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Ren L, Chen DB, Yan X, She S, Yang Y, Zhang X, Liao W, Chen H. Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma. J Hepatocell Carcinoma 2024; 11:2359-2372. [PMID: 39619602 PMCID: PMC11608547 DOI: 10.2147/jhc.s423549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Accepted: 11/19/2024] [Indexed: 01/04/2025] Open
Abstract
Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and the third leading cause of cancer-related deaths. Imaging plays a crucial role in the screening, diagnosis, and monitoring of HCC; however, the potential mechanism regarding phenotypes or molecular subtyping remains underexplored. Radiomics significantly expands the selection of features available by extracting quantitative features from imaging data. Radiogenomics bridges the gap between imaging and genetic/transcriptomic information by associating imaging features with critical genes and pathways, thereby providing biological annotations to these features. Despite challenges in interpreting these connections, assessing their universality, and considering the diversity in HCC etiology and genetic information across different populations, radiomics and radiogenomics offer new perspectives for precision treatment in HCC. This article provides an up-to-date summary of the advancements in radiomics and radiogenomics throughout the HCC care continuum, focusing on the clinical applications, advantages, and limitations of current techniques and offering prospects. Future research should aim to overcome these challenges to improve the prognosis of HCC patients and leverage imaging information for patient benefit.
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Affiliation(s)
- Liying Ren
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Dong Bo Chen
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Xuanzhi Yan
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, 541001, People’s Republic of China
| | - Shaoping She
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Yao Yang
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Xue Zhang
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
| | - Weijia Liao
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, 541001, People’s Republic of China
| | - Hongsong Chen
- Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, 100044, People’s Republic of China
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Hong S, Hong S, Oh E, Lee WJ, Jeong WK, Kim K. Development of a flexible feature selection framework in radiomics-based prediction modeling: Assessment with four real-world datasets. Sci Rep 2024; 14:29297. [PMID: 39592859 PMCID: PMC11599926 DOI: 10.1038/s41598-024-80863-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 11/21/2024] [Indexed: 11/28/2024] Open
Abstract
There are several important challenges in radiomics research; one of them is feature selection. Since many quantitative features are non-informative, feature selection becomes essential. Feature selection methods have been mixed with filter, wrapper, and embedded methods without a rule of thumb. This study aims to develop a framework for optimal feature selection in radiomics research. We developed the framework that the optimal features were selected to quickly through controlling relevance and redundancy among features. A 'FeatureMap' was generated containing information for each step and used as a platform. Through this framework, we can explore the optimal combination of radiomics features and evaluate the predictive performance using only selected features. We assessed the framework using four real datasets. The FeatureMap generated 6 combinations, with the number of features selected varying for each combination. The predictive models obtained high performances; the highest test area under the curves (AUCs) were 0.792, 0.820, 0.846 and 0.738 in the cross-validation method, respectively. We developed a flexible framework for feature selection methods in radiomics research and assessed its usefulness using various real-world data. Our framework can assist clinicians in efficiently developing predictive models based on radiomics.
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Affiliation(s)
- Sungsoo Hong
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Sungjun Hong
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
- Medical AI Research Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Eunsun Oh
- Department of Radiology, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Won Jae Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Woo Kyoung Jeong
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Kyunga Kim
- Department of Digital Health, Samsung Advanced Institute of Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
- Biomedical Statistics Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Huang Y, Qian H. Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis. J Hepatocell Carcinoma 2024; 11:2159-2168. [PMID: 39525830 PMCID: PMC11546143 DOI: 10.2147/jhc.s493227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2024] [Accepted: 10/25/2024] [Indexed: 11/16/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and is associated with high mortality rates due to late detection and aggressive progression. Peritumoral radiomics, an emerging technique that quantitatively analyzes the tissue surrounding the tumor, has shown significant potential in enhancing the management of HCC. This paper examines the role of peritumoral radiomics in improving diagnostic accuracy, guiding personalized treatment strategies, and refining prognostic assessments. By offering unique insights into the tumor microenvironment, peritumoral radiomics enables more precise patient stratification and informs clinical decision-making. However, the integration of peritumoral radiomics into routine clinical practice faces several challenges. Addressing these challenges through continued research and innovation is crucial for the successful implementation of peritumoral radiomics in HCC management, ultimately leading to improved patient outcomes.
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Affiliation(s)
- Yanhua Huang
- Department of Ultrasound, Shaoxing People’s Hospital, Shaoxing, People’s Republic of China
| | - Hongwei Qian
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People’s Hospital, Shaoxing, People’s Republic of China
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, People’s Republic of China
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Cox V, Javle M, Sun J, Kang H. Radiogenomics of Intrahepatic Cholangiocarcinoma: Correlation of Imaging Features With BAP1 and FGFR Molecular Subtypes. J Comput Assist Tomogr 2024; 48:868-874. [PMID: 38968316 DOI: 10.1097/rct.0000000000001638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024]
Abstract
PURPOSE Clinical research has shown unique tumor behavioral characteristics of BRCA -associated protein-1- ( BAP1 -) and fibroblast growth factor receptor ( FGFR )-mutated intrahepatic cholangiocarcinomas (CCAs), with BAP1 -mutated tumors demonstrating more aggressive forms of disease and FGFR -altered CCAs showing more indolent behavior. We performed a retrospective case-control study to evaluate for unique imaging features associated with BAP1 and FGFR genomic markers in intrahepatic CCA (iCCA). METHODS Multiple imaging features of iCCA at first staging were analyzed by 2 abdominal radiologists blinded to genomic data. Growth and development of metastases at available follow-up imaging were also recorded, as were basic clinical cohort data. Types of iCCA analyzed included those with BAP1 , FGFR , or both alterations, as well as cases with low mutational burden or mutations with low clinical impact, which served as a control or "wild-type" group. There were 18 cases in the FGFR group, 10 with BAP1 mutations, and 31 wild types (controls). RESULTS Cases with BAP1 mutations showed significantly larger growth at first year of follow-up ( P = 0.03) and more frequent tumor-associated biliary ductal dilatation ( P = 0.04) compared with controls. FGFR -altered cases showed more infiltrative margins compared with controls ( P = 0.047) and demonstrated less enhancement between arterial to portal venous phases ( P = 0.02). BAP1 and FGFR groups had more cases with stage IV disease at presentation than controls ( P = 0.025, P = 0.006). CONCLUSION Compared with wild-type iCCAs, FGFR -mutated tumors often demonstrate infiltrative margins, and BAP1 tumors show increased biliary ductal dilatation at presentation. BAP1 -mutated cases had significantly larger growth at first-year restaging.
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Affiliation(s)
| | - Milind Javle
- Division of Cancer Medicine, Department of Gastrointestinal Medical Oncology
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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Shin J. Inter-reader agreement for LR-M imaging features: a premise for better imaging-based diagnosis in liver imaging. JOURNAL OF LIVER CANCER 2024; 24:124-125. [PMID: 39134467 PMCID: PMC11449583 DOI: 10.17998/jlc.2024.08.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Accepted: 08/06/2024] [Indexed: 10/05/2024]
Affiliation(s)
- Jaeseung Shin
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Xiao Y, Wu F, Hou K, Wang F, Zhou C, Huang P, Yang C, Zeng M. MR radiomics to predict microvascular invasion status and biological process in combined hepatocellular carcinoma-cholangiocarcinoma. Insights Imaging 2024; 15:172. [PMID: 38981992 PMCID: PMC11233482 DOI: 10.1186/s13244-024-01741-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 06/09/2024] [Indexed: 07/11/2024] Open
Abstract
OBJECTIVES To establish an MRI-based radiomics model for predicting the microvascular invasion (MVI) status of cHCC-CCA and to investigate biological processes underlying the radiomics model. METHODS The study consisted of a retrospective dataset (82 in the training set, 36 in the validation set) and a prospective dataset (25 patients in the test set) from two hospitals. Based on the training set, logistic regression analyses were employed to develop the clinical-imaging model, while radiomic features were extracted to construct a radiomics model. The diagnosis performance was further validated in the validation and test sets. Prognostic aspects of the radiomics model were investigated using the Kaplan-Meier method and log-rank test. Differential gene expression analysis and gene ontology (GO) analysis were conducted to explore biological processes underlying the radiomics model based on RNA sequencing data. RESULTS One hundred forty-three patients (mean age, 56.4 ± 10.5; 114 men) were enrolled, in which 73 (51.0%) were confirmed as MVI-positive. The radiomics model exhibited good performance in predicting MVI status, with the area under the curve of 0.935, 0.873, and 0.779 in training, validation, and test sets, respectively. Overall survival (OS) was significantly different between the predicted MVI-negative and MVI-positive groups (median OS: 25 vs 18 months, p = 0.008). Radiogenomic analysis revealed associations between the radiomics model and biological processes involved in regulating the immune response. CONCLUSION A robust MRI-based radiomics model was established for predicting MVI status in cHCC-CCA, in which potential prognostic value and underlying biological processes that regulate immune response were demonstrated. CRITICAL RELEVANCE STATEMENT MVI is a significant manifestation of tumor invasiveness, and the MR-based radiomics model established in our study will facilitate risk stratification. Furthermore, underlying biological processes demonstrated in the radiomics model will offer valuable insights for guiding immunotherapy strategies. KEY POINTS MVI is of prognostic significance in cHCC-CCA, but lacks reliable preoperative assessment. The MRI-based radiomics model predicts MVI status effectively in cHCC-CCA. The MRI-based radiomics model demonstrated prognostic value and underlying biological processes. The radiomics model could guide immunotherapy and risk stratification in cHCC-CCA.
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Affiliation(s)
- Yuyao Xiao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fei Wu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Kai Hou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fang Wang
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Changwu Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Peng Huang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
- Shanghai Institute of Medical Imaging, Shanghai, China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
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Chen L, Yin G, Wang Z, Liu Z, Sui C, Chen K, Song T, Xu W, Qi L, Li X. A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma. Biosci Trends 2024; 18:263-276. [PMID: 38853000 DOI: 10.5582/bst.2024.01121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.
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Affiliation(s)
- Lu Chen
- Department of Hepatobiliary Cancer, Liver Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Guotao Yin
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Ziyang Wang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Zifan Liu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Tianqiang Song
- Department of Hepatobiliary Cancer, Liver Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Lisha Qi
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Bartnik K, Krzyziński M, Bartczak T, Korzeniowski K, Lamparski K, Wróblewski T, Grąt M, Hołówko W, Mech K, Lisowska J, Januszewicz M, Biecek P. A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation. Sci Rep 2024; 14:14779. [PMID: 38926517 PMCID: PMC11208561 DOI: 10.1038/s41598-024-65630-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.
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Affiliation(s)
- Krzysztof Bartnik
- Second Department of Radiology, Medical University of Warsaw, Banacha 1a st., 02-097, Warsaw, Poland.
| | - Mateusz Krzyziński
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75 st., Warsaw, Poland
| | - Tomasz Bartczak
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75 st., Warsaw, Poland
| | - Krzysztof Korzeniowski
- Second Department of Radiology, Medical University of Warsaw, Banacha 1a st., 02-097, Warsaw, Poland
| | - Krzysztof Lamparski
- Second Department of Radiology, Medical University of Warsaw, Banacha 1a st., 02-097, Warsaw, Poland
| | - Tadeusz Wróblewski
- Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Banacha 1a st., Warsaw, Poland
| | - Michał Grąt
- Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Banacha 1a st., Warsaw, Poland
| | - Wacław Hołówko
- Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Banacha 1a st., Warsaw, Poland
| | - Katarzyna Mech
- Department of General, Gastroenterological and Oncological Surgery, Medical University of Warsaw, Banacha 1a st., Warsaw, Poland
| | - Joanna Lisowska
- Department of General, Gastroenterological and Oncological Surgery, Medical University of Warsaw, Banacha 1a st., Warsaw, Poland
| | - Magdalena Januszewicz
- Second Department of Radiology, Medical University of Warsaw, Banacha 1a st., 02-097, Warsaw, Poland
| | - Przemysław Biecek
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75 st., Warsaw, Poland
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Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, Shu D, Chen K, Wang Y, Chen G. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med 2024; 173:108337. [PMID: 38547656 DOI: 10.1016/j.compbiomed.2024.108337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.
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Affiliation(s)
- Zhiyuan Bo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiatao Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qikuan He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ziyan Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Danyang Shu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kaiyu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Zhou G, Zhou Y, Xu X, Zhang J, Xu C, Xu P, Zhu F. MRI-based radiomics signature: a potential imaging biomarker for prediction of microvascular invasion in combined hepatocellular-cholangiocarcinoma. Abdom Radiol (NY) 2024; 49:49-59. [PMID: 37831165 DOI: 10.1007/s00261-023-04049-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 10/14/2023]
Abstract
PURPOSE To investigate the potential of radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in preoperatively predicting microvascular invasion (MVI) in patients with combined hepatocellular-cholangiocarcinoma (cHCC-CC) before surgery. METHODS A cohort of 91 patients with histologically confirmed cHCC-CC who underwent preoperative liver DCE-MRI were enrolled and divided into a training cohort (27 MVI-positive and 37 MVI-negative) and a validation cohort (11 MVI-positive and 16 MVI-negative). Clinical characteristics and MR features of the patients were evaluated. Radiomics features were extracted from DCE-MRI, and a radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. Prediction performance of the developed radiomics signature was evaluated by utilizing the receiver operating characteristic (ROC) analysis. RESULTS Larger tumor size and higher Radscore were associated with the presence of MVI in the training cohort (p = 0.026 and < 0.001, respectively), and theses findings were also confirmed in the validation cohort (p = 0.040 and 0.001, respectively). The developed radiomics signature, composed of 4 stable radiomics features, showed high prediction performance in both the training cohort (AUC = 0.866, 95% CI 0.757-0.938, p < 0.001) and validation cohort (AUC = 0.841, 95% CI 0.650-0.952, p < 0.001). CONCLUSIONS The radiomics signature developed from DCE-MRI can be a reliable imaging biomarker to preoperatively predict MVI in cHCC-CC.
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Affiliation(s)
- Guofeng Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yang Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Xun Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Chen Xu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Pengju Xu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Shanghai Institute of Medical Imaging, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
| | - Feipeng Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
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Liu N, Wu Y, Tao Y, Zheng J, Huang X, Yang L, Zhang X. Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics. Cancers (Basel) 2023; 15:5373. [PMID: 38001633 PMCID: PMC10670473 DOI: 10.3390/cancers15225373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
The purpose of this study was to investigate the efficacy of magnetic resonance imaging (MRI) radiomics in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). The clinical and MRI data of 129 pathologically confirmed HCC patients and 48 ICC patients treated at the Affiliated Hospital of North Sichuan Medical College between April 2016 and December 2021 were retrospectively analyzed. The patients were randomly divided at a ratio of 7:3 into a training group of 124 patients (90 with HCC and 34 with ICC) and a validation group of 53 patients (39 with HCC and 14 with ICC). Radiomic features were extracted from axial fat suppression T2-weighted imaging (FS-T2WI) and axial arterial-phase (AP) and portal-venous-phase (PVP) dynamic-contrast-enhanced MRI (DCE-MRI) sequences, and the corresponding datasets were generated. The least absolute shrinkage and selection operator (LASSO) method was used to select the best radiomic features. Logistic regression was used to establish radiomic models for each sequence (FS-T2WI, AP and PVP models), a clinical model for optimal clinical variables (C model) and a joint radiomics model (JR model) integrating the radiomics features of all the sequences as well as a radiomics-clinical model combining optimal radiomic features and clinical risk factors (RC model). The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). The AUCs of the FS-T2WI, AP, PVP, JR, C and RC models for distinguishing HCC from ICC were 0.693, 0.863, 0.818, 0.914, 0.936 and 0.977 in the training group and 0.690, 0.784, 0.727, 0.802, 0.860 and 0.877 in the validation group, respectively. The results of this study suggest that MRI-based radiomics may help noninvasively differentiate HCC from ICC. The model integrating the radiomics features and clinical risk factors showed a further improvement in performance.
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Affiliation(s)
- Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
- Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region (Hospital. C.T.), Chengdu 610041, China
| | - Yaokun Wu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Yunyun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Xiaohua Huang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Xiaoming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
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12
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Han Q, Du L, Zhu L, Yu D. Review of the Application of Dual Drug Delivery Nanotheranostic Agents in the Diagnosis and Treatment of Liver Cancer. Molecules 2023; 28:7004. [PMID: 37894483 PMCID: PMC10608862 DOI: 10.3390/molecules28207004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/16/2023] [Accepted: 09/29/2023] [Indexed: 10/29/2023] Open
Abstract
Liver cancer has high incidence and mortality rates and its treatment generally requires the use of a combination treatment strategy. Therefore, the early detection and diagnosis of liver cancer is crucial to achieving the best treatment effect. In addition, it is imperative to explore multimodal combination therapy for liver cancer treatment and the synergistic effect of two liver cancer treatment drugs while preventing drug resistance and drug side effects to maximize the achievable therapeutic effect. Gold nanoparticles are used widely in applications related to optical imaging, CT imaging, MRI imaging, biomarkers, targeted drug therapy, etc., and serve as an advanced platform for integrated application in the nano-diagnosis and treatment of diseases. Dual-drug-delivery nano-diagnostic and therapeutic agents have drawn great interest in current times. Therefore, the present report aims to review the effectiveness of dual-drug-delivery nano-diagnostic and therapeutic agents in the field of anti-tumor therapy from the particular perspective of liver cancer diagnosis and treatment.
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Affiliation(s)
- Qinghe Han
- Radiology Department, The Second Affiliated Hospital of Jilin University, Changchun 130062, China; (Q.H.); (L.D.); (L.Z.)
| | - Lianze Du
- Radiology Department, The Second Affiliated Hospital of Jilin University, Changchun 130062, China; (Q.H.); (L.D.); (L.Z.)
| | - Lili Zhu
- Radiology Department, The Second Affiliated Hospital of Jilin University, Changchun 130062, China; (Q.H.); (L.D.); (L.Z.)
| | - Duo Yu
- Department of Radiotherapy, The Second Affiliated Hospital of Jilin University, Changchun 130062, China
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13
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Li Z, Li H, Ralescu AL, Dillman JR, Parikh NA, He L. A novel collaborative self-supervised learning method for radiomic data. Neuroimage 2023; 277:120229. [PMID: 37321358 PMCID: PMC10440826 DOI: 10.1016/j.neuroimage.2023.120229] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/19/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method will have the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.
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Affiliation(s)
- Zhiyuan Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Anca L Ralescu
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, U niversity of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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14
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Bo Z, Chen B, Yang Y, Yao F, Mao Y, Yao J, Yang J, He Q, Zhao Z, Shi X, Chen J, Yu Z, Yang Y, Wang Y, Chen G. Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study. Eur J Nucl Med Mol Imaging 2023; 50:2501-2513. [PMID: 36922449 DOI: 10.1007/s00259-023-06184-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/28/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection. METHODS Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS 127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03). CONCLUSION ML radiomics models based on CECT are valuable in predicting ER in ICC.
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Affiliation(s)
- Zhiyuan Bo
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Chen
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yi Yang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Fei Yao
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yicheng Mao
- Department of Optometry and Ophthalmology College, Wenzhou Medical University, Wenzhou, China
| | - Jiangqiao Yao
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jinhuan Yang
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qikuan He
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhengxiao Zhao
- Department of Oncology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xintong Shi
- Department of Hepatobiliary Surgery, the Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Jicai Chen
- Department of General Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhengping Yu
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yunjun Yang
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
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15
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Criss C, Nagar AM, Makary MS. Hepatocellular carcinoma: State of the art diagnostic imaging. World J Radiol 2023; 15:56-68. [PMID: 37035828 PMCID: PMC10080581 DOI: 10.4329/wjr.v15.i3.56] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 02/12/2023] [Accepted: 03/22/2023] [Indexed: 03/27/2023] Open
Abstract
Primary liver cancer is the fourth most common malignancy worldwide, with hepatocellular carcinoma (HCC) comprising up to 90% of cases. Imaging is a staple for surveillance and diagnostic criteria for HCC in current guidelines. Because early diagnosis can impact treatment approaches, utilizing new imaging methods and protocols to aid in differentiation and tumor grading provides a unique opportunity to drastically impact patient prognosis. Within this review manuscript, we provide an overview of imaging modalities used to screen and evaluate HCC. We also briefly discuss emerging uses of new imaging techniques that offer the potential for improving current paradigms for HCC characterization, management, and treatment monitoring.
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Affiliation(s)
- Cody Criss
- Heritage College of Osteopathic Medicine, Ohio University, Athens, OH 45701, United States
| | - Arpit M Nagar
- Department of Radiology, The Ohio State University Medical Center, Columbus, OH 43210, United States
| | - Mina S Makary
- Department of Radiology, The Ohio State University Medical Center, Columbus, OH 43210, United States
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16
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Stoehr F, Kloeckner R, Pinto dos Santos D, Schnier M, Müller L, Mähringer-Kunz A, Dratsch T, Schotten S, Weinmann A, Galle PR, Mittler J, Düber C, Hahn F. Radiomics-Based Prediction of Future Portal Vein Tumor Infiltration in Patients with HCC-A Proof-of-Concept Study. Cancers (Basel) 2022; 14:cancers14246036. [PMID: 36551521 PMCID: PMC9775514 DOI: 10.3390/cancers14246036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Portal vein infiltration (PVI) is a typical complication of HCC. Once diagnosed, it leads to classification as BCLC C with an enormous impact on patient management, as systemic therapies are henceforth recommended. Our aim was to investigate whether radiomics analysis using imaging at initial diagnosis can predict the occurrence of PVI in the course of disease. Between 2008 and 2018, we retrospectively identified 44 patients with HCC and an in-house, multiphase CT scan at initial diagnosis who presented without CT-detectable PVI but developed it in the course of disease. Accounting for size and number of lesions, growth type, arterial enhancement pattern, Child-Pugh stage, AFP levels, and subsequent therapy, we matched 44 patients with HCC who did not develop PVI to those developing PVI in the course of disease (follow-up ended December 2021). After segmentation of the tumor at initial diagnosis and texture analysis, we used LASSO regression to find radiomics features suitable for PVI detection in this matched set. Using an 80:20 split between training and holdout validation dataset, 17 radiomics features remained in the fitted model. Applying the model to the holdout validation dataset, sensitivity to detect occurrence of PVI was 0.78 and specificity was 0.78. Radiomics feature extraction had the ability to detect aggressive HCC morphology likely to result in future PVI. An additional radiomics evaluation at initial diagnosis might be a useful tool to identify patients with HCC at risk for PVI during follow-up benefiting from a closer surveillance.
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Affiliation(s)
- Fabian Stoehr
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Roman Kloeckner
- Institute of Interventional Radiology, University Hospital Schleswig-Holstein—Campus Luebeck, 23562 Luebeck, Germany
| | - Daniel Pinto dos Santos
- Institute of Diagnostic and Interventional Radiology, University Hospital Frankfurt, 60590 Frankfurt, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, 50937 Cologne, Germany
| | - Mira Schnier
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Aline Mähringer-Kunz
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Thomas Dratsch
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, 50937 Cologne, Germany
| | - Sebastian Schotten
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, Helios Dr. Horst Schmidt Kliniken Wiesbaden, 65199 Wiesbaden, Germany
| | - Arndt Weinmann
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Peter Robert Galle
- Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Jens Mittler
- Department of General, Visceral and Transplant Surgery, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Christoph Düber
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
- Correspondence: ; Tel.: +49-6131172019
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17
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Zhang X, Wang C, Zheng D, Liao Y, Wang X, Huang Z, Zhong Q. Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation. Front Oncol 2022; 12:1013770. [PMID: 36439458 PMCID: PMC9686343 DOI: 10.3389/fonc.2022.1013770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 10/24/2022] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND There are few studies on the application of radiomics in the risk prediction of early recurrence (ER) after radiofrequency ablation (RFA). This study evaluated the value of a multi-parametric magnetic resonance imaging (MRI, mpMRI)-based radiomics nomogram in predicting ER of small hepatocellular carcinoma (HCC) after RFA. MATERIALS AND METHODS A retrospective analysis was performed on 90 patients with small HCC who were treated with RFA. Patients were divided into two groups according to recurrence within 2 years: the ER group (n=38) and the non-ER group (n=52). Preoperative T1WI, T2WI, and contrast-enhanced MRI (CE-MRI) were used for radiomic analysis. Tumor segmentation was performed on the images and applied to extract 1316 radiomics features. The most predictive features were selected using analysis of variance + Mann-Whitney, Spearman's rank correlation test, random forest (importance), and least absolute shrinkage and selection operator analysis. Radiomics models based on each sequence or combined sequences were established using logistic regression analysis. A predictive nomogram was constructed based on the radiomics score (rad-score) and clinical predictors. The predictive efficiency of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to evaluate the clinical efficacy of the nomogram. RESULTS The radiomics model mpMRI, which is based on T1WI, T2WI, and CE-MRI sequences, showed the best predictive performance, with an AUC of 0.812 for the validation cohort. Combined with the clinical risk factors of albumin level, number of tumors, and rad-score of mpMRI, the AUC of the preoperative predictive nomogram in the training and validation cohorts were 0.869 and 0.812, respectively. DCA demonstrated that the combined nomogram is clinically useful. CONCLUSIONS The multi-parametric MRI-based radiomics nomogram has a high predictive value for ER of small HCC after RFA, which could be helpful for personalized risk stratification and further treatment decision-making for patients with small HCC.
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Affiliation(s)
- Xiaojuan Zhang
- Department of Radiology, Fujian Medical University Xiamen Humanity Hospital, Xiamen, China
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Chuandong Wang
- Department of Thyroid and Breast Surgery, Fujian Medical University Xiamen Humanity Hospital, Xiamen, China
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Dan Zheng
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Yuting Liao
- Institute of Precision Medicine, GE Healthcare, Shanghai, China
| | - Xiaoyang Wang
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Zhifeng Huang
- Department of Radiology, 900th Hospital of Joint Logistics Support Force, Fuzhou, China
| | - Qun Zhong
- Department of Radiology, The Affiliated People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
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18
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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19
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Macias RIR, Cardinale V, Kendall TJ, Avila MA, Guido M, Coulouarn C, Braconi C, Frampton AE, Bridgewater J, Overi D, Pereira SP, Rengo M, Kather JN, Lamarca A, Pedica F, Forner A, Valle JW, Gaudio E, Alvaro D, Banales JM, Carpino G. Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions. Gut 2022; 71:1669-1683. [PMID: 35580963 DOI: 10.1136/gutjnl-2022-327099] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/22/2022] [Indexed: 02/06/2023]
Abstract
Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers.
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Affiliation(s)
- Rocio I R Macias
- Experimental Hepatology and Drug Targeting (HEVEPHARM) group, University of Salamanca, IBSAL, Salamanca, Spain
- Center for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain
| | - Vincenzo Cardinale
- Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy
| | - Timothy J Kendall
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
| | - Matias A Avila
- Center for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain
- Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Maria Guido
- Department of Medicine - DIMED, University of Padua, Padua, Italy
| | - Cedric Coulouarn
- UMR_S 1242, COSS, Centre de Lutte contre le Cancer Eugène Marquis, INSERM University of Rennes 1, Rennes, France
| | - Chiara Braconi
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Adam E Frampton
- Department of Clinical and Experimental Medicine, University of Surrey, Guildford, Surrey, UK
| | - John Bridgewater
- Department of Medical Oncology, UCL Cancer Institute, London, UK
| | - Diletta Overi
- Department of Anatomical, Histological, Forensic Medicine and Orthopaedic Sciences, Sapienza University of Rome, Rome, Italy
| | - Stephen P Pereira
- Institute for Liver & Digestive Health, University College London, London, UK
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Jakob N Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Angela Lamarca
- Medical Oncology/Institute of Cancer Sciences, The Christie NHS Foundation Trust/University of Manchester, Manchester, UK
| | - Federica Pedica
- Department of Pathology, San Raffaele Scientific Institute, Milan, Italy
| | - Alejandro Forner
- Center for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain
- BCLC group, Liver Unit, Hospital Clínic Barcelona. IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Juan W Valle
- Medical Oncology/Institute of Cancer Sciences, The Christie NHS Foundation Trust/University of Manchester, Manchester, UK
| | - Eugenio Gaudio
- Department of Anatomical, Histological, Forensic Medicine and Orthopaedic Sciences, Sapienza University of Rome, Rome, Italy
| | - Domenico Alvaro
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Jesus M Banales
- Center for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain
- Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute, Donostia University Hospital, University of the Basque Country (UPV/EHU), Ikerbasque, San Sebastian, Spain
- Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Guido Carpino
- Department of Movement, Human and Health Sciences, University of Rome 'Foro Italico', Rome, Italy
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20
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Yu C, Sun C. Diagnostic Value of Multislice Spiral Computed Tomography Combined with Serum AFP, TSGF, and GP73 Assay in the Diagnosis of Primary Liver Cancer. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:6581127. [PMID: 35711497 PMCID: PMC9197641 DOI: 10.1155/2022/6581127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/06/2022] [Accepted: 05/16/2022] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To explore the diagnostic value of multislice spiral computed tomography (MSCT) scan combined with serum alpha-fetoprotein (AFP), tumor-specific growth factor (TSGF), and Golgi protein73 (GP73) assays in the diagnosis of primary liver cancer (PLC). METHODS Totally, 60 patients with PLC admitted to The Second Hospital of Dalian Medical University from January 2019 to January 2020 were included in group A, 60 patients with liver cirrhosis were included in group B, and 60 healthy subjects were included in group C. The serum AFP, TSGF, and GP73 levels were determined, and all participants received MSCT scanning. The diagnostic efficacy of MSCT, assays of serum AFP, TSGF, and GP73, and their combined detection was analyzed. RESULTS Group A had the highest levels of AFP, TSGF, and GP73, followed by group B, and then group C. The sensitivity, specificity, positive predictive value, and negative predictive value of MSCT for PLC were 80.0%,91.7%, 82.8%, and 90.2%, respectively, while those of combined detection of MSCT plus serum AFP, TSGF, and GP73 for PLC were 100.0%, 93.3%, 88.2%, and 100.0%. The combined detection was associated with significantly a higher detection rate of PLC versus stand-alone detection. CONCLUSION MSCT plus serum AFP, TSGF, and GP73 has a higher detection rate versus stand-alone detection, which shows great potential in the diagnosis of PLC.
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Affiliation(s)
- Chuanwen Yu
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian 116004, Liaoning, China
| | - Chuang Sun
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian 116004, Liaoning, China
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21
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An J, Oh M, Kim SY, Oh YJ, Oh B, Oh JH, Kim W, Jung JH, Kim HI, Kim JS, Sung CO, Shim JH. PET-Based Radiogenomics Supports mTOR Pathway Targeting for Hepatocellular Carcinoma. Clin Cancer Res 2022; 28:1821-1831. [PMID: 35191466 DOI: 10.1158/1078-0432.ccr-21-3208] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/22/2021] [Accepted: 02/09/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE This work aimed to explore in depth the genomic and molecular underpinnings of hepatocellular carcinoma (HCC) with increased 2[18F]fluoro-2-deoxy-d-glucose (FDG) uptake in PET and to identify therapeutic targets based on this imaging-genomic surrogate. EXPERIMENTAL DESIGN We used RNA sequencing and whole-exome sequencing data obtained from 117 patients with HCC who underwent hepatic resection with preoperative FDG-PET/CT imaging as a discovery cohort. The primary radiogenomic results were validated with transcriptomes from a second cohort of 81 patients with more advanced tumors. All patients were allocated to an FDG-avid or FDG-non-avid group according to the PET findings. We also screened potential drug candidates targeting FDG-avid HCCs in vitro and in vivo. RESULTS High FDG avidity conferred worse recurrence-free survival after HCC resection. Whole transcriptome analysis revealed upregulation of mTOR pathway signals in the FDG-avid tumors, together with higher abundance of associated mutations. These clinical and genomic findings were replicated in the validation set. A molecular signature of FDG-avid HCCs identified in the discovery set consistently predicted poor prognoses in the public-access datasets of two cohorts. Treatment with an mTOR inhibitor resulted in decreased FDG uptake followed by effective tumor control in both the hyperglycolytic HCC cell lines and xenograft mouse models. CONCLUSIONS Our PET-based radiogenomic analysis indicates that mTOR pathway genes are markedly activated and altered in HCCs with high FDG retention. This nuclear imaging biomarker may stimulate umbrella trials and tailored treatments in precision care of patients with HCC.
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Affiliation(s)
- Jihyun An
- Gastroenterology and Hepatology, Hanyang University College of Medicine, Guri, Gyeonggi, Republic of Korea
| | - Minyoung Oh
- Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seog-Young Kim
- Convergence Medicine Research Center, Asan Medical Center, Seoul, Republic of Korea
- Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yoo-Jin Oh
- Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea
| | - Bora Oh
- Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea
| | - Ji-Hye Oh
- Center for Cancer Genome Discovery, Asan Institute for Life Science, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Wonkyung Kim
- Center for Cancer Genome Discovery, Asan Institute for Life Science, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Jin Hwa Jung
- Convergence Medicine Research Center, Asan Medical Center, Seoul, Republic of Korea
| | - Ha Il Kim
- Gastroenterology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
| | - Jae-Seung Kim
- Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Chang Ohk Sung
- Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ju Hyun Shim
- Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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22
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Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma. Cancers (Basel) 2022; 14:cancers14082008. [PMID: 35454914 PMCID: PMC9028737 DOI: 10.3390/cancers14082008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/05/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Mantle cell lymphoma (MCL) is an aggressive lymphoid tumour with a poor prognosis. There exist no routine biomarkers for the early prediction of relapse. Our study compared the potential of radiomics-based machine learning and 3D deep learning models as non-invasive biomarkers to risk-stratify MCL patients, thus promoting precision imaging in clinical oncology. Abstract Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL.
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Lysdahlgaard S. Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study. Radiography (Lond) 2022; 28:718-724. [PMID: 35428570 DOI: 10.1016/j.radi.2022.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 03/07/2022] [Accepted: 03/29/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Liver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset. METHODS The Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy. RESULTS The performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%-99.91%) to 100.00% (95% CI: 86.77%-100.00%), 91.30% (95% CI: 71.96%-98.93%) to 100.00% (95% CI: 83.89%-100.00%)and 94.00% (95% CI: 83.45%-98.75%) to 100.00% (95% CI: 92.45%-100.00%), respectively. CONCLUSION ML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT. IMPLICATIONS FOR PRACTICE Differentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.
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Affiliation(s)
- S Lysdahlgaard
- Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark; Imaging Research Initiative Southwest (IRIS), Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark.
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24
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Lee YS, Lee JE, Yi HS, Jung YK, Jun DW, Kim JH, Seo YS, Yim HJ, Kim BH, Kim JW, Lee CH, Yeon JE, Lee J, Um SH, Byun KS. MRE-based NASH score for diagnosis of nonalcoholic steatohepatitis in patients with nonalcoholic fatty liver disease. Hepatol Int 2022; 16:316-324. [PMID: 35254642 DOI: 10.1007/s12072-022-10300-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 01/07/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND AND AIMS As the prevalence of nonalcoholic fatty liver disease (NAFLD) is approximately 30% in the general population, it is important to develop a non-invasive biomarker for the diagnosis of nonalcoholic steatohepatitis (NASH). This prospective cross-sectional study aimed to develop a scoring system for NASH diagnosis through multiparametric magnetic resonance (MR) and clinical indicators. METHODS Medical history, laboratory tests, and MR parameters of patients with NAFLD were assessed. A scoring system was developed using a logistic regression model. In total, 127 patients (58 with nonalcoholic fatty liver [NAFL] and 69 with NASH) were enrolled. After evaluating 23 clinical characteristics of the patients (4 categorical and 19 numeric variables) for the NASH diagnostic model, an equation for MR elastography (MRE)-based NASH score was obtained using 3 demographic factors, 2 laboratory variables, and MRE. RESULTS The MRE-based NASH score showed a satisfactory accuracy for NASH diagnosis (c-statistics, 0.841; 95% CI 0.772-0.910). At a cut-off MRE-based NASH score of 0.68 for NASH diagnosis, its sensitivity was 0.68 and specificity was 0.91. When an MRE-based NASH score of 0.37 was used as a cut-off for NASH exclusion, the sensitivity was 0.91 and specificity was 0.55. Overall, 35% (44/127) of patients were in the gray zone (between 0.37 and 0.68). Internal validation via bootstrapping also indicated the satisfactory accuracy of NASH diagnosis (optimism-corrected statistics, 0.811). CONCLUSION MRE-based NASH score is a useful and accurate non-invasive biomarker for diagnosis of NASH in patients with NAFLD.
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Affiliation(s)
- Young-Sun Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Ji Eun Lee
- Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hyon-Seung Yi
- Department of Internal Medicine, School of Medicine, Chungnam National University, Daejeon, 35015, Republic of Korea
| | - Young Kul Jung
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Dae Won Jun
- Department of Internal Medicine, Hanyang University School of Medicine, Seoul, Republic of Korea
| | - Ji Hoon Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Yeon Seok Seo
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Hyung Joon Yim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Baek-Hui Kim
- Department of Pathology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jeong Woo Kim
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Chang Hee Lee
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jong Eun Yeon
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea.
| | - Juneyoung Lee
- Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea.
| | - Soon Ho Um
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Kwan Soo Byun
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
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25
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Li X, Cheng L, Li C, Hu X, Hu X, Tan L, Li Q, Liu C, Wang J. Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning. J Clin Transl Hepatol 2022; 10:63-71. [PMID: 35233374 PMCID: PMC8845145 DOI: 10.14218/jcth.2021.00023] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 05/07/2021] [Accepted: 05/18/2021] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND AIMS The relationship between quantitative magnetic resonance imaging (MRI) imaging features and gene-expression signatures associated with the recurrence of hepatocellular carcinoma (HCC) is not well studied. METHODS In this study, we generated multivariable regression models to explore the correlation between the preoperative MRI features and Golgi membrane protein 1 (GOLM1), SET domain containing 7 (SETD7), and Rho family GTPase 1 (RND1) gene expression levels in a cohort study including 92 early-stage HCC patients. A total of 307 imaging features of tumor texture and shape were computed from T2-weighted MRI. The key MRI features were identified by performing a multi-step feature selection procedure including the correlation analysis and the application of RELIEFF algorithm. Afterward, regression models were generated using kernel-based support vector machines with 5-fold cross-validation. RESULTS The features computed from higher specificity MRI better described GOLM1 and RND1 gene-expression levels, while imaging features computed from lower specificity MRI data were more descriptive for the SETD7 gene. The GOLM1 regression model generated with three features demonstrated a moderate positive correlation (p<0.001), and the RND1 model developed with five variables was positively associated (p<0.001) with gene expression levels. Moreover, RND1 regression model integrating four features was moderately correlated with expressed RND1 levels (p<0.001). CONCLUSIONS The results demonstrated that MRI radiomics features could help quantify GOLM1, SETD7, and RND1 expression levels noninvasively and predict the recurrence risk for early-stage HCC patients.
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Affiliation(s)
- Xiaoming Li
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Lin Cheng
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Chuanming Li
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Xianling Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Liang Tan
- Department of Neurosurgery, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Qing Li
- MR Collaborations, Siemens Healthcare Ltd., Shanghai, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
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Tian Y, Komolafe TE, Chen T, Zhou B, Yang X. Prediction of TACE Treatment Response in a Preoperative MRI via Analysis of Integrating Deep Learning and Radiomics Features. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00692-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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27
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Lisson CS, Lisson CG, Achilles S, Mezger MF, Wolf D, Schmidt SA, Thaiss WM, Bloehdorn J, Beer AJ, Stilgenbauer S, Beer M, Götz M. Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL). Cancers (Basel) 2022; 14:393. [PMID: 35053554 PMCID: PMC8773890 DOI: 10.3390/cancers14020393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying "high-risk MCL" was evaluated by receiver operating characteristics (ROC). The four radiomic features, "Uniformity", "Entropy", "Skewness" and "Difference Entropy" showed predictive significance for relapse (p < 0.05)-in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature "Uniformity" (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter "Short Axis," were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.
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Affiliation(s)
- Catharina Silvia Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Christoph Gerhard Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Sherin Achilles
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Marc Fabian Mezger
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Stefan Andreas Schmidt
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Wolfgang M Thaiss
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Johannes Bloehdorn
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Ambros J Beer
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Stephan Stilgenbauer
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Comprehensive Cancer Center Ulm (CCCU), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Michael Götz
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- German Cancer Research Center (DKFZ), Division Medical Image Computing, 69120 Heidelberg, Germany
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Cho K, Moon H, Seo SH, Ro SW, Kim BK. Pharmacological Inhibition of Sonic Hedgehog Signaling Suppresses Tumor Development in a Murine Model of Intrahepatic Cholangiocarcinoma. Int J Mol Sci 2021; 22:13214. [PMID: 34948011 PMCID: PMC8707521 DOI: 10.3390/ijms222413214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/29/2021] [Accepted: 12/02/2021] [Indexed: 12/11/2022] Open
Abstract
Cholangiocarcinoma (CCC) is the second most primary liver cancer with an aggressive biological behavior, and its incidence increases steadily. An aberrant up-regulation of the sonic hedgehog signaling pathway has been reported in a variety of hepatic diseases including hepatic inflammation, fibrosis, as well as cancer. In this study, we determined the effect of a sonic hedgehog inhibitor, vismodegib, on the development of CCC. Through database analyses, we found sonic hedgehog signaling was up-regulated in human CCC, based on overexpression of its target genes, GLI1 and GLI2. Further, human CCC cells were highly sensitive to the treatment with vismodegib in vitro. Based on the data, we investigated the in vivo anti-cancer efficacy of vismodegib in CCC employing a murine model of CCC developed by hydrodynamic tail vein injection method. In the murine model, CCC induced by constitutively active forms of TAZ and PI3K exhibited up-regulated sonic hedgehog signaling. Treatment of vismodegib significantly suppressed tumor development in the murine CCC model, based on comparison of gross morphologies and liver weight/body weight. It is expected that pharmacological inhibition of sonic hedgehog signaling would be an effective molecular target therapy for CCC.
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Affiliation(s)
- Kyungjoo Cho
- Brain Korea 21 Plus Project for Medical Science College of Medicine, Yonsei University, Seoul 03722, Korea; (K.C.); (S.H.S.)
| | - Hyuk Moon
- Department of Genetics and Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 17104, Korea;
| | - Sang Hyun Seo
- Brain Korea 21 Plus Project for Medical Science College of Medicine, Yonsei University, Seoul 03722, Korea; (K.C.); (S.H.S.)
| | - Simon Weonsang Ro
- Department of Genetics and Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 17104, Korea;
| | - Beom Kyung Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Korea
- Institute of Gastroenterology, Yonsei University College of Medicine, Seoul 03722, Korea
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Tao Q, Zeng Q, Liu W, Liu J, Jiang L, Tu X, Li K, Zhao P, Tang X, Liu Z, Wang L, Xu Q, Zheng Y. A novel prognostic nomogram for hepatocellular carcinoma after thermal ablation. Am J Cancer Res 2021; 11:5126-5140. [PMID: 34765316 PMCID: PMC8569373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 08/31/2021] [Indexed: 06/13/2023] Open
Abstract
It remains impossible to accurately assess the prognosis after thermal ablation in patients with hepatocellular carcinoma (HCC). Our aim was to build a nomogram to predict the survival rate of HCC patients after thermal ablation. We developed and validated a nomogram using data of 959 HCC patients after thermal ablation from two centers. Harrell's concordance index (C-index), calibration plot and Decision curve analysis (DCA) were used to measure the performance of the nomogram, and we compared it with the Barcelona Clinic Liver Cancer (BCLC) staging system and a previous nomogram. Six variables including age, serum albumin, operation method, risk area, tumor number and early recurrence were selected to construct the nomogram. In the training cohort, internal validation cohort, and external validation cohort, the nomogram all had a higher C-index to predict survival rate than both the BCLC staging system and the previous nomogram (0.736, 0.558 and 0.698, respectively; 0.763, 0.621 and 0.740, respectively; and 0.825, 0.551 and 0.737, respectively). Calibration plots showed a high degree of consistency between prediction and actual observation. Decision curve analysis (DCA) presented that compared with BCLC system and the previous nomogram, our nomogram had the highest net benefit. In all three cohorts, the nomogram could accurately divide patients into three subgroups according to predicted survival risk. A nomogram was developed and validated to predict survival of HCC patients who underwent thermal ablation, which is helpful for prognostic prediction and individual surveillance in clinical practice.
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Affiliation(s)
- Qiang Tao
- Department of Liver Surgery, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
- State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
| | - Qingjing Zeng
- Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-sen UniversityGuangzhou 510635, Guangdong, China
| | - Wenwu Liu
- State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
- Department of Gastric Surgery, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
| | - Jia Liu
- Department of Neurology, The Seventh Affiliated Hospital, Sun Yat-sen UniversityShenzhen 518000, Guangdong, China
| | - Lingmin Jiang
- Department of Liver Surgery, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
- State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
| | - Xinyue Tu
- Department of Liver Surgery, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
- State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
| | - Kai Li
- Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-sen UniversityGuangzhou 510635, Guangdong, China
| | - Peng Zhao
- Department of Liver Surgery, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
- State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
| | - Xiang Tang
- Department of Liver Surgery, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
- State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
| | - Zonghao Liu
- Department of Liver Surgery, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
- State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
| | - Liang Wang
- The First Department of General Surgery, The First People’s Hospital of Kashi PrefectureKashi 844000, The Xinjiang Uygur Autonomous Region, China
| | - Qilin Xu
- The Second Department of General Surgery, The First People’s Hospital of Kashi PrefectureKashi 844000, The Xinjiang Uygur Autonomous Region, China
| | - Yun Zheng
- Department of Liver Surgery, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
- State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer CenterGuangzhou 510060, Guangdong, China
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Liu X, Liang X, Ruan L, Yan S. A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer. Front Oncol 2021; 11:633852. [PMID: 34631512 PMCID: PMC8493033 DOI: 10.3389/fonc.2021.633852] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 08/31/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives The aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC). Methods A total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram. Results The clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility. Conclusion The findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.
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Affiliation(s)
- Xingyu Liu
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoyuan Liang
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Lingxiang Ruan
- Department of Radiology, The First Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
| | - Sheng Yan
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
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Tan J, Sun X, Wang S, Ma B, Chen Z, Shi Y, Zhang L, Shah MA. Evaluation of Angiogenesis and Pathological Classification of Extrahepatic Cholangiocarcinoma by Dynamic MR Imaging for E-Healthcare. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8666498. [PMID: 34671450 PMCID: PMC8523230 DOI: 10.1155/2021/8666498] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/07/2021] [Accepted: 09/01/2021] [Indexed: 12/16/2022]
Abstract
For staging cholangiocarcinoma and determining respectability, MR is an accurate noninvasive method which provides size of tumor and vascular patency information. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive inspection method for evaluating the vascular structure and functional characteristics of tumor tissue. However, some limitations should be noted about the technology. At present, the technology cannot be used alone, which is just an assisted method during the conventional MRI examination. 50 ECC patients, admitted to Indira Gandhi Medical College and Hospital between 2016 and 2019, were selected as research subjects. They were classified pathologically according to the Steiner classification system. After image processing, regions of interest (ROIs) were selected from the image to measure the rate constant (Kep), extravascular space volume fraction (Ve), and tissue volume transfer constant (Ktrans). There were 15 cases with highly differentiated carcinoma, 23 cases with moderately differentiated carcinoma, and 12 cases with lowly differentiated carcinoma. Non-VEGF expression was noted in 21 cases, with low expression noted in 15 cases, moderate expression noted in 14 cases, and no high expression case noted. The relevant parameters in the dynamic MRI image can quantitatively reflect the angiogenesis and pathological classification of ECC, which is suggested in the clinical treatment of ECC. The Ktrans, Kep, and Ve values of the ECC patients were all not associated with the pathological classification, with no significant difference (P < 0.05). Besides, due to the fact that the patient cannot completely hold his breath, the air leak reduces the image quality.
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Affiliation(s)
- Jinyun Tan
- Department of Hepatobiliary and Pancreatic Surgery, Lanzhou Second People's Hospital, Lanzhou, Gansu Province, China
| | - Xijun Sun
- Department of Medical Imaging, The Second People's Hospital of Lanzhou, Lanzhou, Gansu Province, China
| | - Shaoyu Wang
- MR Scientific Marketing,Siemens Healthineers, Shanghai, China
| | - Baoqin Ma
- Department of Medical Imaging, The Second People's Hospital of Lanzhou, Lanzhou, Gansu Province, China
| | - Zhaohui Chen
- Department of Medical Imaging, The Second People's Hospital of Lanzhou, Lanzhou, Gansu Province, China
| | - Yaowei Shi
- Department of Hepatobiliary and Pancreatic Surgery, Lanzhou Second People's Hospital, Lanzhou, Gansu Province, China
| | - Li Zhang
- Department of Medical Imaging, The Second People's Hospital of Lanzhou, Lanzhou, Gansu Province, China
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Katabathina VS, Marji H, Khanna L, Ramani N, Yedururi S, Dasyam A, Menias CO, Prasad SR. Decoding Genes: Current Update on Radiogenomics of Select Abdominal Malignancies. Radiographics 2021; 40:1600-1626. [PMID: 33001791 DOI: 10.1148/rg.2020200042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Technologic advances in chromosomal analysis and DNA sequencing have enabled genome-wide analysis of cancer cells, yielding considerable data on the genetic basis of malignancies. Evolving knowledge of tumor genetics and oncologic pathways has led to a better understanding of histopathologic features, tumor classification, tumor biologic characteristics, and imaging findings and discovery of targeted therapeutic agents. Radiogenomics is a rapidly evolving field of imaging research aimed at correlating imaging features with gene mutations and gene expression patterns, and it may provide surrogate imaging biomarkers that may supplant genetic tests and be used to predict treatment response and prognosis and guide personalized treatment options. Multidetector CT, multiparametric MRI, and PET with use of multiple radiotracers are some of the imaging techniques commonly used to assess radiogenomic associations. Select abdominal malignancies demonstrate characteristic imaging features that correspond to gene mutations. Recent advances have enabled us to understand the genetics of steatotic and nonsteatotic hepatocellular adenomas, a plethora of morphologic-molecular subtypes of hepatic malignancies, a variety of clear cell and non-clear cell renal cell carcinomas, a myriad of hereditary and sporadic exocrine and neuroendocrine tumors of the pancreas, and the development of targeted therapeutic agents for gastrointestinal stromal tumors based on characteristic KIT gene mutations. Mutations associated with aggressive phenotypes of these malignancies can sometimes be predicted on the basis of their imaging characteristics. Radiologists should be familiar with the genetics and pathogenesis of common cancers that have associated imaging biomarkers, which can help them be integral members of the cancer management team and guide clinicians and pathologists. Online supplemental material is available for this article. ©RSNA, 2020 See discussion on this article by Luna (pp 1627-1630).
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Affiliation(s)
- Venkata S Katabathina
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Haneen Marji
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Lokesh Khanna
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Nisha Ramani
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Sireesha Yedururi
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Anil Dasyam
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Christine O Menias
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Srinivasa R Prasad
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
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Nie P, Wang N, Pang J, Yang G, Duan S, Chen J, Xu W. CT-Based Radiomics Nomogram: A Potential Tool for Differentiating Hepatocellular Adenoma From Hepatocellular Carcinoma in the Noncirrhotic Liver. Acad Radiol 2021; 28:799-807. [PMID: 32386828 DOI: 10.1016/j.acra.2020.04.027] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 04/08/2020] [Accepted: 04/18/2020] [Indexed: 02/06/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate the value of a radiomics nomogram for preoperative differentiating hepatocellular adenoma (HCA) from hepatocellular carcinoma (HCC) in the noncirrhotic liver. MATERIALS AND METHODS One hundred and thirty-one patients with HCA (n = 46) and HCC (n = 85) were divided into a training set (n = 93) and a test set (n = 38). Clinical data and CT findings were analyzed. Radiomics features were extracted from the triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm and a radiomics score was calculated. Combined with the radiomics score and independent clinical factors, a radiomics nomogram was developed by multivariate logistic regression analysis. The performance of the radiomics nomogram was assessed by calibration, discrimination and clinical usefulness. RESULTS Gender, age, and enhancement pattern were the independent clinical factors. Three thousand seven hundred and sixty-eight features were extracted and reduced to 7 features as the optimal discriminators to build the radiomics signature. The radiomics nomogram (area under the curve [AUC], 0.96; 95% confidence interval [CI], 0.93-0.99) and the clinical factors model (AUC, 0.93; 95%CI, 0.88-0.99) showed better discrimination capability (p = 0.001 and 0.047) than the radiomics signature (AUC, 0.83; 95%CI, 0.74-0.92) in the training set. In the test set, the radiomics nomogram (AUC, 0.94; 95%CI, 0.87-1.00) performed better (p = 0.013) than the radiomics signature (AUC, 0.75; 95%CI, 0.59-0.91). Decision curve analysis showed the radiomics nomogram outperformed the clinical factors model and the radiomics signature in terms of clinical usefulness. CONCLUSION The CT-based radiomics nomogram has the potential to accurately differentiate HCA from HCC in the noncirrhotic liver.
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Cheung HMC, Rubin D. Challenges and opportunities for artificial intelligence in oncological imaging. Clin Radiol 2021; 76:728-736. [PMID: 33902889 DOI: 10.1016/j.crad.2021.03.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/15/2021] [Indexed: 02/08/2023]
Abstract
Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, and complications of treatment or disease. The current use of clinical oncology is predominantly qualitative in nature with some relatively crude size-based measurements of tumours for assessment of disease progression or treatment response; however, it is increasingly understood that there may be significantly more information about oncological disease that can be obtained from imaging that is not currently utilized. Artificial intelligence (AI) has the potential to harness quantitative techniques to improve oncological imaging. These may include improving the efficiency or accuracy of traditional roles of imaging such as diagnosis or detection. These may also include new roles for imaging such as risk-stratifying patients for different types of therapy or determining biological tumour subtypes. This review article outlines several major areas in oncological imaging where there may be opportunities for AI technology. These include (1) screening and detection of cancer, (2) diagnosis and risk stratification, (3) tumour segmentation, (4) precision oncology, and (5) predicting prognosis and assessing treatment response. This review will also address some of the potential barriers to AI research in oncological imaging.
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Affiliation(s)
- H M C Cheung
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Canada
| | - D Rubin
- Department of Radiology, Stanford University, CA, USA.
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He T, Fong JN, Moore LW, Ezeana CF, Victor D, Divatia M, Vasquez M, Ghobrial RM, Wong STC. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer. Comput Med Imaging Graph 2021; 89:101894. [PMID: 33725579 PMCID: PMC8054468 DOI: 10.1016/j.compmedimag.2021.101894] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/04/2021] [Accepted: 03/08/2021] [Indexed: 01/27/2023]
Abstract
INTRODUCTION Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients with small HCC (<5 cm) are given priority over others for transplantation due to clinical allocation policies based on tumor size. Attempting to shift from the prevalent paradigm that successful transplantation and longer disease-free survival can only be achieved in patients with small HCC to expanding the transplantation option to patients with HCC of the highest tumor burden (>5 cm), we developed a convergent artificial intelligence (AI) model that combines transient clinical data with quantitative histologic and radiomic features for more objective risk assessment of liver transplantation for HCC patients. METHODS Patients who received a LT for HCC between 2008-2019 were eligible for inclusion in the analysis. All patients with post-LT recurrence were included, and those without recurrence were randomly selected for inclusion in the deep learning model. Pre- and post-transplant magnetic resonance imaging (MRI) scans and reports were compressed using CapsNet networks and natural language processing, respectively, as input for a multiple feature radial basis function network. We applied a histological image analysis algorithm to detect pathologic areas of interest from explant tissue of patients who recurred. The multilayer perceptron was designed as a feed-forward, supervised neural network topology, with the final assessment of recurrence risk. We used area under the curve (AUC) and F-1 score to assess the predictability of different network combinations. RESULTS A total of 109 patients were included (87 in the training group, 22 in the testing group), of which 20 were positive for cancer recurrence. Seven models (AUC; F-1 score) were generated, including clinical features only (0.55; 0.52), magnetic resonance imaging (MRI) only (0.64; 0.61), pathological images only (0.64; 0.61), MRI plus pathology (0.68; 0.65), MRI plus clinical (0.78, 0.75), pathology plus clinical (0.77; 0.73), and a combination of clinical, MRI, and pathology features (0.87; 0.84). The final combined model showed 80 % recall and 89 % precision. The total accuracy of the implemented model was 82 %. CONCLUSION We validated that the deep learning model combining clinical features and multi-scale histopathologic and radiomic image features can be used to discover risk factors for recurrence beyond tumor size and biomarker analysis. Such a predictive, convergent AI model has the potential to alter the LT allocation system for HCC patients and expand the transplantation treatment option to patients with HCC of the highest tumor burden.
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Affiliation(s)
- Tiancheng He
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - Joy Nolte Fong
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Linda W Moore
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States; Center for Outcomes Research, Houston Methodist Research Institute, Houston, TX, 77030, United States
| | - Chika F Ezeana
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - David Victor
- JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX, 77030, United States; Department of Medicine, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Mukul Divatia
- Department of Clinical Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Matthew Vasquez
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - R Mark Ghobrial
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States; JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX, 77030, United States.
| | - Stephen T C Wong
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States.
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Bari H, Wadhwani S, Dasari BVM. Role of artificial intelligence in hepatobiliary and pancreatic surgery. World J Gastrointest Surg 2021; 13:7-18. [PMID: 33552391 PMCID: PMC7830072 DOI: 10.4240/wjgs.v13.i1.7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/08/2020] [Accepted: 12/17/2020] [Indexed: 02/06/2023] Open
Abstract
Over the past decade, enhanced preoperative imaging and visualization, improved delineation of the complex anatomical structures of the liver and pancreas, and intra-operative technological advances have helped deliver the liver and pancreatic surgery with increased safety and better postoperative outcomes. Artificial intelligence (AI) has a major role to play in 3D visualization, virtual simulation, augmented reality that helps in the training of surgeons and the future delivery of conventional, laparoscopic, and robotic hepatobiliary and pancreatic (HPB) surgery; artificial neural networks and machine learning has the potential to revolutionize individualized patient care during the preoperative imaging, and postoperative surveillance. In this paper, we reviewed the existing evidence and outlined the potential for applying AI in the perioperative care of patients undergoing HPB surgery.
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Affiliation(s)
- Hassaan Bari
- Department of HPB and Liver Transplantation Surgery, Queen Elizabeth Hospital, Birmingham B15 2TH, United Kingdom
| | - Sharan Wadhwani
- Department of Radiology, Queen Elizabeth Hospital, Birmingham B15 2TH, United Kingdom
| | - Bobby V M Dasari
- Department of HPB and Liver Transplantation Surgery, Queen Elizabeth Hospital, Birmingham B15 2TH, United Kingdom
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Vidal RIDO, Vidal EIDO, Pereira BDB, Assane CC, Ribeiro A, do Nascimento EM, Romeiro FG, Ribeiro Filho J. Risk Factors for Hepatocellular Carcinoma Recurrence and Survival after Liver Transplantation in Patients with HCV-Related Cirrhosis. BIOMED RESEARCH INTERNATIONAL 2020; 2020:1487593. [PMID: 33134370 PMCID: PMC7591978 DOI: 10.1155/2020/1487593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 10/05/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE We aimed to identify prognostic factors for survival and recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT) for patients with HCC and hepatitis C virus-related cirrhosis (HCV-cirrhosis). METHODS This retrospective cohort study followed all adult patients with HCV-cirrhosis who underwent LT because of HCC or had incidental HCC identified through pathologic examination of the explanted liver at a university hospital in Rio de Janeiro, Brazil, over 11 years (1998-2008). We used Cox regression models to assess the following risk factors regarding HCC recurrence or death after LT: age, Model for End-stage Liver Disease score, Child-Pugh classification, alpha-fetoprotein (AFP), whether patients had undergone locoregional treatment before transplantation, the number of packed red blood cell units (PRBCU) transfused during surgery, the number and size of HCC lesions in the explanted liver, and the presence of microvascular invasion and necrotic areas within HCC lesions. RESULTS Seventy-six patients were followed up for a median (interquartile range (IQR)) of 4.4 (0.7-6.6) years. Thirteen (17%) patients had HCC recurrence during the follow-up period, and 26 (34%) died. The median survival time was 6.6 years (95% CI: 2.4-12.0), and the 5-year survival was 52.5% (95% CI: 42.3-65.0%). The final regression model for overall survival included four variables: age (hazard ratio (HR): 1.02, 95% CI: 0.96-1.08, P = 0.603), transplantation waiting time (HR: 1.00, 95% CI: 1.00-1.00, P = 0.190), preoperative AFP serum levels (HR: 1.01, 95% CI: 1.00-1.02, P = 0.006), and whether >4 PRBCU were transfused during surgery (HR: 1.15, 95% CI: 1.05-1.25, P = 0.001). The final cause-specific Cox regression model for HCC recurrence included only microvascular invasion (HR: 14.86, 95% CI: 4.47-49.39, P < 0.001). CONCLUSION In this study of LT for HCV-cirrhosis, preoperative AFP levels and the number of PRBCU transfused during surgery were associated with overall survival, whereas microvascular invasion with HCC recurrence.
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Affiliation(s)
- Raphael Iglesias de Oliveira Vidal
- Department of Surgery, Faculty of Medicine, Federal University of Rio de Janeiro (UFRJ), Rua Rodolpho Paulo Rocco, 255-Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, Brazil 21941-902
| | - Edison Iglesias de Oliveira Vidal
- Internal Medicine Department, Botucatu Medical School, Sao Paulo State University (UNESP), Av. Prof. Mario Rubens Guimaraes Montenegro, S/N, Botucatu, SP, Brazil 18618-687
| | - Basilio de Bragança Pereira
- Preventive Medicine Department, Faculty of Medicine, Federal University of Rio de Janeiro (UFRJ), Cidade Universitária, Ilha do Fundão, P.O. Box: 68507, Rio de Janeiro, RJ, Brazil 21941-972
| | - Cachimo Combo Assane
- Department of Mathematics and Informatics, Faculty of Sciences, Universidade Eduardo Mondlane, Av. Julius Nyerere/Campus 3453, P.O. Box 257, Maputo, Mozambique
| | - Alexandre Ribeiro
- Department of Surgery, Faculty of Medicine, Federal University of Rio de Janeiro (UFRJ), Rua Rodolpho Paulo Rocco, 255-Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, Brazil 21941-902
| | - Emilia Matos do Nascimento
- Centro Universitário da Zona Oeste, UEZO-Unidade de Engenharia de Produção, Engenharia de Produção, Avenida Manuel Caldeira de Alvarenga, Campo Grande, Rio de Janeiro, RJ, Brazil 23070-200
| | - Fernando Gomes Romeiro
- Internal Medicine Department, Botucatu Medical School, Sao Paulo State University (UNESP), Av. Prof. Mario Rubens Guimaraes Montenegro, S/N, Botucatu, SP, Brazil 18618-687
| | - Joaquim Ribeiro Filho
- Department of Surgery, Faculty of Medicine, Federal University of Rio de Janeiro (UFRJ), Rua Rodolpho Paulo Rocco, 255-Cidade Universitária, Ilha do Fundão, Rio de Janeiro, RJ, Brazil 21941-902
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Bagante F, Tripepi M, Spolverato G, Tsilimigras DI, Pawlik TM. Assessing prognosis in cholangiocarcinoma: a review of promising genetic markers and imaging approaches. Expert Opin Orphan Drugs 2020. [DOI: 10.1080/21678707.2020.1801410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Fabio Bagante
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Marzia Tripepi
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Gaya Spolverato
- Clinica Chirurgica I, Department of Surgical, Oncological and Gastroenterological Sciences (Discog), University of Padova, Padova, Italy
| | - Diamantis I. Tsilimigras
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Timothy M. Pawlik
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
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Current status and quality of radiomics studies in lymphoma: a systematic review. Eur Radiol 2020; 30:6228-6240. [PMID: 32472274 DOI: 10.1007/s00330-020-06927-1] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/25/2020] [Accepted: 04/28/2020] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To perform a systematic review regarding the developments in the field of radiomics in lymphoma. To evaluate the quality of included articles by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), the phases classification criteria for image mining studies, and the radiomics quality scoring (RQS) tool. METHODS We searched for eligible articles in the MEDLINE/PubMed and EMBASE databases using the terms "radiomics", "texture" and "lymphoma". The included studies were divided into two categories: diagnosis-, therapy response- and outcome-related studies. The diagnosis-related studies were evaluated using the QUADAS-2; all studies were evaluated using the phases classification criteria for image mining studies and the RQS tool by two reviewers. RESULTS Forty-five studies were included; thirteen papers (28.9%) focused on the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). Thirty-two (71.1%) studies were classified as discovery science according to the phase classification criteria for image mining studies. The mean RQS score of all studies was 14.2% (ranging from 0.0 to 40.3%), and 23 studies (51.1%) were given a score of < 10%. CONCLUSION The radiomics features could serve as diagnostic and prognostic indicators in lymphoma. However, the current conclusions should be interpreted with caution due to the suboptimal quality of the studies. In order to introduce radiomics into lymphoma clinical settings, the lesion segmentation and selection, the influence of the pathological pattern and the extraction of multiple modalities and multiple time points features need to be further studied. KEY POINTS • The radiomics approach may provide useful information for diagnosis, prediction of the therapy response, and outcome of lymphoma. • The quality of published radiomics studies in lymphoma has been suboptimal to date. • More studies are needed to examine lesion selection and segmentation, the influence of pathological patterns, and the extraction of multiple modalities and multiple time point features.
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Simpson G, Spieler B, Dogan N, Portelance L, Mellon EA, Kwon D, Ford JC, Yang F. Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic ablative body radiotherapy of pancreatic cancer: A pilot study. Med Phys 2020; 47:3682-3690. [DOI: 10.1002/mp.14200] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 03/31/2020] [Accepted: 04/06/2020] [Indexed: 12/14/2022] Open
Affiliation(s)
- Garrett Simpson
- Department of Radiation Oncology University of Miami Miami FL 33136 USA
| | - Benjamin Spieler
- Department of Radiation Oncology University of Miami Miami FL 33136 USA
| | - Nesrin Dogan
- Department of Radiation Oncology University of Miami Miami FL 33136 USA
| | | | - Eric A. Mellon
- Department of Radiation Oncology University of Miami Miami FL 33136 USA
| | - Deukwoo Kwon
- Department of Radiation Oncology University of Miami Miami FL 33136 USA
| | - John C. Ford
- Department of Radiation Oncology University of Miami Miami FL 33136 USA
| | - Fei Yang
- Department of Radiation Oncology University of Miami Miami FL 33136 USA
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Dong Y, Zhou L, Xia W, Zhao XY, Zhang Q, Jian JM, Gao X, Wang WP. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images. Front Oncol 2020; 10:353. [PMID: 32266138 PMCID: PMC7096379 DOI: 10.3389/fonc.2020.00353] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 02/28/2020] [Indexed: 02/06/2023] Open
Abstract
Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross- and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models. Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603-0.812), 0.710 (95% CI, 0.609-0.811), and 0.726 (95% CI, 0.625-0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646-0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667-0.944). Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.
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Affiliation(s)
- Yi Dong
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Liu Zhou
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Wei Xia
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Xing-Yu Zhao
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Qi Zhang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jun-Ming Jian
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Xin Gao
- Suzhou Institute of Biomedical Engineering and Technology (CAS), Suzhou, China
| | - Wen-Ping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
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Nie P, Yang G, Guo J, Chen J, Li X, Ji Q, Wu J, Cui J, Xu W. A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver. Cancer Imaging 2020; 20:20. [PMID: 32093786 PMCID: PMC7041197 DOI: 10.1186/s40644-020-00297-z] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 02/19/2020] [Indexed: 02/07/2023] Open
Abstract
Background The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiating focal nodular hyperplasia (FNH) from hepatocellular carcinoma (HCC) in the non-cirrhotic liver. Methods A total of 156 patients with FNH (n = 55) and HCC (n = 101) were divided into a training set (n = 119) and a validation set (n = 37). Radiomics features were extracted from triphasic contrast CT images. A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm, and a radiomics score (Rad-score) was calculated. Clinical data and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed by multivariate logistic regression analysis. Nomogram performance was assessed with respect to discrimination and clinical usefulness. Results Four thousand two hundred twenty-seven features were extracted and reduced to 10 features as the most important discriminators to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.964; 95% confidence interval [CI], 0.934–0.995) and the validation set (AUC, 0.865; 95% CI, 0.725–1.000). Age, Hepatitis B virus infection, and enhancement pattern were the independent clinical factors. The radiomics nomogram, which incorporated the Rad-score and clinical factors, showed good discrimination in the training set (AUC, 0.979; 95% CI, 0.959–0.998) and the validation set (AUC, 0.917; 95% CI, 0.800–1.000), and showed better discrimination capability (P < 0.001) compared with the clinical factors model (AUC, 0.799; 95% CI, 0.719–0.879) in the training set. Decision curve analysis showed the nomogram outperformed the clinical factors model in terms of clinical usefulness. Conclusions The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating FNH from HCC in the non-cirrhotic liver, which might facilitate clinical decision-making process.
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Affiliation(s)
- Pei Nie
- Department of Radiology, the Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, Shandong, China
| | - Guangjie Yang
- Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jian Guo
- Department of Radiology, the Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, Shandong, China
| | - Jingjing Chen
- Department of Radiology, the Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, Shandong, China
| | - Xiaoli Li
- Department of Radiology, the Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, Shandong, China
| | - Qinglian Ji
- Department of Radiology, the Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, Shandong, China
| | - Jie Wu
- Department of Pathology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jingjing Cui
- Huiying Medical Technology Co., Ltd, Beijing, China
| | - Wenjian Xu
- Department of Radiology, the Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, Shandong, China.
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Peña-Nogales Ó, Hernando D, Aja-Fernández S, de Luis-Garcia R. Determination of optimized set of b-values for Apparent Diffusion Coefficient mapping in liver Diffusion-Weighted MRI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 310:106634. [PMID: 31710951 DOI: 10.1016/j.jmr.2019.106634] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 10/21/2019] [Accepted: 10/25/2019] [Indexed: 06/10/2023]
Abstract
In this manuscript we derive the Cramér-Rao Lower Bound (CRLB) of the monoexponential diffusion-weighted signal model under a realistic noise assumption, and propose a formulation to obtain optimized sets of b-values that maximize the noise performance of the Apparent Diffusion Coefficient (ADC) maps given a target ADC and a signal-to-noise ratio. Therefore, for various sets of parameters (S0 and ADC), signal-to-noise ratios (SNR) and noise distribution, we computed optimized sets of b-values using CRLB-based analysis in two different ways: (i) through a greedy algorithm where b-values from a pool of candidates were added iteratively to the set, and (ii) through a two b-value search algorithm were all two b-value combinations of the pool of candidates were tested. Further, optimized sets of b-values were computed from synthetic data, phantoms, and in-vivo liver diffusion-weighted imaging (DWI) experiments to validate the CRLB-based analysis. The optimized sets of b-values obtained through the proposed CRLB-based analysis showed good agreement with the optimized sets obtained experimentally from synthetic, phantoms, and in-vivo liver data. The variance of the ADC maps decreased when employing the optimized set of b-values compared to various sets of b-values proposed in the literature for in-vivo liver DWI, although differences of notable magnitude between noise models and optimization strategies were not found. In addition, the higher b-values decreased for lower SNR under the Rician noise distribution. Optimization of the set b-values is critical to maximize the noise performance (i.e., maximize the precision and minimize the variance) of the estimated ADC maps in diffusion-weighted MRI. Hence, the proposed approach may help to optimize and standardize liver diffusion-weighted MRI acquisitions by computing optimized set of b-values for a given set of parameters.
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Affiliation(s)
- Óscar Peña-Nogales
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain. http://www.lpi.tel.uva.es
| | - Diego Hernando
- Departments of Radiology, Medical Physics, and Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
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Chen H, He Y, Jia W. Precise hepatectomy in the intelligent digital era. Int J Biol Sci 2020; 16:365-373. [PMID: 32015674 PMCID: PMC6990894 DOI: 10.7150/ijbs.39387] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 11/11/2019] [Indexed: 12/13/2022] Open
Abstract
In the past 20 years, the concept of surgery has undergone profound changes. Surgical practice has shifted from emphasizing the complete elimination of lesions to achieving optimal rehabilitation in patients. Collaborative optimization of surgery consists of three core elements, removal of lesions, organ protection and injury close monitoring, and controlled surgical intervention. As a result, the traditional surgical paradigm has quietly transformed into a modern precision surgical paradigm. In this review, we summarized the latest breakthroughs and applications of precision medicine in liver surgery. In addition, we also outlined the progresses that have been made in precision liver surgery, the opportunities and challenges that may encountered in the future.
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Affiliation(s)
- Hao Chen
- Department of Hepatic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, HeFei, 230001, China.,Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, HeFei, 230001, China
| | - Yuchen He
- Xiangya School of Medicine, Central South University, ChangSha, 410008, China
| | - Weidong Jia
- Department of Hepatic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, HeFei, 230001, China.,Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, HeFei, 230001, China
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A Comparison of the Efficiency of Using a Deep CNN Approach with Other Common Regression Methods for the Prediction of EGFR Expression in Glioblastoma Patients. J Digit Imaging 2019; 33:391-398. [PMID: 31797142 DOI: 10.1007/s10278-019-00290-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
To estimate epithermal growth factor receptor (EGFR) expression level in glioblastoma (GBM) patients using radiogenomic analysis of magnetic resonance images (MRI). A comparative study using a deep convolutional neural network (CNN)-based regression, deep neural network, least absolute shrinkage and selection operator (LASSO) regression, elastic net regression, and linear regression with no regularization was carried out to estimate EGFR expression of 166 GBM patients. Except for the deep CNN case, overfitting was prevented by using feature selection, and loss values for each method were compared. The loss values in the training phase for deep CNN, deep neural network, Elastic net, LASSO, and the linear regression with no regularization were 2.90, 8.69, 7.13, 14.63, and 21.76, respectively, while in the test phase, the loss values were 5.94, 10.28, 13.61, 17.32, and 24.19 respectively. These results illustrate that the efficiency of the deep CNN approach is better than that of the other methods, including Lasso regression, which is a regression method known for its advantage in high-dimension cases. A comparison between deep CNN, deep neural network, and three other common regression methods was carried out, and the efficiency of the CNN deep learning approach, in comparison with other regression models, was demonstrated.
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46
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Loponte S, Lovisa S, Deem AK, Carugo A, Viale A. The Many Facets of Tumor Heterogeneity: Is Metabolism Lagging Behind? Cancers (Basel) 2019; 11:E1574. [PMID: 31623133 PMCID: PMC6826850 DOI: 10.3390/cancers11101574] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/03/2019] [Accepted: 10/09/2019] [Indexed: 12/13/2022] Open
Abstract
Tumor functional heterogeneity has been recognized for decades, and technological advancements are fueling renewed interest in uncovering the cell-intrinsic and extrinsic factors that influence tumor development and therapeutic response. Intratumoral heterogeneity is now arguably one of the most-studied topics in tumor biology, leading to the discovery of new paradigms and reinterpretation of old ones, as we aim to understand the profound implications that genomic, epigenomic, and functional heterogeneity hold with regard to clinical outcomes. In spite of our improved understanding of the biological complexity of cancer, characterization of tumor metabolic heterogeneity has lagged behind, lost in a century-old controversy debating whether glycolysis or mitochondrial respiration is more influential. But is tumor metabolism really so simple? Here, we review historical and current views of intratumoral heterogeneity, with an emphasis on summarizing the emerging data that begin to illuminate just how vast the spectrum of metabolic strategies a tumor can employ may be, and what this means for how we might interpret other tumor characteristics, such as mutational landscape, contribution of microenvironmental influences, and treatment resistance.
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Affiliation(s)
- Sara Loponte
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
| | - Sara Lovisa
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
| | - Angela K Deem
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
| | - Alessandro Carugo
- TRACTION platform, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
| | - Andrea Viale
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
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Wakabayashi T, Ouhmich F, Gonzalez-Cabrera C, Felli E, Saviano A, Agnus V, Savadjiev P, Baumert TF, Pessaux P, Marescaux J, Gallix B. Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int 2019; 13:546-559. [PMID: 31473947 PMCID: PMC7613479 DOI: 10.1007/s12072-019-09973-0] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/26/2019] [Indexed: 12/24/2022]
Abstract
Radiomics is an emerging field which extracts quantitative radiology data from medical images and explores their correlation with clinical outcomes in a non-invasive manner. This review aims to assess whether radiomics is a useful and reproducible method for clinical management of hepatocellular carcinoma (HCC) by reviewing the strengths and weaknesses of current radiomics literature pertaining specifically to HCC. From an initial set of 48 articles recovered through database searches, 23 articles were retained to be included in this review after full screening. Among these 23 studies, 7 used a radiomics approach in magnetic resonance imaging (MRI). Only two studies applied radiomics to positron emission tomography-computed tomography (PET-CT). In the remaining 14 articles, a radiomics analysis was performed on computed tomography (CT). Eight studies dealt with the relationship between biological signatures and imaging findings, and can be classified as radiogenomic studies. For each study included in our review, we computed a Radiomics Quality Score (RQS) as proposed by Lambin et al. We found that the RQS (mean ± standard deviation) was 8.35 ± 5.38 (out of a possible maximum value of 36). Although these scores are fairly low, and radiomics has not yet reached clinical utility in HCC, it is important to underscore the fact that these early studies pave the way for the radiomics field with a focus on HCC. Radiomics is still a very young field, and is far from being mature, but it remains a very promising technology for the future for developing adequate personalized treatment as a non-invasive approach, for complementing or replacing tumor biopsies, as well as for developing novel prognostic biomarkers in HCC patients.
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Affiliation(s)
- Taiga Wakabayashi
- Institut de Recherche Contre les Cancers de l'Appareil Digestif (IRCAD), Strasbourg, France
| | - Farid Ouhmich
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France
| | - Cristians Gonzalez-Cabrera
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France
| | - Emanuele Felli
- Institut de Recherche Contre les Cancers de l'Appareil Digestif (IRCAD), Strasbourg, France
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France
- General, Digestive, and Endocrine Surgery, Nouvel Hôpital Civil, Université de Strasbourg, Strasbourg, France
- Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Université de Strasbourg, Strasbourg, France
- Pôle Hépato-digestif, Hôpitaux Universitaires, Strasbourg, France
| | - Antonio Saviano
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France
- Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Université de Strasbourg, Strasbourg, France
- Pôle Hépato-digestif, Hôpitaux Universitaires, Strasbourg, France
| | - Vincent Agnus
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France
| | - Peter Savadjiev
- Department of Diagnostic Radiology, McGill University, Montreal, Canada
| | - Thomas F Baumert
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France
- Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Université de Strasbourg, Strasbourg, France
- Pôle Hépato-digestif, Hôpitaux Universitaires, Strasbourg, France
| | - Patrick Pessaux
- Institut de Recherche Contre les Cancers de l'Appareil Digestif (IRCAD), Strasbourg, France
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France
- General, Digestive, and Endocrine Surgery, Nouvel Hôpital Civil, Université de Strasbourg, Strasbourg, France
- Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Université de Strasbourg, Strasbourg, France
- Pôle Hépato-digestif, Hôpitaux Universitaires, Strasbourg, France
| | - Jacques Marescaux
- Institut de Recherche Contre les Cancers de l'Appareil Digestif (IRCAD), Strasbourg, France
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France
- General, Digestive, and Endocrine Surgery, Nouvel Hôpital Civil, Université de Strasbourg, Strasbourg, France
| | - Benoit Gallix
- Institut hospitalo-universitaire (IHU), Institute for Minimally Invasive Hybrid Image-Guided Surgery, Université de Strasbourg, 1 Place de l'Hôpital, 67000, Strasbourg, France.
- Department of Diagnostic Radiology, McGill University, Montreal, Canada.
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48
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Kim CG, Lee HW, Choi HJ, Lee JI, Lee HW, Kim SU, Park JY, Kim DY, Ahn SH, Han K, Kim HS, Kim KH, Choi SJ, Kim Y, Lee KS, Kim GM, Kim MD, Won JY, Lee DY, Kim BK. Development and validation of a prognostic model for patients with hepatocellular carcinoma undergoing radiofrequency ablation. Cancer Med 2019; 8:5023-5032. [PMID: 31290618 PMCID: PMC6718586 DOI: 10.1002/cam4.2417] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 06/27/2019] [Accepted: 06/30/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND There are large variations in prognosis among hepatocellular carcinoma (HCC) patients undergoing radiofrequency ablation (RFA). However, current staging or scoring systems hardly discriminate the outcome of HCC patients treated with RFA. METHODS A total of 757 treatment-naïve HCC patients undergoing RFA (derivation cohort) were analyzed to establish a nomogram for disease-free survival (DFS) based on Cox proportional hazard regression model. Accuracy of the nomogram was assessed and compared with conventional staging or scoring systems. Furthermore, external validation was performed in an independent cohort including 208 patients (validation cohort). RESULTS Tumor size, tumor number, alpha-fetoprotein, prothrombin induced by vitamin K absence-II, lymphocyte count, albumin, and presence of ascites were adopted to construct the prognostic nomogram from the derivation cohort. Calibration curves to predict probability of DFS at 3 and 5 years after RFA showed good agreements between the nomogram and actual observations. The concordance index of the present nomogram was 0.759 (95% confidence interval 0.728-0.790), which was superior to those of conventional staging or scoring systems (range 0.505-0.683, all P < .001). These results were also reproduced in the validation cohort. CONCLUSION Our simple-to-use nomogram optimized for treatment-naïve HCC patients undergoing RFA provided better prognostic performance than conventional staging or scoring systems.
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Affiliation(s)
- Chang Gon Kim
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea
| | - Hyun Woong Lee
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
| | - Hye Jin Choi
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Jung Il Lee
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
| | - Hye Won Lee
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
| | - Seung Up Kim
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
- Yonsei Liver CenterSeverance HospitalSeoulRepublic of Korea
| | - Jun Yong Park
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
- Yonsei Liver CenterSeverance HospitalSeoulRepublic of Korea
| | - Do Young Kim
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
- Yonsei Liver CenterSeverance HospitalSeoulRepublic of Korea
| | - Sang Hoon Ahn
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
- Yonsei Liver CenterSeverance HospitalSeoulRepublic of Korea
| | - Kwang‐Hyub Han
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
- Yonsei Liver CenterSeverance HospitalSeoulRepublic of Korea
| | - Han Sang Kim
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | - Kyung Hwan Kim
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea
| | - Seong Jin Choi
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea
| | - Yongun Kim
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea
| | - Kwan Sik Lee
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
| | - Gyoung Min Kim
- Department of RadiologyYonsei University College of MedicineSeoulRepublic of Korea
| | - Man Deuk Kim
- Department of RadiologyYonsei University College of MedicineSeoulRepublic of Korea
| | - Jong Yoon Won
- Department of RadiologyYonsei University College of MedicineSeoulRepublic of Korea
| | - Do Yun Lee
- Department of RadiologyYonsei University College of MedicineSeoulRepublic of Korea
| | - Beom Kyung Kim
- Department of Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
- Institute of GastroenterologyYonsei University College of MedicineSeoulRepublic of Korea
- Yonsei Liver CenterSeverance HospitalSeoulRepublic of Korea
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Ni M, Zhou X, Lv Q, Li Z, Gao Y, Tan Y, Liu J, Liu F, Yu H, Jiao L, Wang G. Radiomics models for diagnosing microvascular invasion in hepatocellular carcinoma: which model is the best model? Cancer Imaging 2019; 19:60. [PMID: 31455432 PMCID: PMC6712704 DOI: 10.1186/s40644-019-0249-x] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 08/14/2019] [Indexed: 12/16/2022] Open
Abstract
Objectives To explore the feasibility of diagnosing microvascular invasion (MVI) with radiomics, to compare the diagnostic performance of different models established by each method, and to determine the best diagnostic model based on radiomics. Methods A retrospective analysis was conducted with 206 cases of hepatocellular carcinoma (HCC) confirmed through surgery and pathology in our hospital from June 2015 to September 2018. Among the samples, 88 were MVI-positive, and 118 were MVI-negative. The radiomics analysis process included tumor segmentation, feature extraction, data preprocessing, dimensionality reduction, modeling and model evaluation. Results A total of 1044 sets of texture feature parameters were extracted, and 21 methods were used for the radiomics analysis. All research methods could be used to diagnose MVI. Of all the methods, the LASSO+GBDT method had the highest accuracy, the LASSO+RF method had the highest sensitivity, the LASSO+BPNet method had the highest specificity, and the LASSO+GBDT method had the highest AUC. Through Z-tests of the AUCs, LASSO+GBDT, LASSO+K-NN, LASSO+RF, PCA + DT, and PCA + RF had Z-values greater than 1.96 (p<0.05). The DCA results showed that the LASSO + GBDT method was better than the other methods when the threshold probability was greater than 0.22. Conclusions Radiomics can be used for the preoperative, noninvasive diagnosis of MVI, but different dimensionality reduction and modeling methods will affect the diagnostic performance of the final model. The model established with the LASSO+GBDT method had the optimal diagnostic performance and the greatest diagnostic value for MVI.
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Affiliation(s)
- Ming Ni
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Xiaoming Zhou
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
| | - Qian Lv
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Yuanxiang Gao
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Yongqi Tan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Jihua Liu
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Fang Liu
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Haiyang Yu
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Linlin Jiao
- Intervention Medical Center, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China
| | - Gang Wang
- Department of Radiology, The Affiliated Hospital of QingDao University, QingDao, ShanDong, China.
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Ouhmich F, Agnus V, Noblet V, Heitz F, Pessaux P. Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks. Int J Comput Assist Radiol Surg 2019; 14:1275-1284. [PMID: 31041697 DOI: 10.1007/s11548-019-01989-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Accepted: 04/24/2019] [Indexed: 12/24/2022]
Abstract
PURPOSE We address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on multiphase CT images using a deep learning approach. METHODS We devise a cascaded convolutional neural network based on the U-Net architecture. Two strategies for dealing with multiphase information are compared: Single-phase images are concatenated in a multi-dimensional features map on the input layer, or output maps are computed independently for each phase before being merged to produce the final segmentation. Each network of the cascade is specialized in the segmentation of a specific tissue. The performances of these networks taken separately and of the cascaded architecture are assessed on both single-phase and on multiphase images. RESULTS In terms of Dice coefficients, the proposed method is on par with a state-of-the-art method designed for automatic MR image segmentation and outperforms previously used technique for interactive CT image segmentation. We validate the hypothesis that several cascaded specialized networks have a higher prediction accuracy than a single network addressing all tasks simultaneously. Although the portal venous phase alone seems to provide sufficient contrast for discriminating tumors from healthy parenchyma, the multiphase information brings significant improvement for the segmentation of cancerous tissues (active versus necrotic part). CONCLUSION The proposed cascaded multiphase architecture showed promising performances for the automatic segmentation of liver tissues, allowing to reliably estimate the necrosis rate, a valuable imaging biomarker of the clinical outcome.
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Affiliation(s)
- Farid Ouhmich
- Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l'Hôpital, 67000, Strasbourg, France.
| | - Vincent Agnus
- Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l'Hôpital, 67000, Strasbourg, France
| | - Vincent Noblet
- ICube UMR 7357, University of Strasbourg, CNRS, FMTS, 300 bd Sébastien Brant, 67412, Illkirch, France
| | - Fabrice Heitz
- ICube UMR 7357, University of Strasbourg, CNRS, FMTS, 300 bd Sébastien Brant, 67412, Illkirch, France
| | - Patrick Pessaux
- Department of Hepato-Biliary and Pancreatic Surgery, Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l'Hôpital, 67000, Strasbourg, France
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