|
1
|
Goto M, Futamura Y, Makishima H, Saito T, Sakamoto N, Iijima T, Tamaki Y, Okumura T, Sakurai T, Sakurai H. Development of a deep learning-based model to evaluate changes during radiotherapy using cervical cancer digital pathology. JOURNAL OF RADIATION RESEARCH 2025; 66:144-156. [PMID: 40051384 PMCID: PMC11932348 DOI: 10.1093/jrr/rraf004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2024] [Revised: 12/20/2024] [Accepted: 01/24/2025] [Indexed: 03/25/2025]
Abstract
This study aims to create a deep learning-based classification model for cervical cancer biopsy before and during radiotherapy, visualize the results on whole slide images (WSIs), and explore the clinical significance of obtained features. This study included 95 patients with cervical cancer who received radiotherapy between April 2013 and December 2020. Hematoxylin-eosin stained biopsies were digitized to WSIs and divided into small tiles. Our model adopted the feature extractor of DenseNet121 and the classifier of the support vector machine. About 12 400 tiles were used for training the model and 6000 tiles for testing. The model performance was assessed on a per-tile and per-WSI basis. The resultant probability was defined as radiotherapy status probability (RSP) and its color map was visualized on WSIs. Survival analysis was performed to examine the clinical significance of the RSP. In the test set, the trained model had an area under the receiver operating characteristic curve of 0.76 per-tile and 0.95 per-WSI. In visualization, the model focused on viable tumor components and stroma in tumor biopsies. While survival analysis failed to show the prognostic impact of RSP during treatment, cases with low RSP at diagnosis had prolonged overall survival compared to those with high RSP (P = 0.045). In conclusion, we successfully developed a model to classify biopsies before and during radiotherapy and visualized the result on slide images. Low RSP cases before treatment had a better prognosis, suggesting that tumor morphologic features obtained using the model may be useful for predicting prognosis.
Collapse
Affiliation(s)
- Masaaki Goto
- Department of Radiation Oncology & Proton Medical Research Center, Institute of Medicine, University of Tsukuba, 2-1-1 Amakubo, Tsubuka, Ibaraki 305-8576, Japan
- Department of Radiation Oncology, Japan Red Cross Medical Center, 4-1-22 Hiroo, Shibuya, Tokyo 150-8935, Japan
| | - Yasunori Futamura
- Center for Artificial Intelligence Research, University of Tsukuba, 1-1-1 Tennodai, Tsubuka, Ibaraki 305-8577, Japan
- Department of Computer Science, University of Tsukuba, 1-1-1 Tennodai, Tsubuka, Ibaraki 305-8577, Japan
| | - Hirokazu Makishima
- Department of Radiation Oncology & Proton Medical Research Center, Institute of Medicine, University of Tsukuba, 2-1-1 Amakubo, Tsubuka, Ibaraki 305-8576, Japan
- QST Hospital, National Institute for Quantum Science and Technology, 4-9-1 Anagawa, Inage, Chiba 263-8555, Japan
| | - Takashi Saito
- Department of Radiation Oncology & Proton Medical Research Center, Institute of Medicine, University of Tsukuba, 2-1-1 Amakubo, Tsubuka, Ibaraki 305-8576, Japan
| | - Noriaki Sakamoto
- Department of Diagnostic Pathology, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsubuka, Ibaraki 305-8575, Japan
| | - Tatsuo Iijima
- Department of Diagnostic Pathology, Ibaraki Prefectural Central Hospital, 6528 Koibuchi, Kasama, Ibaraki 309-1793, Japan
| | - Yoshio Tamaki
- Department of Radiation Oncology, Fukushima Rosai Hospital, 3 Numaziri, Uchigotsuzuramachi, Iwaki, Fukushima 973-8403, Japan
| | - Toshiyuki Okumura
- Department of Radiation Oncology, Ibaraki Prefectural Central Hospital, 6528 Koibuchi, Kasama, Ibaraki 309-1793, Japan
| | - Tetsuya Sakurai
- Center for Artificial Intelligence Research, University of Tsukuba, 1-1-1 Tennodai, Tsubuka, Ibaraki 305-8577, Japan
- Department of Computer Science, University of Tsukuba, 1-1-1 Tennodai, Tsubuka, Ibaraki 305-8577, Japan
| | - Hideyuki Sakurai
- Department of Radiation Oncology & Proton Medical Research Center, Institute of Medicine, University of Tsukuba, 2-1-1 Amakubo, Tsubuka, Ibaraki 305-8576, Japan
| |
Collapse
|
|
2
|
Shah SNA, Parveen R. Differential gene expression analysis and machine learning identified structural, TFs, cytokine and glycoproteins, including SOX2, TOP2A, SPP1, COL1A1, and TIMP1 as potential drivers of lung cancer. Biomarkers 2025; 30:200-215. [PMID: 39888730 DOI: 10.1080/1354750x.2025.2461698] [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: 08/13/2024] [Accepted: 01/26/2025] [Indexed: 02/02/2025]
Abstract
BACKGROUND Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal is to slow disease progression and intervene early to prevent the development of advanced lung cancer cases. Hence, there's an urgent need for new biomarkers that can detect lung cancer in its early stages. METHODS The study conducted RNA-Seq analysis of lung cancer samples from the publicly available SRA database (NCBI SRP009408), including both control and tumour samples. The genes with differential expression between tumour and healthy tissues were identified using R and Bioconductor. Machine learning (ML) techniques, Random Forest, Lasso, XGBoost, Gradient Boosting and Elastic Net were employed to pinpoint significant genes followed by classifiers, Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbours (k-NN). Gene ontology and pathway analyses were performed on the significant differentially expressed genes (DEGs). The top genes from DEG and machine learning analyses were combined for protein-protein interaction (PPI) analysis, identifying 10 hub genes essential for lung cancer progression. RESULTS The integrated analysis of ML and DEGs revealed the significance of specific genes in lung cancer samples, identified the top 5 upregulated genes (COL11A1, TOP2A, SULF1, DIO2, MIR196A2) and the top 5 downregulated genes (PDK4, FOSB, FLYWCH1, CYB5D2, MIR328), along with their associated genes implicated in pathways or co-expression networks were identified. Among the various algorithms employed, Random Forest and XGBoost proved effective in identifying common genes, underscoring their potential significance in lung cancer pathogenesis. The MLP exhibited the highest accuracy in classifying samples using all genes. Additionally, the protein-protein interaction (PPI) analysis identified 10 hub genes that are pivotal in lung cancer pathogenesis: COL1A1, SOX2, SPP1, THBS2, POSTN, COL5A1, COL11A1, TIMP1, TOP2A and PKP1. CONCLUSION The study contributes to the early prediction of lung cancer by identifying potential biomarkers that could enhance early diagnosis and pave the way for practical clinical applications in the future. Integrating DEGs and machine learning-derived significant genes for PPI analysis offers a robust approach to uncovering critical molecular targets for lung cancer treatment.
Collapse
Affiliation(s)
| | - Rafat Parveen
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India
| |
Collapse
|
|
3
|
Santos CS, Amorim-Lopes M. Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review. BMC Med Res Methodol 2025; 25:45. [PMID: 39984835 PMCID: PMC11843972 DOI: 10.1186/s12874-025-02463-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: 11/09/2023] [Accepted: 01/03/2025] [Indexed: 02/23/2025] Open
Abstract
BACKGROUND This scoping review systematically maps externally validated machine learning (ML)-based models in cancer patient care, quantifying their performance, and clinical utility, and examining relationships between models, cancer types, and clinical decisions. By synthesizing evidence, this study identifies, strengths, limitations, and areas requiring further research. METHODS The review followed the Joanna Briggs Institute's methodology, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, and the Population, Concept, and Context mnemonic. Searches were conducted across Embase, IEEE Xplore, PubMed, Scopus, and Web of Science (January 2014-September 2022), targeting English-language quantitative studies in Q1 journals (SciMago Journal and Country Ranking > 1) that used ML to evaluate clinical outcomes for human cancer patients with commonly available data. Eligible models required external validation, clinical utility assessment, and performance metric reporting. Studies involving genetics, synthetic patients, plants, or animals were excluded. Results were presented in tabular, graphical, and descriptive form. RESULTS From 4023 deduplicated abstracts and 636 full-text reviews, 56 studies (2018-2022) met the inclusion criteria, covering diverse cancer types and applications. Convolutional neural networks were most prevalent, demonstrating high performance, followed by gradient- and decision tree-based algorithms. Other algorithms, though underrepresented, showed promise. Lung and digestive system cancers were most frequently studied, focusing on diagnosis and outcome predictions. Most studies were retrospective and multi-institutional, primarily using image-based data, followed by text-based and hybrid approaches. Clinical utility assessments involved 499 clinicians and 12 tools, indicating improved clinician performance with AI assistance and superior performance to standard clinical systems. DISCUSSION Interest in ML-based clinical decision-making has grown in recent years alongside increased multi-institutional collaboration. However, small sample sizes likely impacted data quality and generalizability. Persistent challenges include limited international validation across ethnicities, inconsistent data sharing, disparities in validation metrics, and insufficient calibration reporting, hindering model comparison reliability. CONCLUSION Successful integration of ML in oncology decision-making requires standardized data and methodologies, larger sample sizes, greater transparency, and robust validation and clinical utility assessments. OTHER Financed by FCT-Fundação para a Ciência e a Tecnologia (Portugal, project LA/P/0063/2020, grant 2021.09040.BD) as part of CSS's Ph.D. This work was not registered.
Collapse
Affiliation(s)
- Catarina Sousa Santos
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal.
| | - Mário Amorim-Lopes
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal
| |
Collapse
|
|
4
|
Zeng Q, Jia F, Tang S, He H, Fu Y, Wang X, Zhang J, Tan Z, Tang H, Wang J, Yi X, Chen BT. Ensemble learning-based radiomics model for discriminating brain metastasis from glioblastoma. Eur J Radiol 2025; 183:111900. [PMID: 39733718 DOI: 10.1016/j.ejrad.2024.111900] [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: 04/26/2024] [Revised: 10/24/2024] [Accepted: 12/21/2024] [Indexed: 12/31/2024]
Abstract
OBJECTIVE Differentiating between brain metastasis (BM) and glioblastoma (GBM) preoperatively is challenging due to their similar imaging features on conventional brain MRI. This study aimed to enhance diagnostic accuracy through a machine learning model based on MRI radiomics data. METHODS This retrospective study included 235 patients with confirmed solitary BM and 273 patients with GBM. Patients were randomly assigned to the training (n = 356) or the validation (n = 152) cohort. Conventional brain MRI sequences including T1-weighted imaging (T1WI), contrast-enhanced_T1WI, and T2-weighted imaging (T2WI) were acquired. Brain tumors were delineated on all three sequences and segmented. Features were selected from demographic, clinical, and radiomic data. An integrated ensemble machine learning model, i.e., the elastic regression-SVM-SVM model (ERSS) and a multivariable logistic regression (LR) model combining demographic, clinical, and radiomic data were built for predictive modeling. Model efficiency was evaluated using discrimination, calibration, and decision curve analyses. Additionally, external validation was performed using an independent cohort consisting of 47 patients with GBM and 43 patients with isolated BM to assess the ERSS model generalizability. RESULTS The ERSS model demonstrated more optimal classification performance (AUC: 0.9548, 95% CI: 0.9337-0.9734 in training cohort; AUC: 0.9716, 95% CI: 0.9485-0.9895 in validation cohort) as compared to the LR model according to the receiver operating characteristic (ROC) curve and decision curve for the internal cohort. The external validation cohort had less optimal but still robust performance (AUC: 0.7174, 95% CI: 0.6172-0.8024). The ERSS model with integration of multiple classifiers, including elastic net, random forest and support vector machine, produced robust predictive performance and outperformed the LR method. CONCLUSION The results suggested that the integrated machine learning model, i.e., the ERSS model, had the potential for efficient and accurate preoperative differentiation of BM from GBM, which may improve clinical decision-making and outcomes of patients with brain tumors.
Collapse
Affiliation(s)
- Qi Zeng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - Fangxu Jia
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - Shengming Tang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - Haoling He
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, PR China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha 410008, Hunan, PR China
| | - Xueying Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - Jinfan Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - Zeming Tan
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China
| | - Haiyun Tang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China.
| | - Jing Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China.
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, PR China; Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, PR China.
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
| |
Collapse
|
|
5
|
Xiao Y, Benedict S, Cui Y, Glide-Hurst C, Graves S, Jia X, Kry SF, Li H, Lin L, Matuszak M, Newpower M, Paganetti H, Qi XS, Roncali E, Rong Y, Sgouros G, Simone CB, Sunderland JJ, Taylor PA, Tchelebi L, Weldon M, Zou JW, Wuthrick EJ, Machtay M, Le QT, Buchsbaum JC. Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions. Int J Radiat Oncol Biol Phys 2025:S0360-3016(25)00057-4. [PMID: 39848295 DOI: 10.1016/j.ijrobp.2025.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Revised: 12/20/2024] [Accepted: 01/12/2025] [Indexed: 01/25/2025]
Abstract
This white paper examines the potential of pioneering technologies and artificial intelligence-driven solutions in advancing clinical trials involving radiation therapy. As the field of radiation therapy evolves, the integration of cutting-edge approaches such as radiopharmaceutical dosimetry, FLASH radiation therapy, image guided radiation therapy, and artificial intelligence promises to improve treatment planning, patient care, and outcomes. Additionally, recent advancements in quantum science, linear energy transfer/relative biological effect, and the combination of radiation therapy and immunotherapy create new avenues for innovation in clinical trials. The paper aims to provide an overview of these emerging technologies and discuss their challenges and opportunities in shaping the future of radiation oncology clinical trials. By synthesizing knowledge from experts across various disciplines, this white paper aims to present a foundation for the successful integration of these innovations into radiation therapy research and practice, ultimately enhancing patient outcomes and revolutionizing cancer care.
Collapse
Affiliation(s)
- Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Stanley Benedict
- Department of Radiation Oncology, University of California at Davis, Comprehensive Cancer Center, Davis, California
| | - Yunfeng Cui
- Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Carri Glide-Hurst
- Department of Human Oncology, University of Wisconsin, Madison, Wisconsin
| | - Stephen Graves
- Department of Radiology, Division of Nuclear Medicine, University of Iowa, Iowa City, Iowa
| | - Xun Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Stephen F Kry
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Heng Li
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Liyong Lin
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Mark Newpower
- Department of Radiation Oncology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - X Sharon Qi
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California
| | - Emilie Roncali
- Department of Radiology, University of California at Davis, Davis, California
| | - Yi Rong
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - George Sgouros
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | | | - John J Sunderland
- Department of Radiology, Division of Nuclear Medicine, University of Iowa, Iowa City, Iowa
| | - Paige A Taylor
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Leila Tchelebi
- Department of Radiation Oncology, Northwell Health, Mt. Kisco, New York
| | - Michael Weldon
- Department of Radiation Oncology, The Ohio State University Medical Center, Columbus, Ohio
| | - Jennifer W Zou
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Evan J Wuthrick
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Mitchell Machtay
- Department of Radiation Oncology, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Jeffrey C Buchsbaum
- Division of Cancer Treatment and Diagnosis, Radiation Research Program, National Cancer Institute, Bethesda, Maryland.
| |
Collapse
|
|
6
|
Ramiah D, Mmereki D. Synthesizing Efficiency Tools in Radiotherapy to Increase Patient Flow: A Comprehensive Literature Review. Clin Med Insights Oncol 2024; 18:11795549241303606. [PMID: 39677332 PMCID: PMC11645725 DOI: 10.1177/11795549241303606] [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: 07/08/2024] [Accepted: 11/07/2024] [Indexed: 12/17/2024] Open
Abstract
The promise of novel technologies to increase access to radiotherapy in low- and middle-income countries (LMICs) is crucial, given that the cost of equipping new radiotherapy centres or upgrading existing machinery remains a major obstacle to expanding access to cancer treatment. The study aims to provide a thorough analysis overview of how technological advancement may revolutionize radiotherapy (RT) to improve level of care provided to cancer patients. A comprehensive literature review following some steps of systematic review (SLR) was performed using the Web of Science (WoS), PubMed, and Scopus databases. The study findings are classified into different technologies. Artificial intelligence (AI), knowledge-based planning, remote planning, radiotherapy, and scripting are all ways to increase patient flow across radiation oncology, including initial consultation, treatment planning, delivery, verification, and patient follow-up. This review found that these technologies improve delineation of organ at risks (OARs) and considerably reduce waiting times when compared with conventional treatment planning in RT. In this review, AI, knowledge-based planning, remote radiotherapy treatment planning, and scripting reduced waiting times and improved organ at-risk delineation compared with conventional RT treatment planning. A combination of these technologies may lower cancer patients' risk of disease progression due to reduced workload, quality of therapy, and individualized treatment. Efficiency tools, such as the application of AI, knowledge-based planning, remote radiotherapy planning, and scripting, are urgently needed to reduce waiting times and improve OAR delineation accuracy in cancer treatment compared with traditional treatment planning methods. The study's contribution is to present the potential of technological advancement to optimize RT planning process, thereby improving patient care and resource utilization. The study may be extended in the future to include digital integration and technology's impact on patient safety, outcomes, and risk. Therefore, in radiotherapy, research on more efficient tools pioneers the development and implementation of high-precision radiotherapy for cancer patients.
Collapse
Affiliation(s)
- Duvern Ramiah
- Division of Radiation Oncology, Department of Radiation Sciences, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Daniel Mmereki
- Division of Radiation Oncology, Department of Radiation Sciences, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| |
Collapse
|
|
7
|
Choi SH, Park JW, Cho Y, Yang G, Yoon HI. Automated Organ Segmentation for Radiation Therapy: A Comparative Analysis of AI-Based Tools Versus Manual Contouring in Korean Cancer Patients. Cancers (Basel) 2024; 16:3670. [PMID: 39518109 PMCID: PMC11544936 DOI: 10.3390/cancers16213670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2024] [Revised: 10/24/2024] [Accepted: 10/28/2024] [Indexed: 11/16/2024] Open
Abstract
BACKGROUND Accurate delineation of tumors and organs at risk (OARs) is crucial for intensity-modulated radiation therapy. This study aimed to evaluate the performance of OncoStudio, an AI-based auto-segmentation tool developed for Korean patients, compared with Protégé AI, a globally developed tool that uses data from Korean cancer patients. METHODS A retrospective analysis of 1200 Korean cancer patients treated with radiotherapy was conducted. Auto-contours generated via OncoStudio and Protégé AI were compared with manual contours across the head and neck and thoracic, abdominal, and pelvic organs. Accuracy was assessed using the Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD). Feedback was obtained from 10 participants, including radiation oncologists, residents, and radiation therapists, via an online survey with a Turing test component. RESULTS OncoStudio outperformed Protégé AI in 85% of the evaluated OARs (p < 0.001). For head and neck organs, OncoStudio achieved a similar DSC (0.70 vs. 0.70, p = 0.637) but significantly lower MSD and 95% HD values (p < 0.001). In thoracic organs, OncoStudio performed excellently in 90% of cases, with a significantly greater DSC (male: 0.87 vs. 0.82, p < 0.001; female: 0.95 vs. 0.87, p < 0.001). OncoStudio also demonstrated superior accuracy in abdominal (DSC 0.88 vs. 0.81, p < 0.001) and pelvic organs (male: DSC 0.95 vs. 0.85, p < 0.001; female: DSC 0.82 vs. 0.73, p < 0.001). Clinicians favored OncoStudio in 70% of cases, with 90% endorsing its clinical suitability for Korean patients. CONCLUSIONS OncoStudio, which is tailored for Korean patients, demonstrated superior segmentation accuracy across multiple anatomical regions, suggesting its suitability for radiotherapy planning in this population.
Collapse
Affiliation(s)
- Seo Hee Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.H.C.)
| | - Jong Won Park
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.H.C.)
| | - Yeona Cho
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Gowoon Yang
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.H.C.)
- Department of Radiation Oncology, Cha University Ilsan Cha Hospital, Cha University School of Medicine, Goyang 10414, Republic of Korea
| | - Hong In Yoon
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.H.C.)
| |
Collapse
|
|
8
|
Xiao T, Kong S, Zhang Z, Hua D, Liu F. A review of big data technology and its application in cancer care. Comput Biol Med 2024; 176:108577. [PMID: 38739981 DOI: 10.1016/j.compbiomed.2024.108577] [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: 11/19/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.
Collapse
Affiliation(s)
- Tianyun Xiao
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Shanshan Kong
- College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
| | - Zichen Zhang
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Dianbo Hua
- Beijing Sitairui Cancer Data Analysis Joint Laboratory, Beijing, 101149, China
| | - Fengchun Liu
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei, China; Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, Hebei, China
| |
Collapse
|
|
9
|
Futami S, Miwa T. Comprehensive Equilibrium Function Tests for an Accurate Diagnosis in Vertigo: A Retrospective Analysis. J Clin Med 2024; 13:2450. [PMID: 38730980 PMCID: PMC11084401 DOI: 10.3390/jcm13092450] [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: 03/11/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/13/2024] Open
Abstract
Background/Objectives: An accurate diagnosis of vertigo is crucial in patient care. Traditional balance function tests often fail to offer independent, conclusive diagnoses. This study aimed to bridge the gap between traditional diagnostic approaches and the evolving landscape of automated diagnostic tools, laying the groundwork for advancements in vertigo care. Methods: A cohort of 1400 individuals with dizziness underwent a battery of equilibrium function tests, and diagnoses were established based on the criteria by the Japanese Society for Vertigo and Equilibrium. A multivariate analysis identified the key diagnostic factors for various vestibudata nlar disorders, including Meniere's disease, vestibular neuritis, and benign paroxysmal positional vertigo. Results: This study underscored the complexity of diagnosing certain disorders such as benign paroxysmal positional vertigo, where clinical symptoms play a crucial role. Additionally, it highlighted the utility of specific physical balance function tests for differentiating central diseases. These findings bolster the reliability of established diagnostic tools, such as audiometry for Meniere's disease and spontaneous nystagmus for vestibular neuritis. Conclusions: This study concluded that a multifaceted approach integrating multiple diagnostic indicators is crucial for accurate clinical decisions in vestibular disorders. Future studies should incorporate novel tests, quantitative assessments, and advanced technologies to enhance the diagnostic capabilities of vestibular medicine.
Collapse
Affiliation(s)
- Shumpei Futami
- Department of Otolaryngology, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan;
| | - Toru Miwa
- Department of Otolaryngology, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan;
- Department of Otolaryngology-Head and Neck Surgery, Graduate of School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
| |
Collapse
|
|
10
|
Cobanaj M, Corti C, Dee EC, McCullum L, Boldrini L, Schlam I, Tolaney SM, Celi LA, Curigliano G, Criscitiello C. Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow. Eur J Cancer 2024; 198:113504. [PMID: 38141549 PMCID: PMC11362966 DOI: 10.1016/j.ejca.2023.113504] [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: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/25/2023]
Abstract
Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.
Collapse
Affiliation(s)
- Marisa Cobanaj
- National Center for Radiation Research in Oncology, OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Chiara Corti
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy.
| | - Edward C Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucas McCullum
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Boldrini
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Ilana Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara M Tolaney
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Leo A Celi
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| |
Collapse
|
|
11
|
Zhu Y, Lyu X, Tao X, Wu L, Yin A, Liao F, Hu S, Wang Y, Zhang M, Huang L, Wang J, Zhang C, Gong D, Jiang X, Zhao L, Yu H. A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination. BMC Gastroenterol 2024; 24:10. [PMID: 38166722 PMCID: PMC10759410 DOI: 10.1186/s12876-023-03067-w] [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: 04/08/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE. DESIGN AND METHODS A total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus. RESULTS For the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts. CONCLUSIONS We established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.
Collapse
Affiliation(s)
- Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoguang Lyu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiao Tao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Anning Yin
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Fei Liao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shan Hu
- School of Computer Science, Wuhan University, Wuhan, China
| | - Yang Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengjiao Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Junxiao Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chenxia Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liang Zhao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
| |
Collapse
|
|
12
|
Hoebers FJP, Wee L, Likitlersuang J, Mak RH, Bitterman DS, Huang Y, Dekker A, Aerts HJWL, Kann BH. Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods. BJR Open 2024; 6:tzae039. [PMID: 39583148 PMCID: PMC11585305 DOI: 10.1093/bjro/tzae039] [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: 09/12/2023] [Revised: 08/20/2024] [Accepted: 11/04/2024] [Indexed: 11/26/2024] Open
Abstract
The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice. Part of the limited clinical uptake of these models may be explained by the need for broader knowledge, among practising clinicians within the medical community, about the processes of AI development. This lack of understanding could lead to low commitment to AI research, widespread scepticism, and low levels of trust. This attitude towards AI may be further negatively impacted by the perception that deep learning is a "black box" with inherently low transparency. Thus, there is an unmet need to train current and future clinicians in the development and application of AI in medicine. Improving clinicians' AI-related knowledge and skills is necessary to enhance multidisciplinary collaboration between data scientists and physicians, that is, involving a clinician in the loop during AI development. Increased knowledge may also positively affect the acceptance and trust of AI. This paper describes the necessary steps involved in AI research and development, and thus identifies the possibilities, limitations, challenges, and opportunities, as seen from the perspective of a practising radiation oncologist. It offers the clinician with limited knowledge and experience in AI valuable tools to evaluate research papers related to an AI model application.
Collapse
Affiliation(s)
- Frank J P Hoebers
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, MA 02115, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, MA 02115, United States
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, 6229ET, the Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, 6229ET, the Netherlands
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, MA 02115, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, MA 02115, United States
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, MA 02115, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, MA 02115, United States
| | - Danielle S Bitterman
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, MA 02115, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, MA 02115, United States
| | - Yanqi Huang
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, 6229ET, the Netherlands
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, 6229ET, the Netherlands
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, MA 02115, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, MA 02115, United States
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, 6229ER, the Netherlands
| | - Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, MA 02115, United States
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, MA 02115, United States
| |
Collapse
|
|
13
|
Huang Z, Bucklin MA, Guo W, Martin JT. Disease progression and clinical outcomes in latent osteoarthritis phenotypes: Data from the Osteoarthritis Initiative. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.14.23299525. [PMID: 38168330 PMCID: PMC10760291 DOI: 10.1101/2023.12.14.23299525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The prevalence of knee osteoarthritis (OA) is widespread and the heterogeneous patient factors and clinical symptoms in OA patients impede developing personalized treatments for OA patients. In this study, we used unsupervised and supervised machine learning to organize the heterogeneity in knee OA patients and predict disease progression in individuals from the Osteoarthritis Initiative (OAI) dataset. We identified four distinct knee OA phenotypes using unsupervised learning that were defined by nutrition, disability, stiffness, and pain (knee and back) and were strongly related to disease fate. Interestingly, the absence of supplemental vitamins from an individual's diet was protective from disease progression. Moreover, we established a phenotyping tool and prognostic model from 5 variables (WOMAC disability score of the right knee, WOMAC total score of the right knee, WOMAC total score of the left knee, supplemental vitamins and minerals frequency, and antioxidant combination multivitamins frequency) that can be utilized in clinical practice to determine the risk of knee OA progression in individual patients. We also developed a prognostic model to estimate the risk for total knee replacement and provide suggestions for modifiable variables to improve long-term knee health. This combination of unsupervised and supervised data-driven tools provides a framework to identify knee OA phenotype in a clinical scenario and personalize treatment strategies.
Collapse
|
|
14
|
Shanbhag NM, Bin Sumaida A, Binz T, Hasnain SM, El-Koha O, Al Kaabi K, Saleh M, Al Qawasmeh K, Balaraj K. Integrating Artificial Intelligence Into Radiation Oncology: Can Humans Spot AI? Cureus 2023; 15:e50486. [PMID: 38098735 PMCID: PMC10719429 DOI: 10.7759/cureus.50486] [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] [Accepted: 12/13/2023] [Indexed: 12/17/2023] Open
Abstract
Introduction Artificial intelligence (AI) is transforming healthcare, particularly in radiation oncology. AI-based contouring tools like Limbus are designed to delineate Organs at Risk (OAR) and Target Volumes quickly. This study evaluates the accuracy and efficiency of AI contouring compared to human radiation oncologists and the ability of professionals to differentiate between AI-generated and human-generated contours. Methods At a recent AI conference in Abu Dhabi, a blind comparative analysis was performed to assess AI's performance in radiation oncology. Participants included four human radiation oncologists and the Limbus® AI software. They contoured specific regions from CT scans of a breast cancer patient. The audience, consisting of healthcare professionals and AI experts, was challenged to identify the AI-generated contours. The exercise was repeated twice to observe any learning effects. Time taken for contouring and audience identification accuracy were recorded. Results Initially, only 28% of the audience correctly identified the AI contours, which slightly increased to 31% in the second attempt. This indicated a difficulty in distinguishing between AI and human expertise. The AI completed contouring in up to 60 seconds, significantly faster than the human average of 8 minutes. Discussion The results indicate that AI can perform radiation contouring comparably to human oncologists but much faster. The challenge faced by professionals in identifying AI versus human contours highlights AI's advanced capabilities in medical tasks. Conclusion AI shows promise in enhancing radiation oncology workflow by reducing contouring time without quality compromise. Further research is needed to confirm AI contouring's clinical efficacy and its integration into routine practice.
Collapse
Affiliation(s)
- Nandan M Shanbhag
- Oncology/Palliative Care, Tawam Hospital, Al Ain, ARE
- Oncology/Radiation Oncolgy, Tawam Hospital, Al Ain, ARE
| | | | - Theresa Binz
- Radiotherapy Technology, Tawam Hospital, Al Ain, ARE
| | | | | | | | | | | | - Khalid Balaraj
- Oncology/Radiation Oncology, Tawam Hospital, Al Ain, ARE
| |
Collapse
|
|
15
|
Pfeifer B, Chereda H, Martin R, Saranti A, Clemens S, Hauschild AC, Beißbarth T, Holzinger A, Heider D. Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification. Bioinformatics 2023; 39:btad703. [PMID: 37988152 PMCID: PMC10684359 DOI: 10.1093/bioinformatics/btad703] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/06/2023] [Accepted: 11/20/2023] [Indexed: 11/22/2023] Open
Abstract
SUMMARY Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA). AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/pievos101/Ensemble-GNN, and the data at Zenodo (DOI: 10.5281/zenodo.8305122).
Collapse
Affiliation(s)
- Bastian Pfeifer
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
| | - Hryhorii Chereda
- Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany
| | - Roman Martin
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
| | - Anna Saranti
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences, Vienna 1190, Austria
| | - Sandra Clemens
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
| | - Anne-Christin Hauschild
- Institute for Medical Informatics, University Medical Center Göttingen, Göttingen 37075, Germany
| | - Tim Beißbarth
- Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany
| | - Andreas Holzinger
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz 8036, Austria
- Human-Centered AI Lab, University of Natural Resources and Life Sciences, Vienna 1190, Austria
| | - Dominik Heider
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Marburg 35043, Germany
| |
Collapse
|
|
16
|
Cho H, Lewis AL, Storey KM, Zittle AC. An adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:17986-18017. [PMID: 38052545 DOI: 10.3934/mbe.2023799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models requires detailed clinical data. This raises questions about the type and quantity of data that should be collected and when, in order to maximize the information gain about the model behavior while still minimizing the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using an adaptive score function to determine the optimal data collection times and measurement types. The novel score function introduced in this work eliminates the need for a penalization parameter used in a previous study, while yielding model predictions that are superior to those obtained using two potential pre-determined data collection protocols for two different prostate cancer model scenarios: one in which we fit a simple ODE system to synthetic data generated from a cellular automaton model using radiotherapy as the imposed treatment, and a second scenario in which a more complex ODE system is fit to clinical patient data for patients undergoing intermittent androgen suppression therapy. We also conduct a robust analysis of the calibration results, using both error and uncertainty metrics in combination to determine when additional data acquisition may be terminated.
Collapse
Affiliation(s)
- Heyrim Cho
- Department of Mathematics, University of California, Riverside CA, USA
| | - Allison L Lewis
- Department of Mathematics, Lafayette College, Easton PA, USA
| | | | - Anna C Zittle
- Department of Mathematics, Lafayette College, Easton PA, USA
| |
Collapse
|
|
17
|
Ecker S, Kirisits C, Schmid M, Knoth J, Heilemann G, De Leeuw A, Sturdza A, Kirchheiner K, Jensen N, Nout R, Jürgenliemk-Schulz I, Pötter R, Spampinato S, Tanderup K, Eder-Nesvacil N. EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer. Radiother Oncol 2023; 186:109748. [PMID: 37330055 DOI: 10.1016/j.radonc.2023.109748] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/19/2023]
Abstract
PURPOSE To develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC). METHODS A system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated. One TCP model for local tumor control, and five NTCP models for OAR morbidities. RESULTS EviGUIDE incorporates TCP-NTCP graphs to help users visualize the clinical impact of different treatment plans and provides feedback on achievable doses based on a large reference population. It enables holistic assessment of the interplay between multiple clinical endpoints and tumour and treatment variables. Retrospective analysis of 45 patients treated with MR-IGABT showed that there exists a sub-cohort of patients (20%) with increased risk factors, that could greatly benefit from the quantitative and visual feedback. CONCLUSION A novel digital concept was developed that can enhance clinical decision- making and facilitate personalized treatment. It serves as a proof of concept for a new generation of decision support systems in radiation oncology, which incorporate outcome models and high-quality reference data, and aids the dissemination of evidence-based knowledge about optimal treatment and serve as a blueprint for other sites in radiation oncology.
Collapse
Affiliation(s)
- Stefan Ecker
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.
| | - Christian Kirisits
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Maximilian Schmid
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Johannes Knoth
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Gerd Heilemann
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Astrid De Leeuw
- University Medical Centre Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands
| | - Alina Sturdza
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Kathrin Kirchheiner
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Nina Jensen
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
| | - Remi Nout
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, the Netherlands
| | - Ina Jürgenliemk-Schulz
- University Medical Centre Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands
| | - Richard Pötter
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Sofia Spampinato
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
| | - Kari Tanderup
- Aarhus University Hospital, Department of Oncology, Aarhus, Denmark
| | | |
Collapse
|
|
18
|
Lempart M, Scherman J, Nilsson MP, Jamtheim Gustafsson C. Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy. J Appl Clin Med Phys 2023; 24:e14022. [PMID: 37177830 PMCID: PMC10476996 DOI: 10.1002/acm2.14022] [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: 10/03/2022] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023] Open
Abstract
Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guidelines exist; however, information on the used guideline is not provided with the delineation. Extraction of training data with coherent guidelines can therefore be challenging. We present a supervised classification method for pelvis structure delineations where bowel cavity, femoral heads, bladder, and rectum data, with two guidelines, were classified. The impact on DL-based segmentation quality using mixed guideline training data was also demonstrated. Bowel cavity was manually delineated on CT images for anal cancer patients (n = 170) according to guidelines Devisetty and RTOG. The DL segmentation quality from using training data with coherent or mixed guidelines was investigated. A supervised 3D squeeze-and-excite SENet-154 model was trained to classify two bowel cavity delineation guidelines. In addition, a pelvis CT dataset with manual delineations from prostate cancer patients (n = 1854) was used where data with an alternative guideline for femoral heads, rectum, and bladder were generated using commercial software. The model was evaluated on internal (n = 200) and external test data (n = 99). By using mixed, compared to coherent, delineation guideline training data mean DICE score decreased 3% units, mean Hausdorff distance (95%) increased 5 mm and mean surface distance (MSD) increased 1 mm. The classification of bowel cavity test data achieved 99.8% unweighted classification accuracy, 99.9% macro average precision, 97.2% macro average recall, and 98.5% macro average F1. Corresponding metrics for the pelvis internal test data were all 99% or above and for the external pelvis test data they were 96.3%, 96.6%, 93.3%, and 94.6%. Impaired segmentation performance was observed for training data with mixed guidelines. The DL delineation classification models achieved excellent results on internal and external test data. This can facilitate automated guideline-specific data extraction while avoiding the need for consistent and correct structure labels.
Collapse
Affiliation(s)
- Michael Lempart
- Radiation Physics, Department of HematologyOncology, and Radiation PhysicsSkåne University HospitalLundSweden
- Department of Translational MedicineMedical Radiation PhysicsLund UniversityMalmöSweden
| | - Jonas Scherman
- Radiation Physics, Department of HematologyOncology, and Radiation PhysicsSkåne University HospitalLundSweden
| | - Martin P. Nilsson
- Department of HematologyOncology, and Radiation PhysicsSkåne University HospitalLundSweden
| | - Christian Jamtheim Gustafsson
- Radiation Physics, Department of HematologyOncology, and Radiation PhysicsSkåne University HospitalLundSweden
- Department of Translational MedicineMedical Radiation PhysicsLund UniversityMalmöSweden
| |
Collapse
|
|
19
|
Lucia F, Lovinfosse P, Schick U, Le Pennec R, Pradier O, Salaun PY, Hustinx R, Bourbonne V. Radiotherapy modification based on artificial intelligence and radiomics applied to ( 18F)-fluorodeoxyglucose positron emission tomography/computed tomography. Cancer Radiother 2023; 27:542-547. [PMID: 37481344 DOI: 10.1016/j.canrad.2023.06.001] [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: 05/30/2023] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 07/24/2023]
Abstract
Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy.
Collapse
Affiliation(s)
- F Lucia
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France; Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium.
| | - P Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium
| | - U Schick
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
| | - R Le Pennec
- Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
| | - O Pradier
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
| | - P-Y Salaun
- Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
| | - R Hustinx
- Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium
| | - V Bourbonne
- Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
| |
Collapse
|
|
20
|
Prabhakaran S, Choong KWK, Prabhakaran S, Choy KT, Kong JC. Accuracy of deep neural learning models in the imaging prediction of pathological complete response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer: a systematic review. Langenbecks Arch Surg 2023; 408:321. [PMID: 37594552 DOI: 10.1007/s00423-023-03039-4] [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: 03/18/2023] [Accepted: 08/01/2023] [Indexed: 08/19/2023]
Abstract
PURPOSE Up to 15-27% of patients achieve pathologic complete response (pCR) following neoadjuvant chemoradiotherapy (CRT) for locally advanced rectal cancer (LARC). Deep neural learning (DL) algorithms have been suggested to be a useful adjunct to allow accurate prediction of pCR and to identify patients who could potentially avoid surgery. This systematic review aims to interrogate the accuracy of DL algorithms at predicting pCR. METHODS Embase (PubMed, MEDLINE) databases and Google Scholar were searched to identify eligible English-language studies, with the search concluding in July 2022. Studies reporting on the accuracy of DL models in predicting pCR were selected for review and information pertaining to study characteristics and diagnostic measures was extracted from relevant studies. Risk of bias was evaluated using the Newcastle-Ottawa scale (NOS). RESULTS Our search yielded 85 potential publications. Nineteen full texts were reviewed, and a total of 12 articles were included in this systematic review. There were six retrospective and six prospective cohort studies. The most common DL algorithm used was the Convolutional Neural Network (CNN). Performance comparison was carried out via single modality comparison. The median performance for each best-performing algorithm was an AUC of 0.845 (range 0.71-0.99) and Accuracy of 0.85 (0.83-0.98). CONCLUSIONS There is a promising role for DL models in the prediction of pCR following neoadjuvant-CRT for LARC. Further studies are needed to provide a standardised comparison in order to allow for large-scale clinical application. PROPERO REGISTRATION PROSPERO 2021 CRD42021269904 Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021269904 .
Collapse
Affiliation(s)
- Sowmya Prabhakaran
- Department of General Surgical Specialties, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
| | | | - Swetha Prabhakaran
- Department of Colorectal Surgery, Alfred Hospital, Melbourne, Victoria, Australia
| | - Kay Tai Choy
- Department of Surgery, Austin Health, Melbourne, Victoria, Australia
| | - Joseph Ch Kong
- Department of Colorectal Surgery, Alfred Hospital, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| |
Collapse
|
|
21
|
Roman A, Al-Sharif L, Al Gharyani M. The Expanding Role of ChatGPT (Chat-Generative Pre-Trained Transformer) in Neurosurgery: A Systematic Review of Literature and Conceptual Framework. Cureus 2023; 15:e43502. [PMID: 37719492 PMCID: PMC10500385 DOI: 10.7759/cureus.43502] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2023] [Indexed: 09/19/2023] Open
Abstract
The objective of this study is to explore the use of ChatGPT (Chat-Generative Pre-Trained Transformer) in neurosurgery and its potential impact on the field. The authors aim to discuss, through a systematic review of current literature, how this rising new artificial intelligence (AI) technology may prove to be a useful tool in the future, weighing its potential benefits and limitations. The authors conducted a comprehensive and systematic literature review of the use of ChatGPT and its applications in healthcare and different neurosurgery topics. Through a systematic review of the literature, with a search strategy using the databases such as PubMed, Google Scholar, and Embase, we analyzed the advantages and limitations of using ChatGPT in neurosurgery and evaluated its potential impact. ChatGPT has demonstrated promising results in various applications, such as natural language processing, language translation, and text summarization. In neurosurgery, ChatGPT can assist in different areas such as surgical planning, image recognition, medical diagnosis, patient care, and scientific production. A total of 128 articles were retrieved from databases, where the final 22 articles were included for thorough analysis. The studies reviewed demonstrate the potential of AI and deep learning (DL), through language models such as ChatGPT, to improve the accuracy and efficiency of neurosurgical procedures, as well as diagnosis, treatment, and patient outcomes across various medical specialties, including neurosurgery. There are, however, limitations to its use, including the need for large datasets and the potential for errors in the output, which most authors concur will need human verification for the final application. Our search demonstrated the potential that ChatGPT holds for the present and future, in accordance with the studies' authors' findings herein analyzed and expert opinions. Further research and development are required to fully understand its capabilities and limitations. AI technology can serve as a useful tool to augment human intelligence; however, it is essential to use it in a responsible and ethical manner.
Collapse
Affiliation(s)
- Alex Roman
- Neurological Surgery, Cleveland Clinic Abu Dhabi, Abu Dhabi, ARE
| | - Lubna Al-Sharif
- Physiology, Pharmacology and Toxicology, An-Najah National University, Nablus, PSE
| | | |
Collapse
|
|
22
|
Klau JH, Maj C, Klinkhammer H, Krawitz PM, Mayr A, Hillmer AM, Schumacher J, Heider D. AI-based multi-PRS models outperform classical single-PRS models. Front Genet 2023; 14:1217860. [PMID: 37441549 PMCID: PMC10335560 DOI: 10.3389/fgene.2023.1217860] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance. Results showed that multi-PRS models outperform single-PRS models significantly on different diseases. Moreover, replacing regression models with machine learning models, i.e., deep learning, can also improve overall accuracy.
Collapse
Affiliation(s)
- Jan Henric Klau
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| | - Carlo Maj
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Hannah Klinkhammer
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University Bonn, Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University Bonn, Bonn, Germany
| | - Peter M. Krawitz
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University Bonn, Bonn, Germany
| | - Andreas Mayr
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University Bonn, Bonn, Germany
| | - Axel M. Hillmer
- Institute of Pathology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | | | - Dominik Heider
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| |
Collapse
|
|
23
|
Du RC, Ouyang YB, Hu Y. Research trends on artificial intelligence and endoscopy in digestive diseases: A bibliometric analysis from 1990 to 2022. World J Gastroenterol 2023; 29:3561-3573. [PMID: 37389238 PMCID: PMC10303508 DOI: 10.3748/wjg.v29.i22.3561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/03/2023] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
BACKGROUND Recently, artificial intelligence (AI) has been widely used in gastrointestinal endoscopy examinations.
AIM To comprehensively evaluate the application of AI-assisted endoscopy in detecting different digestive diseases using bibliometric analysis.
METHODS Relevant publications from the Web of Science published from 1990 to 2022 were extracted using a combination of the search terms “AI” and “endoscopy”. The following information was recorded from the included publications: Title, author, institution, country, endoscopy type, disease type, performance of AI, publication, citation, journal and H-index.
RESULTS A total of 446 studies were included. The number of articles reached its peak in 2021, and the annual citation numbers increased after 2006. China, the United States and Japan were dominant countries in this field, accounting for 28.7%, 16.8%, and 15.7% of publications, respectively. The Tada Tomohiro Institute of Gastroenterology and Proctology was the most influential institution. “Cancer” and “polyps” were the hotspots in this field. Colorectal polyps were the most concerning and researched disease, followed by gastric cancer and gastrointestinal bleeding. Conventional endoscopy was the most common type of examination. The accuracy of AI in detecting Barrett’s esophagus, colorectal polyps and gastric cancer from 2018 to 2022 is 87.6%, 93.7% and 88.3%, respectively. The detection rates of adenoma and gastrointestinal bleeding from 2018 to 2022 are 31.3% and 96.2%, respectively.
CONCLUSION AI could improve the detection rate of digestive tract diseases and a convolutional neural network-based diagnosis program for endoscopic images shows promising results.
Collapse
Affiliation(s)
- Ren-Chun Du
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Yao-Bin Ouyang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
- Department of Oncology, Mayo Clinic, Rochester, MN 55905, United States
| | - Yi Hu
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong 999077, China
| |
Collapse
|
|
24
|
He M, Cao Y, Chi C, Yang X, Ramin R, Wang S, Yang G, Mukhtorov O, Zhang L, Kazantsev A, Enikeev M, Hu K. Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives. Front Oncol 2023; 13:1189370. [PMID: 37546423 PMCID: PMC10400334 DOI: 10.3389/fonc.2023.1189370] [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: 03/19/2023] [Accepted: 05/30/2023] [Indexed: 08/08/2023] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.
Collapse
Affiliation(s)
- Mingze He
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Yu Cao
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Changliang Chi
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
| | - Xinyi Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Rzayev Ramin
- Department of Radiology, The Second University Clinic, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Shuowen Wang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Guodong Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Otabek Mukhtorov
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Liqun Zhang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, China
| | - Anton Kazantsev
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Mikhail Enikeev
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Kebang Hu
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
| |
Collapse
|
|
25
|
Huang Z, Guo W, Martin JT. Socioeconomic status, mental health, and nutrition are the principal traits for low back pain phenotyping: Data from the osteoarthritis initiative. JOR Spine 2023; 6:e1248. [PMID: 37361325 PMCID: PMC10285761 DOI: 10.1002/jsp2.1248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/12/2022] [Accepted: 12/15/2022] [Indexed: 06/28/2023] Open
Abstract
Background Low back pain (LBP) is a heterogeneous disease with biological, physical, and psychosocial etiologies. Models for predicting LBP severity and chronicity have not made a clinical impact, perhaps due to difficulty deciphering multidimensional phenotypes. In this study, our objective was to develop a computational framework to comprehensively screen metrics related to LBP severity and chronicity and identify the most influential. Methods We identified individuals from the observational, longitudinal Osteoarthritis Initiative cohort (N = 4796) who reported LBP at enrollment (N = 215). OAI descriptor variables (N = 1190) were used to cluster individuals via unsupervised learning and uncover latent LBP phenotypes. We also developed a dimensionality reduction algorithm to visualize clusters/phenotypes using Uniform Manifold Approximation and Projection (UMAP). Next, to predict chronicity, we identified those with acute LBP (N = 40) and persistent LBP over 8 years of follow-up (N = 66) and built logistic regression and supervised machine learning models. Results We identified three LBP phenotypes: a "high socioeconomic status, low pain severity group", a "low socioeconomic status, high pain severity group", and an intermediate group. Mental health and nutrition were also key clustering variables, while traditional biomedical factors (e.g., age, sex, BMI) were not. Those who developed chronic LBP were differentiated by higher pain interference and lower alcohol consumption (a correlate to poor physical fitness and lower soceioeconomic status). All models for predicting chronicity had satisfactory performance (accuracy 76%-78%). Conclusions We developed a computational pipeline capable of screening hundreds of variables and visualizing LBP cohorts. We found that socioeconomic status, mental health, nutrition, and pain interference were more influential in LBP than traditional biomedical descriptors like age, sex, and BMI.
Collapse
Affiliation(s)
- ZeYu Huang
- Department of Orthopaedic Surgery, West China Hospital, West China Medical SchoolSiChuan UniversityChengDuSiChuan ProvincePeople's Republic of China
- Department of Orthopaedic Surgery, School of MedicineDuke UniversityDurhamNorth CarolinaUSA
| | - Weihua Guo
- Department of Immuno‐OncologyCity of Hope Comprehensive Cancer CenterDuarteCaliforniaUSA
| | - John T. Martin
- Department of Orthopaedic Surgery, School of MedicineDuke UniversityDurhamNorth CarolinaUSA
- Department of Orthopedic SurgeryRush University Medical CenterChicagoIllinoisUSA
| |
Collapse
|
|
26
|
Zhang A, Qiao D, Wang Y, Yang C, Wang Y, Sun N, Hu X, Liu Z, Zhang K. Distinguishing between bipolar depression and unipolar depression based on the reward circuit activities and clinical characteristics: A machine learning analysis. J Affect Disord 2023; 327:46-53. [PMID: 36708957 DOI: 10.1016/j.jad.2023.01.080] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/31/2022] [Accepted: 01/21/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Differentiating bipolar depression (BD) from unipolar depression (UD) is a major clinical challenge. Identifying the potential classifying biomarkers between these two diseases is vital to optimize personalized management of depressed individuals. METHODS Here, we aimed to integrate neuroimaging and clinical data with machine learning method to classify BD and UD at the individual level. Data were collected from 31 healthy controls (HC group) and 80 depressive patients with an average follow-up period of 7.51 years. Of these patients, 32 got diagnosis conversion from major depressive disorder (MDD) to BD (BD group) and 48 remain persistent diagnosis of MDD (MDD group). Using graph theory and functional connectivity (FC) analysis, we investigated the differences in reward circuit properties among three groups. Then we applied a support vector machine and leave-one-out cross-validation methods to classify BD and UD patients based on neuroimaging and clinical data. RESULTS Compared with MDD and HC, BD showed decreased degree centrality of right mediodorsal thalamus (MD) and nodal efficiency (NE) of left ventral pallidum. Compared with BD and HC, MDD showed decreased NE of right MD and increased FC between right MD and bilateral dorsolateral prefrontal cortex and left ventromedial prefrontal cortex. Notably, the classifier obtained high classification accuracies (87.50 %) distinguishing BD and UD patients based on reward circuit properties and clinical features. LIMITATIONS The classifying model requires out-of-sample replication analysis. CONCLUSION The reward circuit dysfunction can not only provide additional information to assist clinical differential diagnosis, but also in turn informed treatment decision of depressive patients.
Collapse
Affiliation(s)
- Aixia Zhang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Dan Qiao
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Yuchen Wang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Chunxia Yang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Yanfang Wang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Ning Sun
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Xiaodong Hu
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Zhifen Liu
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China.
| | - Kerang Zhang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China.
| |
Collapse
|
|
27
|
Alizadeh B, Alibabaei A, Ahmadi S, Maroufi SF, Ghafouri-Fard S, Nateghinia S. Designing predictive models for appraisal of outcome of neurosurgery patients using machine learning-based techniques. INTERDISCIPLINARY NEUROSURGERY 2023. [DOI: 10.1016/j.inat.2022.101658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
|
|
28
|
Chen M, Kong C, Qiao E, Chen Y, Chen W, Jiang X, Fang S, Zhang D, Chen M, Chen W, Ji J. Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI. Insights Imaging 2023; 14:38. [PMID: 36854872 PMCID: PMC9975141 DOI: 10.1186/s13244-023-01380-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/29/2023] [Indexed: 03/02/2023] Open
Abstract
OBJECTIVES This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance. METHODS The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN). RESULTS DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735). CONCLUSIONS DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.
Collapse
Affiliation(s)
- Mingzhen Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Chunli Kong
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Enqi Qiao
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Yaning Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China
| | - Weiyue Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Xiaole Jiang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Shiji Fang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Dengke Zhang
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Minjiang Chen
- grid.469539.40000 0004 1758 2449Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000 China ,grid.268099.c0000 0001 0348 3990Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000 China ,grid.440824.e0000 0004 1757 6428Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000 China
| | - Weiqian Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. .,Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. .,Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, School of Medicine, Lishui, 323000, China. .,Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China. .,Clinical College of the Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
| |
Collapse
|
|
29
|
Buchanan L, Hamdan S, Zhang Y, Chen X, Li XA. Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study. Front Oncol 2023; 13:939951. [PMID: 36741025 PMCID: PMC9889647 DOI: 10.3389/fonc.2023.939951] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 01/06/2023] [Indexed: 01/19/2023] Open
Abstract
Purpose Fast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learning to quickly predict deliverable adaptive plans based on a target dose distribution for MRgOART/MRgRART. Methods A conditional generative adversarial network (cGAN) was trained to predict the MLC leaf sequence corresponding to a target dose distribution based on reference plan created prior to MRgOART using a 1.5T MR-Linac. The training dataset included 50 ground truth dose distributions and corresponding beam parameters (aperture shapes and weights) created during MRgOART for 10 pancreatic cancer patients (each with five fractions). The model input was the dose distribution from each individual beam and the output was the predicted corresponding field segments with specific shape and weight. Patient-based leave-one-out-cross-validation was employed and for each model trained, four (44 training beams) out of five fractionated plans of the left-out patient were set aside for testing purposes. We deliberately kept a single fractionated plan in the training dataset so that the model could learn to replan the patient based on a prior plan. The model performance was evaluated by calculating the gamma passing rate of the ground truth dose vs. the dose from the predicted adaptive plan and calculating max and mean dose metrics. Results The average gamma passing rate (95%, 3mm/3%) among 10 test cases was 88%. In general, we observed 95% of the prescription dose to PTV achieved with an average 7.6% increase of max and mean dose, respectively, to OARs for predicted replans. Complete adaptive plans were predicted in ≤20 s using a GTX 1660TI GPU. Conclusion We have proposed and demonstrated a deep learning method to generate adaptive plans automatically and rapidly for MRgOART. With further developments using large datasets and the inclusion of patient contours, the method may be implemented to accelerate MRgOART process or even to facilitate MRgRART.
Collapse
|
|
30
|
Haidar A, Field M, Batumalai V, Cloak K, Al Mouiee D, Chlap P, Huang X, Chin V, Aly F, Carolan M, Sykes J, Vinod SK, Delaney GP, Holloway L. Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach. Cancers (Basel) 2023; 15:cancers15030564. [PMID: 36765523 PMCID: PMC9913464 DOI: 10.3390/cancers15030564] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/01/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.
Collapse
Affiliation(s)
- Ali Haidar
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- Correspondence: or
| | - Matthew Field
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Vikneswary Batumalai
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- GenesisCare, Alexandria, NSW 2015, Australia
| | - Kirrily Cloak
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Daniel Al Mouiee
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Phillip Chlap
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Xiaoshui Huang
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- University of Sydney, Camperdown, NSW 2006, Australia
| | - Vicky Chin
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Farhannah Aly
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Martin Carolan
- Illawarra Cancer Care Center, Wollongong, NSW 2522, Australia
- University of Wollongong, Wollongong, NSW 2522, Australia
| | - Jonathan Sykes
- University of Sydney, Camperdown, NSW 2006, Australia
- Blacktown Hospital, Blacktown, NSW 2148, Australia
| | - Shalini K. Vinod
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Geoffrey P. Delaney
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool, NSW 2170, Australia
- South Western Sydney Clinical School, University of New South Wales, Liverpool, NSW 2170, Australia
- University of Sydney, Camperdown, NSW 2006, Australia
| |
Collapse
|
|
31
|
Rana SP, Dey M, Loretoni R, Duranti M, Ghavami M, Dudley S, Tiberi G. Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model. Tomography 2023; 9:105-129. [PMID: 36648997 PMCID: PMC9844448 DOI: 10.3390/tomography9010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/08/2023] [Accepted: 01/09/2023] [Indexed: 01/14/2023] Open
Abstract
Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.
Collapse
Affiliation(s)
| | - Maitreyee Dey
- School of Engineering, London South Bank University, London SE1 0AA, UK
| | - Riccardo Loretoni
- Breast Screening and Diagnostic Breast Cancer Unit, AUSL Umbria 2, 06034 Foligno, Italy
| | - Michele Duranti
- Department of Diagnostic Imaging, Perugia Hospital, 06156 Perugia, Italy
| | - Mohammad Ghavami
- School of Engineering, London South Bank University, London SE1 0AA, UK
| | - Sandra Dudley
- School of Engineering, London South Bank University, London SE1 0AA, UK
| | - Gianluigi Tiberi
- School of Engineering, London South Bank University, London SE1 0AA, UK
- Umbria Bioengineering Technologies (UBT) Srl, 06081 Perugia, Italy
| |
Collapse
|
|
32
|
Lu SC, Swisher CL, Chung C, Jaffray D, Sidey-Gibbons C. On the importance of interpretable machine learning predictions to inform clinical decision making in oncology. Front Oncol 2023; 13:1129380. [PMID: 36925929 PMCID: PMC10013157 DOI: 10.3389/fonc.2023.1129380] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient's future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions. In this paper, we describe how different algorithms can be interpreted and introduce some techniques for interpreting complex nonlinear algorithms.
Collapse
Affiliation(s)
- Sheng-Chieh Lu
- Section of Patient-Centered Analytics, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Christine L Swisher
- The Ronin Project, San Mateo, CA, United States.,The Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, United States
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - David Jaffray
- Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Chris Sidey-Gibbons
- Section of Patient-Centered Analytics, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| |
Collapse
|
|
33
|
Hu Q, Li K, Yang C, Wang Y, Huang R, Gu M, Xiao Y, Huang Y, Chen L. The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges. Front Oncol 2023; 13:1133164. [PMID: 36959810 PMCID: PMC10028142 DOI: 10.3389/fonc.2023.1133164] [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: 12/28/2022] [Accepted: 02/20/2023] [Indexed: 03/09/2023] Open
Abstract
Objectives Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC). Materials and methods A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis. Results Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability. Conclusion AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.
Collapse
Affiliation(s)
- Qiuyuan Hu
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Ke Li
- Department of Cancer Biotherapy Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Conghui Yang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yue Wang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Rong Huang
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Mingqiu Gu
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yuqiang Xiao
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
| | - Yunchao Huang
- Department of Thoracic Surgery I, Key Laboratory of Lung Cancer of Yunnan Province, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
- *Correspondence: Long Chen, ; Yunchao Huang,
| | - Long Chen
- Department of positron emission tomography/computed tomography (PET/CT) Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Cancer Center of Yunnan Province, Kunming, Yunnan, China
- *Correspondence: Long Chen, ; Yunchao Huang,
| |
Collapse
|
|
34
|
Vaandering A, Jansen N, Weltens C, Moretti L, Stellamans K, Vanhoutte F, Scalliet P, Remouchamps V, Lievens Y. Radiotherapy-specific quality indicators at national level: How to make it happen. Radiother Oncol 2023; 178:109433. [PMID: 36464181 DOI: 10.1016/j.radonc.2022.11.022] [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: 08/12/2022] [Revised: 11/22/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022]
Abstract
PURPOSE /OBJECTIVE To promote best practice and quality of care, the Belgian College of Physicians for Radiotherapy Centers established a set of radiotherapy specific quality indicators for benchmarking on a national level. This paper describes the development, the collected QIs, the observed trends and the departments' evaluation of this initiative. MATERIAL AND METHODS The Donabedian approach was used, focussing on structural, process and outcome QIs. The criteria for QI selection were availability, required for low-threshold regular collection, and applicability to guidelines and good practice. The QIs were collected yearly and individualized reports were sent out to all RT departments. In 2021, a national survey was held to evaluate the ease of data collection and submission, and the perceived importance and validity of the collected QIs. RESULTS 18 structural QI and 37 process and outcome parameters (n = 25 patients/pathology/department) were collected. The participation rate amounted to 95 % overall. The analysis gave a national overview of RT activity, resources, clinical practice and reported acute toxicities. The individualized reports allowed departments to benchmark their performance. The 2021 survey indicated that the QIs were overall easy to collect, relevant and reliable. The collection of acute recorded toxicities was deemed a weak point due to inter-observer variabilities and lack of follow-up time. CONCLUSION QI collection on a national level is a valuable process in steering quality improvement initiatives. The feasibility and relevance was demonstrated with a high level of participation. The national initiative will continue to evolve as a quality monitoring and improvement tool.
Collapse
Affiliation(s)
- Aude Vaandering
- UCL Cliniques Universitaires St Luc, Department of radiation oncology, Brussels, Belgium; Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Brussels, Belgium.
| | - Nicolas Jansen
- University Hospital of Liège, Department of radiation oncology, Liège, Belgium
| | - Caroline Weltens
- Department of Radiation Oncology, University Hospitals Leuven, KU Leuven, Belgium
| | - Luigi Moretti
- Institut Jules Bordet, Department of radiation oncology, Brussels, Belgium
| | - Karin Stellamans
- AZ Groeninge, Department of radiation oncology, Kortrijk, Belgium
| | - Frederik Vanhoutte
- Ghent University Hospital and Ghent University, Department of radiation oncology, Ghent, Belgium
| | - Pierre Scalliet
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Brussels, Belgium
| | - Vincent Remouchamps
- CHU-UCL Namur - site Saint Elisabeth, Department of radiation oncology, Namur, Belgium
| | - Yolande Lievens
- Ghent University Hospital and Ghent University, Department of radiation oncology, Ghent, Belgium
| | | |
Collapse
|
|
35
|
Abdalvand N, Sadeghi M, Mahdavi SR, Abdollahi H, Qasempour Y, Mohammadian F, Birgani MJT, Hosseini K. Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters. Brachytherapy 2022; 21:769-782. [PMID: 35933272 DOI: 10.1016/j.brachy.2022.06.007] [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: 03/12/2022] [Revised: 06/09/2022] [Accepted: 06/26/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE To predict clinical response in locally advanced cervical cancer (LACC) patients by a combination of measures, including clinical and brachytherapy parameters and several machine learning (ML) approaches. METHODS Brachytherapy features such as insertion approaches, source metrics, dosimetric, and clinical measures were used for modeling. Four different ML approaches, including LASSO, Ridge, support vector machine (SVM), and Random Forest (RF), were applied to extracted measures for model development alone or in combination. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics curve, sensitivity, specificity, and accuracy. Our results were compared with a reference model developed by simple logistic regression applied to three distinct clinical features identified by previous papers. RESULTS One hundred eleven LACC patients were included. Nine data sets were obtained based on the features, and 36 predictive models were built. In terms of AUC, the model developed using RF applied to dosimetric, physical, and total BT sessions features were found as the most predictive [AUC; 0.82 (0.95 confidence interval (CI); 0.79 -0.93), sensitivity; 0.79, specificity; 0.76, and accuracy; 0.77]. The AUC (0.95 CI), sensitivity, specificity, and accuracy for the reference model were found as 0.56 (0.52 ...0.68), 0.51, 0.51, and 0.48, respectively. Most RF models had significantly better performance than the reference model (Bonferroni corrected p-value < 0.0014). CONCLUSION Brachytherapy response can be predicted using dosimetric and physical parameters extracted from treatment parameters. Machine learning algorithms, including Random Forest, could play a critical role in such predictive modeling.
Collapse
Affiliation(s)
- Neda Abdalvand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mahdi Sadeghi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Younes Qasempour
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Fatemeh Mohammadian
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| | | | - Khadijeh Hosseini
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| |
Collapse
|
|
36
|
Minami S, Saso K, Miyoshi N, Fujino S, Kato S, Sekido Y, Hata T, Ogino T, Takahashi H, Uemura M, Yamamoto H, Doki Y, Eguchi H. Diagnosis of Depth of Submucosal Invasion in Colorectal Cancer with AI Using Deep Learning. Cancers (Basel) 2022; 14:5361. [PMID: 36358780 PMCID: PMC9656054 DOI: 10.3390/cancers14215361] [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: 09/20/2022] [Revised: 10/23/2022] [Accepted: 10/24/2022] [Indexed: 08/30/2023] Open
Abstract
The submucosal invasion depth predicts prognosis in early colorectal cancer. Although colorectal cancer with shallow submucosal invasion can be treated via endoscopic resection, colorectal cancer with deep submucosal invasion requires surgical colectomy. However, accurately diagnosing the depth of submucosal invasion via endoscopy is difficult. We developed a tool to diagnose the depth of submucosal invasion in early colorectal cancer using artificial intelligence. We reviewed data from 196 patients who had undergone a preoperative colonoscopy at the Osaka University Hospital and Osaka International Cancer Institute between 2011 and 2018 and were diagnosed pathologically as having shallow submucosal invasion or deep submucosal invasion colorectal cancer. A convolutional neural network for predicting invasion depth was constructed using 706 images from 91 patients between 2011 and 2015 as the training dataset. The diagnostic accuracy of the constructed convolutional neural network was evaluated using 394 images from 49 patients between 2016 and 2017 as the validation dataset. We also prospectively tested the tool from 56 patients in 2018 with suspected early-stage colorectal cancer. The sensitivity, specificity, accuracy, and area under the curve of the convolutional neural network for diagnosing deep submucosal invasion colorectal cancer were 87.2% (258/296), 35.7% (35/98), 74.4% (293/394), and 0.758, respectively. The positive predictive value was 84.4% (356/422) and the sensitivity was 75.7% (356/470) in the test set. The diagnostic accuracy of the constructed convolutional neural network seemed to be as high as that of a skilled endoscopist. Thus, endoscopic image recognition by deep learning may be able to predict the submucosal invasion depth in early-stage colorectal cancer in clinical practice.
Collapse
Affiliation(s)
- Soichiro Minami
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Innovative Oncology Research and Regenerative Medicine, Osaka International Cancer Institute, Osaka 541-8567, Japan
| | - Kazuhiro Saso
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Innovative Oncology Research and Regenerative Medicine, Osaka International Cancer Institute, Osaka 541-8567, Japan
| | - Norikatsu Miyoshi
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Innovative Oncology Research and Regenerative Medicine, Osaka International Cancer Institute, Osaka 541-8567, Japan
| | - Shiki Fujino
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Innovative Oncology Research and Regenerative Medicine, Osaka International Cancer Institute, Osaka 541-8567, Japan
| | - Shinya Kato
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Innovative Oncology Research and Regenerative Medicine, Osaka International Cancer Institute, Osaka 541-8567, Japan
| | - Yuki Sekido
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Tsuyoshi Hata
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Takayuki Ogino
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Hidekazu Takahashi
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Mamoru Uemura
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Hirofumi Yamamoto
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Yuichiro Doki
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| |
Collapse
|
|
37
|
Lahmi L, Mamzer MF, Burgun A, Durdux C, Bibault JE. Ethical Aspects of Artificial Intelligence in Radiation Oncology. Semin Radiat Oncol 2022; 32:442-448. [DOI: 10.1016/j.semradonc.2022.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
|
38
|
Kumagai Y, Takubo K, Kawada K, Ohue M, Higashi M, Ishiguro T, Hatano S, Toyomasu Y, Matsuyama T, Mochiki E, Ishida H. Endocytoscopic Observation of Esophageal Lesions: Our Own Experience and a Review of the Literature. Diagnostics (Basel) 2022; 12:2222. [PMID: 36140623 PMCID: PMC9498282 DOI: 10.3390/diagnostics12092222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/02/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022] Open
Abstract
This review outlines the process of the development of the endocytoscope (EC) with reference to previously reported studies including our own. The EC is an ultra-high-magnification endoscope capable of imaging at the cellular level. The esophagus is the most suitable site for EC observation because it is amenable to vital staining. The diagnosis of esophageal lesions using EC is based on nuclear density and nuclear abnormality, allowing biopsy histology to be omitted. The observation of nuclear abnormality requires a magnification of ×600 or higher using digital technology. Several staining methods have been proposed, but single staining with toluidine blue or methylene blue is most suitable because the contrast at the border of a cancerous area can be easily identified. A three-tier classification of esophageal lesions visualized by EC is proposed: Type 1 (non-cancerous), Type 2 (endocytoscopic borderline), and Type 3 (cancerous). Since characteristic EC images reflecting pathology can be obtained from non-cancerous esophageal lesions, a modified form of classification with four additional characteristic non-cancerous EC features has also been proposed. Recently, deep-learning AI for analysis of esophageal EC images has revealed that its diagnostic accuracy is comparable to that of expert pathologists.
Collapse
Affiliation(s)
- Youichi Kumagai
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Kaiyo Takubo
- Research Team for Geriatric Pathology, Tokyo Metropolitan Institute of Gerontology, Tokyo 173-0015, Japan
| | - Kenro Kawada
- Department of Esophageal and General Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Masayuki Ohue
- Department of Surgery, Osaka International Cancer Center, Osaka 541-8567, Japan
| | - Morihiro Higashi
- Department of Pathology, Saitama Medical Center, Saitama Medical University, Saitama 350-0495, Japan
| | - Toru Ishiguro
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Satoshi Hatano
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Yoshitaka Toyomasu
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Takatoshi Matsuyama
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Erito Mochiki
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Hideyuki Ishida
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| |
Collapse
|
|
39
|
Kodaka Y, Futagami S, Watanabe Y, Shichijo S, Uedo N, Aono H, Kirita K, Kato Y, Ueki N, Agawa S, Yamawaki H, Iwakiri K, Tada T. Determination of gastric atrophy with artificial intelligence compared to the assessments of the modified Kyoto and
OLGA
classifications. JGH Open 2022; 6:704-710. [PMID: 36262541 PMCID: PMC9575326 DOI: 10.1002/jgh3.12810] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 07/19/2022] [Accepted: 08/09/2022] [Indexed: 12/03/2022]
Abstract
Background and Aim Gastric atrophy is a precancerous lesion. We aimed to clarify whether gastric atrophy determined by artificial intelligence (AI) correlates with the diagnosis made by expert endoscopists using several endoscopic classifications, the Operative Link on Gastritis Assessment (OLGA) classification based on histological findings, and genotypes associated with gastric atrophy and cancer. Methods Two hundred seventy Helicobacter pylori‐positive outpatients were enrolled. All patients' endoscopy data were retrospectively evaluated based on the Kimura‐Takemoto, modified Kyoto, and OLGA classifications. The AI‐trained neural network generated a continuous number between 0 and 1 for gastric atrophy. Nucleotide variance of some candidate genes was confirmed or selectively assessed for a variety of genotypes, including the COX‐21195, IL‐1β 511, and mPGES‐1 genotypes. Results There were significant correlations between determinations of gastric atrophy by AI and by expert endoscopists using not only the Kimura‐Takemoto classification (P < 0.001), but also the modified Kyoto classification (P = 0.046 and P < 0.001 for the two criteria). Moreover, there was a significant correlation with the OLGA classification (P = 0.009). Nucleotide variance of the COX‐2, IL‐1β, and mPGES‐1genes was not significantly associated with gastric atrophy determined by AI. The area under the curve values of the combinations of AI and the modified Kyoto classification (0.746) and AI and the OLGA classification (0.675) were higher than in AI alone (0.665). Conclusion Combinations of AI and the modified Kyoto classification or of AI and the OLGA classification could be useful tools for evaluating gastric atrophy in patients with H. pylori infection as the risk of gastric cancer.
Collapse
Affiliation(s)
- Yasuhiro Kodaka
- Division of Gastroenterology Nippon Medical School Tokyo Japan
| | - Seiji Futagami
- Division of Gastroenterology Nippon Medical School Tokyo Japan
| | | | - Satoki Shichijo
- Department of Gastrointestinal Oncology Osaka International Cancer Institute Osaka Japan
| | - Noriya Uedo
- Department of Gastrointestinal Oncology Osaka International Cancer Institute Osaka Japan
| | | | - Kumiko Kirita
- Division of Gastroenterology Nippon Medical School Tokyo Japan
| | | | - Nobue Ueki
- Division of Gastroenterology Nippon Medical School Tokyo Japan
| | - Shuhei Agawa
- Division of Gastroenterology Nippon Medical School Tokyo Japan
| | | | | | - Tomohiro Tada
- AI Medical Service Inc. Tokyo Japan
- Tada Tomohiro Institute of Gastroenterology and Proctology Saitama Japan
| |
Collapse
|
|
40
|
Barbosa B, Bravo I, Oliveira C, Antunes L, Couto JG, McFadden S, Hughes C, McClure P, Dias AG. Digital skills of therapeutic radiographers/radiation therapists - Document analysis for a European educational curriculum. Radiography (Lond) 2022; 28:955-963. [PMID: 35842952 DOI: 10.1016/j.radi.2022.06.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/14/2022] [Accepted: 06/23/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION It is estimated that around 50% of cancer patients require Radiotherapy (RT) at some point during their treatment, hence Therapeutic Radiographers/Radiation Therapists (TR/RTTs) have a key role to play in patient management. It is essential for TR/RTTs to keep abreast with new technologies and continuously develop the digital skills necessary for safe RT practice. The RT profession and education is not regulated at European Union level, which leads to heterogeneity in the skills developed and practised among countries. This study aimed to explore the white and grey literature to collate data on the relevant digital skills required for TR/RTTs practice. METHODS An exhaustive systematic search was conducted to identify literature discussing digital skills of TR/RTTs; relevant grey literature was also identified. A thematic analysis was performed to identify and organise these skills into themes and sub-themes. RESULTS 195 digital skills were identified, organised in 35 sub-themes and grouped into six main themes: (i) Transversal Digital Skills, (ii) RT Planning Image, (iii) RT Treatment Planning, (iv) RT Treatment Administration, (v) Quality, Safety and Risk Management, and (vi) Management, Education and Research. CONCLUSION This list can be used as a reference to close current gaps in knowledge or skills of TR/RTTs while anticipating future needs regarding the rapid development of new technologies (such as Artificial Intelligence or Big Data). IMPLICATIONS FOR PRACTICE It is imperative to align education with current and future RT practice to ensure that all RT patients receive the best care. Filling the gaps in TR/RTTs skill sets will improve current practice and provide TR/RTTs with the support needed to develop more advanced skills.
Collapse
Affiliation(s)
- B Barbosa
- Radiotherapy Department, Instituto Português de Oncologia do Porto (IPO Porto), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal; Escola Internacional de Doutoramento, Universidad de Vigo, Circunvalación ao Campus Universitario, 36310 Vigo, Pontevedra, Spain; Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Porto Comprehensive Cancer Center (Porto.CCC) & Rise@CI-IPOP (Health Research Network), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal.
| | - I Bravo
- Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Porto Comprehensive Cancer Center (Porto.CCC) & Rise@CI-IPOP (Health Research Network), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal.
| | - C Oliveira
- Radiotherapy Department, Instituto Português de Oncologia do Porto (IPO Porto), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal; Escola Internacional de Doutoramento, Universidad de Vigo, Circunvalación ao Campus Universitario, 36310 Vigo, Pontevedra, Spain.
| | - L Antunes
- School of Health, Polytechnic Institute of Porto, Rua Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal.
| | - J G Couto
- Radiography Department, Faculty of Health Sciences, University of Malta, Msida MSD2080, Malta.
| | - S McFadden
- Institute of Nursing and Health Research, School of Health Sciences, Ulster University, Jordanstown, United Kingdom.
| | - C Hughes
- Institute of Nursing and Health Research, School of Health Sciences, Ulster University, Jordanstown, United Kingdom.
| | - P McClure
- Institute of Nursing and Health Research, School of Health Sciences, Ulster University, Jordanstown, United Kingdom.
| | - A G Dias
- Medical Physics, Radiobiology and Radiation Protection Group, IPO Porto Research Center (CI-IPOP), Porto Comprehensive Cancer Center (Porto.CCC) & Rise@CI-IPOP (Health Research Network), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal; Medical Physics Department, Instituto Português de Oncologia do Porto (IPO Porto), R. Dr. António Bernardino de Almeida 865, 4200-072 Porto, Portugal.
| |
Collapse
|
|
41
|
Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 2022; 159:333-346. [PMID: 35761160 DOI: 10.1007/s11060-022-04068-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/11/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
Collapse
|
|
42
|
Yahara Y, Tamura M, Seki S, Kondo Y, Makino H, Watanabe K, Kamei K, Futakawa H, Kawaguchi Y. A deep convolutional neural network to predict the curve progression of adolescent idiopathic scoliosis: a pilot study. BMC Musculoskelet Disord 2022; 23:610. [PMID: 35751051 PMCID: PMC9229131 DOI: 10.1186/s12891-022-05565-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 06/17/2022] [Indexed: 11/17/2022] Open
Abstract
Background Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that predominantly occurs in girls. While skeletal growth and maturation influence the development of AIS, accurate prediction of curve progression remains difficult because the prognosis for deformity differs among individuals. The purpose of this study is to develop a new diagnostic platform using a deep convolutional neural network (DCNN) that can predict the risk of scoliosis progression in patients with AIS. Methods Fifty-eight patients with AIS (49 females and 9 males; mean age: 12.5 ± 1.4 years) and a Cobb angle between 10 and 25 degrees (mean angle: 18.7 ± 4.5) were divided into two groups: those whose Cobb angle increased by more than 10 degrees within two years (progression group, 28 patients) and those whose Cobb angle changed by less than 5 degrees (non-progression group, 30 patients). The X-ray images of three regions of interest (ROIs) (lung [ROI1], abdomen [ROI2], and total spine [ROI3]), were used as the source data for learning and prediction. Five spine surgeons also predicted the progression of scoliosis by reading the X-rays in a blinded manner. Results The prediction performance of the DCNN for AIS curve progression showed an accuracy of 69% and an area under the receiver-operating characteristic curve of 0.70 using ROI3 images, whereas the diagnostic performance of the spine surgeons showed inferior at 47%. Transfer learning with a pretrained DCNN contributed to improved prediction accuracy. Conclusion Our developed method to predict the risk of scoliosis progression in AIS by using a DCNN could be a valuable tool in decision-making for therapeutic interventions for AIS.
Collapse
Affiliation(s)
- Yasuhito Yahara
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan. .,Department of Molecular and Medical Pharmacology, Faculty of Medicine, University of Toyama, Toyama, Japan.
| | - Manami Tamura
- Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata, 951-8518, Japan
| | - Shoji Seki
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Yohan Kondo
- Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata, 951-8518, Japan.
| | - Hiroto Makino
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Kenta Watanabe
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Katsuhiko Kamei
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Hayato Futakawa
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Yoshiharu Kawaguchi
- Department of Orthopaedic Surgery, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| |
Collapse
|
|
43
|
Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis? Eur Radiol 2022; 32:8540-8549. [PMID: 35731290 DOI: 10.1007/s00330-022-08882-5] [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: 04/28/2022] [Revised: 04/28/2022] [Accepted: 05/12/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To explore the utility of radiomics and deep learning model in assessing the risk factors for sepsis after flexible ureteroscopy lithotripsy (FURL) or percutaneous nephrolithotomy (PCNL) in patients with ureteral calculi. METHODS This retrospective analysis included 847 patients with treatment-naive proximal ureteral calculi who received FURL or PCNL. All participants were preoperatively conducted non-contrast computed tomography scans, and relevant clinical information was meanwhile collected. After propensity score matching, the radiomics model was established to predict the onset of sepsis. A deep learning model was also adapted to further improve the prediction accuracy. Performance of these trained models was verified in another independent external validation set including 40 cases of ureteral calculi patients. RESULTS The overall incidence of sepsis after FURL or PCNL was 5.9%. The least absolute shrinkage and selection operator (LASSO) regression analysis revealed 26 predictive variables, with an overall AUC of 0.881 (95% CI, 0.813-0.931) and an AUC of 0.783 (95% CI, 0.766-0.801) in external validation cohort. Judicious adaption of a deep neural network (DNN) model to our dataset improved the AUC to 0.920 (95% CI, 0.906-0.933) in the internal validation. To eliminate the overfitting, external validation was carried out for DNN model (AUC = 0.874 (95% CI, 0.858-0.891)). CONCLUSIONS The DNN was more effective than the LASSO model in revealing risk factors for sepsis after FURL or PCNL in single ureteral calculi patients, and females are more susceptible to sepsis than males. Deep learning models have the potential to act as gatekeepers to facilitate patient stratification. KEY POINTS • Both the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models were shown to be effective in sepsis prediction. • The DNN model achieved superior prediction capability, with an AUC of 0.920 (95% CI, 0.906-0.933). • DNN-assisted model has potential to serve as a gatekeeper to facilitate patient stratification.
Collapse
|
|
44
|
Medici F, Bazzocchi A, Buwenge M, Zamagni A, Macchia G, Deodato F, Cilla S, De Iaco P, Perrone AM, Strigari L, Rizzo S, Morganti AG. Impact and Treatment of Sarcopenia in Patients Undergoing Radiotherapy: A Multidisciplinary, AMSTAR-2 Compliant Review of Systematic Reviews and Metanalyses. Front Oncol 2022; 12:887156. [PMID: 35692790 PMCID: PMC9177942 DOI: 10.3389/fonc.2022.887156] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/26/2022] [Indexed: 12/11/2022] Open
Abstract
Background Sarcopenia (SP) is defined as the quantitative and functional impairment of skeletal muscles. SP is commonly related to older age and is frequent in patients with cancer. To provide an overview of SP in patients treated with radiotherapy (RT) and to evaluate the current evidence, we analyzed the available systematic reviews and meta-analyses. Methods Reviews were identified using PubMed, Scopus, and Cochrane library databases, without date restriction. Only systematic reviews and meta-analyses on the prognostic impact of SP and on any treatments aimed at reducing SP effect, in patients undergoing RT, were included in this review. The analyses not separately reporting the results in patients treated with RT were excluded. The quality assessment was performed using AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews). Results From the 84 papers identified, five reviews met the inclusion criteria with four reports mainly including non-randomized trials. Three reviews on the effect of SP showed a significantly negative impact on overall survival in patients undergoing RT and/or chemoradiation for H&N cancers (HR: 1.63-2.07). Two reviews on interventional studies showed the possibility of 1) improving physical functions through nutritional and physical interventions and 2) avoiding muscle wasting by means of sufficient protein intake. The quality assessment of the included review showed that two and three analyses are classifiable as having low and moderate overall confidence rating, respectively. Conclusions The analyzed reviews uniformly confirmed the negative impact of SP in patients with H&N tumors undergoing RT and the possibility of improving muscle mass and function through nutritional and physical interventions. These results justify further research on this topic based on a more uniform SP definition and on a complete evaluation of the potentially confounding parameters.
Collapse
Affiliation(s)
- Federica Medici
- Department of Experimental, Diagnostic and Specialty Medicine-DIMES, Alma Mater Studiorum University of Bologna, Bologna, Italy
- *Correspondence: Federica Medici,
| | - Alberto Bazzocchi
- Diagnostic and Interventional Radiology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Milly Buwenge
- Department of Experimental, Diagnostic and Specialty Medicine-DIMES, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Alice Zamagni
- Department of Experimental, Diagnostic and Specialty Medicine-DIMES, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Gabriella Macchia
- Radiation Oncology Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Francesco Deodato
- Radiation Oncology Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Savino Cilla
- Medical Physics Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Pierandrea De Iaco
- Division of Oncologic Gynecology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Centro di Studio e Ricerca delle Neoplasie Ginecologiche (CSR), University of Bologna, Bologna, Italy
| | - Anna Myriam Perrone
- Division of Oncologic Gynecology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Centro di Studio e Ricerca delle Neoplasie Ginecologiche (CSR), University of Bologna, Bologna, Italy
| | - Lidia Strigari
- Medical Physics Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Stefania Rizzo
- Service of Radiology, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
| | - Alessio G. Morganti
- Department of Experimental, Diagnostic and Specialty Medicine-DIMES, Alma Mater Studiorum University of Bologna, Bologna, Italy
- Radiation Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| |
Collapse
|
|
45
|
Vision for Improving Pregnancy Health: Innovation and the Future of Pregnancy Research. Reprod Sci 2022; 29:2908-2920. [PMID: 35534766 PMCID: PMC9537127 DOI: 10.1007/s43032-022-00951-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/15/2022] [Indexed: 10/25/2022]
Abstract
Understanding, predicting, and preventing pregnancy disorders have been a major research target. Nonetheless, the lack of progress is illustrated by research results related to preeclampsia and other hypertensive pregnancy disorders. These remain a major cause of maternal and infant mortality worldwide. There is a general consensus that the rate of progress toward understanding pregnancy disorders lags behind progress in other aspects of human health. In this presentation, we advance an explanation for this failure and suggest solutions. We propose that progress has been impeded by narrowly focused research training and limited imagination and innovation, resulting in the failure to think beyond conventional research approaches and analytical strategies. Investigations have been largely limited to hypothesis-generating approaches constrained by attempts to force poorly defined complex disorders into a single "unifying" hypothesis. Future progress could be accelerated by rethinking this approach. We advise taking advantage of innovative approaches that will generate new research strategies for investigating pregnancy abnormalities. Studies should begin before conception, assessing pregnancy longitudinally, before, during, and after pregnancy. Pregnancy disorders should be defined by pathophysiology rather than phenotype, and state of the art agnostic assessment of data should be adopted to generate new ideas. Taking advantage of new approaches mandates emphasizing innovation, inclusion of large datasets, and use of state of the art experimental and analytical techniques. A revolution in understanding pregnancy-associated disorders will depend on networks of scientists who are driven by an intense biological curiosity, a team spirit, and the tools to make new discoveries.
Collapse
|
|
46
|
Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
Collapse
Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| |
Collapse
|
|
47
|
Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models. Sci Rep 2022; 12:5722. [PMID: 35388124 PMCID: PMC8986767 DOI: 10.1038/s41598-022-09803-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/29/2022] [Indexed: 12/21/2022] Open
Abstract
This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature selection methods and five classification algorithms. The model set with the highest average AUC value was selected, in which some models were selected and divided into Groups A, B, and C. Individual and joint voting predictions were performed in each group for the entire data. The model set based on MRI combined with 18F-FDG-PET had the highest average AUC compared with isolated MRI or 18F-FDG-PET. Joint voting prediction showed better performance than the individual prediction when all models reached an agreement. In conclusion, radiomics derived from MRI and 18F-FDG-PET could help differentiate GBM from SBM preoperatively. The combined application of multiple models can provide greater benefits.
Collapse
|
|
48
|
Qiao D, Liu H, Zhang X, Lei L, Sun N, Yang C, Li G, Guo M, Zhang Y, Zhang K, Liu Z. Exploring the potential of thyroid hormones to predict clinical improvements in depressive patients: A machine learning analysis of the real-world based study. J Affect Disord 2022; 299:159-165. [PMID: 34856305 DOI: 10.1016/j.jad.2021.11.055] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/17/2021] [Accepted: 11/19/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Although undergoing antidepressant treatments, many patients continue to struggle with chronic depression episode. Seeking the potential biomarkers and establishing a predictive model of clinical improvements is vital to optimize personalized management of depression. Mounting evidence showed thyroid hormones changes are central to leading paradigms of depression. METHODS Here, we conducted a real-world based retrospective study using clinical and biochemical data of 2086 depressive inpatients during period of 2014-2020. We first performed regression analyses to evaluate the contributing effect of free triiodothyronine (FT3), free thyroxine (FT4) and thyroid stimulating hormone (TSH) in predicting the clinical outcomes of depression. Then we established 7 predictive models using different combination of such hormones by supervised learning methods and tested the actual prediction efficacy on clinical outcomes, in order to select the one with the best predictive power. RESULTS The results showed that lower values of FT3 and FT4 can both predict a poor clinical outcome in depression. Further, a model with the best performance was selected (sensitivity=0.91, specificity=0.79, and ROC-AUC=0.86), including the values of FT3 and FT4, and the scores of Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) as features. LIMITATIONS The predictive model requires further external validation, and multi-center researches to confirm its clinical applicability. CONCLUSIONS Our findings present a crucial role of thyroid measurements in predicting clinical outcomes of depression. Assessment of thyroid hormone should be extended to routine practice settings to determine which patients should be most in need of earlier or intensive interventions for preventing continued dysfunction.
Collapse
Affiliation(s)
- Dan Qiao
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Huishan Liu
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Xuemin Zhang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Lei Lei
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Ning Sun
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Chunxia Yang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Gaizhi Li
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Meng Guo
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Yu Zhang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Kerang Zhang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China.
| | - Zhifen Liu
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China.
| |
Collapse
|
|
49
|
Bonzanini AD, Shao K, Stancampiano A, Graves DB, Mesbah A. Perspectives on Machine Learning-Assisted Plasma Medicine: Toward Automated Plasma Treatment. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3055727] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
|
50
|
Chang V, Srilikhita G, Xu QA, Hossain MA, Guizani M. Analyzing the Impact of Machine Learning on Cancer Treatments. INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2022. [DOI: 10.4018/ijdst.304429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The survival rate of breast cancer prediction has been a significant issue for researchers. Nowadays, the health care industry has completely transformed by using modern technologies and their applications for medical services. Among those technologies, machine learning is one of them, which has gained attention by people that its new advanced technology would give accurate results by using modeling methods for prediction. As this is a branch of artificial intelligence, it employs various statics, probabilistic and optimistic tools. This is applied to medical services, especially which are based on proteomic and genomic measurements. The aim is to use the dataset of cancer treatment and predict the results of patients by using machine learning with its modeling methods for accurate results. Recently experts have even used this machine learning in cancer for prognosis and forecasting.
Collapse
Affiliation(s)
| | | | | | - M. A. Hossain
- Cambodia University of Technology and Science, Cambodia
| | - Mohsen Guizani
- Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)
| |
Collapse
|