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Tixier F, Lopez-Ramirez F, Blanco A, Javed AA, Chu LC, Hruban RH, Yasrab M, Fouladi DF, Shayesteh S, Ghandili S, Fishman EK, Kawamoto S. Diagnostic Performance of Combined Conventional CT Imaging Features and Radiomics Signature in Differentiating Grade 1 Tumors from Higher-Grade Pancreatic Neuroendocrine Neoplasms. Cancers (Basel) 2025; 17:1047. [PMID: 40149380 PMCID: PMC11941307 DOI: 10.3390/cancers17061047] [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: 02/04/2025] [Revised: 03/16/2025] [Accepted: 03/18/2025] [Indexed: 03/29/2025] Open
Abstract
BACKGROUND/OBJECTIVES Accurate identification of grade 1 (G1) pancreatic neuroendocrine tumors (PanNETs) is crucial due to their rising incidence and emerging nonsurgical management strategies. This study evaluated whether combining conventional CT imaging features, CT radiomics features, and clinical data improves differentiation of G1 PanNETs from higher-grade tumors (G2/G3 PanNETs and pancreatic neuroendocrine carcinomas [PanNECs]) compared to using these features individually. METHODS A retrospective analysis included 133 patients with pathologically confirmed PanNETs or PanNECs (70 males, 63 females; mean age, 58.5 years) who underwent pancreas protocol CT. A total of 28 conventional imaging features, 4892 radiomics features, and clinical data (age, gender, and tumor location) were analyzed using a support vector machine (SVM) model. Data were divided into 70% training and 30% testing sets. RESULTS The SVM model using the top 10 conventional imaging features (e.g., suspicious lymph nodes and hypoattenuating tumors) achieved 75% sensitivity, 81% specificity, and 79% accuracy for identifying higher-grade tumors (G2/G3 PanNETs and PanNECs). The top 10 radiomics features yielded 94% sensitivity, 46% specificity, and 69% accuracy. Combining all features (imaging, radiomics, and clinical data) improved performance, with 94% sensitivity, 69% specificity, 79% accuracy, and an F1-score of 0.77. The radiomics score demonstrated an AUC of 0.85 in the training and 0.83 in the testing set. CONCLUSIONS Conventional imaging features provided higher specificity, while radiomics offered greater sensitivity for identifying higher-grade tumors. Integrating all three features improved diagnostic accuracy, highlighting their complementary roles. This combined model may serve as a valuable tool for distinguishing higher-grade tumors from G1 PanNETs and potentially guiding patient management.
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Affiliation(s)
- Florent Tixier
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Felipe Lopez-Ramirez
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Alejandra Blanco
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Ammar A. Javed
- Department of Surgery, The NYU Grossman School of Medicine and NYU Langone Health, New York, NY 10016, USA;
| | - Linda C. Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Ralph H. Hruban
- The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, School of Medicine, Hopkins University, Baltimore, MD 21205, USA;
- Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Mohammad Yasrab
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Daniel Fadaei Fouladi
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Shahab Shayesteh
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Saeed Ghandili
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Elliot K. Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (F.T.); (F.L.-R.); (A.B.); (L.C.C.); (M.Y.); (D.F.F.); (S.S.); (S.G.); (E.K.F.)
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Xie Y, Abaydulla E, Zhang S, Liu H, Hang H, Li Q, Qiu Y, Cheng H. Preoperative prediction of pancreatic neuroendocrine tumors grade based on computed tomography, magnetic resonance imaging and endoscopic ultrasonography. Abdom Radiol (NY) 2025:10.1007/s00261-025-04865-4. [PMID: 40105959 DOI: 10.1007/s00261-025-04865-4] [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: 12/18/2024] [Revised: 02/24/2025] [Accepted: 02/28/2025] [Indexed: 03/22/2025]
Abstract
PURPOSE To establish a preoperative prediction model for pathological grade of PanNETs based on computed tomography (CT), magnetic resonance imaging (MRI) and endoscopic ultrasonography (EUS). METHODS Clinical data of 58 patients with pathologically confirmed PanNETs were included in this retrospectively study and they were divided into grade 1 and grade 2/3. CT, MRI and EUS images were collected within one week before surgery. A clinical predictive model based on the independent clinical risk factors and significant radiological features was established. The area under receiver operating characteristic curve (AUC) was performed to assess the model. RESULTS Gender, pancreatic duct dilatation (PDD) and portal enhancement ratio (PER) were the independent predictors for PanNETs grading (P < 0.05). PanNETs grade 1 and grade 2/3 had statistical difference in elastography score (P = 0.001). The combination of gender, PDD and PER had better predictive efficiency than each of these three predictors alone, with a high AUC of 0.925. The elastography score also achieved an AUC of 0.838. CONCLUSION We proposed a comprehensive model based on preoperative CT, MRI and EUS to predict grade 1 and grade 2/3 of PanNETs and better informs clinicians on individualized diagnosis and treatment of patients with PanNETs.
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Affiliation(s)
- Yu Xie
- Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Elyar Abaydulla
- Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Song Zhang
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Haobai Liu
- Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hexing Hang
- Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qi Li
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yudong Qiu
- Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
| | - Hao Cheng
- Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
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Mo S, Huang C, Wang Y, Qin S. Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors. BMC Med Imaging 2025; 25:22. [PMID: 39827128 PMCID: PMC11743008 DOI: 10.1186/s12880-025-01555-x] [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: 03/25/2024] [Accepted: 01/03/2025] [Indexed: 01/22/2025] Open
Abstract
OBJECTIVES The objective is to develop and validate intratumoral and peritumoral ultrasomics models utilizing endoscopic ultrasonography (EUS) to predict pathological grading in pancreatic neuroendocrine tumors (PNETs). METHODS Eighty-one patients, including 51 with grade 1 PNETs and 30 with grade 2/3 PNETs, were included in this retrospective study after confirmation through pathological examination. The patients were randomly allocated to the training or test group in a 6:4 ratio. Univariate and multivariate logistic regression were used for screening clinical and ultrasonic characteristics. Ultrasomics is ultrasound-based radiomics. Ultrasomics features were extracted from both the intratumoral and peritumoral regions of conventional EUS images. Subsequently, the dimensionality of these radiomics features was reduced using the least absolute shrinkage and selection operator (LASSO) algorithm. A machine learning algorithm, namely multilayer perception (MLP), was employed to construct prediction models using only the nonzero coefficient features and retained clinical features, respectively. RESULTS One hundred seven ultrasomics features based on EUS were extracted, and only features with nonzero coefficients were ultimately retained. Among all the models, the combined ultrasomics model achieved the greatest performance, with an AUC of 0.858 (95% CI, 0.7512 - 0.9642) in the training group and 0.842 (95% CI, 0.7061 - 0.9785) in the test group. A calibration curve and a decision curve analysis (DCA) also demonstrated its accuracy and utility. CONCLUSIONS The integrated model using EUS ultrasomics features from intratumoral and peritumoral tumors accurately predicts PNETs' pathological grades pre-surgery, aiding personalized treatment planning. TRIAL REGISTRATION ChiCTR2400091906.
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Affiliation(s)
- Shuangyang Mo
- Gastroenterology Department/Clinical Nutrition Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Cheng Huang
- Oncology Department, Liuzhou Peoples' Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Yingwei Wang
- Gastroenterology Department/Clinical Nutrition Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Shanyu Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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Michcik A, Jopek M, Pęksa R, Choma P, Garbacewicz Ł, Polcyn A, Wach T, Sikora M, Drogoszewska B. Virtual Tumor Mapping: A New Standard for Surgeon-Pathologist Collaboration in Treating Oral Squamous Cell Carcinoma. Cancers (Basel) 2024; 16:3761. [PMID: 39594716 PMCID: PMC11591874 DOI: 10.3390/cancers16223761] [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: 10/06/2024] [Revised: 11/02/2024] [Accepted: 11/05/2024] [Indexed: 11/28/2024] Open
Abstract
Background: The histopathological assessment is critical in the comprehensive treatment process for patients diagnosed with oral squamous cell carcinoma (OSCC). A detailed and precise specimen characterization is essential to facilitate effective surgeon-pathologist communication. Methods: In response to this need, a user-friendly virtual communication protocol utilizing a 3D scanner has been developed. This study involved 50 patients with OSCC, whose resected tumors were directly scanned in the operating room and subsequently annotated and characterized using available software. Results: The direct application of annotations and descriptions onto the virtual tumor specimens significantly enhanced the quantity and accuracy of information conveyed to the pathologist. Conclusions: The proposed solution's repeatability and standardized approach make integration into routine clinical practice feasible, thereby establishing a potential new standard in the field.
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Affiliation(s)
- Adam Michcik
- Department of Maxillofacial Surgery, Medical University of Gdansk, Mariana Smoluchowskiego 17, 80-214 Gdansk, Poland; (P.C.); (Ł.G.); (A.P.); (B.D.)
| | - Maksym Jopek
- Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology of the University of Gdańsk and the Medical University of Gdańsk, Dębinki 1, 80-211 Gdańsk, Poland;
- Centre of Biostatistics and Bioinformatics, Medical University of Gdansk, Mariana Smoluchowskiego 17, 80-214 Gdansk, Poland
| | - Rafał Pęksa
- Department of Pathomorphology, Medical University of Gdańsk, Mariana Smoluchowskiego 17, 80-214 Gdansk, Poland;
| | - Piotr Choma
- Department of Maxillofacial Surgery, Medical University of Gdansk, Mariana Smoluchowskiego 17, 80-214 Gdansk, Poland; (P.C.); (Ł.G.); (A.P.); (B.D.)
| | - Łukasz Garbacewicz
- Department of Maxillofacial Surgery, Medical University of Gdansk, Mariana Smoluchowskiego 17, 80-214 Gdansk, Poland; (P.C.); (Ł.G.); (A.P.); (B.D.)
| | - Adam Polcyn
- Department of Maxillofacial Surgery, Medical University of Gdansk, Mariana Smoluchowskiego 17, 80-214 Gdansk, Poland; (P.C.); (Ł.G.); (A.P.); (B.D.)
| | - Tomasz Wach
- Department of Maxillofacial Surgery, Medical University of Lodz, Zeromskiego 113, 90-549 Lodz, Poland;
| | - Maciej Sikora
- National Medical Institute of the Ministry of Interior and Administration, Wołoska 137 Str., 02-507 Warsaw, Poland;
- Department of Maxillofacial Surgery, Hospital of the Ministry of Interior, Wojska Polskiego 51, 25-375 Kielce, Poland
- Department of Biochemistry and Medical Chemistry, Pomeranian Medical University, Powstanców Wielkopolskich 72, 70-111 Szczecin, Poland
| | - Barbara Drogoszewska
- Department of Maxillofacial Surgery, Medical University of Gdansk, Mariana Smoluchowskiego 17, 80-214 Gdansk, Poland; (P.C.); (Ł.G.); (A.P.); (B.D.)
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Ahn B, Park HJ, Kim HJ, Hong SM. Radiologic tumor border can further stratify prognosis in patients with pancreatic neuroendocrine tumor. Pancreatology 2024; 24:753-763. [PMID: 38796309 DOI: 10.1016/j.pan.2024.05.524] [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: 03/13/2024] [Revised: 04/30/2024] [Accepted: 05/14/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND AND OBJECTIVES Pancreatic neuroendocrine tumor (PanNET), although rare in incidence, is increasing in recent years. Several clinicopathologic and molecular factors have been suggested for patient stratification due to the extensive heterogeneity of PanNETs. We aimed to discover the prognostic role of assessing the tumor border of PanNETs with pre-operative computed tomography (CT) images and correlate them with other clinicopathologic factors. METHODS The radiologic, macroscopic, and microscopic tumor border of 183 surgically resected PanNET cases was evaluated using preoperative CT images (well defined vs. poorly defined), gross images (expansile vs. infiltrative), and hematoxylin and eosin-stained slides (pushing vs. infiltrative). The clinicopathologic and prognostic significance of the tumor border status was compared with other clinicopathologic factors. RESULTS A poorly defined radiologic tumor border was observed in 65 PanNET cases (35.5 %), and were more frequent in male patients (P = 0.031), and tumor with larger size, infiltrative macroscopic growth pattern, infiltrative microscopic tumor border, higher tumor grade, higher pT category, lymph node metastasis, lymphovascular and perineural invasions (all, P < 0.001). Patients with PanNET with a poorly defined radiologic tumor border had significantly worse overall survival (OS) and recurrence-free survival (RFS; both, P < 0.001). Multivariable analysis revealed that PanNET with a poorly defined radiologic border is an independent poor prognostic factor for both OS (P = 0.049) and RFS (P = 0.027). CONCLUSION Pre-operative CT-based tumor border evaluation can provide additional information regarding survival and recurrence in patients with PanNET.
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Affiliation(s)
- Bokyung Ahn
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyoung Jung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Seung-Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Mo S, Wang Y, Huang C, Wu W, Qin S. A novel endoscopic ultrasomics-based machine learning model and nomogram to predict the pathological grading of pancreatic neuroendocrine tumors. Heliyon 2024; 10:e34344. [PMID: 39130461 PMCID: PMC11315146 DOI: 10.1016/j.heliyon.2024.e34344] [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: 03/13/2024] [Revised: 07/08/2024] [Accepted: 07/08/2024] [Indexed: 08/13/2024] Open
Abstract
OBJECTIVES This research aimed to retrospectively construct and authenticate ultrasomics models using endoscopic ultrasonography (EUS) images for forecasting the pathological grading of pancreatic neuroendocrine tumors (PNETs). METHODS After confirmation through pathological examination, a retrospective analysis of 79 patients was conducted, including 49 with grade 1 PNETs and 30 with grade 2/3 PNETs. These patients were randomized to the training or test cohort in a 6:4 proportion. The least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimensionality of ultrasomics features derived from standard EUS images. These nonzero coefficient features were retained and applied to construct prediction models via eight machine-learning algorithms. The optimum ulstrasomics model was determined, followed by creating and evaluating a nomogram. RESULTS Ultrasomics features of 107 were extracted, and only those with coefficients greater than zero were retained. The XGboost ultrasomics model performed exceptionally well, achieving AUCs of 0.987 and 0.781 in the training and test cohorts, respectively. Furthermore, an effective nomogram was developed and visually represented. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curve (CIC) displayed in the ulstrasomics model and nomogram demonstrated high accuracy. They provided significant net benefits for clinical decision-making. CONCLUSIONS A novel ulstrasomics model and nomogram were created and certified to predict the pathological grading of PNETs using EUS images. This study has the potential to provide valuable insights that improve the clinical applicability and efficacy of EUS in predicting the grading of PNETs.
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Affiliation(s)
- Shuangyang Mo
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Gastroenterology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Yingwei Wang
- Gastroenterology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Cheng Huang
- Oncology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Wenhong Wu
- Gastroenterology Department, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Shanyu Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Yan Q, Chen Y, Liu C, Shi H, Han M, Wu Z, Huang S, Zhang C, Hou B. Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis. Front Oncol 2024; 14:1332387. [PMID: 38725633 PMCID: PMC11080013 DOI: 10.3389/fonc.2024.1332387] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 04/02/2024] [Indexed: 05/12/2024] Open
Abstract
Background Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis. Method A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool. Results A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%. Conclusion AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.
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Affiliation(s)
- Qian Yan
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Yubin Chen
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Chunsheng Liu
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Hexian Shi
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Mingqian Han
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zelong Wu
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Shanzhou Huang
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Chuanzhao Zhang
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Baohua Hou
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
- Department of General Surgery, Heyuan People’s Hospital, Heyuan, China
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Wang K, Karalis JD, Elamir A, Bifolco A, Wachsmann M, Capretti G, Spaggiari P, Enrico S, Balasubramanian K, Fatimah N, Pontecorvi G, Nebbia M, Yopp A, Kaza R, Pedrosa I, Zeh H, Polanco P, Zerbi A, Wang J, Aguilera T, Ligorio M. Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients. Ann Surg Oncol 2024; 31:2608-2620. [PMID: 38151623 PMCID: PMC10908610 DOI: 10.1245/s10434-023-14805-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/06/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Neoadjuvant therapy (NAT) emerged as the standard of care for patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgery; however, surgery is morbid, and tools to predict resection margin status (RMS) and prognosis in the preoperative setting are needed. Radiomic models, specifically delta radiomic features (DRFs), may provide insight into treatment dynamics to improve preoperative predictions. METHODS We retrospectively collected clinical, pathological, and surgical data (patients with resectable, borderline, locally advanced, and metastatic disease), and pre/post-NAT contrast-enhanced computed tomography (CT) scans from PDAC patients at the University of Texas Southwestern Medical Center (UTSW; discovery) and Humanitas Hospital (validation cohort). Gross tumor volume was contoured from CT scans, and 257 radiomics features were extracted. DRFs were calculated by direct subtraction of pre/post-NAT radiomic features. Cox proportional models and binary prediction models, including/excluding clinical variables, were constructed to predict overall survival (OS), disease-free survival (DFS), and RMS. RESULTS The discovery and validation cohorts comprised 58 and 31 patients, respectively. Both cohorts had similar clinical characteristics, apart from differences in NAT (FOLFIRINOX vs. gemcitabine/nab-paclitaxel; p < 0.05) and type of surgery resections (pancreatoduodenectomy, distal or total pancreatectomy; p < 0.05). The model that combined clinical variables (pre-NAT carbohydrate antigen (CA) 19-9, the change in CA19-9 after NAT (∆CA19-9), and resectability status) and DRFs outperformed the clinical feature-based models and other radiomics feature-based models in predicting OS (UTSW: 0.73; Humanitas: 0.66), DFS (UTSW: 0.75; Humanitas: 0.64), and RMS (UTSW 0.73; Humanitas: 0.69). CONCLUSIONS Our externally validated, predictive/prognostic delta-radiomics models, which incorporate clinical variables, show promise in predicting the risk of predicting RMS in NAT-treated PDAC patients and their OS or DFS.
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Affiliation(s)
- Kai Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - John D Karalis
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ahmed Elamir
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alessandro Bifolco
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Megan Wachsmann
- Department of Pathology, Veterans Affairs North Texas Health Care System, Dallas, TX, USA
| | - Giovanni Capretti
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Paola Spaggiari
- Department of Pathology, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Sebastian Enrico
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Nafeesah Fatimah
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Giada Pontecorvi
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Martina Nebbia
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Adam Yopp
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ravi Kaza
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Herbert Zeh
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Patricio Polanco
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alessandro Zerbi
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Todd Aguilera
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Matteo Ligorio
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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9
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Wei C, Jiang T, Wang K, Gao X, Zhang H, Wang X. GEP-NETs radiomics in action: a systematical review of applications and quality assessment. Clin Transl Imaging 2024; 12:287-326. [DOI: 10.1007/s40336-024-00617-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/03/2024] [Indexed: 01/05/2025]
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10
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Zhang J, Zhu H, Shen L, Li J, Zhang X, Bai C, Zhou Z, Yu X, Li Z, Li E, Yuan X, Lou W, Chi Y, Xu N, Yin Y, Bai Y, Zhang T, Xiu D, Chen J, Qin S, Wang X, Yang Y, Shi H, Luo X, Fan S, Su W, Lu M, Xu J. Baseline radiologic features as predictors of efficacy in patients with pancreatic neuroendocrine tumors with liver metastases receiving surufatinib. Chin J Cancer Res 2023; 35:526-535. [PMID: 37969958 PMCID: PMC10643338 DOI: 10.21147/j.issn.1000-9604.2023.05.09] [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: 07/24/2023] [Accepted: 09/15/2023] [Indexed: 11/17/2023] Open
Abstract
Objective Currently, pre-treatment prediction of patients with pancreatic neuroendocrine tumors with liver metastases (PNELM) receiving surufatinib treatment was unsatisfying. Our objective was to examine the association between radiological characteristics and efficacy/prognosis. Methods We enrolled patients with liver metastases in the phase III, SANET-p trial (NCT02589821) and obtained contrast-enhanced computed tomography (CECT) images. Qualitative and quantitative parameters including hepatic tumor margins, lesion volumes, enhancement pattern, localization types, and enhancement ratios were evaluated. The progression-free survival (PFS) and hazard ratio (HR) were calculated using Cox's proportional hazard model. Efficacy was analyzed by logistic-regression models. Results Among 152 patients who had baseline CECT assessments and were included in this analysis, the surufatinib group showed statistically superior efficacy in terms of median PFS compared to placebo across various qualitative and quantitative parameters. In the multivariable analysis of patients receiving surufatinib (N=100), those with higher arterial phase standardized enhancement ratio-peri-lesion (ASER-peri) exhibited longer PFS [HR=0.039; 95% confidence interval (95% CI): 0.003-0.483; P=0.012]. Furthermore, patients with a high enhancement pattern experienced an improvement in the objective response ratio [31.3% vs. 14.7%, odds ratio (OR)=3.488; 95% CI: 1.024-11.875; P=0.046], and well-defined tumor margins were associated with a higher disease control rate (DCR) (89.3% vs. 68.2%, OR=4.535; 95% CI: 1.285-16.011; P=0.019) compared to poorly-defined margins. Conclusions These pre-treatment radiological features, namely high ASER-peri, high enhancement pattern, and well-defined tumor margins, have the potential to serve as predictive markers of efficacy in patients with PNELM receiving surufatinib.
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Affiliation(s)
- Jianwei Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Haibin Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Lin Shen
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jie Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Xiaoyan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Chunmei Bai
- Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Zhiwei Zhou
- Department of Gastric Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Centre for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510062, China
| | - Xianrui Yu
- Department of Pancreatic and Hepatobiliary Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Zhiping Li
- Department of Abdominal Oncology, West China Hospital, Sichuan University, Chengdu 332001, China
| | - Enxiao Li
- Department of Medical Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Xianglin Yuan
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wenhui Lou
- Department of General Surgery, Zhongshan Hospital of Fudan University, Shanghai 200032, China
| | - Yihebali Chi
- Department of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Nong Xu
- Department of Medical Oncology, the First Affiliated Hospital of Zhejiang University, Hangzhou 310009, China
| | - Yongmei Yin
- Department of Medical Oncology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yuxian Bai
- Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin 150081, China
| | - Tao Zhang
- Department of Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Dianrong Xiu
- Department of General Surgery, Peking University Third Hospital, Beijing 100191, China
| | - Jia Chen
- Department of Medical Oncology, Jiangsu Cancer Hospital, Nanjing 214206, China
| | - Shukui Qin
- Cancer Center of Jinling Hospital, Nanjing University of Chinese Medicine, Nanjing 210016, China
| | - Xiuwen Wang
- Department of Medical Oncology, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Yujie Yang
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Haoyun Shi
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Xian Luo
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Songhua Fan
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Weiguo Su
- Department of Clinical and Regulatory Affairs, HUTCHMED Limited, Shanghai 200001, China
| | - Ming Lu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jianming Xu
- Department of Gastrointestinal Oncology, the Fifth Medical Center, Chinese PLA General Hospital, Beijing 100071, China
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11
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Park YJ, Park YS, Kim ST, Hyun SH. A Machine Learning Approach Using [ 18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor. Mol Imaging Biol 2023; 25:897-910. [PMID: 37395887 DOI: 10.1007/s11307-023-01832-7] [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: 01/11/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Abstract
PURPOSE We sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET)-based radiomics and clinical features in patients with pancreatic neuroendocrine tumors (PNETs). PROCEDURES A total of 58 patients with PNETs who underwent pretherapeutic [18F]FDG PET/computed tomography (CT) were retrospectively enrolled. PET-based radiomics extracted from segmented tumor and clinical features were selected to develop prediction models by the least absolute shrinkage and selection operator feature selection method. The predictive performances of ML models using neural network (NN) and random forest algorithms were compared by the areas under the receiver operating characteristic curves (AUROCs) and validated by stratified five-fold cross validation. RESULTS We developed two separate ML models for predicting high-grade tumors (Grade 3) and tumors with poor prognosis (disease progression within two years). The integrated models consisting of clinical and radiomic features with NN algorithm showed the best performances than the other models (stand-alone clinical or radiomics models). The performance metrics of the integrated model by NN algorithm were AUROC of 0.864 in the tumor grade prediction model and AUROC of 0.830 in the prognosis prediction model. In addition, AUROC of the integrated clinico-radiomics model with NN was significantly higher than that of tumor maximum standardized uptake model in predicting prognosis (P < 0.001). CONCLUSIONS Integration of clinical features and [18F]FDG PET-based radiomics using ML algorithms improved the prediction of high-grade PNET and poor prognosis in a non-invasive manner.
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Affiliation(s)
- Yong-Jin Park
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Nuclear Medicine, Ajou University Medical Center, Ajou University School of Medicine, 164, Worldcup-ro, Yeongtong-gu, Suwon, 16499, South Korea
| | - Young Suk Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Hyup Hyun
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
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12
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Shiozaki H, Gocho T, Shirai Y, Takano Y, Ohki K, Suka M, Okamoto T, Fujioka S, Toya N, Ikegami T. A Novel Observational Strategy for Nonfunctional Pancreatic Neuroendocrine Neoplasms With Texture Analysis: A Multicenter Retrospective Study. CANCER DIAGNOSIS & PROGNOSIS 2023; 3:543-550. [PMID: 37671308 PMCID: PMC10475916 DOI: 10.21873/cdp.10253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 08/04/2023] [Indexed: 09/07/2023]
Abstract
Background/Aim Surgical resection is recommended for nonfunctional pancreatic neuroendocrine neoplasms (NF-pNENs). However, metastasis is rare in patients with small lesions with histological grade 1 (G1); thus, observation is an optional treatment approach for small NF-pNENs. Texture analysis (TA) is an imaging analysis mode for quantification of heterogeneity by extracting quantitative parameters from images. We retrospectively evaluated the utility of TA in predicting histological grade of resected NF-pNENs in a multicenter retrospective study. Patients and Methods The utility of TA in preoperative prediction of grade were evaluated with 29 patients treated by pancreatectomy for NF-pNEN who underwent preoperative dynamic computed tomography scan between January 1, 2013 and December 31, 2020 at three hospitals affiliated with the Jikei University School of Medicine. TA was performed with dedicated software for medical imaging processing for determining histological tumor grade using dynamic computed tomography images. Results Histological tumor grades based on the 2017 World Health Organization Classification for Pancreatic Neuroendocrine Neoplasms were grade 1, 2 and 3 in 18, 10 and one patient, respectively. Preoperative grades by TA were 1 and 2/3 in 15 and 14 patients, respectively. The sensitivity, specificity and area under the curve for TA-oriented grade 1 lesions were 1.00, 0.889 and 0.965 (95% confidence interval=0.901-1.000), respectively. Conclusion TA is useful for predicting grade 2/3 NF-pNEN and can provide a safe option for observation for patients with small grade 1 lesions.
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Affiliation(s)
- Hironori Shiozaki
- Department of Surgery, The Jikei University Kashiwa Hospital, Chiba, Japan
| | - Takeshi Gocho
- Department of Surgery, The Jikei University School of Medicine, Tokyo, Japan
| | - Yoshihiro Shirai
- Department of Surgery, The Jikei University School of Medicine, Tokyo, Japan
| | - Yuki Takano
- Department of Surgery, The Jikei University Kashiwa Hospital, Chiba, Japan
| | - Kazuyoshi Ohki
- Department of Radiology, The Jikei University School of Medicine, Tokyo, Japan
| | - Machi Suka
- Department of Public Health and Environmental Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Tomoyoshi Okamoto
- Department of Surgery, The Jikei University Daisan Hospital, Tokyo, Japan
| | - Shuichi Fujioka
- Department of Surgery, The Jikei University Kashiwa Hospital, Chiba, Japan
| | - Naoki Toya
- Department of Surgery, The Jikei University Kashiwa Hospital, Chiba, Japan
| | - Toru Ikegami
- Department of Surgery, The Jikei University School of Medicine, Tokyo, Japan
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Ligero M, Hernando J, Delgado E, Garcia-Ruiz A, Merino-Casabiel X, Ibrahim T, Fazio N, Lopez C, Teulé A, Valle JW, Tafuto S, Custodio A, Reed N, Raderer M, Grande E, Garcia-Carbonero R, Jimenez-Fonseca P, Garcia-Alvarez A, Escobar M, Casanovas O, Capdevila J, Perez-Lopez R. Radiomics and outcome prediction to antiangiogenic treatment in advanced gastroenteropancreatic neuroendocrine tumours: findings from the phase II TALENT trial. BJC REPORTS 2023; 1:9. [PMID: 39516643 PMCID: PMC11523983 DOI: 10.1038/s44276-023-00010-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 11/16/2024]
Abstract
BACKGROUND More accurate predictive biomarkers in patients with gastroenteropancreatic neuroendocrine tumours (GEP-NETs) are needed. This study aims to investigate radiomics-based tumour phenotypes as a surrogate biomarker of the tumour vasculature and response prediction to antiangiogenic targeted agents in patients with GEP-NETs. METHODS In this retrospective study, a radiomics signature was developed in patients with GEP-NETs and liver metastases receiving lenvatinib. Patients were selected from the multicentre phase II TALENT trial (NCT02678780) (development cohort). Radiomics variables were extracted from liver metastases in the pre-treatment CT-scans and selected using LASSO regression and minimum redundancy maximum relevance (mRMR). Logistic regression and Cox proportional-hazards models for radiomics and combined radiomics with clinical data were explored. The performance of the models was tested in an external cohort of patients treated with sunitinib (test cohort). Associations between the radiomics score and vascularisation factors in plasma were studied using hierarchical clustering and Mann-Whitney U test. RESULTS A total of 89 patients were included in the study, 408 liver metastases were analysed. The CT-based radiomics signature was associated with clinical benefit in the development (training and validation sets) and test cohorts (AUC 0.75 [0.66-0.90], 0.67 [0.49-0.92] and 0.67 [0.43-0.91], respectively). The combined radiomics-clinical signature (including the radiomics score, Ki-67 index and primary tumour site) improved on radiomics-only signature performance (AUC 0.79 [95% CI 0.64-0.93]; p < 0.001). A higher radiomics score indicated longer progression-free survival (hazard ration of 0.11 [0.03-0.45]; p = 0.002) and was associated with vascularisation factors (p = 0.01). CONCLUSIONS Radiomics-based phenotypes can provide valuable information about tumour characteristics, including the vasculature, that are associated with response to antiangiogenics. CLINICAL TRIAL REGISTRATION This is a study of the Lenvatinib Efficacy in Metastatic Neuroendocrine Tumours (TALENT) phase II clinical trial (NCT02678780).
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Affiliation(s)
- Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Jorge Hernando
- Oncology Department, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Eric Delgado
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Alonso Garcia-Ruiz
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | | | - Toni Ibrahim
- Osteoncology and Rare Tumours Centre, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST), Meldola, Italy
| | - Nicola Fazio
- Units of Gastrointestinal and Neuroendocrine Tumours, European Institute of Oncology, Milan, Italy
| | - Carlos Lopez
- Oncology Department, Marques de Valdecilla University Hospital (IDIVAL), Santander, Spain
| | - Alexandre Teulé
- Oncology Department, Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat (Barcelona), Spain
| | - Juan W Valle
- University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Salvatore Tafuto
- S.C. Sarcomi e Tumori Rari, Istituto Nazionale Tumori, IRCCS, Fondazione "G. Pascale", Naples, Italy
| | - Ana Custodio
- Oncology Department, La Paz University Hospital, Madrid, Spain
| | - Nicholas Reed
- Gartnavel Hospital, Beatson Oncology Centre, Glasgow, UK
| | - Markus Raderer
- Department of Oncology and Internal Medicine, Medical University of Vienna, Vienna, Austria
| | - Enrique Grande
- Oncology Department, MD Anderson Cancer Center, Madrid, Spain
| | | | | | | | - Manuel Escobar
- Radiology Department, Vall d'Hebron University Hospital (VHUH), Barcelona, Spain
| | - Oriol Casanovas
- Oncology Department, Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat (Barcelona), Spain
| | - Jaume Capdevila
- Oncology Department, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.
- Radiology Department, Vall d'Hebron University Hospital (VHUH), Barcelona, Spain.
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14
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Prognostic value of tumor-to-parenchymal contrast enhancement ratio on portal venous-phase CT in pancreatic neuroendocrine neoplasms. Eur Radiol 2023; 33:2713-2724. [PMID: 36378252 DOI: 10.1007/s00330-022-09235-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/07/2022] [Accepted: 10/12/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVES We aimed to evaluate the prognostic value of tumor-to-parenchymal contrast enhancement ratio on portal venous-phase CT (CER on PVP) and compare its prognostic performance to prevailing grading and staging systems in pancreatic neuroendocrine neoplasms (PanNENs). METHODS In this retrospective study, data on 465 patients (development cohort) who underwent upfront curative-intent resection for PanNEN were used to assess the performance of CER on PVP and tumor size measured by CT (CT-Size) in predicting recurrence-free survival (RFS) using Harrell's C-index and to determine their optimal cutoffs to stratify RFS using a multi-way partitioning algorithm. External data on 184 patients (test cohort) were used to validate the performance of CER on PVP in predicting RFS and overall survival (OS) and compare its predictive performance with those of CT-Size, 2019 World Health Organization classification system (WHO), and the 8th American Joint Committee on Cancer staging system (AJCC). RESULTS In the test cohort, CER on PVP showed C-indexes of 0.83 (95% confidence interval [CI], 0.74-0.91) and 0.84 (95% CI, 0.73-0.95) for predicting RFS and OS, respectively, which were higher than those for the WHO (C-index: 0.73 for RFS [p = .002] and 0.72 for OS [p = .004]) and AJCC (C-index, 0.67 for RFS [p = .002] and 0.58 for OS [p = .002]). CT-Size obtained C-indexes of 0.71 for RFS and 0.61 for OS. CONCLUSIONS CER on PVP showed superior predictive performance on postoperative survival in PanNEN than current grading and staging systems, indicating its potential as a noninvasive preoperative prognostic tool. KEY POINTS • In pancreatic neuroendocrine neoplasms, the tumor-to-parenchymal enhancement ratio on portal venous-phase CT (CER on PVP) showed acceptable predictive performance of postoperative outcomes. • CER on PVP showed superior predictive performance of postoperative survival over the current WHO classification and AJCC staging system.
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15
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Gu XL, Cui Y, Zhu HT, Li XT, Pei X, He XX, Yang L, Lu M, Li ZW, Sun YS. Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography-Based Radiomics Analysis. J Comput Assist Tomogr 2023; 47:361-368. [PMID: 36944109 DOI: 10.1097/rct.0000000000001443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
OBJECTIVE The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs). METHODS Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC). RESULTS In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621). CONCLUSIONS The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.
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Affiliation(s)
- Xiao-Lei Gu
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
| | - Yong Cui
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
| | - Hai-Tao Zhu
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
| | - Xiao-Ting Li
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
| | - Xiang Pei
- Department of Radiology, Beijing Shunyi District Hospital, Beijing
| | - Xiao-Xiao He
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang
| | - Li Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang
| | - Ming Lu
- Departments of Gastrointestinal Oncology and
| | - Zhong-Wu Li
- Pathology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Ying-Shi Sun
- From the Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute
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Jiang ZY, Qi LS, Li JT, Cui N, Li W, Liu W, Wang KZ. Radiomics: Status quo and future challenges. Artif Intell Med Imaging 2022; 3:87-96. [DOI: 10.35711/aimi.v3.i4.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/08/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Noninvasive imaging (computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography) as an important part of the clinical workflow in the clinic, but it still provides limited information for diagnosis, treatment effect evaluation and prognosis prediction. In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to variable results in the same case. With accumulation of medical imaging data, radiomics emerges as a relatively new approach for analysis. Via artificial intelligence techniques, high-throughput quantitative data which is invisible to the naked eyes extracted from original images can be used in the process of patients’ management. Several studies have evaluated radiomics combined with clinical factors, pathological, or genetic information would assist in the diagnosis, particularly in the prediction of biological characteristics, risk of recurrence, and survival with encouraging results. In various clinical settings, there are limitations and challenges needing to be overcome before transformation. Therefore, we summarize the concepts and method of radiomics including image acquisition, region of interest segmentation, feature extraction and model development. We also set forth the current applications of radiomics in clinical routine. At last, the limitations and related deficiencies of radiomics are pointed out to direct the future opportunities and development.
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Affiliation(s)
- Zhi-Yun Jiang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Li-Shuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Jia-Tong Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Nan Cui
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Wei Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
- Department of Interventional Vascular Surgery, The 4th Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - Wei Liu
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Ke-Zheng Wang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
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17
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Qualitative imaging features of pancreatic neuroendocrine neoplasms predict histopathologic characteristics including tumor grade and patient outcome. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3971-3985. [PMID: 35166939 DOI: 10.1007/s00261-022-03430-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 01/18/2023]
Abstract
OBJECTIVES To identify PanNEN imaging features associated with tumor grade and aggressive histopathological features. METHODS Associations between histopathological and imaging features of resected PanNEN were retrospectively tested. Histopathologic features included WHO grade, lymphovascular invasion (LVI), growth pattern (infiltrative, circumscribed), and intratumoral fibrosis (mature, immature). Imaging features included size, degree/uniformity of enhancement, progressive enhancement, contour, infiltrative appearance (infiltrativeim), calcifications, cystic components, tumor thrombus, vascular occlusion (VO), duct dilatation, and atrophy. Multinomial logistic regression analyses evaluated the magnitude of associations. Association of variables with outcome was assessed using Cox-proportional hazards regression. RESULTS 133 patients were included. 3 imaging features (infiltrativeim, ill-defined contour [contourill], and VO) were associated with all histopathologic parameters and poor outcome. Increase in grade increased odds of contourill by 15.6 times (p = 0.0001, 95% CI 3.8-64.4). PanNEN with VO were 51.1 times (p = 0.0002, 6.5-398.6) more likely to demonstrate LVI. For PanNEN with contourill, infiltrative growth pattern was 51.3 times (p < 0.0001, 9.1-288.4), and fibrosis was 14 times (p = 0.0065, 2.1-93.7) more likely. Contourill was associated with decreased recurrence-free survival (p = 0.0003, HR 18.29, 3.83-87.3) and VO (p = 0.0004, HR6.08, 2.22-16.68) with decreased overall survival. CONCLUSIONS Infiltrativeim, contourill, and VO on imaging are associated with higher grade/histopathological parameters linked to tumor aggression, and poor outcome.
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Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY) 2022; 47:3118-3160. [PMID: 34292365 DOI: 10.1007/s00261-021-03216-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.
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Laino ME, Ammirabile A, Lofino L, Mannelli L, Fiz F, Francone M, Chiti A, Saba L, Orlandi MA, Savevski V. Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review. Healthcare (Basel) 2022; 10:1511. [PMID: 36011168 PMCID: PMC9408381 DOI: 10.3390/healthcare10081511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 12/19/2022] Open
Abstract
The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Ludovica Lofino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | | | - Francesco Fiz
- Nuclear Medicine Unit, Department of Diagnostic Imaging, E.O. Ospedali Galliera, 56321 Genoa, Italy
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital, 72074 Tübingen, Germany
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09124 Cagliari, Italy
| | | | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
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Staal FCR, Aalbersberg EA, van der Velden D, Wilthagen EA, Tesselaar MET, Beets-Tan RGH, Maas M. GEP-NET radiomics: a systematic review and radiomics quality score assessment. Eur Radiol 2022; 32:7278-7294. [PMID: 35882634 DOI: 10.1007/s00330-022-08996-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/25/2022] [Accepted: 06/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence. METHODS PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data. RESULTS In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%. CONCLUSION Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce. KEY POINTS • The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.
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Affiliation(s)
- Femke C R Staal
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands
| | - Else A Aalbersberg
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Nuclear Medicine, The Netherlands Cancer Institute Amsterdam, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Daphne van der Velden
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Erica A Wilthagen
- Scientific Information Service, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Margot E T Tesselaar
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Medical Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,Faculty of Health Sciences, University of Southern Denmark, J. B. Winsløws Vej 19, 3, 5000, Odense, Denmark
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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21
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Ramachandran A, Madhusudhan KS. Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms. World J Gastroenterol 2022; 28:3008-3026. [PMID: 36051339 PMCID: PMC9331531 DOI: 10.3748/wjg.v28.i26.3008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/30/2021] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis, hormonal syndromes produced, biological behavior and consequently, in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies. Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question. Also, with the increased availability of cross sectional imaging, these neoplasms are increasingly being detected incidentally in routine radiology practice. This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms, along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons, mostly in the research stage.
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Affiliation(s)
- Anupama Ramachandran
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
| | - Kumble Seetharama Madhusudhan
- Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi 110029, India
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Zhu Y, Hu P, Li X, Tian Y, Bai X, Liang T, Li J. Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning. Med Phys 2022; 49:5799-5818. [PMID: 35833617 DOI: 10.1002/mp.15827] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 04/28/2022] [Accepted: 05/27/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Computer-aided automatic pancreas segmentation is essential for early diagnosis and treatment of pancreatic diseases. However, the annotation of pancreas images requires professional doctors and considerable expenditure. Due to imaging differences among various institution population, scanning devices and imaging protocols etc., significant degradation in the performance of model inference results is prone to occur when models trained with domain-specific (usually institution-specific) datasets are directly applied to new (other centers/institutions) domain data. In this paper, we propose a novel unsupervised domain adaptation method based on adversarial learning to address pancreas segmentation challenges with the lack of annotations and domain shift interference. METHODS A 3D semantic segmentation model with attention module and residual module is designed as the backbone pancreas segmentation model. In both segmentation model and domain adaptation discriminator network, a multiscale progressively weighted structure is introduced to acquire different field of views. Features of labeled data and unlabeled data are fed in pairs into the proposed multiscale discriminator to learn domain-specific characteristics. Then the unlabeled data features with pseudo-domain label are fed to the discriminator to acquire domain-ambiguous information. With this adversarial learning strategy, the performance of the segmentation network is enhanced to segment unseen unlabeled data. RESULTS Experiments were conducted on two public annotated datasets as source datasets respectively and one private dataset as target dataset, where annotations were not used for the training process but only for evaluation. The 3D segmentation model achieves comparative performance with state-of-the-art pancreas segmentation methods on source domain. After implementing our domain adaptation architecture, the average Dice Similarity Coefficient(DSC) of the segmentation model trained on the NIH-TCIA source dataset increases from 58.79% to 72.73% on the local hospital dataset, while the performance of the target domain segmentation model transferred from the MSD source dataset rises from 62.34% to 71.17%. CONCLUSIONS Correlation of features across data domains are utilized to train the pancreas segmentation model on unlabeled data domain, improving the generalization of the model. Our results demonstrate that the proposed method enables the segmentation model to make meaningful segmentation for unseen data of the training set. In the future, the proposed method has the potential to apply segmentation model trained on public dataset to clinical unannotated CT images from local hospital, effectively assisting radiologists in clinical practice. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yan Zhu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Peijun Hu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.,Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311100, China
| | - Xiang Li
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.,Zhejiang Provincial Key Laboratory of Pancreatic Disease, Hangzhou, 310006, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Xueli Bai
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.,Zhejiang Provincial Key Laboratory of Pancreatic Disease, Hangzhou, 310006, China
| | - Tingbo Liang
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.,Zhejiang Provincial Key Laboratory of Pancreatic Disease, Hangzhou, 310006, China
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.,Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311100, China
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Nießen A, Bechtiger FA, Hinz U, Lewosinska M, Billmann F, Hackert T, Büchler MW, Schimmack S. Enucleation Is a Feasible Procedure for Well-Differentiated pNEN-A Matched Pair Analysis. Cancers (Basel) 2022; 14:cancers14102570. [PMID: 35626174 PMCID: PMC9139922 DOI: 10.3390/cancers14102570] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023] Open
Abstract
The extent of surgical resection in the treatment of pancreatic neuroendocrine neoplasms (pNEN) is still controversial. This study aimed to evaluate the outcomes of enucleation for well-differentiated non-functional (nf) pNEN. Patients undergoing enucleation (2001−2020) were analyzed. Clinicopathological parameters, perioperative outcomes and survival were assessed. The analysis was performed as a nested case-control study and matched-pair analysis with formal resection. Sixty-one patients undergoing enucleation were identified. Compared to patients undergoing formal resection, enucleation was associated with a significantly shorter median length of operative time (128 (IQR 95−170) versus 263 (172−337) minutes, p < 0.0001) and a significantly lower rate of postoperative diabetes (2% versus 21%, p = 0.0020). There was no significant difference in postoperative pancreatic fistula rate (18% versus 16% type B/C, p = 1.0), Clavien−Dindo ≥ III complications (20% versus 26%, p = 0.5189), readmission rate (12% versus 15%, p = 0.6022) or length of hospital stay (8 (7−11) versus 10 (8−17) days, p = 0.0652). There was no 30-day mortality after enucleation compared to 1.6% (n = 1) after formal resection. 10-year overall survival (OS) and disease-free survival (DFS) was similar between the two groups (OS: 89% versus 77%, p = 0.2756; DFS: 98% versus 91%, p = 0.0873). Enucleation presents a safe surgical approach for well-differentiated nf-pNEN with good long-term outcomes for selected patients.
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van der Velden D, Staal F, Aalbersberg E, Castagnoli F, Wilthagen E, Beets-Tan R. Prognostic value of CT characteristics in GEP-NET: a systematic review. Crit Rev Oncol Hematol 2022; 175:103713. [DOI: 10.1016/j.critrevonc.2022.103713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/04/2022] [Accepted: 05/16/2022] [Indexed: 11/16/2022] Open
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Ren S, Tang HJ, Zhao R, Duan SF, Chen R, Wang ZQ. Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma. Curr Med Sci 2022; 42:217-225. [PMID: 35089491 DOI: 10.1007/s11596-022-2535-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 06/28/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. RESULTS In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). CONCLUSION The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA.
| | - Hui-Juan Tang
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
- Department of Clinical and Molecular Sciences, Marche Polytechnic University, Ancona, 60126, Italy
| | - Rui Zhao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | | | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA
| | - Zhong-Qiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
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Utility of Quantitative Metrics from Dual-Layer Spectral-Detector CT for Differentiation of Pancreatic Neuroendocrine Tumor and Neuroendocrine Carcinoma. AJR Am J Roentgenol 2022; 218:999-1009. [PMID: 35043668 DOI: 10.2214/ajr.21.27017] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background: The 2019 WHO classification separates neuroendocrine neoplasms (NENs) into neuroendocrine tumors (NET) and neuroendocrine carcinomas (NEC), which are considered to represent pathologically distinct entities warranting different management approaches. Dual-layer spectral-detector CT (DLCT) may aid their differentiation through specific material decomposition. Objective: To assess the utility of quantitative metrics derived from DLCT for the differentiation of pancreatic NET and NEC. Methods: This retrospective study included 104 patients (mean age 51±13 years; 53 women, 51 men) with pathologically confirmed NEN [89 NET, including 22 grade 1, 48 grade 2, and 19 grade 3 (G3); 15 NEC], who underwent multiphase DLCT within 15 days before biopsy or resection. Two radiologists independently placed ROIs to record tumor attenuation, iodine concentration (IC), and effective atomic number (Zeff) across phases, and also assessed qualitative features (composition, homogeneity, margins, calcifications, main pancreatic duct dilation, vascular invasion, lymphadenopathy). Interreader agreement was assessed. Mean values between readers were obtained for quantitative measures; consensus was reached for qualitative features. NET and NEC were compared using multivariable regression analysis and ROC analysis. Results: Interobserver agreement, expressed as intraclass correlation coefficients, ranged from 0.879 to 0.992 for quantitative metrics, and, expressed as kappa coefficients, from 0.763 to 0.823 for qualitative features. In multivariable analysis of qualitative and quantitative features, significant independent predictors of NEC (P<.05) were IC in portal venous phase (1.3 mg/mL in NEC vs 2.7 mg/mL in NET), Zeff in portal venous phase (8.1 vs 8.6), and attenuation in portal venous phase (78.2 vs 113.5 HU). AUC for predicting NEC was 0.897 for IC, 0.884 for Zeff, 0.921 for combination of IC and Zeff, and 0.855 for attenuation. Predicted probability based on combination of IC and Zeff achieved sensitivity of 93.3% and specificity of 80.9% for NEC. Significant independent predictors (P<.05) for differentiating G3 NET and NEC were IC (1.3 vs 2.0 mg/mL; AUC=0.789) and attenuation (78.2 vs 90.3 HU; AUC=0.647), both measured in portal venous phase. Conclusion: Incorporation of DLCT-metrics improves differentiation of NET and NEC compared with conventional CT attenuation and qualitative features. Clinical Impact: DLCT may help select patients with pancreatic NENs for platinum-based chemotherapies.
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Cardobi N, De Robertis R, D’Onofrio M. Advanced Imaging of Pancreatic Neoplasms. IMAGING AND PATHOLOGY OF PANCREATIC NEOPLASMS 2022:481-493. [DOI: 10.1007/978-3-031-09831-4_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2025]
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Enhanced computed tomography features predict pancreatic neuroendocrine neoplasm with Ki-67 index less than 5. Eur J Radiol 2021; 147:110100. [PMID: 34972060 DOI: 10.1016/j.ejrad.2021.110100] [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: 09/08/2021] [Revised: 11/16/2021] [Accepted: 12/07/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND Several studies have suggested that patients with pancreatic neuroendocrine neoplasm (pNEN) with the Ki-67 index of < 5% are more likely to show better prognosis after clinical intervention. Moreover, the Ki-67 index at 5% has also been suggested as a potential threshold by the 2016 European Neuroendocrine Tumor Society guidelines. OBJECTIVE Based on preoperative enhanced computed tomography (CT), this study aimed to investigate imaging characteristics eligible to discriminate the ≤ 5% Ki-67 group from the > 5% Ki-67 group of patients with nonmetastatic pNEN. METHODS Patients with pathologically diagnosed pNEN and preoperative multiphase CT were enrolled. Their Ki-67 index was calculated and grouped according to the 5% cutoff value. The following CT imaging characteristics and some serum biomarkers were assessed between the two groups: the diameter, location, tumor margin, calcification, pancreatic atrophy, distal pancreatic duct dilation, vessel involvement, and enhancement pattern characteristics of both arterial phase (AP) and portal vein phase (PVP). RESULTS A total of 142 patients with pNEN were enrolled in this study, comprising 104 in the low (Ki-67, 1%-5%) and 38 in the high index group (Ki-67, >5%). Alpha fetoprotein and cancer antigen 125 were significantly different between the two groups (P-values, 0.030 and 0.049, respectively). The diameter (P < 0.0001), margin (P = 0.003), distal main ductal dilation (P = 0.021), vessel involvement (P = 0.002), AP hypoenhancement (P < 0.0001), PVP hypoenhancement (P = 0.003), AP ratio (P = 0.0001), and PVP ratio (P = 0.0003) were significantly different between the low and high index groups. The area under the curve of the multivariate logistic regression model was 0.853. CONCLUSION Nonmetastatic pNENs with larger diameter, ill-defined margin, distal main ductal dilation, and tumor hypoenhancement in AP in preoperative enhanced CT tend to have a Ki-67 index of > 5%.The results of this study provide an alternative method to clinicians to decide whether surgery is appropriate.
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Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study. Abdom Radiol (NY) 2021; 46:4266-4277. [PMID: 33813624 DOI: 10.1007/s00261-021-03044-5] [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: 11/29/2020] [Revised: 03/01/2021] [Accepted: 03/05/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE To predict the histologic grade and type of small papillary renal cell carcinomas (pRCCs) using texture analysis and machine learning algorithms. METHODS This was a retrospective HIPAA-compliant study. 24 noncontrast (NC), 22 corticomedullary (CM) phase, and 24 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected pRCCs were identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade and type 1 or 2. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (20 gray-level co-occurrences and 11 gray-level run-lengths) features were calculated for each tumor in each phase. Feature values in low- versus high-grade and type 1 versus 2 pRCCs were compared. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of histologic grade and type of pRCCs in each phase. Histogram, texture, and combined histogram and texture feature sets were used to train and test three classification algorithms (support vector machine (SVM), random forest, and histogram-based gradient boosting decision tree (HGBDT)) with stratified shuffle splits and threefold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of histologic grade and type of pRCCs. RESULTS Individual histogram and texture features did not have statistically significant differences between low- and high-grade or type 1 and type 2 pRCCs across all phases. Individual features had low predictive power for tumor grade or type in all phases (AUC < 0.70). HGBDT was highly accurate at predicting pRCC histologic grade and type using histogram, texture or combined histogram and texture feature data from the CM phase (AUCs = 0.97-1.0). All algorithms had highest AUCs using CM phase feature data sets; AUCs decreased using feature sets from NC or NG phases. CONCLUSIONS The histologic grade and type of small pRCCs can be predicted with classification algorithms using CM histogram and texture features, which outperform NC and NG phase image data. The accurate prediction of pRCC histologic grade and type may be able to further guide management of patients with small (< 4 cm) pRCCs being considered for active surveillance.
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Karmazanovsky G, Gruzdev I, Tikhonova V, Kondratyev E, Revishvili A. Computed tomography-based radiomics approach in pancreatic tumors characterization. LA RADIOLOGIA MEDICA 2021; 126:10.1007/s11547-021-01405-0. [PMID: 34386897 DOI: 10.1007/s11547-021-01405-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/27/2021] [Indexed: 12/26/2022]
Abstract
Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging.
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Affiliation(s)
- Grigory Karmazanovsky
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
- Radiology Department, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Ivan Gruzdev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia.
| | - Valeriya Tikhonova
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Evgeny Kondratyev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Amiran Revishvili
- Arrhythmology Department, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
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Target Heterogeneity in Oncology: The Best Predictor for Differential Response to Radioligand Therapy in Neuroendocrine Tumors and Prostate Cancer. Cancers (Basel) 2021; 13:cancers13143607. [PMID: 34298822 PMCID: PMC8304541 DOI: 10.3390/cancers13143607] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/04/2021] [Accepted: 07/07/2021] [Indexed: 12/27/2022] Open
Abstract
Simple Summary In the era of precision medicine, novel targets have emerged on the surface of cancer cells, which have been exploited for the purpose of radioligand therapy. However, there have been variations in the way these receptors are expressed, especially in prostate cancers and neuroendocrine tumors. This variable expression of receptors across the grades of cancers led to the concept of ‘target heterogeneity’, which has not just impacted therapeutic decisions but also their outcomes. Radiopharmaceuticals targeting receptors need to be used when there are specific indicators—either clinical, radiological, or at molecular level—warranting their use. In addition, response to these radioligands can be assessed using different techniques, whereby we can prognosticate further outcomes. We shall also discuss, in this review, the conventional as well as novel approaches of detecting heterogeneity in prostate cancers and neuroendocrine tumors. Abstract Tumor or target heterogeneity (TH) implies presence of variable cellular populations having different genomic characteristics within the same tumor, or in different tumor sites of the same patient. The challenge is to identify this heterogeneity, as it has emerged as the most common cause of ‘treatment resistance’, to current therapeutic agents. We have focused our discussion on ‘Prostate Cancer’ and ‘Neuroendocrine Tumors’, and looked at the established methods for demonstrating heterogeneity, each with its advantages and drawbacks. Also, the available theranostic radiotracers targeting PSMA and somatostatin receptors combined with targeted systemic agents, have been described. Lu-177 labeled PSMA and DOTATATE are the ‘standard of care’ radionuclide therapeutic tracers for management of progressive treatment-resistant prostate cancer and NET. These approved therapies have shown reasonable benefit in treatment outcome, with improvement in quality of life parameters. Various biomarkers and predictors of response to radionuclide therapies targeting TH which are currently available and those which can be explored have been elaborated in details. Imaging-based features using artificial intelligence (AI) need to be developed to further predict the presence of TH. Also, novel theranostic tools binding to newer targets on surface of cancer cell should be explored to overcome the treatment resistance to current treatment regimens.
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Liu Y, Fang Q, Jiang A, Meng Q, Pang G, Deng X. Texture analysis based on U-Net neural network for intracranial hemorrhage identification predicts early enlargement. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 206:106140. [PMID: 33979753 DOI: 10.1016/j.cmpb.2021.106140] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 04/24/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Early hemorrhage enlargement in hypertensive cerebral hemorrhage indicates a poor prognosis. This study aims to predict the early enlargement of cerebral hemorrhage through the intelligent texture analysis of cerebral hemorrhage after segmentation. METHODS A total of 54 patients with hypertensive intracerebral hemorrhage were selected and divided into enlarged hematoma (enlarged group) and non-enlarged hematoma (negative group). The U-Net Neural network model and contour recognition were used to extract the brain parenchymal region, and Mazda texture analysis software was used to extract regional features. The texture features were reduced by Fisher coefficient (Fisher), classification error probability combined average correlation coefficients (POE + ACC), and mutual information (MI) to select the best feature parameters. B11 module was used to analyze the selected features. The misclassified rate of feature parameters screened by different dimensionality reduction methods was calculated. RESULTS The neural network based on U-Net can accurately identify the lesion of cerebral hemorrhage. Among the 54 patients, 18 were in the enlarged group and 36 in the negative group. The parameters of gray level co-occurrence matrix and gray level run length matrix can be used to predict the enlargement of intracerebral hemorrhage. Among the features screened by Fisher, POE + ACC and MI, the texture features of MI showed the lowest misclassified rate, which was 0. CONCLUSION The texture analysis based on U-Net neural network is helpful to predict the early expansion of hypertensive cerebral hemorrhage, and the parameters of gray level co-occurrence matrix and gray level run length matrix under MI dimensionality reduction have the most excellent predictive value.
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Affiliation(s)
- Yu Liu
- Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
| | - Qiong Fang
- Department of Basic Medicine, Anhui Medical College, Hefei 230601, China.
| | - Anhong Jiang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China.
| | - Qingling Meng
- Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
| | - Gang Pang
- Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
| | - Xuefei Deng
- Department of Anatomy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
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Frank V, Shariati S, Budai BK, Fejér B, Tóth A, Orbán V, Bérczi V, Kaposi PN. CT texture analysis of abdominal lesions – Part II: Tumors of the Kidney and Pancreas. IMAGING 2021; 13:25-36. [DOI: 10.1556/1647.2021.00020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2025] Open
Abstract
AbstractIt has been proven in a few early studies that radiomic analysis offers a promising opportunity to detect or differentiate between organ lesions based on their unique texture parameters. Recently, the utilization of CT texture analysis (CTTA) has been receiving significant attention, especially for response evaluation and prognostication of different oncological diagnoses. In this review article, we discuss the unique ability of radiomics and its subfield CTTA to diagnose lesions in the pancreas and kidney. We review studies in which CTTA was used for the classification of histology grades in pancreas and kidney tumors. We also review the role of radiogenomics in the prediction of the molecular and genetic subtypes of pancreatic tumors. Furthermore, we provide a short report on recent advancements of radiomic analysis in predicting prognosis and survival of patients with pancreatic and renal cancers.
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Affiliation(s)
- Veronica Frank
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Sonaz Shariati
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Bettina Katalin Budai
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Bence Fejér
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Ambrus Tóth
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Vince Orbán
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Viktor Bérczi
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
| | - Pál Novák Kaposi
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Faculty of Medicine, Budapest, Hungary
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CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias. Radiol Med 2021; 126:1037-1043. [PMID: 34043146 PMCID: PMC8155795 DOI: 10.1007/s11547-021-01370-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 05/05/2021] [Indexed: 01/08/2023]
Abstract
Purpose To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. Material and Methods CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C−), respectively. C− patients, however, presented with interstitial lung involvement. A subgroup of C−, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. Results The first model classified C + and C− pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C− (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). Conclusion Whole lung ML models based on radiomics can classify C + and C− interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.
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Bevilacqua A, Calabrò D, Malavasi S, Ricci C, Casadei R, Campana D, Baiocco S, Fanti S, Ambrosini V. A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours. Diagnostics (Basel) 2021; 11:diagnostics11050870. [PMID: 34065981 PMCID: PMC8150289 DOI: 10.3390/diagnostics11050870] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/06/2021] [Accepted: 05/11/2021] [Indexed: 12/17/2022] Open
Abstract
Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest area under the curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), and C (using the cross-validation on the whole dataset). The second-order normalized homogeneity and entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC = 0.90, sensitivity = 0.88, specificity = 0.89), followed by model C (median test AUC = 0.87, sensitivity = 0.83, specificity = 0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a “hybrid” (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT.
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Affiliation(s)
- Alessandro Bevilacqua
- Advanced Research Center for Electronic Systems (ARCES), University of Bologna, I-40125 Bologna, Italy; (S.M.); (S.B.)
- Department of Computer Science and Engineering (DISI), University of Bologna, I-40136 Bologna, Italy
- Correspondence: ; Tel.: +39-051-209-5409
| | - Diletta Calabrò
- Department of Nuclear Medicine, DIMES, Alma Mater Studiorum, University of Bologna, I-40126 Bologna, Italy; (D.C.); (S.F.)
| | - Silvia Malavasi
- Advanced Research Center for Electronic Systems (ARCES), University of Bologna, I-40125 Bologna, Italy; (S.M.); (S.B.)
- Research Institute on Global Challenges and Climate Change (Alma Climate), University of Bologna, I-40126 Bologna, Italy
| | - Claudio Ricci
- IRCCS Azienda Ospedaliero Universitaria di Bologna, I-40138 Bologna, Italy; (C.R.); (R.C.); (D.C.)
- Department of Surgery, DIMEC Alma Mater Studiorum, University of Bologna, S.Orsola-Malpighi Hospital, I-40138 Bologna, Italy
- NET Team Bologna, ENETS Center of Excellence, I-40138 Bologna, Italy
| | - Riccardo Casadei
- IRCCS Azienda Ospedaliero Universitaria di Bologna, I-40138 Bologna, Italy; (C.R.); (R.C.); (D.C.)
- Department of Surgery, DIMEC Alma Mater Studiorum, University of Bologna, S.Orsola-Malpighi Hospital, I-40138 Bologna, Italy
- NET Team Bologna, ENETS Center of Excellence, I-40138 Bologna, Italy
| | - Davide Campana
- IRCCS Azienda Ospedaliero Universitaria di Bologna, I-40138 Bologna, Italy; (C.R.); (R.C.); (D.C.)
- NET Team Bologna, ENETS Center of Excellence, I-40138 Bologna, Italy
- Department of Oncology, DIMES Alma Mater Studiorum, University of Bologna, S.Orsola-Malpighi Hospital, I-40126 Bologna, Italy
| | - Serena Baiocco
- Advanced Research Center for Electronic Systems (ARCES), University of Bologna, I-40125 Bologna, Italy; (S.M.); (S.B.)
| | - Stefano Fanti
- Department of Nuclear Medicine, DIMES, Alma Mater Studiorum, University of Bologna, I-40126 Bologna, Italy; (D.C.); (S.F.)
- IRCCS Azienda Ospedaliero Universitaria di Bologna, I-40138 Bologna, Italy; (C.R.); (R.C.); (D.C.)
- NET Team Bologna, ENETS Center of Excellence, I-40138 Bologna, Italy
| | - Valentina Ambrosini
- Department of Nuclear Medicine, DIMES, Alma Mater Studiorum, University of Bologna, I-40126 Bologna, Italy; (D.C.); (S.F.)
- IRCCS Azienda Ospedaliero Universitaria di Bologna, I-40138 Bologna, Italy; (C.R.); (R.C.); (D.C.)
- NET Team Bologna, ENETS Center of Excellence, I-40138 Bologna, Italy
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Abunahel BM, Pontre B, Kumar H, Petrov MS. Pancreas image mining: a systematic review of radiomics. Eur Radiol 2021; 31:3447-3467. [PMID: 33151391 DOI: 10.1007/s00330-020-07376-6] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/25/2020] [Accepted: 10/05/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES To systematically review published studies on the use of radiomics of the pancreas. METHODS The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study. RESULTS A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003). CONCLUSIONS Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice. KEY POINTS • Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.
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Affiliation(s)
| | - Beau Pontre
- School of Medical Sciences, University of Auckland, Auckland, New Zealand
| | - Haribalan Kumar
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Maxim S Petrov
- School of Medicine, University of Auckland, Auckland, New Zealand.
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D’Onofrio M, De Robertis R, Aluffi G, Cadore C, Beleù A, Cardobi N, Malleo G, Manfrin E, Bassi C. CT Simplified Radiomic Approach to Assess the Metastatic Ductal Adenocarcinoma of the Pancreas. Cancers (Basel) 2021; 13:cancers13081843. [PMID: 33924363 PMCID: PMC8069159 DOI: 10.3390/cancers13081843] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 12/31/2022] Open
Abstract
The aim of this study was to perform a simplified radiomic analysis of pancreatic ductal adenocarcinoma based on qualitative and quantitative tumor features and to compare the results between metastatic and non-metastatic patients. A search of our radiological, surgical, and pathological databases identified 1218 patients with a newly diagnosed pancreatic ductal adenocarcinoma who were referred to our Institution between January 2014 and December 2018. Computed Tomography (CT) examinations were reviewed analyzing qualitative and quantitative features. Two hundred eighty-eight patients fulfilled the inclusion criteria and were included in this study. Overall, metastases were present at diagnosis in 86/288 patients, while no metastases were identified in 202/288 patients. Ill-defined margins and a hypodense appearance on portal-phase images were significantly more common among patients with metastases compared to non-metastatic patients (p < 0.05). Metastatic tumors showed a significantly larger size and significantly lower arterial index, perfusion index, and permeability index compared to non-metastatic tumors (p < 0.05). In the management of pancreatic ductal adenocarcinoma, early detection and correct staging are key elements. The study of computerized tomography characteristics of pancreatic ductal adenocarcinoma showed substantial differences, both qualitative and quantitative, between metastatic and non-metastatic disease.
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Affiliation(s)
- Mirko D’Onofrio
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy; (G.A.); (C.C.); (A.B.)
- Correspondence:
| | - Riccardo De Robertis
- Department of Radiology, Ospedale Civile Maggiore, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy; (R.D.R.); (N.C.)
| | - Gregorio Aluffi
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy; (G.A.); (C.C.); (A.B.)
| | - Camilla Cadore
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy; (G.A.); (C.C.); (A.B.)
| | - Alessandro Beleù
- Department of Radiology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy; (G.A.); (C.C.); (A.B.)
| | - Nicolò Cardobi
- Department of Radiology, Ospedale Civile Maggiore, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy; (R.D.R.); (N.C.)
| | - Giuseppe Malleo
- Unit of General and Pancreatic Surgery, The Pancreas Institute, Policlinico GB Rossi, University of Verona, 37129 Verona, Italy; (G.M.); (C.B.)
| | - Erminia Manfrin
- Department of Pathology, G.B. Rossi Hospital, University of Verona, 37129 Verona, Italy;
| | - Claudio Bassi
- Unit of General and Pancreatic Surgery, The Pancreas Institute, Policlinico GB Rossi, University of Verona, 37129 Verona, Italy; (G.M.); (C.B.)
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Crinó SF, Brandolese A, Vieceli F, Paiella S, Conti Bellocchi MC, Manfrin E, Bernardoni L, Sina S, D'Onofrio M, Marchegiani G, Larghi A, Frulloni L, Landoni L, Gabbrielli A. Endoscopic Ultrasound Features Associated with Malignancy and Aggressiveness of Nonhypovascular Solid Pancreatic Lesions: Results from a Prospective Observational Study. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2021; 42:167-177. [PMID: 31597179 DOI: 10.1055/a-1014-2766] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND STUDY AIMS On contrast-enhanced imaging studies, nonhypovascular (i. e., isovascular and hypervascular) patterns can be observed in solid pancreatic lesions (SPLs) of different nature, prognosis, and management. We aimed to identify endoscopic ultrasound (EUS) features of nonhypovascular SPLs associated with malignancy/aggressiveness. The secondary aims were EUS tissue acquisition (EUS-TA) outcome and safety in this setting of patients. PATIENTS AND METHODS This prospective observational study included patients with nonhypovascular SPLs detected on cross-sectional imaging and referred for EUS-TA. Lesion features (size, site, margins, echotexture, vascular pattern, and upstream dilation of the main pancreatic duct) were recorded. Malignancy/aggressiveness was determined by evidence of carcinoma at biopsy/surgical pathology, signs of aggressiveness (perineural invasion, lymphovascular invasion, and/or microscopic tumor extension/infiltration or evidence of metastatic lymph nodes) in the surgical specimen, radiologic detection of lymph nodes or distant metastases, and/or tumor growth > 5 mm/6 months. Uni- and multivariate analyses were performed to assess the primary aim. RESULTS A total of 154 patients with 161 SPLs were enrolled. 40 (24.8 %) lesions were defined as malignant/aggressive. Irregular margins and size > 20 mm were independent factors associated with malignancy/aggressiveness (p < 0.001, OR = 5.2 and p = 0.003, OR = 2.1, respectively). However, size > 20 mm was not significant in the subgroup of other-than-neuroendocrine tumor (NET) lesions. The EUS-TA accuracy was 92 %, and the rate of adverse events was 4 %. CONCLUSION Irregular margins on EUS are associated with malignancy/aggressiveness of nonhypovascular SPLs. Size > 20 mm should be considered a malignancy-related feature only in NET patients. EUS-TA is safe and highly accurate for differential diagnosis in this group of patients.
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Affiliation(s)
- Stefano Francesco Crinó
- Gastroenterology and Digestive Endoscopy Unit, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
| | - Alessandro Brandolese
- Gastroenterology and Digestive Endoscopy Unit, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
| | - Filippo Vieceli
- Gastroenterology and Digestive Endoscopy Unit, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
| | - Salvatore Paiella
- Unit of General and Pancreatic Surgery, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
| | | | - Erminia Manfrin
- Department of Diagnostics and Public Health, Integrated University-Hospital of Verona, Italy
| | - Laura Bernardoni
- Gastroenterology and Digestive Endoscopy Unit, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
| | - Sokol Sina
- Department of Diagnostics and Public Health, Integrated University-Hospital of Verona, Italy
| | - Mirko D'Onofrio
- Department of Radiology, Integrated University-Hospital of Verona, Italy
| | - Giovanni Marchegiani
- Unit of General and Pancreatic Surgery, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
| | - Alberto Larghi
- Digestive Endoscopy Unit, University-Hospital Agostino Gemelli, Roma, Italy
| | - Luca Frulloni
- Gastroenterology and Digestive Endoscopy Unit, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
| | - Luca Landoni
- Unit of General and Pancreatic Surgery, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
| | - Armando Gabbrielli
- Gastroenterology and Digestive Endoscopy Unit, The Pancreas-Institute, Integrated University-Hospital of Verona, Italy
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Zhang T, Zhang Y, Liu X, Xu H, Chen C, Zhou X, Liu Y, Ma X. Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades. Front Oncol 2021; 10:521831. [PMID: 33643890 PMCID: PMC7905094 DOI: 10.3389/fonc.2020.521831] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 12/11/2020] [Indexed: 02/05/2023] Open
Abstract
Purpose To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics. Materials and Methods A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model. Result Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively. Conclusion In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.
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Affiliation(s)
- Tao Zhang
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - YueHua Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xinglong Liu
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hanyue Xu
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xuan Zhou
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yichun Liu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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40
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Xu W, Yan H, Xu L, Li M, Gao W, Jiang K, Wu J, Miao Y. Correlation between radiologic features on contrast-enhanced CT and pathological tumor grades in pancreatic neuroendocrine neoplasms. J Biomed Res 2021; 35:179-188. [PMID: 33637654 PMCID: PMC8193709 DOI: 10.7555/jbr.34.20200039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Contrast-enhanced computed tomography (CT) contributes to the increasing detection of pancreatic neuroendocrine neoplasms (PNENs). Nevertheless, its value for differentiating pathological tumor grades is not well recognized. In this report, we have conducted a retrospective study on the relationship between the 2017 World Health Organization (WHO) classification and CT imaging features in 94 patients. Most of the investigated features eventually provided statistically significant indicators for discerning PNENs G3 from PNENs G1/G2, including tumor size, shape, margin, heterogeneity, intratumoral blood vessels, vascular invasion, enhancement pattern in both contrast phases, enhancement degree in both phases, tumor-to-pancreas contrast ratio in both phases, common bile duct dilatation, lymph node metastases, and liver metastases. Ill-defined tumor margin was an independent predictor for PNENs G3 with the highest area under the curve (AUC) of 0.906 in the multivariable logistic regression and receiver operating characteristic curve analysis. The portal enhancement ratio (PER) was shown the highest AUC of 0.855 in terms of quantitative features. Our data suggest that the traditional contrast-enhanced CT still plays a vital role in differentiation of tumor grades and heterogeneity analysis prior to treatment.
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Affiliation(s)
- Wenbin Xu
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.,Pancreas Institute of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Han Yan
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.,Pancreas Institute of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Lulu Xu
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Mingna Li
- Department of Pathology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Wentao Gao
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.,Pancreas Institute of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Kuirong Jiang
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.,Pancreas Institute of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Junli Wu
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.,Pancreas Institute of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yi Miao
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.,Pancreas Institute of Nanjing Medical University, Nanjing, Jiangsu 210029, China
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Ren S, Qian L, Daniels MJ, Duan S, Chen R, Wang Z. Evaluation of contrast-enhanced computed tomography for the differential diagnosis of hypovascular pancreatic neuroendocrine tumors from chronic mass-forming pancreatitis. Eur J Radiol 2020; 133:109360. [PMID: 33126171 DOI: 10.1016/j.ejrad.2020.109360] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/27/2020] [Accepted: 10/15/2020] [Indexed: 12/28/2022]
Abstract
PURPOSE To assess the role of contrast-enhanced computed tomography (CECT) for differentiation of hypovascular pancreatic neuroendocrine tumors (hypo-PNETs) from chronic mass-forming pancreatitis (CMFP). METHODS A retrospective study of 59 patients (27 hypo-PNETs patients vs 32 CMFP patients) who underwent preoperative CECT between July 2012 and July 2019 was performed. Qualitative and quantitative analysis was performed, including mass location, size, margin, cystic changes, calcification, pancreatic or bile duct dilatation, pancreatic atrophy, local vessels involvement, mass contrast enhancement and mass-to-pancreas enhancement ratio. Multivariate logistic regression analyses were used to identify relevant CT imaging findings in differentiation between hypo-PNETs and CMFP. RESULTS When compared to CMFP, hypo-PNETs more frequently had a well-defined margin and cystic changes and less frequently had a history of pancreatitis and calcification. CMFP had higher mass contrast enhancement and mass-to-pancreas enhancement ratio in the portal and delayed phases than hypo-PNETs. After multivariate logistic regression analyses, areas under the curve (AUCs) were 0.795 (95 % CI: 0.652-0.899), 0.752 (95 % CI: 0.604-0.866), 0.859 (95 % CI: 0.726-0.943), and 0.929 (95 % CI: 0.814-0.983) for Model 1(clinical factors), Model 2 (qualitative parameters), Model 3 (quantitative parameters), and their combinations, respectively. CONCLUSION Combined assessment of clinical factors, qualitative, and quantitative imaging characteristics can improve the differentiation between hypo-PNETs and CMFP at CECT.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China; Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, Guangdong Province, China; The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China; Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA
| | - Lichao Qian
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China
| | - Marcus J Daniels
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA
| | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China; Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, Guangdong Province, China.
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Gruzdev IS, Zamyatina KA, Tikhonova VS, Kondratyev EV, Glotov AV, Karmazanovsky GG, Revishvili AS. Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur J Radiol 2020; 133:109371. [PMID: 33126173 DOI: 10.1016/j.ejrad.2020.109371] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To evaluate the reproducibility of textural features of pancreatic neuroendocrine neoplasms (PNENs), obtained under various CT-scanning conditions. METHODS AND MATERIALS We included 12 patients with PNENs and 2 contrast enhanced CT (CECT): 1) from our center according to standard CT-protocol; 2) from another institution. Two radiologists independently segmented the entire neoplasm volume using a 3D region of interest by LIFEx application on the arterial phase and then copied it to the other phases. 52 texture features were calculated for each phase. As a criterion for the segmentation consistency, a value of neoplasm volume was compared using the Bland-Altman method. The Kendall concordance coefficient was calculated to assess the texture features reproducibility in three scenarios: 1) different radiologists, same CECT; 2) same radiologist, different CECT; 3) different radiologists, different CECT. RESULTS For the scenario 1 the neoplasm volumes (except one large PNEN) were found within two standard deviations; this indicates high consistency of the segmentation. For the first scenario, Kendall's coefficient exceeded a threshold of 0.7 for all 52 features for all CT phases. For the second and third scenario, the concordance coefficient exceeded a threshold of 0.7 in 38, 28, 42, 45 and in 36, 25, 36, 44 features for the native, arterial, venous and delayed phases, respectively. CONCLUSION The highest reproducibility was found in the first scenario compared to the second and third: 100 % vs. 74 % and 67 %. Reproducible texture features can be reliably used to assess the PNENs structure.
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Affiliation(s)
- I S Gruzdev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia.
| | - K A Zamyatina
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia.
| | - V S Tikhonova
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia.
| | - E V Kondratyev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia.
| | - A V Glotov
- Pathological Department, A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia.
| | - G G Karmazanovsky
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia; Radiology Department, Pirogov Russian National Research Medical University, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia.
| | - A Sh Revishvili
- Arrhythmology Department, A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia.
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Liang P, Xu C, Tan F, Li S, Chen M, Hu D, Kamel I, Duan Y, Li Z. Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis. Cancer Med 2020; 10:595-604. [PMID: 33263225 PMCID: PMC7877354 DOI: 10.1002/cam4.3628] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/19/2020] [Accepted: 11/10/2020] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVES To investigate the diagnostic value of contrast-enhanced computed tomography (CECT) histogram analysis in predicting the World Health Organization (WHO) grade of rectal neuroendocrine tumors (R-NETs). MATERIALS AND METHODS A total of 61 (35 G1, 12 G2, 10 G3, and 4 NECs) patients who underwent preoperative CECT and treated with surgery to be confirmed as R-NETs were included in this study from January 2014 to May 2019. We depicted ROIs and measured the CECT texture parameters (mean, median, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis, and entropy) from arterial phase (AP) and venous phase (VP) images by two radiologists. We calculated intraclass correlation coefficient (ICC) and compared the histogram parameters between low-grade (G1) and higher grade (HG) (G2/G3/NECs) by applying appropriate statistical method. We obtained the optimal parameters to identify G1 from HG using receiver operating characteristic (ROC) curves. RESULTS The capability of AP and VP histogram parameters for differentiating G1 from HG was similar in several histogram parameters (mean, median, 10th, 25th, 75th, and 90th percentiles) (all p < 0.001). Skewness, kurtosis, and entropy on AP images showed no significant differences between G1 and HG (p = 0.853, 0.512, 0.557, respectively). Entropy on VP images was significantly different (p = 0.017) between G1 and HG, however, skewness and kurtosis showed no significant differences (p = 0.654, 0.172, respectively). ROC analysis showed a good predictive performance between G1 and HG, and the 75th (AP) generated the highest area under the curve (AUC = 0.871), followed by the 25th (AP), mean (VP), and median (VP) (AUC = 0.864). Combined the size of tumor and the 75th (AP) generated the highest AUC. CONCLUSIONS CECT histogram parameters, including arterial and venous phases, can be used as excellent indicators for predicting G1 and HG of rectal neuroendocrine tumors, and the size of the tumor is also an important independent predictor.
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Affiliation(s)
- Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chuou Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Fangqin Tan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mingzhen Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ihab Kamel
- Russell H. Morgan Department of Radiology and Radiological Science, the Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Yaqi Duan
- Department of Pathology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Bartoli M, Barat M, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Cassinotto C, Chassagnon G, Soyer P. CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol 2020; 38:1111-1124. [PMID: 33085029 DOI: 10.1007/s11604-020-01057-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023]
Abstract
Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.
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Affiliation(s)
- Marion Bartoli
- Department of Radiology, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
| | - Maxime Barat
- Department of Radiology, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
- Université de Paris, Descartes-Paris 5, F-75006, Paris, France
| | - Anthony Dohan
- Department of Radiology, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
- Université de Paris, Descartes-Paris 5, F-75006, Paris, France
| | - Sébastien Gaujoux
- Université de Paris, Descartes-Paris 5, F-75006, Paris, France
- Department of Abdominal Surgery, Cochin Hospital, AP-HP, 75014, Paris, France
| | - Romain Coriat
- Université de Paris, Descartes-Paris 5, F-75006, Paris, France
- Department of Gastroenterology, Cochin Hospital, AP-HP, 75014, Paris, France
| | - Christine Hoeffel
- Department of Radiology, Robert Debré Hospital, 51092, Reims, France
| | - Christophe Cassinotto
- Department of Radiology, CHU Montpellier, University of Montpellier, Saint-Éloi Hospital, 34000, Montpellier, France
| | - Guillaume Chassagnon
- Department of Radiology, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
- Université de Paris, Descartes-Paris 5, F-75006, Paris, France
| | - Philippe Soyer
- Department of Radiology, Cochin Hospital, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France.
- Université de Paris, Descartes-Paris 5, F-75006, Paris, France.
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Ohki K, Igarashi T, Ashida H, Takenaga S, Shiraishi M, Nozawa Y, Ojiri H. Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps. Jpn J Radiol 2020; 39:66-75. [PMID: 32885378 DOI: 10.1007/s11604-020-01038-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 08/21/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2-3) in patients with pancreatic neuroendocrine tumor (PNET). MATERIALS AND METHODS Thirty-three PNETs (22 G1 and 11 G2-3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs). RESULTS G2-G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively. CONCLUSION Texture analysis may aid in differentiating between G1 and G2-3 PNET.
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Affiliation(s)
- Kazuyoshi Ohki
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan.
| | - Takao Igarashi
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Hirokazu Ashida
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Shinsuke Takenaga
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Megumi Shiraishi
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Yosuke Nozawa
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | - Hiroya Ojiri
- Department of Radiology, The Jikei University School of Medicine, 3-25-8, Nishi-Shimbashi, Minato-ku, Tokyo, Japan
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Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1:37-50. [DOI: 10.35712/aig.v1.i2.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/22/2020] [Accepted: 08/27/2020] [Indexed: 02/06/2023] Open
Abstract
Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.
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Affiliation(s)
- Pei Feng
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Zhen-Dong Wang
- Department of Ultrasound, Beijing Sihui Hospital of Traditional Chinese Medicine, Beijing 100022, China
| | - Wei Fan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Heng Liu
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Jing-Jing Pan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
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Chu LC, Park S, Kawamoto S, Yuille AL, Hruban RH, Fishman EK. Pancreatic Cancer Imaging: A New Look at an Old Problem. Curr Probl Diagn Radiol 2020; 50:540-550. [PMID: 32988674 DOI: 10.1067/j.cpradiol.2020.08.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/21/2020] [Indexed: 12/18/2022]
Abstract
Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.
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Affiliation(s)
- Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Seyoun Park
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alan L Yuille
- Department of Computer Science, Johns Hopkins University, Baltimore, MD
| | - Ralph H Hruban
- Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
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Ren S, Zhao R, Cui W, Qiu W, Guo K, Cao Y, Duan S, Wang Z, Chen R. Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma. Front Oncol 2020; 10:1618. [PMID: 32984030 PMCID: PMC7477956 DOI: 10.3389/fonc.2020.01618] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 07/27/2020] [Indexed: 12/15/2022] Open
Abstract
PURPOSE The purpose was to assess the predictive ability of computed tomography (CT)-based radiomics signature in differential diagnosis between pancreatic adenosquamous carcinoma (PASC) and pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS Eighty-one patients (63.6 ± 8.8 years old) with PDAC and 31 patients (64.7 ± 11.1 years old) with PASC who underwent preoperative CE-CT were included. A total of 792 radiomics features were extracted from the late arterial phase (n = 396) and portal venous phase (n = 396) for each case. Significantly different features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy and maximum relevance method. A radiomics signature was constructed using random forest method, the robustness and the reliability of which was validated using 10-times leave group out cross-validation (LGOCV) method. RESULTS Seven radiomics features from late arterial phase images and three from portal venous phase images were finally selected. The radiomics signature performed well in differential diagnosis between PASC and PDAC, with 94.5% accuracy, 98.3% sensitivity, 90.1% specificity, 91.9% positive predictive value (PPV), and 97.8% negative predictive value (NPV). Moreover, the radiomics signature was proved to be robust and reliable using the LGOCV method, with 76.4% accuracy, 91.1% sensitivity, 70.8% specificity, 56.7% PPV, and 96.2% NPV. CONCLUSION CT-based radiomics signature may serve as a promising non-invasive method in differential diagnosis between PASC and PDAC.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Rui Zhao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenjing Cui
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenli Qiu
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Kai Guo
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Yingying Cao
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | | | - Zhongqiu Wang
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
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Azoulay A, Cros J, Vullierme MP, de Mestier L, Couvelard A, Hentic O, Ruszniewski P, Sauvanet A, Vilgrain V, Ronot M. Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma. Diagn Interv Imaging 2020; 101:821-830. [PMID: 32709455 DOI: 10.1016/j.diii.2020.06.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/28/2020] [Accepted: 06/29/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE To compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC). MATERIALS AND METHODS Patients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared. RESULTS Thirty-seven patients (21 men, 16 women; mean age, 56±13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60±46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70±51 [SD] mm [range: 18 - 196mm] vs. 42±24 [SD] mm [range: 8 - 94mm], respectively; P=0.039), with more tumor necrosis (75% vs. 33%, respectively; P=0.030) and lower attenuation on precontrast (30±4 [SD] HU [range: 25-39 HU] vs. 37±6 [SD] [range: 25-45 HU], respectively; P=0.002) and on portal venous phase CT images (75±18 [SD] HU [range: 43 - 108 HU] vs. 92±19 [SD] HU [range: 46 - 117 HU], respectively; P=0.014). Hemorrhagic content on MRI was only observed in NEC (P=0.007). The mean ADC value was lower in NEC ([1.1±0.1 (SD)]×10-3 mm2/s [range: (0.91 - 1.3)×10-3 mm2/s] vs. [1.4±0.2 (SD)]×10-3 mm2/s [range: (1.1 - 1.6)×10-3 mm2/s]; P=0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7±0.2 [SD] [range: 4.2-5.1] vs. 4.5±0.4 [SD] [range: 3.7-4.9]; P=0.023). CONCLUSION Pancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.
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Affiliation(s)
- A Azoulay
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France
| | - J Cros
- Department of Pathology, University Hospitals Paris Nord Val de Seine, Beaujon Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France; Université de Paris, Diderot Paris 7, 75010 Paris, France; INSERM U1149, CRI, Paris, France
| | - M-P Vullierme
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France
| | - L de Mestier
- Université de Paris, Diderot Paris 7, 75010 Paris, France; Department of Pancreatology, University Hospitals Paris Nord Val de Seine, Beaujon Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France; INSERM U1149, CRI, Paris, France
| | - A Couvelard
- Department of Pathology, University Hospitals Paris Nord Val de Seine, Beaujon Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France; Université de Paris, Diderot Paris 7, 75010 Paris, France; INSERM U1149, CRI, Paris, France
| | - O Hentic
- Department of Pancreatology, University Hospitals Paris Nord Val de Seine, Beaujon Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France
| | - P Ruszniewski
- Université de Paris, Diderot Paris 7, 75010 Paris, France; Department of Pancreatology, University Hospitals Paris Nord Val de Seine, Beaujon Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France; INSERM U1149, CRI, Paris, France
| | - A Sauvanet
- Department of HPB Surgery, University Hospitals Paris Nord Val de Seine, Beaujon Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France
| | - V Vilgrain
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France; Université de Paris, Diderot Paris 7, 75010 Paris, France; INSERM U1149, CRI, Paris, France
| | - M Ronot
- Department of Radiology, University Hospitals Paris Nord Val de Seine, Beaujon, Assistance Publique-Hôpitaux de Paris, 92118 Clichy, France; Université de Paris, Diderot Paris 7, 75010 Paris, France; INSERM U1149, CRI, Paris, France.
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Chen BB. Artificial intelligence in pancreatic disease. Artif Intell Med Imaging 2020; 1:19-30. [DOI: 10.35711/aimi.v1.i1.19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/18/2020] [Accepted: 06/19/2020] [Indexed: 02/06/2023] Open
Affiliation(s)
- Bang-Bin Chen
- Department of Medical Imaging, National Taiwan University Hospital, Taipei 10016, Taiwan
- Department of Radiology, College of Medicine, National Taiwan University, Taipei 10016, Taiwan
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