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Yin XN, Wang ZH, Zou L, Yang CW, Shen CY, Liu BK, Yin Y, Liu XJ, Zhang B. Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor. World J Gastrointest Oncol 2024; 16:1296-1308. [PMID: 38660646 PMCID: PMC11037038 DOI: 10.4251/wjgo.v16.i4.1296] [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: 10/08/2023] [Revised: 01/23/2024] [Accepted: 02/25/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. AIM To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. METHODS A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. RESULTS The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. CONCLUSION Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.
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Affiliation(s)
- Xiao-Nan Yin
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zi-Hao Wang
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Li Zou
- Department of Paediatric Surgery, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Cai-Wei Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Chao-Yong Shen
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bai-Ke Liu
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yuan Yin
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xi-Jiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bo Zhang
- Department of Gastrointestinal Surgery, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
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Ji X, Shang Y, Tan L, Hu Y, Liu J, Song L, Zhang J, Wang J, Ye Y, Zhang H, Peng T, An P. Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery. Clin Med Insights Oncol 2024; 18:11795549241245698. [PMID: 38628841 PMCID: PMC11020727 DOI: 10.1177/11795549241245698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/20/2024] [Indexed: 04/19/2024] Open
Abstract
Background Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling. Methods A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created. Results Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence (P < .05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST. Conclusions The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.
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Affiliation(s)
- Xianqun Ji
- Department of Radiology and Surgery, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Emergency Internal Medicine and Orthopedics, Hubei Province Clinical Research Center of Parkinson’s Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yu Shang
- Department of Radiology and Surgery, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Stomatology and Laboratory, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Infectious Disease and Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Lin Tan
- Department of Radiology and Surgery, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Emergency Internal Medicine and Orthopedics, Hubei Province Clinical Research Center of Parkinson’s Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Stomatology and Laboratory, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yan Hu
- Department of Radiology and Surgery, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Stomatology and Laboratory, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Infectious Disease and Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Junjie Liu
- Department of Radiology and Surgery, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Stomatology and Laboratory, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Oncology, Gynaecology and Obstetrics, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Lina Song
- Department of Radiology and Surgery, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Oncology, Gynaecology and Obstetrics, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Junyan Zhang
- Department of Stomatology and Laboratory, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Infectious Disease and Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
- Department of Oncology, Gynaecology and Obstetrics, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Jingxian Wang
- Department of Emergency Internal Medicine and Orthopedics, Hubei Province Clinical Research Center of Parkinson’s Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Infectious Disease and Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Yingjian Ye
- Department of Stomatology and Laboratory, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Oncology, Gynaecology and Obstetrics, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Haidong Zhang
- Department of Stomatology and Laboratory, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Oncology, Gynaecology and Obstetrics, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Tianfang Peng
- Department of Emergency Internal Medicine and Orthopedics, Hubei Province Clinical Research Center of Parkinson’s Disease, Xiangyang Key Laboratory of Movement Disorders, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Oncology, Gynaecology and Obstetrics, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Peng An
- Department of Radiology and Surgery, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Stomatology and Laboratory, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Oncology, Gynaecology and Obstetrics, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
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Prinzi F, Currieri T, Gaglio S, Vitabile S. Shallow and deep learning classifiers in medical image analysis. Eur Radiol Exp 2024; 8:26. [PMID: 38438821 PMCID: PMC10912073 DOI: 10.1186/s41747-024-00428-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 01/03/2024] [Indexed: 03/06/2024] Open
Abstract
An increasingly strong connection between artificial intelligence and medicine has enabled the development of predictive models capable of supporting physicians' decision-making. Artificial intelligence encompasses much more than machine learning, which nevertheless is its most cited and used sub-branch in the last decade. Since most clinical problems can be modeled through machine learning classifiers, it is essential to discuss their main elements. This review aims to give primary educational insights on the most accessible and widely employed classifiers in radiology field, distinguishing between "shallow" learning (i.e., traditional machine learning) algorithms, including support vector machines, random forest and XGBoost, and "deep" learning architectures including convolutional neural networks and vision transformers. In addition, the paper outlines the key steps for classifiers training and highlights the differences between the most common algorithms and architectures. Although the choice of an algorithm depends on the task and dataset dealing with, general guidelines for classifier selection are proposed in relation to task analysis, dataset size, explainability requirements, and available computing resources. Considering the enormous interest in these innovative models and architectures, the problem of machine learning algorithms interpretability is finally discussed, providing a future perspective on trustworthy artificial intelligence.Relevance statement The growing synergy between artificial intelligence and medicine fosters predictive models aiding physicians. Machine learning classifiers, from shallow learning to deep learning, are offering crucial insights for the development of clinical decision support systems in healthcare. Explainability is a key feature of models that leads systems toward integration into clinical practice. Key points • Training a shallow classifier requires extracting disease-related features from region of interests (e.g., radiomics).• Deep classifiers implement automatic feature extraction and classification.• The classifier selection is based on data and computational resources availability, task, and explanation needs.
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Affiliation(s)
- Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB2 1TN, UK
| | - Tiziana Currieri
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | - Salvatore Gaglio
- Department of Engineering, University of Palermo, Palermo, Italy
- Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), Palermo, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.
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Yang L, Zhang D, Zheng T, Liu D, Fang Y. Predicting the progression-free survival of gastrointestinal stromal tumors after imatinib therapy through multi-sequence magnetic resonance imaging. Abdom Radiol (NY) 2024; 49:801-813. [PMID: 38006414 DOI: 10.1007/s00261-023-04093-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 11/27/2023]
Abstract
PURPOSE Identify radiomics features associated with progression-free survival (PFS) and develop a predictive model for accurate PFS prediction in liver metastatic gastrointestinal stromal tumor patients (GIST). METHODS This multi-center retrospective study involved a comprehensive review of clinical and imaging data pertaining to 211 patients with gastrointestinal stromal tumors (GIST) from Center A and B. A total of 147 patients with hepatic metastatic GIST were included, with 102 cases as the training set and 45 cases as the external validation set. Radiomics features were extracted from non-enhanced MR images, specifically T2WI, DWI, and ADC, and relevant features were selected through LASSO-Cox regression. A radiomics nomogram model was then constructed using multivariable Cox regression analysis to effectively predict PFS. The models performance were evaluated with the concordance index (C-index). RESULTS The median age of the patients was 53 years, with 82 males and 65 females. A total of 21 radiomics features were selected to generate the radiomics signature. Radiomics signature slightly outperformed the clinical model but without significant difference (P > 0.05). Integrated radiomics signature with clinical features to build a nomogram, which exhibited high predictive performance in both training (C-index 0.757, 95% CI 0.692-0.822) and validation cohorts (C-index 0.718, 95% CI 0.618-0.818). Nomogram significantly outperformed the clinical model (P = 0.002 for training cohort, P < 0.001 for validation cohort). Stable long-term predictions shown by time-dependent ROC analysis (AUC 0.765-0.919 in training, 0.766-0.893 in validation). Multivariable Cox regression confirmed radiomics signature as an independent prognostic factor for preoperative survival prediction in hepatic metastatic GIST patients (HR = 3.973). CONCLUSION Radiomics signature is valuable for predicting PFS in metastatic GIST patients. Integrating imaging features and clinical factors into a comprehensive nomogram improves accuracy and effectiveness of survival prognosis, guiding personalized treatment strategies.
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Affiliation(s)
- Linsha Yang
- Department of Medical Imaging, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China
| | - Duo Zhang
- Department of Medical Imaging, Baoding No. 1 Central Hospital, Baoding, People's Republic of China
| | - Tao Zheng
- Department of Medical Imaging, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China
| | - Defeng Liu
- Department of Medical Imaging, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China.
| | - Yuan Fang
- Medical Imaging Center, Chongqing Yubei District People's Hospital, Chongqing, People's Republic of China.
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Zhuo M, Tang Y, Guo J, Qian Q, Xue E, Chen Z. Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics. J Med Ultrason (2001) 2024; 51:71-82. [PMID: 37798591 DOI: 10.1007/s10396-023-01373-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
PURPOSE This study aimed to use conventional ultrasound features, ultrasound radiomics, and machine learning algorithms to establish a predictive model to assess the risk of post-surgical recurrence of gastrointestinal stromal tumors (GISTs). METHODS This retrospective analysis included 230 patients with pathologically diagnosed GISTs. Radiomic features were extracted from manually annotated images. Radiomic features plus conventional ultrasound features were selected using the SelectKbest analysis of variance and stratified tenfold cross-validation recursive elimination methods. Finally, five different machine learning algorithms (logistic regression [LR], support vector machine [SVM], random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) were established to predict risk stratification of GISTs. The predictive performance of the established model was mainly evaluated based on the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy, whereas the predictive performance of the optimal machine learning algorithm and a radiologist's subjective assessment were compared using McNemar's test. RESULTS Seven radiomics features and one conventional ultrasound feature were selected to construct the machine learning models for GIST risk classification. The mentioned five machine learning models were able to predict the malignant potential of GISTs. LR and SVM outperformed other classifiers on the test set, with LR achieving an accuracy of 0.852 (AUC, 0.881; sensitivity, 0.871; specificity, 0.826) and SVM achieving an accuracy of 0.852 (AUC, 0.879; sensitivity, 0.839; specificity, 0.870), and proved significantly better than the radiologist (accuracy, 0.691; sensitivity, 0.645; specificity, 0.813). CONCLUSION Machine learning-based ultrasound radiomics features are able to noninvasively predict the biological risk of GISTs.
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Affiliation(s)
- Minling Zhuo
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian, China
| | - Yi Tang
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian, China
| | - Jingjing Guo
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian, China
| | - Qingfu Qian
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian, China
| | - Ensheng Xue
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian, China
| | - Zhikui Chen
- Department of Ultrasound, Fujian Medical University Affiliated Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian, China.
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Wang P, Yan J, Qiu H, Huang J, Yang Z, Shi Q, Yan C. A radiomics-clinical combined nomogram-based on non-enhanced CT for discriminating the risk stratification in GISTs. J Cancer Res Clin Oncol 2023; 149:12993-13003. [PMID: 37464150 DOI: 10.1007/s00432-023-05170-7] [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: 06/08/2023] [Accepted: 07/09/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To discriminate the risk stratification in gastrointestinal stromal tumors (GISTs) by preoperatively constructing a model of nonenhanced computed tomography (NECT). METHODS A total of 111 GISTs patients (77 in the training group and 34 in the validation Group) from two hospitals between 2015 and 2022 were collected retrospectively. One thousand and thirty-seven radiomics features were extracted from non-contract CT images, and the optimal radiomics signature was determined by univariate analysis and LASSO regression. The radiomics model was developed and validated from the ten optimal radiomics features by three methods. Covariates (clinical features, CT findings, and immunohistochemical characteristics) were collected to establish the clinical model, and both the radiomics features and the covariates were used to build the combined model. The effectiveness of the three models was evaluated by the Delong test. RESULTS The experimental results showed that the clinical models (75.3%, 70.6%), the radiomics models (79.2%, 79.4%) and the combined models (81.8%, 82.4%) all had high accuracy in predicting the pathological risk of GIST in both training and validation groups. The AUC values of the combined models were significantly higher in both the training groups (0.921 vs 0.822, p= 0.032) and the validation groups (0.913 vs 0.792, p= 0.019) than that of the clinical models. According to the calibration curve, the combined model nomogram is clinically useful. CONCLUSIONS The clinical-radiomics combined model and based on NECT performed well in discriminating the risk stratification in GISTs. As a quantitative technique, radiomics is capable of predicting the malignant potential and guiding treatment preoperatively.
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Affiliation(s)
- Peizhe Wang
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China
| | - Jingrui Yan
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Hui Qiu
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China
| | - Jingying Huang
- Department of Medical Imaging, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China
| | - Zhe Yang
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China
| | - Qiang Shi
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China
| | - Chengxin Yan
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, Shandong, China.
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Kim HJ, Gong EJ, Bang CS. Application of Machine Learning Based on Structured Medical Data in Gastroenterology. Biomimetics (Basel) 2023; 8:512. [PMID: 37999153 PMCID: PMC10669027 DOI: 10.3390/biomimetics8070512] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/12/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023] Open
Abstract
The era of big data has led to the necessity of artificial intelligence models to effectively handle the vast amount of clinical data available. These data have become indispensable resources for machine learning. Among the artificial intelligence models, deep learning has gained prominence and is widely used for analyzing unstructured data. Despite the recent advancement in deep learning, traditional machine learning models still hold significant potential for enhancing healthcare efficiency, especially for structured data. In the field of medicine, machine learning models have been applied to predict diagnoses and prognoses for various diseases. However, the adoption of machine learning models in gastroenterology has been relatively limited compared to traditional statistical models or deep learning approaches. This narrative review provides an overview of the current status of machine learning adoption in gastroenterology and discusses future directions. Additionally, it briefly summarizes recent advances in large language models.
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Affiliation(s)
- Hye-Jin Kim
- Department of Internal Medicine, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea; (H.-J.K.); (E.-J.G.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Republic of Korea
- Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea
| | - Eun-Jeong Gong
- Department of Internal Medicine, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea; (H.-J.K.); (E.-J.G.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Republic of Korea
- Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea
| | - Chang-Seok Bang
- Department of Internal Medicine, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea; (H.-J.K.); (E.-J.G.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Republic of Korea
- Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea
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Wang TT, Liu WW, Liu XH, Gao RJ, Zhu CY, Wang Q, Zhao LP, Fan XM, Li J. Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors. World J Gastrointest Oncol 2023; 15:1073-1085. [PMID: 37389110 PMCID: PMC10303000 DOI: 10.4251/wjgo.v15.i6.1073] [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: 03/09/2023] [Revised: 04/02/2023] [Accepted: 04/25/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs).
AIM To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs.
METHODS The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification.
RESULTS There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 ± 0.94 vs 6.63 ± 3.26 cm, P < 0.001) between the low-grade and high-grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours (P = 0.028; OR = 7.750; 95%CI: 1.253-47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively.
CONCLUSION CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.
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Affiliation(s)
- Tian-Tian Wang
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Wei-Wei Liu
- Department of Rheumatology, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Xian-Hai Liu
- Department of Network Information Center, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Rong-Ji Gao
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Chun-Yu Zhu
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Qing Wang
- Department of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Lu-Ping Zhao
- Department of Medical Imaging, The Affiliated Hospital of Ji’ning Medical University, Jining 272000, Shandong Province, China
| | - Xiao-Ming Fan
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
| | - Juan Li
- Department of Medical Imaging, The Second Affiliated Hospital of Shandong First Medical University, Taian 271000, Shandong Province, China
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Lin JX, Wang FH, Wang ZK, Wang JB, Zheng CH, Li P, Huang CM, Xie JW. Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01637-2. [PMID: 37148481 DOI: 10.1007/s11547-023-01637-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/21/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVE The objective is to develop a mitotic prediction model and preoperative risk stratification nomogram for gastrointestinal stromal tumor (GIST) based on computed tomography (CT) radiomic features. METHODS A total of 267 GIST patients from 2009.07 to 2015.09 were retrospectively collected and randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest was delineated from the portal-phase images on contrast-enhanced (CE)-CT, and radiomic features were extracted. Lasso regression method was used to select valuable features to establish a radiomic model for predicting mitotic index in GIST. Finally, the nomogram of preoperative risk stratification was constructed by combining the radiomic features and clinical risk factors. RESULTS Four radiomic features closely related to the level of mitosis were obtained, and a mitotic radiomic model was constructed. The area under the curve (AUC) of the radiomics signature model used to predict mitotic levels in training and validation cohorts (training cohort AUC = 0.752; 95% confidence interval [95%CI] 0.674-0.829; validation cohort AUC = 0.764; 95% CI 0.667-0.862). Finally, the preoperative risk stratification nomogram combining radiomic features was equivalent to the clinically recognized gold standard AUC (0.965 vs. 0.983) (p = 0.117). The Cox regression analysis found that the nomogram score was one of the independent risk factors for the long-term prognosis of the patients. CONCLUSION Preoperative CT radiomic features can effectively predict the level of mitosis in GIST, and combined with preoperative tumor size, accurate preoperative risk stratification can be performed to guide clinical decision-making and individualized treatment.
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Affiliation(s)
- Jian-Xian Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Fu-Hai Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zu-Kai Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Bin Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chao-Hui Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ping Li
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chang-Ming Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China.
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China.
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Jian-Wei Xie
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China.
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China.
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
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10
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Jia X, Wan L, Chen X, Ji W, Huang S, Qi Y, Cui J, Wei S, Cheng J, Chai F, Feng C, Liu Y, Zhang H, Sun Y, Hong N, Rao S, Zhang X, Xiao Y, Ye Y, Tang L, Wang Y. Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis. Eur Radiol 2023; 33:2768-2778. [PMID: 36449061 DOI: 10.1007/s00330-022-09228-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 08/15/2022] [Accepted: 10/09/2022] [Indexed: 12/03/2022]
Abstract
OBJECTIVES To investigate the ability of CT and endoscopic sonography (EUS) in predicting the malignant risk of 1-2-cm gastric gastrointestinal stromal tumors (gGISTs) and to clarify whether radiomics could be applied for risk stratification. METHODS A total of 151 pathologically confirmed 1-2-cm gGISTs from seven institutions were identified by contrast-enhanced CT scans between January 2010 and March 2021. A detailed description of EUS morphological features was available for 73 gGISTs. The association between EUS or CT high-risk features and pathological malignant potential was evaluated. gGISTs were randomly divided into three groups to build the radiomics model, including 74 in the training cohort, 37 in validation cohort, and 40 in testing cohort. The ROIs covering the whole tumor volume were delineated on the CT images of the portal venous phase. The Pearson test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection, and the ROC curves were used to evaluate the model performance. RESULTS The presence of EUS- and CT-based morphological high-risk features, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not differ between very-low and intermediate risk 1-2-cm gGISTs (p > 0.05). The radiomics model consisting of five radiomics features showed favorable performance in discrimination of malignant 1-2-cm gGISTs, with the AUC of the training, validation, and testing cohort as 0.866, 0.812, and 0.766, respectively. CONCLUSIONS Instead of CT- and EUS-based morphological high-risk features, the CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs. KEY POINTS • The presence of EUS- and CT-based morphological high-risk factors, including calcification, necrosis, intratumoral heterogeneity, irregular border, or surface ulceration, did not correlate with the pathological malignant potential of 1-2-cm gGISTs. • The CT radiomics model could potentially be applied for preoperative risk stratification of 1-2-cm gGISTs.
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Affiliation(s)
- Xiaoxuan Jia
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Lijuan Wan
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaoshan Chen
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Wanying Ji
- Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, China
| | - Shaoqing Huang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuangang Qi
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, China
| | - Jingjing Cui
- United Imaging Intelligence (Beijing) Co., Ltd., Yongteng North Road, Haidian District, Beijing, 100094, China
| | - Shengcai Wei
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Jin Cheng
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Fan Chai
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Caizhen Feng
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Yulu Liu
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Hongmei Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yingshi Sun
- Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Shengxiang Rao
- Department of Radiology, Shanghai Institute of Medical Imaging, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Xinhua Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
| | - Youping Xiao
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, China.
| | - Yingjiang Ye
- Department of Gastrointestinal Surgery, Peking University People's Hospital, Beijing, 100044, China.
| | - Lei Tang
- Department of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, China.
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China.
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11
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Liu M, Bian J. Radiomics signatures based on contrast-enhanced CT for preoperative prediction of the Ki-67 proliferation state in gastrointestinal stromal tumors. Jpn J Radiol 2023:10.1007/s11604-023-01391-5. [PMID: 36652141 DOI: 10.1007/s11604-023-01391-5] [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: 10/16/2022] [Accepted: 01/07/2023] [Indexed: 01/19/2023]
Abstract
PURPOSE This study aimed to evaluate the Ki-67 proliferation state in patients with gastrointestinal stromal tumors (GISTs) using radiomics prediction signatures based on contrast-enhanced computed tomography (CE-CT). MATERIALS AND METHODS This single-center, retrospective study involved 103 patients (48 men and 55 women, mean age 61.1 ± 10.6 years) who had pathologically confirmed GISTs after curative resection, including 63 with low Ki-67 proliferation level (Ki-67 labeling index ≤ 6%) and 40 with high Ki-67 proliferation level (Ki-67 labeling index > 6%). Radiomics features of the delineated lesions were preoperatively extracted from three-phase CE-CT images, including the arterial, venous, and delayed phases. The most relevant features were selected to construct the radiomics signatures using a logistic regression algorithm. Significant demographic characteristics and semantic features on CT were selected to develop a nomogram along with the optimal radiomics feature. We calculated the sensitivity, specificity, accuracy, F1 score, and area under the receiver operating characteristic (ROC) curve to evaluate the predictive performance of radiomics signatures. RESULTS Ten quantitative radiomics features (two first-order and eight texture features) were selected to construct radiomics signatures. The radiomics signature based on the three-phase CE-CT images showed better predictive performance than that based on the single-phase CE-CT images, with an area under the curve (AUC) of 0.83 (95% CI 0.73-0.92) and F1 score of 82% in the training dataset and an AUC of 0.80 (95% CI 0.63-0.95) and F1 score of 75% in the testing dataset. The nomogram showed good calibration. CONCLUSION Radiomics signatures using CE-CT images are generalizable and could be used in clinical practice to determine the proliferation state of Ki-67 in GISTs.
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Affiliation(s)
- Meijun Liu
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No.467 Zhongshan Road, Shahekou District, Dalian, 116027, Liaoning Province, China
| | - Jie Bian
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No.467 Zhongshan Road, Shahekou District, Dalian, 116027, Liaoning Province, China.
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12
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Wang Y, Wang Y, Ren J, Jia L, Ma L, Yin X, Yang F, Gao BL. Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center study. Front Oncol 2022; 12:966743. [PMID: 36052224 PMCID: PMC9425090 DOI: 10.3389/fonc.2022.966743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 07/25/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose This study was to investigate the diagnostic efficacy of radiomics models based on the enhanced CT images in differentiating the malignant risk of gastrointestinal stromal tumors (GIST) in comparison with the clinical indicators model and traditional CT diagnostic criteria. Materials and methods A total of 342 patients with GISTs confirmed histopathologically were enrolled from five medical centers. Data of patients wrom two centers comprised the training group (n=196), and data from the remaining three centers constituted the validation group (n=146). After CT image segmentation and feature extraction and selection, the arterial phase model and venous phase model were established. The maximum diameter of the tumor and internal necrosis were used to establish a clinical indicators model. The traditional CT diagnostic criteria were established for the classification of malignant potential of tumor. The performance of the four models was assessed using the receiver operating characteristics curve. Reuslts In the training group, the area under the curves(AUCs) of the arterial phase model, venous phase model, clinical indicators model, and traditional CT diagnostic criteria were 0.930 [95% confidence interval (CI): 0.895-0.965), 0.933 (95%CI 0.898-0.967), 0.917 (95%CI 0.872-0.961) and 0.782 (95%CI 0.717-0.848), respectively. In the validation group, the AUCs of the models were 0.960 (95%CI 0.930-0.990), 0.961 (95% CI 0.930-0.992), 0.922 (95%CI 0.884-0.960) and 0.768 (95%CI 0.692-0.844), respectively. No significant difference was detected in the AUC between the arterial phase model, venous phase model, and clinical indicators model by the DeLong test, whereas a significant difference was observed between the traditional CT diagnostic criteria and the other three models. Conclusion The radiomics model using the morphological features of GISTs play a significant role in tumor risk stratification and can provide a reference for clinical diagnosis and treatment plan.
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Affiliation(s)
- Yun Wang
- Affiliated Hospital of Hebei University/Hebei University (Clinical Medical College), Baoding, China
| | - Yurui Wang
- Tangshan Gongren Hospital, Tangshan, China
| | - Jialiang Ren
- General Electric Pharmaceutical Co., Ltd, Shanghai, China
| | - Linyi Jia
- Xingtai People’s Hospital, Xingtai, China
| | - Luyao Ma
- Affiliated Hospital of Hebei University/Hebei University (Clinical Medical College), Baoding, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University/Hebei University (Clinical Medical College), Baoding, China
- *Correspondence: Xiaoping Yin, ; Fei Yang,
| | - Fei Yang
- Medical Imaging Department, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
- *Correspondence: Xiaoping Yin, ; Fei Yang,
| | - Bu-Lang Gao
- Affiliated Hospital of Hebei University/Hebei University (Clinical Medical College), Baoding, China
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13
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Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment. Diagnostics (Basel) 2022; 12:diagnostics12020388. [PMID: 35204479 PMCID: PMC8870749 DOI: 10.3390/diagnostics12020388] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/28/2022] [Accepted: 01/30/2022] [Indexed: 02/05/2023] Open
Abstract
(1) Background: As outcome of patients with metastatic melanoma treated with anti-PD1 immunotherapy can vary in success, predictors are needed. We aimed to predict at the patients’ levels, overall survival (OS) and progression-free survival (PFS) after one year of immunotherapy, based on their pre-treatment 18F-FDG PET; (2) Methods: Fifty-six metastatic melanoma patients—without prior systemic treatment—were retrospectively included. Forty-five 18F-FDG PET-based radiomic features were computed and the top five features associated with the patient’s outcome were selected. The analyzed machine learning classifiers were random forest (RF), neural network, naive Bayes, logistic regression and support vector machine. The receiver operating characteristic curve was used to compare model performances, which were validated by cross-validation; (3) Results: The RF model obtained the best performance after validation to predict OS and PFS and presented AUC, sensitivities and specificities (IC95%) of 0.87 ± 0.1, 0.79 ± 0.11 and 0.95 ± 0.06 for OS and 0.9 ± 0.07, 0.88 ± 0.09 and 0.91 ± 0.08 for PFS, respectively. (4) Conclusion: A RF classifier, based on pretreatment 18F-FDG PET radiomic features may be useful for predicting the survival status for melanoma patients, after one year of a first line systemic treatment by immunotherapy.
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14
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Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
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Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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15
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Shao M, Niu Z, He L, Fang Z, He J, Xie Z, Cheng G, Wang J. Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors. Front Oncol 2021; 11:737302. [PMID: 34950578 PMCID: PMC8689687 DOI: 10.3389/fonc.2021.737302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features that were extracted from a lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models, and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and model 3 yielded an accuracy of 88.3%, 85.8%, 86.4%, and 84.6%, a sensitivity of 85.4%, 84.2%, 76.8%, and 78.0%, and a specificity of 91.2%, 87.5%, 96.2%, and 91.2% in the training set and an accuracy of 88.4%, 84.1%, 82.6%, and 82.6%, a sensitivity of 88.6%, 77.1%, 74.3%, and 85.7%, and a specificity of 88.2%, 91.2%, 91.2%, and 79.4% in the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the training data set (Delong test, p < 0.05). The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.
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Affiliation(s)
- Meihua Shao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Zhongfeng Niu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Linyang He
- Hangzhou Jianpei Technology Company, Hangzhou, China
| | - Zhaoxing Fang
- Hangzhou Jianpei Technology Company, Hangzhou, China
| | - Jie He
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zongyu Xie
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
| | - Guohua Cheng
- Hangzhou Jianpei Technology Company, Hangzhou, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
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16
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Yang F, Wan Y, Xu L, Wu Y, Shen X, Wang J, Lu D, Shao C, Zheng S, Niu T, Xu X. MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study. Front Oncol 2021; 11:672126. [PMID: 34476208 PMCID: PMC8406635 DOI: 10.3389/fonc.2021.672126] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 07/20/2021] [Indexed: 12/13/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has poor prognosis. Cytokeratin (CK)19-positive (CK19+) HCC is especially aggressive; early identification of this subtype and timely intervention can potentially improve clinical outcomes. In the present study, we developed a preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI)-based radiomics model for noninvasive and accurate classification of CK19+ HCC. A multicenter and time-independent cohort of 257 patients were retrospectively enrolled (training cohort, n = 143; validation cohort A, n = 75; validation cohort B, n = 39). A total of 968 radiomics features were extracted from preoperative multisequence MR images. The maximum relevance minimum redundancy algorithm was applied for feature selection. Multiple logistic regression, support vector machine, random forest, and artificial neural network (ANN) algorithms were used to construct the radiomics model, and the area under the receiver operating characteristic (AUROC) curve was used to evaluate the diagnostic performance of corresponding classifiers. The incidence of CK19+ HCC was significantly higher in male patients. The ANN-derived combined classifier comprising 12 optimal radiomics features showed the best diagnostic performance, with AUROCs of 0.857, 0.726, and 0.790 in the training cohort and validation cohorts A and B, respectively. The combined model based on multisequence MRI radiomics features can be used for preoperative noninvasive and accurate classification of CK19+ HCC, so that personalized management strategies can be developed.
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Affiliation(s)
- Fan Yang
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yidong Wan
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China.,Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lei Xu
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China.,Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yichao Wu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoyong Shen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianguo Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Di Lu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chuxiao Shao
- Department of General Surgery, Lishui Central Hospital, Lishui, China
| | - Shusen Zheng
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, Shulan Health Hangzhou Hospital, Hangzhou, China
| | - Tianye Niu
- Nucelar & Radiological Engineering and Medical Physics Programs, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Xiao Xu
- Department of Hepatobiliary and Pancreatic Surgery, The Center of Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Zhejiang University Cancer Center, Hangzhou, China.,NHC Key Laboratory of Combined Multi-Organ Transplantation, Hangzhou, China.,Institute of Organ Transplantation, Zhejiang University, Hangzhou, China
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