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Barat M, Pellat A, Terris B, Dohan A, Coriat R, Fishman EK, Rowe SP, Chu L, Soyer P. Cinematic Rendering of Gastrointestinal Stromal Tumours: A Review of Current Possibilities and Future Developments. Can Assoc Radiol J 2024; 75:359-368. [PMID: 37982314 DOI: 10.1177/08465371231211278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023] Open
Abstract
Gastrointestinal stromal tumours (GISTs) are defined as CD117-positive primary, spindled or epithelioid, mesenchymal tumours of the gastrointestinal tract, omentum, or mesentery. While computed tomography (CT) is the recommended imaging modality for GISTs, overlap in imaging features between GISTs and other gastrointestinal tumours often make radiological diagnosis and subsequent selection of the optimal therapeutic approach challenging. Cinematic rendering is a novel CT post-processing technique that generates highly photorealistic anatomic images based on a unique lighting model. The global lighting model produces high degrees of surface detail and shadowing effects that generate depth in the final three-dimensional display. Early studies have shown that cinematic rendering produces high-quality images with enhanced detail by comparison with other three-dimensional visualization techniques. Cinematic rendering shows promise in improving the visualization of enhancement patterns and internal architecture of abdominal lesions, local tumour extension, and global disease burden, which may be helpful for lesion characterization and pretreatment planning. This article discusses and illustrates the application of cinematic rendering in the evaluation of GISTs and the unique benefit of using cinematic rendering in the workup of GIST with a specific emphasis on tumour characterization and preoperative planning.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Anna Pellat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
| | - Benoit Terris
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Pathology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Steven P Rowe
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
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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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Cao J, Li Q, Zhang H, Wu Y, Wang X, Ding S, Chen S, Xu S, Duan G, Qiu D, Sun J, Shi J, Liu S. Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study. J Bone Oncol 2024; 45:100599. [PMID: 38601920 PMCID: PMC11004638 DOI: 10.1016/j.jbo.2024.100599] [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: 07/02/2023] [Revised: 12/19/2023] [Accepted: 01/09/2024] [Indexed: 04/12/2024] Open
Abstract
Purpose Spinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them. Methods Enrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score. Results Based on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively. Conclusions The radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.
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Affiliation(s)
- Jiashi Cao
- Department of Orthopedics, Navy Medical Center, the Navy Medical University, No. 338 Huaihai West Road, Shanghai 200052, China
| | - Qiong Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center/Cancer Hospital, No. 651 Dongfeng East Road, Guangzhou 510060, China
| | - Huili Zhang
- School of Communication and Information Engineering, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Yanyan Wu
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Saisai Ding
- School of Communication and Information Engineering, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Song Chen
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Shaochun Xu
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Guangwen Duan
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Defu Qiu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jiuyi Sun
- Department of Orthopedics, Navy Medical Center, the Navy Medical University, No. 338 Huaihai West Road, Shanghai 200052, China
| | - Jun Shi
- School of Communication and Information Engineering, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
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Wang J, Shao M, Hu H, Xiao W, Cheng G, Yang G, Ji H, Yu S, Wan J, Xie Z, Xu M. Convolutional neural network applied to preoperative venous-phase CT images predicts risk category in patients with gastric gastrointestinal stromal tumors. BMC Cancer 2024; 24:280. [PMID: 38429653 PMCID: PMC10908217 DOI: 10.1186/s12885-024-11962-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 02/05/2024] [Indexed: 03/03/2024] Open
Abstract
OBJECTIVE The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs. METHOD A total of 425 patients pathologically diagnosed with gastric GISTs at the authors' medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models. RESULTS In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05). CONCLUSIONS The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.
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Affiliation(s)
- Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
- Department of radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China
| | - Meihua Shao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, The Sir Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenbo Xiao
- Department of radiology,The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | | | - Guangzhao Yang
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Hongli Ji
- Jianpei Technology, Hangzhou, Zhejiang, China
| | - Susu Yu
- Department of radiology,The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Wan
- Jianpei Technology, Hangzhou, Zhejiang, China
| | - Zongyu Xie
- Department of Radiology, The First Affliated Hospital of Bengbu Medical University, Bengbu, Anhui, China
| | - Maosheng Xu
- Department of radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.
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Barat M, Pellat A, Dohan A, Hoeffel C, Coriat R, Soyer P. CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives. Can Assoc Radiol J 2024; 75:107-117. [PMID: 37386745 DOI: 10.1177/08465371231180510] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Anna Pellat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
| | - Christine Hoeffel
- Reims Medical School, Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, Reims, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Paris, France
| | - Philippe Soyer
- Department of Radiology, Hopital Cochin, Paris, France
- Université Paris Cité, Faculté de Médecine, Paris, France
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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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Sun B, Liu J, Li S, Lovell JF, Zhang Y. Imaging of Gastrointestinal Tract Ailments. J Imaging 2023; 9:115. [PMID: 37367463 DOI: 10.3390/jimaging9060115] [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: 04/24/2023] [Revised: 05/20/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management of GI diseases. This review mainly focuses on the imaging of several representative gastrointestinal ailments, such as inflammatory bowel disease, tumors, appendicitis, Meckel's diverticulum, and others. Various imaging modalities commonly used for the gastrointestinal tract, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), and photoacoustic tomography (PAT) and multimodal imaging with mode overlap are summarized. These achievements in single and multimodal imaging provide useful guidance for improved diagnosis, staging, and treatment of the corresponding gastrointestinal diseases. The review evaluates the strengths and weaknesses of different imaging techniques and summarizes the development of imaging techniques used for diagnosing gastrointestinal ailments.
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Affiliation(s)
- Boyang Sun
- Key Laboratory of Systems Bioengineering, School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300350, China
| | - Jingang Liu
- Key Laboratory of Systems Bioengineering, School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300350, China
| | - Silu Li
- Key Laboratory of Systems Bioengineering, School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300350, China
| | - Jonathan F Lovell
- Department of Biomedical Engineering, The State University of New York at Buffalo, Buffalo, NY 14260, USA
| | - Yumiao Zhang
- Key Laboratory of Systems Bioengineering, School of Chemical Engineering and Technology, Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300350, China
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Giudice F, Salerno S, Badalamenti G, Muto G, Pinto A, Galia M, Prinzi F, Vitabile S, Lo Re G. Gastrointestinal Stromal Tumors: Diagnosis, Follow-up and Role of Radiomics in a Single Center Experience. Semin Ultrasound CT MR 2023; 44:194-204. [PMID: 37245884 DOI: 10.1053/j.sult.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Gastrointestinal stromal tumors (GISTs) arise from the interstitial cells of Cajal in the gastrointestinal tract and are the most common intestinal tumors. Usually GISTs are asymptomatic, especially small tumors that may not cause any symptoms and may be found accidentally on abdominal CT scans. Discovering of inhibitor of receptor tyrosine kinases has changed the outcome of patients with high-risk GISTs. This paper will focus on the role of imaging in diagnosis, characterization and follow-up. We shall also report our local experience in radiomics evaluation of GISTs.
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Affiliation(s)
- Francesca Giudice
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
| | - Sergio Salerno
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
| | - Giuseppe Badalamenti
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
| | - Gianluca Muto
- Department of Radiology, University Hospital of Geneve, Geneve, Switzerland.
| | - Antonio Pinto
- Department of Radiology, CTO Hospital, Azienda Ospedaliera dei Colli, Naples, Italy.
| | - Massimo Galia
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
| | - Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
| | - Giuseppe Lo Re
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy.
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Pasini G, Stefano A, Russo G, Comelli A, Marinozzi F, Bini F. Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics? Diagnostics (Basel) 2023; 13:diagnostics13061167. [PMID: 36980475 PMCID: PMC10046953 DOI: 10.3390/diagnostics13061167] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/16/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.
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Affiliation(s)
- Giovanni Pasini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
| | - Albert Comelli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
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10
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Porrello G, Cannella R, Alvarez-Hornia Pérez E, Brancatelli G, Vernuccio F. The Neoplastic Side of the Abdominal Wall: A Comprehensive Pictorial Essay of Benign and Malignant Neoplasms. Diagnostics (Basel) 2023; 13:diagnostics13020315. [PMID: 36673126 PMCID: PMC9858284 DOI: 10.3390/diagnostics13020315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/05/2023] [Accepted: 01/10/2023] [Indexed: 01/18/2023] Open
Abstract
Abdominal wall neoplasms are usually benign and, in the majority of these cases, no further work-up or treatment is indicated. The percentage of malignant abdominal neoplasms, however, is not negligible. Radiologists play a pivotal role in identifying imaging features that should favor malignancy, including larger lesion size, edema, neurovascular involvement, and peripheral or inhomogeneous dynamic enhancement, thus indicating to the clinician the need for further work-up. Histopathology is the reference standard for the characterization of abdominal wall neoplasms. In patients undergoing surgery, radiological assessment is needed to guide the surgeon by providing a comprehensive anatomic guide of the tumor extension. We present a pictorial review of benign and malignant abdominal wall neoplasms that can be encountered on radiological examinations, with a main focus on CT and MRI features that help in narrowing the differential diagnosis.
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Affiliation(s)
- Giorgia Porrello
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnosis (Bi.N.D), University of Palermo, Via del Vespro 129, 90127 Palermo, Italy
- Radiology Unit, Department of Diagnostic and Therapeutic Services, IRCCS ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), Via Tricomi 5, 90127 Palermo, Italy
- Correspondence: (G.P.); (F.V.)
| | - Roberto Cannella
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnosis (Bi.N.D), University of Palermo, Via del Vespro 129, 90127 Palermo, Italy
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, Piazza delle Cliniche, 2, 90127 Palermo, Italy
| | | | - Giuseppe Brancatelli
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnosis (Bi.N.D), University of Palermo, Via del Vespro 129, 90127 Palermo, Italy
| | - Federica Vernuccio
- Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy
- Correspondence: (G.P.); (F.V.)
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11
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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12
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Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28:6363-6379. [PMID: 36533112 PMCID: PMC9753055 DOI: 10.3748/wjg.v28.i45.6363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/25/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Hao-Ming Yan
- School of Clinical Medicine, China Medical University, Shenyang 110013, Liaoning Province, China
| | - Shan Gao
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Province, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Liang Yao
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Zhong-Ren Wang
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Hon Ho Yu
- Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
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13
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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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14
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Inoue A, Ota S, Yamasaki M, Batsaikhan B, Furukawa A, Watanabe Y. Gastrointestinal stromal tumors: a comprehensive radiological review. Jpn J Radiol 2022; 40:1105-1120. [PMID: 35809209 DOI: 10.1007/s11604-022-01305-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 06/08/2022] [Indexed: 11/29/2022]
Abstract
Gastrointestinal stromal tumors (GISTs) originating from the interstitial cells of Cajal in the muscularis propria are the most common mesenchymal tumor of the gastrointestinal tract. Multiple modalities, including computed tomography (CT), magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography, ultrasonography, digital subtraction angiography, and endoscopy, have been performed to evaluate GISTs. CT is most frequently used for diagnosis, staging, surveillance, and response monitoring during molecularly targeted therapy in clinical practice. The diagnosis of GISTs is sometimes challenging because of the diverse imaging findings, such as anatomical location (esophagus, stomach, duodenum, small bowel, colorectum, appendix, and peritoneum), growth pattern, and enhancement pattern as well as the presence of necrosis, calcification, ulceration, early venous return, and metastasis. Imaging findings of GISTs treated with antineoplastic agents are quite different from those of other neoplasms (e.g. adenocarcinomas) because only subtle changes in size are seen even in responsive lesions. Furthermore, the recurrence pattern of GISTs is different from that of other neoplasms. This review discusses the advantages and disadvantages of each imaging modality, describes imaging findings obtained before and after treatment, presents a few cases of complicated GISTs, and discusses recent investigations performed using CT and MRI to predict histological risk grade, gene mutations, and patient outcomes.
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Affiliation(s)
- Akitoshi Inoue
- Department of Radiology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan. .,Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| | - Shinichi Ota
- Department of Radiology, Nagahama Red Cross Hospital, Shiga, Japan
| | - Michio Yamasaki
- Department of Radiology, Kohka Public Hospital, Shiga, Japan
| | - Bolorkhand Batsaikhan
- Graduate School of Human Health Sciences, Department of Radiological Science, Tokyo Metropolitan University, Tokyo, Japan
| | - Akira Furukawa
- Graduate School of Human Health Sciences, Department of Radiological Science, Tokyo Metropolitan University, Tokyo, Japan
| | - Yoshiyuki Watanabe
- Department of Radiology, Shiga University of Medical Science, Seta, Tsukinowa-cho, Otsu, Shiga, 520-2192, Japan
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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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Bartolotta TV, Taibbi A, Randazzo A, Gagliardo C. New frontiers in liver ultrasound: From mono to multi parametricity. World J Gastrointest Oncol 2021; 13:1302-1316. [PMID: 34721768 PMCID: PMC8529919 DOI: 10.4251/wjgo.v13.i10.1302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 07/17/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023] Open
Abstract
Modern liver ultrasonography (US) has become a “one-stop shop” able to provide not only anatomic and morphologic but also functional information about vascularity, stiffness and other various liver tissue properties. Modern US techniques allow a quantitative assessment of various liver diseases. US scanning is no more limited to the visualized plane, but three-dimensional, volumetric acquisition and consequent post-processing are also possible. Further, US scan can be consistently merged and visualized in real time with Computed Tomography and Magnetic Resonance Imaging examinations. Effective and safe microbubble-based contrast agents allow a real time, dynamic study of contrast kinetic for the detection and characterization of focal liver lesions. Ultrasound can be used to guide loco-regional treatment of liver malignancies and to assess tumoral response either to interventional procedures or medical therapies. Microbubbles may also carry and deliver drugs under ultrasound exposure. US plays a crucial role in diagnosing, treating and monitoring focal and diffuse liver disease. On the basis of personal experience and literature data, this paper is aimed to review the main topics involving recent advances in the field of liver ultrasound.
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Affiliation(s)
- Tommaso Vincenzo Bartolotta
- Department of Radiology, University Hospital "Paolo Giaccone", Palermo 90127, Italy
- Radiology Department, Fondazione Istituto G. Giglio Hospital, Cefalù 90015, Italy
| | - Adele Taibbi
- Department of Radiology, University Hospital "Paolo Giaccone", Palermo 90127, Italy
| | - Angelo Randazzo
- Department of Radiology, University Hospital "Paolo Giaccone", Palermo 90127, Italy
| | - Cesare Gagliardo
- Department of Biopathology and Medical and Forensic Biotechnologies, University of Palermo, Palermo 90127, Italy
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17
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Kang B, Yuan X, Wang H, Qin S, Song X, Yu X, Zhang S, Sun C, Zhou Q, Wei Y, Shi F, Yang S, Wang X. Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors. Front Oncol 2021; 11:750875. [PMID: 34631589 PMCID: PMC8496403 DOI: 10.3389/fonc.2021.750875] [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: 07/31/2021] [Accepted: 08/31/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs). Methods Preoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping. Results In the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review. Conclusion The DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.
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Affiliation(s)
- Bing Kang
- Cheeloo College of Medicine, School of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Xianshun Yuan
- Cheeloo College of Medicine, School of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Songnan Qin
- Cheeloo College of Medicine, School of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Xuelin Song
- Department of Radiology, Hospital of Traditional Chinese Medicine of Liaocheng City, Liaocheng, China
| | - Xinxin Yu
- Cheeloo College of Medicine, School of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Shuai Zhang
- School of Medicine, Shandong First Medical University, Jinan, China
| | - Cong Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
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Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. LA RADIOLOGIA MEDICA 2021; 126:1296-1311. [PMID: 34213702 PMCID: PMC8520512 DOI: 10.1007/s11547-021-01389-x] [Citation(s) in RCA: 158] [Impact Index Per Article: 52.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/15/2021] [Indexed: 11/29/2022]
Abstract
Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.
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Affiliation(s)
- Camilla Scapicchio
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Andrea Barucci
- CNR-IFAC Institute of Applied Physics "N. Carrara", 50019, Sesto Fiorentino, Italy
| | - Dania Cioni
- Academic Radiology, Department of Surgical, Medical, Molecular Pathology and Emergency Medicine, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Monserrato (Cagliari),Cagliari, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122, Milano, Italy
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Ao W, Cheng G, Lin B, Yang R, Liu X, Zhou S, Wang W, Fang Z, Tian F, Yang G, Wang J. A novel CT-based radiomic nomogram for predicting the recurrence and metastasis of gastric stromal tumors. Am J Cancer Res 2021; 11:3123-3134. [PMID: 34249449 PMCID: PMC8263673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/17/2021] [Indexed: 06/13/2023] Open
Abstract
Our study aimed to explore the value of applying the CT-based radiomic nomogram for predicting recurrence and/or metastasis (RM) of gastric stromal tumors (GSTs). During the past ten years, a total of 236 patients with GST were analyzed retrospectively. According to the postoperative follow-up classification, the patients were divided into two groups, namely non-recurrence/metastasis group (non-RM) and RM group. All the cases were randomly divided into primary cohort and validation cohort according to the ratio of 7:3. Standardized CT images were segmented by radiologists using ITK-SNAP software manually. Texture features were extracted from all segmented lesions, then radiomic features were selected and the radiomic nomogram was built using least absolute shrinkage and selection operator (LASSO) method. The clinical features with the greatest correlation with RM of GST were selected by univariate analysis, and used as parameters to build the clinical feature model. Eventually, model of radiomic and clinical features were fitted to construct the clinical + radiomic feature model. The performance of each model was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). A total of 1223 features were extracted from all the segmentation regions of each case, and features were selected via the least absolute shrinkage and LASSO binary logistic regression model. After deletion of redundant features, four key features were obtained, which were used as the parameters to build a radiomic signature. The AUCs of radiomic nomogram in primary cohort and validation cohort were 0.816 and 0.946, respectively. The AUCs of clinical + radiomic feature model in primary cohort and validation cohort were 0.833 and 0.937, respectively. Using DeLong test, the differences of AUC values between radiomic nomogram and clinical + radiomic feature model in primary cohort (P = 0.840) and validation cohort (P = 0.857) were not statistically significant. To sum up, CT-based radiomic nomogram is of great potential in predicting the RM of GST non-invasively before operation.
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Affiliation(s)
- Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang ProvinceHangzhou, Zhejiang, China
| | | | - Bin Lin
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of MedicineHangzhou, Zhejiang, China
| | - Rong Yang
- Department of Radiology, First Affiliated Hospital, Zhejiang University School of MedicineHangzhou, Zhejiang, China
| | - Xuebin Liu
- Department of Radiology, First Affiliated Hospital, Zhejiang University School of MedicineHangzhou, Zhejiang, China
| | - Sheng Zhou
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese MedicineLanzhou, Gansu, China
| | - Wenqi Wang
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese MedicineLanzhou, Gansu, China
| | | | - Fengjuan Tian
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineHangzhou, Zhejiang, China
| | - Guangzhao Yang
- Department of Radiology, Tongde Hospital of Zhejiang ProvinceHangzhou, Zhejiang, China
| | - Jian Wang
- Department of Radiology, Tongde Hospital of Zhejiang ProvinceHangzhou, Zhejiang, China
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