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Marsilla J, Weiss J, Ye XY, Welch M, Milosevic M, Lyng H, Hompland T, Bruheim K, Tadic T, Haibe-Kains B, Han K. A T2-weighted MRI-based radiomic signature for disease-free survival in locally advanced cervical cancer following chemoradiation: An international, multicentre study. Radiother Oncol 2024; 199:110463. [PMID: 39067707 DOI: 10.1016/j.radonc.2024.110463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 07/30/2024]
Abstract
INTRODUCTION To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based radiomic signature associated with disease-free survival (DFS) in locally advanced cervical cancer. MATERIALS AND METHODS The study comprised a training dataset of 132 patients (93 Norwegian; 39 The Cancer Imaging Archive (TCIA) and an independent validation Canadian dataset of 199 patients with FIGO stage IB-IVA cervical cancer treated with chemoradiation. Radiomic features were extracted using PyRadiomics. A radiomic signature was developed based on a multivariable radiomic prognostic model for DFS built using the training dataset, with minimal redundancy maximum relevancy feature selection method. Univariate and multivariable Cox regression analyses were then conducted to examine the association of the derived radiomic signature with DFS. RESULTS A radiomic signature was prognostic for DFS in the training cohort (Norwegian hazard ratio [HR] 5.54, p = 0.002; TCIA HR 3.59, p = 0.04). The radiomic signature remained independently associated with DFS (HR 3.70, p = 0.004) when adjusted for stage and tumor volume. The radiomic signature was also prognostic for DFS in the validation cohort, both on univariate analysis (HR 2.22, p = 0.003), and multivariable analysis adjusted for stage and tumor volume (HR 1.84, p = 0.04). The 4-year DFS rates of patients with radiomic signature score > 0 vs ≤ 0 were 48.2 % vs 87.9 %, and 56.4 % vs 80.8 % for training and validation cohorts respectively. CONCLUSION An MRI-based radiomic signature can be used as a prognostic biomarker for DFS in patients with locally advanced cervical cancer undergoing chemoradiation.
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Affiliation(s)
- Joseph Marsilla
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Jessica Weiss
- Department of Biostatistics, University Health Network, Toronto, Ontario, Canada
| | - Xiang Y Ye
- Department of Biostatistics, University Health Network, Toronto, Ontario, Canada
| | - Mattea Welch
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada; Cancer Digital Intelligence Program, University Health Network, Toronto, Ontario, Canada
| | - Michael Milosevic
- Radiation Medicine Program, Princess Margaret Cancer Center, University Health Network, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Heidi Lyng
- Department of Radiation Biology, Oslo University Hospital, Oslo, Norway
| | - Tord Hompland
- Department of Physics, University of Oslo, Oslo, Norway
| | - Kjersti Bruheim
- Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Center, University Health Network, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada; Cancer Digital Intelligence Program, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
| | - Kathy Han
- Radiation Medicine Program, Princess Margaret Cancer Center, University Health Network, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
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Li B, Zhu J, Wang Y, Xu Y, Gao Z, Shi H, Nie P, Zhang J, Zhuang Y, Wang Z, Yang G. Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study. Cancer Imaging 2024; 24:103. [PMID: 39107799 PMCID: PMC11302839 DOI: 10.1186/s40644-024-00744-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 07/24/2024] [Indexed: 08/10/2024] Open
Abstract
OBJECTIVES To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC). METHODS Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index). RESULTS Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients. CONCLUSIONS The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.
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Affiliation(s)
- Ben Li
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
- School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
| | - Jie Zhu
- Department of Scientific Research Management and Foreign Affairs, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yanmei Wang
- GE Healthcare China, Pudong New Town, Shanghai, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, China
| | - Zhaisong Gao
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Hailei Shi
- Department of Pathology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao, 266003, Shandong, China
| | - Ju Zhang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Yuan Zhuang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China
| | - Zhenguang Wang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
| | - Guangjie Yang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao, 266061, Shandong, China.
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Cai W, Guo K, Chen Y, Shi Y, Chen J. Sub-regional CT Radiomics for the Prediction of Ki-67 Proliferation Index in Gastrointestinal Stromal Tumors: A Multi-center Study. Acad Radiol 2024:S1076-6332(24)00421-5. [PMID: 39033048 DOI: 10.1016/j.acra.2024.06.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/18/2024] [Accepted: 06/22/2024] [Indexed: 07/23/2024]
Abstract
RATIONALE AND OBJECTIVES The objective was to assess and examine radiomics models derived from contrast-enhanced CT for their predictive capacity using the sub-regional radiomics regarding the Ki-67 proliferation index (PI) in patients with pathologically confirmed gastrointestinal stromal tumors (GIST). METHODS In this retrospective study, a total of 412 GIST patients across three institutions (223 from center 1, 106 from center 2, and 83 from center 3) was enrolled. Radiomic features were derived from various sub-regions of the tumor region of interest employing the K-means approach. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify features correlated with Ki-67 PI level in GIST patients. A support vector machine (SVM) model was then constructed to predict the high level of Ki-67 (Ki-67 index >8%), drawing on the radiomics features from each sub-region within the training cohort. RESULTS After features selection process, 6, 9, 9, 7 features were obtained to construct SVM models based on sub-region 1, 2, 3 and the entire tumor, respectively. Among different models, the model developed by the sub-region 1 achieved an area under the receiver operating characteristic curve (AUC) of 0.880 (95% confidence interval [CI]: 0.830 to 0.919), 0.852 (95% CI: 0.770-0.914), 0.799 (95% CI: 0.697-0.879) in the training, external test set 1, and 2, respectively. CONCLUSION The results of the present study suggested that SVM model based on the sub-regional radiomics features had the potential of predicting Ki-67 PI level in patients with GIST.
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Affiliation(s)
- Wemin Cai
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, China; Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Kun Guo
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yongxian Chen
- Department of Chest cancer, Xiamen Second People's Hospital, Xiamen 36100, China
| | - Yubo Shi
- Department of Pulmonary, Yueqing People's Hospital, Wenzhou 325000, China
| | - Junkai Chen
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou 325000, 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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Xie Z, Suo S, Zhang W, Zhang Q, Dai Y, Song Y, Li X, Zhou Y. Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases. Eur Radiol 2024; 34:2223-2232. [PMID: 37773213 PMCID: PMC10957607 DOI: 10.1007/s00330-023-10249-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/12/2023] [Accepted: 07/23/2023] [Indexed: 10/01/2023]
Abstract
OBJECTIVES To evaluate and analyze radiomics models based on non-contrast-enhanced computed tomography (CT) and different phases of contrast-enhanced CT in predicting Ki-67 proliferation index (PI) among patients with pathologically confirmed gastrointestinal stromal tumors (GISTs). METHODS A total of 383 patients with pathologically proven GIST were divided into a training set (n = 218, vendor 1) and 2 validation sets (n = 96, vendor 2; n = 69, vendors 3-5). Radiomics features extracted from the most recent non-contrast-enhanced and three contrast-enhanced CT scan prior to pathological examination. Random forest models were trained for each phase to predict tumors with high Ki-67 proliferation index (Ki-67>10%) and were evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics on the validation sets. RESULTS Out of 107 radiomics features extracted from each phase of CT images, four were selected for analysis. The model trained using the non-contrast-enhanced phase achieved an AUC of 0.792 in the training set and 0.822 and 0.711 in the two validation sets, similar to models trained on different contrast-enhanced phases (p > 0.05). Several relevant features, including NGTDM Busyness and tumor size, remained predictive in non-contrast-enhanced and different contrast-enhanced images. CONCLUSION The results of this study indicate that a radiomics model based on non-contrast-enhanced CT matches that of models based on different phases of contrast-enhanced CT in predicting the Ki-67 PI of GIST. GIST may exhibit similar radiological patterns irrespective of the use of contrast agent, and such radiomics features may help quantify these patterns to predict Ki-67 PI of GISTs. CLINICAL RELEVANCE STATEMENT GIST may exhibit similar radiomics patterns irrespective of contrast agent; thus, radiomics models based on non-contrast-enhanced CT could be an alternative for risk stratification in GIST patients with contraindication to contrast agent. KEY POINTS • Performance of radiomics models in predicting Ki-67 proliferation based on different CT phases is evaluated. • Non-contrast-enhanced CT-based radiomics models performed similarly to contrast-enhanced CT in risk stratification in GIST patients. • NGTDM Busyness remains stable to contrast agents in GISTs in radiomics models.
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Affiliation(s)
- Zhenhui Xie
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shiteng Suo
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wang Zhang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qingwei Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Xiaobo Li
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Zhu X, He Y, Wang M, Shu Y, Lai X, Gan C, Liu L. Intratumoral and Peritumoral Multiparametric MRI-Based Radiomics Signature for Preoperative Prediction of Ki-67 Proliferation Status in Glioblastoma: A Two-Center Study. Acad Radiol 2024; 31:1560-1571. [PMID: 37865602 DOI: 10.1016/j.acra.2023.09.010] [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: 06/28/2023] [Revised: 08/28/2023] [Accepted: 09/08/2023] [Indexed: 10/23/2023]
Abstract
RATIONALE AND OBJECTIVES To assess the predictive ability of intratumoral and peritumoral multiparametric magnetic resonance imaging (MRI)-based radiomics signature (RS) for preoperative prediction of Ki-67 proliferation status in glioblastoma. MATERIALS AND METHODS: A total of 205 patients with glioblastoma at two institutions were retrospectively analyzed. Data from institution 1 (n = 158) were used to develop the predictive model, and as an internal test dataset, data from institution 2 (n = 47) constitute the external test dataset. Feature selection was performed using spearman correlation coefficient, univariate ranking method, and the least absolute shrinkage and selection operator algorithm. RSs were established using a logistic regression algorithm. The predictive performance of the RSs was assessed using calibration curve, decision curve analysis (DCA), and area under the curve (AUC). RESULTS In the RSs based on single-parametric (contrast-enhanced T1-weighted image, T2-weighted image, or apparent diffusion coefficient maps), the AUCs of intratumoral, peritumoral, and combined area (intratumoral and peritumoral) were 0.60-0.67, with no significant difference among them. The RSs that using multiparametric features (integrating the previously mentioned three sequences) showed improved AUC compared to the single-parametric RSs; AUC reached 0.75-0.89. Among them, the multiparametric RS based on radiomics features of the combined area (Multi-Com) exhibited the highest performance, with an internal test dataset AUC of 0.89 (95% confidence interval (CI) 0.75-1.00) and an external test dataset AUC of 0.88 (95% CI 0.78-0.97). The calibration curve and DCA display RS (Multi-Com) have good calibration ability and clinical applicability. CONCLUSION The multiparametric MRI-based RS combining intratumoral and peritumoral features can serve as a noninvasive and effective tool for preoperative assessment of Ki-67 proliferation status in glioblastoma.
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Affiliation(s)
- Xuechao Zhu
- Departments of Radiology, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China (X.Z., Y.S., C.G., L.L.)
| | - Yulin He
- Departments of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (Y.H., M.W., X.L.)
| | - Mengting Wang
- Departments of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (Y.H., M.W., X.L.)
| | - Yuqin Shu
- Departments of Radiology, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China (X.Z., Y.S., C.G., L.L.)
| | - Xunfu Lai
- Departments of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (Y.H., M.W., X.L.)
| | - Cuihua Gan
- Departments of Radiology, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China (X.Z., Y.S., C.G., L.L.)
| | - Lan Liu
- Departments of Radiology, Jiangxi Tumor Hospital, Nanchang, Jiangxi, China (X.Z., Y.S., C.G., L.L.).
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Luo X, Zheng R, Zhang J, He J, Luo W, Jiang Z, Li Q. CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1329801. [PMID: 38384802 PMCID: PMC10879429 DOI: 10.3389/fonc.2024.1329801] [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: 10/29/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Background Radiomics, an emerging field, presents a promising avenue for the accurate prediction of biomarkers in different solid cancers. Lung cancer remains a significant global health challenge, contributing substantially to cancer-related mortality. Accurate assessment of Ki-67, a marker reflecting cellular proliferation, is crucial for evaluating tumor aggressiveness and treatment responsiveness, particularly in non-small cell lung cancer (NSCLC). Methods A systematic review and meta-analysis conducted following the preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guidelines. Two authors independently conducted a literature search until September 23, 2023, in PubMed, Embase, and Web of Science. The focus was on identifying radiomics studies that predict Ki-67 expression in lung cancer. We evaluated quality using both Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. For statistical analysis in the meta-analysis, we used STATA 14.2 to assess sensitivity, specificity, heterogeneity, and diagnostic values. Results Ten retrospective studies were pooled in the meta-analysis. The findings demonstrated that the use of computed tomography (CT) scan-based radiomics for predicting Ki-67 expression in lung cancer exhibited encouraging diagnostic performance. Pooled sensitivity, specificity, and area under the curve (AUC) in training cohorts were 0.78, 0.81, and 0.85, respectively. In validation cohorts, these values were 0.78, 0.70, and 0.81. Quality assessment using QUADAS-2 and RQS indicated generally acceptable study quality. Heterogeneity in training cohorts, attributed to factors like contrast-enhanced CT scans and specific Ki-67 thresholds, was observed. Notably, publication bias was detected in the training cohort, indicating that positive results are more likely to be published than non-significant or negative results. Thus, journals are encouraged to publish negative results as well. Conclusion In summary, CT-based radiomics exhibit promise in predicting Ki-67 expression in lung cancer. While the results suggest potential clinical utility, additional research efforts should concentrate on enhancing diagnostic accuracy. This could pave the way for the integration of radiomics methods as a less invasive alternative to current procedures like biopsy and surgery in the assessment of Ki-67 expression.
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Affiliation(s)
- Xinmin Luo
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Renying Zheng
- Department of Oncology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Jiao Zhang
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Juan He
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Wei Luo
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Zhi Jiang
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Qiang Li
- Department of Radiology, Yuechi County Traditional Chinese Medicine Hospital in Sichuan Province, Guang’an, Sichuan, 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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Liu Y, He C, Fang W, Peng L, Shi F, Xia Y, Zhou Q, Zhang R, Li C. Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT. Eur Radiol 2023; 33:7609-7617. [PMID: 37266658 DOI: 10.1007/s00330-023-09727-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVE To study the value of radiomics models based on plain and multiphase contrast-enhanced CT to predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). METHODS A total of 215 patients with GISTs were retrospectively analyzed, including 150 patients in one hospital as the training set and 65 patients in another hospital as the external verification set. The tumor at the largest level of CT images was delineated as the region of interest (ROI). The maximum diameter of the ROI was defined as the tumor size. A total of 851 radiomics features were extracted from each ROI by 3D Slicer Radiomics. After dimensionality reduction, three machine learning classification algorithms including logistic regression (LR), random forest (RF), and support vector machine (SVM) were used for Ki-67 expression prediction. Using a multivariable logistic model, a nomogram was established to predict the expression of Ki-67 individually. RESULTS Delong tests showed that the SVM models had the highest accuracy in the arterial phase (Z value 0.217-1.139) and venous phase (Z value 0.022-1.396). For the plain phase, LR and SVM models had the highest accuracy (Z value 0.874-1.824, 1.139-1.763). For the delayed phase, LR models had the highest accuracy (Z value 0.056-1.824). For the combined phase, RF models had the highest accuracy (Z value 0.232-1.978). There was no significant difference among the above models for KI-67 expression prediction (Z value 0.022-1.978). A nomogram was developed with a C-index of 0.913 (95% CI, 0.878 to 0.956). CONCLUSIONS Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. CLINICAL RELEVANCE STATEMENT CT radiomics could accurately predict the expression of Ki-67 in GIST, which has a great clinical value in reflecting the proliferative activity of tumor cells and helping determine whether a patient is suitable for adjuvant therapy with imatinib. KEY POINTS • Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. • For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. • A radiomics nomogram was developed to allow personalized preoperative evaluation with high accuracy.
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Affiliation(s)
- Yun Liu
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - ChangYin He
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Weidong Fang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Peng
- Department of Pathology, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Qing Zhou
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Ronggui Zhang
- Department of Urology, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China.
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China.
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Yao X, Ao W, Zhu X, Tian S, Han X, Hu J, Xu W, Mao G, Deng S. A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study. BMC Med Imaging 2023; 23:168. [PMID: 37891502 PMCID: PMC10612175 DOI: 10.1186/s12880-023-01123-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. METHODS Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. RESULTS Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. CONCLUSION The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.
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Affiliation(s)
- Xiuzhen Yao
- Department of Ultrasound, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, Hangzhou, Zhejiang Province, 310012, China
| | - Xiandi Zhu
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, Hangzhou, Zhejiang Province, 310012, China
| | - Shuyuan Tian
- Department of Ultrasound, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Xiaoyu Han
- Department of Pathology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Jinwen Hu
- Department of Radiology, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wenjie Xu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Guoqun Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, Hangzhou, Zhejiang Province, 310012, China.
| | - Shuitang Deng
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234 Gucui Road, Hangzhou, Zhejiang Province, 310012, China.
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Zhang QW, Zhang RY, Yan ZB, Zhao YX, Wang XY, Jin JZ, Qiu QX, Chen JJ, Xie ZH, Lin J, Cao H, Zhou Y, Chen HM, Li XB. Personalized radiomics signature to screen for KIT-11 mutation genotypes among patients with gastrointestinal stromal tumors: a retrospective multicenter study. J Transl Med 2023; 21:726. [PMID: 37845765 PMCID: PMC10577986 DOI: 10.1186/s12967-023-04520-w] [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: 06/30/2023] [Accepted: 09/11/2023] [Indexed: 10/18/2023] Open
Abstract
OBJECTIVES Gastrointestinal stromal tumors (GISTs) carrying different KIT exon 11 (KIT-11) mutations exhibit varying prognoses and responses to Imatinib. Herein, we aimed to determine whether computed tomography (CT) radiomics can accurately stratify KIT-11 mutation genotypes to benefit Imatinib therapy and GISTs monitoring. METHODS Overall, 1143 GISTs from 3 independent centers were separated into a training cohort (TC) or validation cohort (VC). In addition, the KIT-11 mutation genotype was classified into 4 categories: no KIT-11 mutation (K11-NM), point mutations or duplications (K11-PM/D), KIT-11 557/558 deletions (K11-557/558D), and KIT-11 deletion without codons 557/558 involvement (K11-D). Subsequently, radiomic signatures (RS) were generated based on the arterial phase of contrast CT, which were then developed as KIT-11 mutation predictors using 1408 quantitative image features and LASSO regression analysis, with further evaluation of its predictive capability. RESULTS The TC AUCs for K11-NM, K11-PM/D, K11-557/558D, and K11-D ranged from 0.848 (95% CI 0.812-0.884), 0.759 (95% CI 0.722-0.797), 0.956 (95% CI 0.938-0.974), and 0.876 (95% CI 0.844-0.908), whereas the VC AUCs ranged from 0.723 (95% CI 0.660-0.786), 0.688 (95% CI 0.643-0.732), 0.870 (95% CI 0.824-0.918), and 0.830 (95% CI 0.780-0.878). Macro-weighted AUCs for the KIT-11 mutant genotype ranged from 0.838 (95% CI 0.820-0.855) in the TC to 0.758 (95% CI 0.758-0.784) in VC. TC had an overall accuracy of 0.694 (95%CI 0.660-0.729) for RS-based predictions of the KIT-11 mutant genotype, whereas VC had an accuracy of 0.637 (95%CI 0.595-0.679). CONCLUSIONS CT radiomics signature exhibited good predictive performance in estimating the KIT-11 mutation genotype, especially in prediction of K11-557/558D genotype. RS-based classification of K11-NM, K11-557/558D, and K11-D patients may be an indication for choice of Imatinib therapy.
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Affiliation(s)
- Qing-Wei Zhang
- Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ran-Ying Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Zhi-Bo Yan
- Department of General Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Yu-Xuan Zhao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Xin-Yuan Wang
- Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jing-Zheng Jin
- Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qi-Xuan Qiu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Jie-Jun Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Zhen-Hui Xie
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd., Shanghai, 200127, China
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Hui Cao
- Department of Gastrointestinal Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, No. 160 Pujian Road, Shanghai, 200127, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 160, Pujian Rd., Shanghai, 200127, China.
| | - Hui-Min Chen
- Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Xiao-Bo Li
- Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, State Key Laboratory for Oncogenes and Related Genes, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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12
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Guo C, Zhou H, Chen X, Feng Z. Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors. Heliyon 2023; 9:e20983. [PMID: 37876490 PMCID: PMC10590931 DOI: 10.1016/j.heliyon.2023.e20983] [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: 02/21/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/26/2023] Open
Abstract
Background KIT exon 11 mutation in gastrointestinal stromal tumors (GISTs) is associated with treatment strategies. However, few studies have shown the role of imaging-based texture analysis in KIT exon 11 mutation in GISTs. In this study, we aimed to show the association between computed tomography (CT)-based texture features and KIT exon 11 mutation. Methods Ninety-five GISTs confirmed by surgery and identified with mutational genotype of KIT were included in this study. By amplifying the samples using over-sampling technique, a total of 183 region of interest (ROI) segments were extracted from 63 patients as training cohort. The 63 new ROI segments were extracted from the 63 patients as internal validation cohort. Thirty-two patients who underwent KIT exon 11 mutation test during 2021-2023 was selected as external validation cohort. The textural parameters were evaluated both in training cohort and validation cohort. Least absolute shrinkage and selection operator (LASSO) algorithms and logistic regression analysis were used to select the discriminant features. Results Three of textural features were obtained using LASSO analysis. Logistic regression analysis showed that patients' age, tumor location and radiomics features were significantly associated with KIT exon 11 mutation (p < 0.05). A nomogram was developed based on the associated factors. The area under the curve (AUC) of clinical features, radiomics features and their combination in training cohort was 0.687 (95 % CI: 0.604-0.771), 0.829 (95 % CI: 0.768-0.890) and 0.874 (95 % CI: 0.822-0.926), respectively. The AUC of radiomics features in internal validation cohort and external cohort was 0.880 (95 % CI: 0.796-0.964) and 0.827 (95%CI: 0.667-0.987), respectively. Conclusion The CT texture-based model can be used to predict KIT exon 11 mutation in GISTs.
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Affiliation(s)
- Chuangen Guo
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China
| | - Hao Zhou
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, 155 hanzhong Road, Nanjing, 210029, China
| | - Xiao Chen
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, 155 hanzhong Road, Nanjing, 210029, China
| | - Zhan Feng
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China
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Galluzzo A, Boccioli S, Danti G, De Muzio F, Gabelloni M, Fusco R, Borgheresi A, Granata V, Giovagnoni A, Gandolfo N, Miele V. Radiomics in gastrointestinal stromal tumours: an up-to-date review. Jpn J Radiol 2023; 41:1051-1061. [PMID: 37171755 DOI: 10.1007/s11604-023-01441-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/29/2023] [Indexed: 05/13/2023]
Abstract
Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.
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Affiliation(s)
- Antonio Galluzzo
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Sofia Boccioli
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, Pisa, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126, Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria Delle Marche", Via Conca 71, 60126, Ancona, Italy
| | - Vincenza Granata
- Department of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione, Pascale-IRCCS di Napoli", 80131, Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126, Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria Delle Marche", Via Conca 71, 60126, Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149, Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
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14
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Zhang Y, Yue X, Zhang P, Zhang Y, Wu L, Diao N, Ma G, Lu Y, Ma L, Tao K, Li Q, Han P. Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study. Front Oncol 2023; 13:1193010. [PMID: 37645430 PMCID: PMC10461453 DOI: 10.3389/fonc.2023.1193010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/26/2023] [Indexed: 08/31/2023] Open
Abstract
Objective gastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs. Methods Overall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1. Results The Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621-0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore. Conclusion The radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance.
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Affiliation(s)
- Yu Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiaofei Yue
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Peng Zhang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuying Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Linxia Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Nan Diao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Guina Ma
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yuting Lu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ling Ma
- He Kang Corporate Management (SH) Co. Ltd., Shanghai, China
| | - Kaixiong Tao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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15
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Jiang J, Wei J, Zhu Y, Wei L, Wei X, Tian H, Zhang L, Wang T, Cheng Y, Zhao Q, Sun Z, Du H, Huang Y, Liu H, Li Y. Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study. Eur Radiol 2023; 33:970-980. [PMID: 36066731 DOI: 10.1007/s00330-022-09116-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 07/11/2022] [Accepted: 08/12/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To develop a clot-based radiomics model using CT imaging radiomic features and machine learning to identify cardioembolic (CE) stroke before mechanical thrombectomy (MTB) in patients with acute ischemic stroke (AIS). MATERIALS AND METHODS This retrospective four-center study consecutively included 403 patients with AIS who sequentially underwent CT and MTB between April 2016 and July 2021. These were grouped into training, testing, and external validation cohorts. Thrombus-extracted radiomic features and basic information were gathered to construct a machine learning model to predict CE stroke. The radiological characteristics and basic information were used to build a routine radiological model. A combined radiomics and radiological features model was also developed. The performances of all models were evaluated and compared in the validation cohort. A histological analysis helped further assess the proposed model in all patients. RESULTS The radiomics model yielded an area under the curve (AUC) of 0.838 (95% confidence interval [CI], 0.771-0.891) for predicting CE stroke in the validation cohort, significantly higher than the radiological model (AUC, 0.713; 95% CI, 0.636-0.781; p = 0.007) but similar to the combined model (AUC, 0.855; 95% CI, 0.791-0.906; p = 0.14). The thrombus radiomic features achieved stronger correlations with red blood cells (|rmax|, 0.74 vs. 0.32) and fibrin and platelet (|rmax|, 0.68 vs. 0.18) than radiological characteristics. CONCLUSION The proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method. KEY POINTS • Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis. • The proposed CT imaging-based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007). • Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |rmax|, 0.74; fibrin and platelet, |rmax|, 0.68) than routine radiological characteristics.
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Affiliation(s)
- Jingxuan Jiang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China.,Department of Radiology, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Jianyong Wei
- Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Yueqi Zhu
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Liming Wei
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Xiaoer Wei
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Hao Tian
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Lei Zhang
- Department of Radiology, Wuxi Second People's Hospital, Wuxi, 214000, China
| | - Tianle Wang
- Department of Radiology, Affiliated No. 1 People's Hospital of Nantong University, Nantong, 226001, China
| | - Yue Cheng
- Department of Radiology, Wuxi Second People's Hospital, Wuxi, 214000, China
| | - Qianqian Zhao
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Zheng Sun
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Haiyan Du
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Yu Huang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Hui Liu
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Yuehua Li
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China.
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Zheng M, Chen Q, Ge Y, Yang L, Tian Y, Liu C, Wang P, Deng K. Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors. Med Phys 2023; 50:947-957. [PMID: 36273307 DOI: 10.1002/mp.16042] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 10/27/2022] [Accepted: 10/27/2022] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast-enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors. METHODS One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast-enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad-score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad-score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model. RESULTS The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80-0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77-1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96-1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88-1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88-0.99) in the training cohort (p < 0.05). The DCA demonstrated that the combined radiomics nomogram provided superior clinical usefulness than clinical factor model and radiomics model. CONCLUSIONS The CT-based radiomics nomogram combining Rad-score and clinical factors exhibits excellent predictive capability for differentiating parotid PA from AL, which might hold promise in assisting radiologists and clinicians in the exact differential diagnosis and formulation of appropriate treatment strategy.
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Affiliation(s)
- Menglong Zheng
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Qi Chen
- Department of Radiology, Kunshan Third People's Hospital, Kunshan, Jiangsu, China
| | | | - Liping Yang
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yulong Tian
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Chang Liu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
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Wu S, Zhang R, Wan X, Yao T, Zhang Q, Chen X, Fan X. Chest computed tomography radiomics to predict the outcome for patients with COVID-19 at an early stage. Diagn Interv Radiol 2023; 29:91-102. [PMID: 36960545 PMCID: PMC10679604 DOI: 10.5152/dir.2022.21576] [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: 06/18/2021] [Accepted: 12/27/2021] [Indexed: 01/14/2023]
Abstract
PURPOSE Early monitoring and intervention for patients with novel coronavirus disease-2019 (COVID-19) will benefit both patients and the medical system. Chest computed tomography (CT) radiomics provide more information regarding the prognosis of COVID-19. METHODS A total of 833 quantitative features of 157 COVID-19 patients in the hospital were extracted. By filtering unstable features using the least absolute shrinkage and selection operator algorithm, a radiomic signature was built to predict the prognosis of COVID-19 pneumonia. The main outcomes were the area under the curve (AUC) of the prediction models for death, clinical stage, and complications. Internal validation was performed using the bootstrapping validation technique. RESULTS The AUC of each model demonstrated good predictive accuracy [death, 0.846; stage, 0.918; complication, 0.919; acute respiratory distress syndrome (ARDS), 0.852]. After finding the optimal cut-off for each outcome, the respective accuracy, sensitivity, and specificity were 0.854, 0.700, and 0.864 for the prediction of the death of COVID-19 patients; 0.814, 0.949, and 0.732 for the prediction of a higher stage of COVID-19; 0.846, 0.920, and 0.832 for the prediction of complications of COVID-19 patients; and 0.814, 0.818, and 0.814 for ARDS of COVID-19 patients. The AUCs after bootstrapping were 0.846 [95% confidence interval (CI): 0.844-0.848] for the death prediction model, 0.919 (95% CI: 0.917-0.922) for the stage prediction model, 0.919 (95% CI: 0.916-0.921) for the complication prediction model, and 0.853 (95% CI: 0.852-0.0.855) for the ARDS prediction model in the internal validation. Based on the decision curve analysis, the radiomics nomogram was clinically significant and useful. CONCLUSION The radiomic signature from the chest CT was significantly associated with the prognosis of COVID-19. A radiomic signature model achieved maximum accuracy in the prognosis prediction. Although our results provide vital insights into the prognosis of COVID-19, they need to be verified by large samples in multiple centers.
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Affiliation(s)
- Shan Wu
- Department of Endoscopy, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ranying Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai, China
| | - Xinjian Wan
- Department of Endoscopy, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Yao
- Department of Infectious Disease, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingwei Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Xiaohua Chen
- Department of Infectious Disease, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaohong Fan
- Department of Respiratory Medicine, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Department of Respiratory and Critical Care, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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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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Wei Y, Lu Z, Ren Y. Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors. Technol Cancer Res Treat 2023; 22:15330338231181260. [PMID: 37296525 PMCID: PMC10272646 DOI: 10.1177/15330338231181260] [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: 01/01/2023] [Revised: 04/28/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
OBJECTIVES To establish and validate a radiomics nomogram model for preoperative prediction of KIT exon 9 mutation status in patients with gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS Eighty-seven patients with pathologically confirmed GISTs were retrospectively enrolled in this study. Imaging and clinicopathological data were collected and randomly assigned to the training set (n = 60) and test set (n = 27) at a ratio of 7:3. Based on contrast-enhanced CT (CE-CT) arterial and venous phase images, the region of interest (ROI) of the tumors were manually drawn layer by layer, and the radiomics features were extracted. The intra-class correlation coefficient (ICC) was used to test the consistency between observers. Least absolute shrinkage and selection operator regression (LASSO) were used to further screen the features. The nomogram of integrated radiomics score (Rad-Score) and clinical risk factors (extra-gastric location and distant metastasis) was drawn on the basis of multivariate logistic regression. The area under the receiver operating characteristic (AUC) curve and decision curve analysis were used to evaluate the predictive efficiency of the nomogram, and the clinical benefits that the decision curve evaluation model may bring to patients. RESULTS The selected radiomics features (arterial phase and venous phase features) were significantly correlated with the KIT exon 9 mutation status of GISTs. The AUC, sensitivity, specificity, and accuracy in the radiomics model were 0.863, 85.7%, 80.4%, and 85.0% for the training group (95% confidence interval [CI]: 0.750-0.938), and 0.883, 88.9%, 83.3%, and 81.5% for the test group (95% CI: 0.701-0.974), respectively. The AUC, sensitivity, specificity, and accuracy in the nomogram model were 0.902 (95% confidence interval [CI]: 0.798-0.964), 85.7%, 86.9%, and 91.7% for the training group, and 0.907 (95% CI: 0.732-0.984), 77.8%, 94.4%, and 88.9% for the test group, respectively. The decision curve showed the clinical application value of the radiomic nomogram. CONCLUSION The radiomics nomogram model based on CE-CT can effectively predict the KIT exon 9 mutation status of GISTs and may be used for selective gene analysis in the future, which is of great significance for the accurate treatment of GISTs.
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Affiliation(s)
- Yuze Wei
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Zaiming Lu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ying Ren
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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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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Huang Z, Zhou P, Li S, Li K. Prediction of the Ki-67 marker index in hepatocellular carcinoma based on Dynamic Contrast-Enhanced Ultrasonography with Sonazoid. Insights Imaging 2022; 13:199. [PMID: 36536262 PMCID: PMC9763522 DOI: 10.1186/s13244-022-01320-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/29/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Ki-67 is widely used as a proliferative and prognostic factor in HCC. This study aimed to analyze the relationship between dynamic contrast-enhanced ultrasonography (DCE-US) parameters and Ki-67 expression. METHODS One hundred and twenty patients with histopathologically confirmed HCC who underwent DCE-US were included in this prospective study. Patients were classified according to the Ki-67 marker index into low Ki-67 (< 10%) (n = 84) and high Ki-67 (≥ 10%) groups (n = 36). Quantitative perfusion parameters were obtained and analyzed. RESULTS Clinicopathological features (pathological grade and microvascular invasion) were significantly different between the high and low Ki-67 expression groups (p = 0.029 and p = 0.020, respectively). In the high Ki-67 expression group, the peak energy (PE) in the arterial phase and fall time (FT) were significantly different between the HCC lesions and distal liver parenchyma (p = 0.016 and p = 0.025, respectively). PE in the Kupffer phase was significantly different between the HCC lesions and the distal liver parenchyma in the low Ki-67 expression group (p = 0.029). The difference in PE in the Kupffer phase between HCC lesions and distal liver parenchyma was significantly different between the high and low Ki-67 expression groups (p = 0.045). The difference in PE in the Kupffer phase between HCC lesions and distal liver parenchyma < - 4.0 × 107 a.u. may contribute to a more accurate diagnosis of the high Ki-67 expression group, and the sensitivity and specificity were 82.9% and 38.7%, respectively. CONCLUSIONS The DCE-US parameters have potential as biomarkers for predicting Ki-67 expression in patients with HCC.
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Affiliation(s)
- Zhe Huang
- grid.412793.a0000 0004 1799 5032Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - PingPing Zhou
- grid.412793.a0000 0004 1799 5032Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - ShanShan Li
- grid.412793.a0000 0004 1799 5032Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kaiyan Li
- grid.412793.a0000 0004 1799 5032Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures. Diagnostics (Basel) 2022; 12:diagnostics12092175. [PMID: 36140576 PMCID: PMC9497787 DOI: 10.3390/diagnostics12092175] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/04/2022] [Accepted: 09/05/2022] [Indexed: 01/27/2023] Open
Abstract
Objectives: Histopathological tumor grade and Ki-67 expression level are key aspects concerning the prognosis of patients with hepatocellular carcinoma (HCC) lesions. The aim of this study was to investigate whether the radiomics model derived from Sonazoid contrast-enhanced (S-CEUS) images could predict histological grades and Ki-67 expression of HCC lesions. Methods: This prospective study included 101 (training cohort: n = 71; validation cohort: n = 30) patients with surgical resection and histopathologically confirmed HCC lesions. Radiomics features were extracted from the B mode and Kupffer phase of S-CEUS images. Maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) were used for feature selection, and a stepwise multivariate logit regression model was trained for prediction. Model accuracy, sensitivity, and specificity in both training and testing datasets were used to evaluate performance. Results: The prediction model derived from Kupffer phase images (CE-model) displayed a significantly better performance in the prediction of stage III HCC patients, with an area under the receiver operating characteristic curve (AUROC) of 0.908 in the training dataset and 0.792 in the testing set. The CE-model demonstrated generalizability in identifying HCC patients with elevated Ki-67 expression (>10%) with a training AUROC of 0.873 and testing AUROC of 0.768, with noticeably higher specificity of 92.3% and 80.0% in training and testing datasets, respectively. Conclusions: The radiomics model constructed from the Kupffer phase of S-CEUS images has the potential for predicting Ki-67 expression and histological stages in patients with HCC.
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Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features. Radiol Med 2022; 127:1085-1097. [DOI: 10.1007/s11547-022-01549-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/18/2022] [Indexed: 10/14/2022]
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Zhu MP, Ding QL, Xu JX, Jiang CY, Wang J, Wang C, Yu RS. Building contrast-enhanced CT-based models for preoperatively predicting malignant potential and Ki67 expression of small intestine gastrointestinal stromal tumors (GISTs). ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3161-3173. [PMID: 33765174 DOI: 10.1007/s00261-021-03040-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/02/2021] [Accepted: 03/05/2021] [Indexed: 01/18/2023]
Abstract
PURPOSE To assess contrast-enhanced computed tomography (CE-CT) features for predicting malignant potential and Ki67 in small intestinal gastrointestinal stromal tumors (GISTs) and the correlation between them. METHODS We retrospectively analyzed the pathological and imaging data for 123 patients (55 male/68 female, mean age: 57.2 years) with a histopathological diagnosis of small intestine GISTs who received CE-CT followed by curative surgery from May 2009 to August 2019. According to postoperatively pathological and immunohistochemical results, patients were categorized by malignant potential and the Ki67 index, respectively. CT features were analyzed to be associated with malignant potential or the Ki67 index using univariate analysis, logistic regression and receiver operating curve analysis. Then, we explored the correlation between the Ki67 index and malignant potential by using the Spearman rank correlation. RESULTS Based on univariate and multivariate analysis, a predictive model of malignant potential of small intestine GISTs, consisting of tumor size (p < 0.001) and presence of necrosis (p = 0.033), was developed with the area under the receiver operating curve (AUC) of 0.965 (95% CI, 0.915-0.990; p < 0.001), with 91.53% sensitivity, 96.87% specificity, 96.43% PPV, 92.54% NPV, 94.31% diagnostic accuracy. For high Ki67 expression, a model made up of tumor size (p = 0.051), presence of ulceration (p = 0.054) and metastasis (p = 0.001) may be the best predictive combination with an AUC of 0.785 (95% CI, 0.702-0.854; p < 0.001), 63.33% sensitivity, 76.34% specificity, 46.34% PPV, 86.59% NPV, 73.17% diagnostic accuracy. Ki67 index showed a moderate positive correlation with mitotic count (r = 0.578, p < 0.001), a weak positive correlation with tumor size (r = 0.339, p < 0.001) and with risk stratification (r = 0.364, p < 0.001). CONCLUSION Features on CE-CT could preoperatively predict malignant potential and high Ki67 expression of small intestine GISTs, and Ki67 index may be a promising prognostic factor in predicting the prognosis of small intestine GISTs, independent of the risk stratification system.
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Affiliation(s)
- Miao-Ping Zhu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
- Department of Radiology, Hangzhou Women's Hospital, Hangzhou, China
| | - Qiao-Ling Ding
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Jian-Xia Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chun-Yan Jiang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
- Department of Radiology, People's Hospital of Songyang County, Lishui, China
| | - Jing Wang
- Department of Radiology, Ningbo Medical Center Lihuili Hospital, Ningbo, China
| | - Chao Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China.
| | - Ri-Sheng Yu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China.
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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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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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Arthur A, Johnston EW, Winfield JM, Blackledge MD, Jones RL, Huang PH, Messiou C. Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We? Front Oncol 2022; 12:892620. [PMID: 35847882 PMCID: PMC9286756 DOI: 10.3389/fonc.2022.892620] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/31/2022] [Indexed: 12/13/2022] Open
Abstract
A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.
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Affiliation(s)
- Amani Arthur
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Edward W. Johnston
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Robin L. Jones
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- Division of Clinical Studies, The Institute of Cancer Research, London, United Kingdom
| | - Paul H. Huang
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton, United Kingdom
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
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28
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Zhan PC, Lyu PJ, Li Z, Liu X, Wang HX, Liu NN, Zhang Y, Huang W, Chen Y, Gao JB. CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma. Front Oncol 2022; 12:900478. [PMID: 35795043 PMCID: PMC9252420 DOI: 10.3389/fonc.2022.900478] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/20/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose The study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively. Materials and Methods From February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed. Results Finally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility. Conclusion We developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively.
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Affiliation(s)
- Peng-Chao Zhan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Pei-jie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xing Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-Xia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Na-Na Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenpeng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
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Bi S, Li J, Wang T, Man F, Zhang P, Hou F, Wang H, Hao D. Multi-parametric MRI-based radiomics signature for preoperative prediction of Ki-67 proliferation status in sinonasal malignancies: a two-centre study. Eur Radiol 2022; 32:6933-6942. [PMID: 35687135 DOI: 10.1007/s00330-022-08780-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 03/18/2022] [Accepted: 03/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To assess the predictive ability of a multi-parametric MRI-based radiomics signature (RS) for the preoperative evaluation of Ki-67 proliferation status in sinonasal malignancies. METHODS A total of 128 patients with sinonasal malignancies that underwent multi-parametric MRIs at two medical centres were retrospectively analysed. Data from one medical centre (n = 77) were used to develop the predictive models and data from the other medical centre (n = 51) constitute the test dataset. Clinical data and conventional MRI findings were reviewed to identify significant predictors. Radiomics features were determined using maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms. Subsequently, RSs were established using a logistic regression (LR) algorithm. The predictive performance of RSs was assessed using calibration, decision curve analysis (DCA), accuracy, and AUC. RESULTS No independent predictors of high Ki-67 proliferation were observed based on clinical data and conventional MRI findings. RS-T1, RS-T2, and RS-T1c (contrast enhancement T1WI) were established based on a single-parametric MRI. RS-Combined (combining T1WI, FS-T2WI, and T1c features) was developed based on multi-parametric MRI and achieved an AUC and accuracy of 0.852 (0.733-0.971) and 86.3%, respectively, on the test dataset. The calibration curve and DCA demonstrated an improved fitness and benefits in clinical practice. CONCLUSIONS A multi-parametric MRI-based RS may be used as a non-invasive, dependable, and accurate tool for preoperative evaluation of the Ki-67 proliferation status to overcome the sampling bias in sinonasal malignancies. KEY POINTS • Multi-parametric MRI-based radiomics signatures (RSs) are used to preoperatively evaluate the proliferation status of Ki-67 in sinonasal malignancies. • Radiomics features are determined using maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. • RSs are established using a logistic regression (LR) algorithm.
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Affiliation(s)
- Shucheng Bi
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China
| | - Jie Li
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China
| | - Tongyu Wang
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China
| | - Fengyuan Man
- The Department of Radiology, The PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China
| | - Peng Zhang
- The Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Feng Hou
- The Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, China
| | - Hexiang Wang
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China.
| | - Dapeng Hao
- The Department of Radiology, The Affiliated Hospital of Qingdao University, 16, Jiangsu Road, Qingdao, 266003, China.
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31
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Wu H, Wang Z, Liang Y, Tan C, Wei X, Zhang W, Yang R, Mo L, Jiang X. A Computed Tomography Nomogram for Assessing the Malignancy Risk of Focal Liver Lesions in Patients With Cirrhosis: A Preliminary Study. Front Oncol 2022; 11:681489. [PMID: 35127463 PMCID: PMC8814623 DOI: 10.3389/fonc.2021.681489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose The detection and characterization of focal liver lesions (FLLs) in patients with cirrhosis is challenging. Accurate information about FLLs is key to their management, which can range from conservative methods to surgical excision. We sought to develop a nomogram that incorporates clinical risk factors, blood indicators, and enhanced computed tomography (CT) imaging findings to predict the nature of FLLs in cirrhotic livers. Method A total of 348 surgically confirmed FLLs were included. CT findings and clinical data were assessed. All factors with P < 0.05 in univariate analysis were included in multivariate analysis. ROC analysis was performed, and a nomogram was constructed based on the multivariate logistic regression analysis results. Results The FLLs were either benign (n = 79) or malignant (n = 269). Logistic regression evaluated independent factors that positively affected malignancy. AFP (OR = 10.547), arterial phase hyperenhancement (APHE) (OR = 740.876), washout (OR = 0.028), satellite lesions (OR = 15.164), ascites (OR = 156.241), and nodule-in-nodule architecture (OR =27.401) were independent predictors of malignancy. The combined predictors had excellent performance in differentiating benign and malignant lesions, with an AUC of 0.959, a sensitivity of 95.24%, and a specificity of 87.5% in the training cohort and AUC of 0.981, sensitivity of 94.74%, and specificity of 93.33% in the test cohort. The C-index was 96.80%, and calibration curves showed good agreement between the nomogram predictions and the actual data. Conclusions The nomogram showed excellent discrimination and calibration for malignancy risk prediction, and it may aid in making FLLs treatment decisions.
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Affiliation(s)
- Hongzhen Wu
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zihua Wang
- Department of Radiology, Foshan Hospital of Traditional Chinese Medicine, Foshan, China
| | - Yingying Liang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Caihong Tan
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wanli Zhang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Lei Mo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinqing Jiang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Palatresi D, Fedeli F, Danti G, Pasqualini E, Castiglione F, Messerini L, Massi D, Bettarini S, Tortoli P, Busoni S, Pradella S, Miele V. Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience. Radiol Med 2022; 127:117-128. [PMID: 35022956 DOI: 10.1007/s11547-021-01446-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/30/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Our primary purpose was to search for computed tomography (CT) radiomic features of gastrointestinal stromal tumors (GISTs) that could potentially correlate with the risk class according to the Miettinen classification. Subsequently, assess the existence of features with possible predictive value in differentiating responder from non-responder patients to first-line therapy with Imatinib. METHODS A retrospective study design was carried out using data from June 2009 to December 2020. We analyzed all the preoperative CTs of patients undergoing surgery for GISTs. We segmented non-contrast-enhanced CT (NCECT) and contrast-enhanced venous CT (CECT) images obtained either on three different CT scans (heterogeneous cohort) or on a single CT scan (homogeneous cohort). We then divided the patients into two groups according to Miettinen classification criteria and based on the predictive value of response to first-line therapy with Imatinib. RESULTS We examined 54 patients with pathological confirmation of GISTs. For the heterogeneous cohort, we found a statistically significant relationship between 57 radiomic features for NCECT and 56 radiomic features for CECT using the Miettinen risk classification. In the homogeneous cohort, we found the same relationship between 8 features for the NCECT and 5 features for CECT, all included in the heterogeneous cohort. The various radiomic features are distributed with different values in the two risk stratification groups according to the Miettinen classification. We also found some features for groups predictive of response to first-line therapy with Imatinib. CONCLUSIONS We found radiomic features that correlate with statistical significance for both the Miettinen risk classification and the molecular subtypes of response. All features found in the homogeneous study cohort were also found in the heterogeneous cohort. CT radiomic features may be useful in assessing the risk class and prognosis of GISTs.
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Affiliation(s)
- Daniele Palatresi
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Filippo Fedeli
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Elisa Pasqualini
- Pathology Unit, Department of Health Sciences, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Francesca Castiglione
- Histopathology and Molecular Diagnostics Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Luca Messerini
- Department of Experimental and Clinical Medicine, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Daniela Massi
- Pathology Unit, Department of Health Sciences, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Bettarini
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Paolo Tortoli
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Simone Busoni
- Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
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Zhou L, Peng H, Ji Q, Li B, Pan L, Chen F, Jiao Z, Wang Y, Huang M, Liu G, Liu Y, Li W. Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma. ANNALS OF TRANSLATIONAL MEDICINE 2022; 9:1665. [PMID: 34988174 PMCID: PMC8667089 DOI: 10.21037/atm-21-5348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/10/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients. METHODS Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models. RESULTS A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods. CONCLUSIONS A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments.
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Affiliation(s)
- Lili Zhou
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hong Peng
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiang Ji
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bo Li
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, China
| | - Lexin Pan
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Feng Chen
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | | | - Yali Wang
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mengqian Huang
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Gaifen Liu
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenbin Li
- Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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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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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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Zheng Z, Gu Z, Xu F, Maskey N, He Y, Yan Y, Xu T, Liu S, Yao X. Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer. Cancer Imaging 2021; 21:65. [PMID: 34863282 PMCID: PMC8642943 DOI: 10.1186/s40644-021-00433-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/12/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND MATERIALS We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status. RESULTS 1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05). CONCLUSIONS The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.
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Affiliation(s)
- Zongtai Zheng
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Zhuoran Gu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Feijia Xu
- Department of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Niraj Maskey
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Yanyan He
- Department of Pathology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yang Yan
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Tianyuan Xu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Department of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shenghua Liu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
| | - Xudong Yao
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
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Zhao Y, Feng M, Wang M, Zhang L, Li M, Huang C. CT Radiomics for the Preoperative Prediction of Ki67 Index in Gastrointestinal Stromal Tumors: A Multi-Center Study. Front Oncol 2021; 11:689136. [PMID: 34595107 PMCID: PMC8476965 DOI: 10.3389/fonc.2021.689136] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/30/2021] [Indexed: 12/15/2022] Open
Abstract
Purpose This study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs). Materials and Methods A total of 344 patients with GISTs from three hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model’s performance and generalization ability. Results After dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761–0.908) in the training set and 0.784 (95% CI: 0.691–0.874) in the external validation cohort. Conclusion The radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.
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Affiliation(s)
- Yilei Zhao
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Meibao Feng
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Minhong Wang
- First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Liang Zhang
- Zhejiang Cancer Hospital, University of Chinese Academy of Sciences, Hangzhou, China
| | - Meirong Li
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chencui Huang
- Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
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A CT-based nomogram for predicting the malignant potential of primary gastric gastrointestinal stromal tumors preoperatively. Abdom Radiol (NY) 2021; 46:3075-3085. [PMID: 33713161 DOI: 10.1007/s00261-021-03026-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 02/19/2021] [Accepted: 02/25/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop and validate a computerized tomography (CT)-based nomogram for predicting the malignant potential of primary gastric gastrointestinal stromal tumors (GISTs). METHODS The primary and validation cohorts consisted of 167 and 39 patients (single center, different time periods) with histologically confirmed primary gastric GISTs. Clinical data and preoperative CT images were reviewed. The association of CT characteristics with malignant potential was analyzed using univariate and stepwise logistic regression analyses. A nomogram based on significant CT findings was developed for predicting malignant potential. The predictive accuracy of the nomogram was determined by the concordance index (C-index) and calibration curves. External validation was performed with the validation cohort. RESULTS CT imaging features including tumor size, tumor location, tumor necrosis, growth pattern, ulceration, enlarged vessels feeding or draining the mass (EVFDM), tumor contour, mesenteric fat infiltration, and direct organ invasion showed significant differences between the low- and high-grade malignant potential groups in univariate analysis (P < 0.05). Only tumor size (> 5 cm vs ≤ 5 cm), location (cardiac/pericardial region vs other), EVFDM, and mesenteric fat infiltration (present vs absent) were significantly associated with high malignant potential in multivariate logistic regression analysis. Incorporating these four independent factors into the nomogram model achieved good C-indexes of 0.946 (95% confidence interval [CI] 0.899-0.975) and 0.952 (95% CI 0.913-0.977) in the primary and validation cohorts, respectively. The cutoff point was 0.33, with sensitivity, specificity, and diagnostic accuracy of 0.865, 0.915, and 0.780, respectively. DISCUSSION Primary gastric GISTs originating in the cardiac/pericardial region appear to be associated with higher malignant potential. The nomogram consisting of CT features, including size, location, EVFDM, and mesenteric fat infiltration, could be used to accurately predict the high malignant potential of primary gastric GISTs.
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La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021; 13:cancers13123015. [PMID: 34208595 PMCID: PMC8234501 DOI: 10.3390/cancers13123015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
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Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
- Correspondence:
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Fabienne Tschanz
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Verena Waller
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Martin Pruschy
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
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Luo P, Fang Z, Zhang P, Yang Y, Zhang H, Su L, Wang Z, Ren J. Radiomics Score Combined with ACR TI-RADS in Discriminating Benign and Malignant Thyroid Nodules Based on Ultrasound Images: A Retrospective Study. Diagnostics (Basel) 2021; 11:diagnostics11061011. [PMID: 34205943 PMCID: PMC8229428 DOI: 10.3390/diagnostics11061011] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 12/12/2022] Open
Abstract
This study aimed to explore the ability of combination model of ultrasound radiomics score (Rad-score) and the thyroid imaging, reporting and data system by the American College of Radiology (ACR TI-RADS) in predicting benign and malignant thyroid nodules (TNs). Up to 286 radiomics features were extracted from ultrasound images of TNs. By using the lowest probability of classification error and average correlation coefficients (POE + ACC) and the least absolute shrinkage and selection operator (LASSO), we finally selected four features to establish Rad-score (Vertl-RLNonUni, Vertl-GLevNonU, WavEnLH-s4 and WavEnHL-s5). DeLong’s test and decision curve analysis (DCA) showed that the method of combining Rad-score and ACR TI-RADS had the best performance (the area under the receiver operating characteristic curve (AUC = 0.913 (95% confidence interval (CI), 0.881–0.939) and 0.899 (95%CI, 0.840–0.942) in the training group and verification group, respectively), followed by ACR TI-RADS (AUC = 0.898 (95%CI, 0.863–0.926) and 0.870 (95%CI, 0.806–0.919) in the training group and verification group, respectively), and followed by Rad-score (AUC = 0.750 (95%CI, 0.704–0.792) and 0.750 (95%CI, 0.672–0.817) in the training group and verification group, respectively). We concluded that the ability of ultrasound Rad-score to distinguish benign and malignant TNs was not as good as that of ACR TI-RADS, and the ability of the combination model of Rad-score and ACR TI-RADS to discriminate benign and malignant TNs was better than ACR TI-RADS or Rad-score alone. Ultrasound Rad-score might play a potential role in improving the differentiation of malignant TNs from benign TNs.
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Affiliation(s)
- Peng Luo
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (P.L.); (P.Z.); (Y.Y.); (H.Z.); (L.S.); (Z.W.)
| | - Zheng Fang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China;
| | - Ping Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (P.L.); (P.Z.); (Y.Y.); (H.Z.); (L.S.); (Z.W.)
| | - Yang Yang
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (P.L.); (P.Z.); (Y.Y.); (H.Z.); (L.S.); (Z.W.)
| | - Hua Zhang
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (P.L.); (P.Z.); (Y.Y.); (H.Z.); (L.S.); (Z.W.)
| | - Lei Su
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (P.L.); (P.Z.); (Y.Y.); (H.Z.); (L.S.); (Z.W.)
| | - Zhigang Wang
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (P.L.); (P.Z.); (Y.Y.); (H.Z.); (L.S.); (Z.W.)
| | - Jianli Ren
- Department of Ultrasound, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China; (P.L.); (P.Z.); (Y.Y.); (H.Z.); (L.S.); (Z.W.)
- Correspondence:
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Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021; 13:cancers13112522. [PMID: 34063937 PMCID: PMC8196591 DOI: 10.3390/cancers13112522] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Part I is an overview aimed to investigate some technical principles and the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy in gastrointestinal cancers, describing mostly the results for each pre-eminent tumor. In particular, this paper provides a general description of the main radiomic drawbacks and future challenges, which limit radiomic application in clinical setting as routine. Further investigations need to standardize and validate the Radiomics as a helpful tool in management of oncologic patients. In that context, Radiomics has been playing a relevant role and could be considered as a future imaging landscape. Abstract Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.
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Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome-Umberto I University Hospital, Viale del Policlinico, 155, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-063-377-5285
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Wu H, Han X, Wang Z, Mo L, Liu W, Guo Y, Wei X, Jiang X. Prediction of the Ki-67 marker index in hepatocellular carcinoma based on CT radiomics features. Phys Med Biol 2020; 65:235048. [PMID: 32756021 DOI: 10.1088/1361-6560/abac9c] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The noninvasive detection of tumor proliferation is of great value and the Ki-67 is a biomarker of tumor proliferation. We hypothesized that radiomics characteristics may be related to tumor proliferation. To evaluate whether computed tomography (CT) radiomics feature analyses could aid in assessing the Ki-67 marker index in hepatocellular carcinoma (HCC), we retrospectively analyzed preoperative CT findings of 74 patients with HCC. The texture feature calculations were computed from MaZda 4.6 software, and the sequential forward selection algorithm was used as the selection method. The correlation between radiomics features and the Ki-67 marker index, as well as the difference between low Ki-67 (<10%) and high Ki-67 (≥10%) groups were evaluated. A simple logistic regression model was used to evaluate the associations between texture features and high Ki-67, and receiver operating characteristic analysis was performed on important parameters to assess the ability of radiomics characteristics to distinguish the high Ki-67 group from the low Ki-67 group. Contrast, correlation, and inverse difference moment (IDM) were significantly different (P < 0.001) between the low and high Ki-67 groups. Contrast (odds ratio [OR] = 0.957; 95% confidence interval [CI]: 0.926-0.990, P = 0.01) and correlation (OR = 2.5☆105; 95% CI: 7.560-8.9☆109; P = 0.019) were considered independent risk factors for combined model building with logistic regression. Angular second moment (r = -0.285, P = 0.014), contrast (r = -0.449, P < 0.001), correlation (r = 0.552, P < 0.001), IDM (r = 0.458, P < 0.001), and entropy (r = 0.285, P = 0.014) strongly correlated with the Ki-67 scores. Contrast, correlation, and the combined predictor were predictive of Ki-67 status (P < 0.001), with areas under the curve ranging from 0.777 to 0.836. The radiomics characteristics of CT have potential as biomarkers for predicting Ki-67 status in patients with HCC. These findings suggest that the radiomics features of CT might be used as a noninvasive measure of cellular proliferation in HCC.
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Affiliation(s)
- Hongzhen Wu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510180, People's Republic of China. Jinan University, Guangzhou 510632, Guangdong, People's Republic of China
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Cannella R, La Grutta L, Midiri M, Bartolotta TV. New advances in radiomics of gastrointestinal stromal tumors. World J Gastroenterol 2020; 26:4729-4738. [PMID: 32921953 PMCID: PMC7459199 DOI: 10.3748/wjg.v26.i32.4729] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/16/2020] [Accepted: 08/01/2020] [Indexed: 02/06/2023] Open
Abstract
Gastrointestinal stromal tumors (GISTs) are uncommon neoplasms of the gastrointestinal tract with peculiar clinical, genetic, and imaging characteristics. Preoperative knowledge of risk stratification and mutational status is crucial to guide the appropriate patients’ treatment. Predicting the clinical behavior and biological aggressiveness of GISTs based on conventional computed tomography (CT) and magnetic resonance imaging (MRI) evaluation is challenging, unless the lesions have already metastasized at the time of diagnosis. Radiomics is emerging as a promising tool for the quantification of lesion heterogeneity on radiological images, extracting additional data that cannot be assessed by visual analysis. Radiomics applications have been explored for the differential diagnosis of GISTs from other gastrointestinal neoplasms, risk stratification and prediction of prognosis after surgical resection, and evaluation of mutational status in GISTs. The published researches on GISTs radiomics have obtained excellent performance of derived radiomics models on CT and MRI. However, lack of standardization and differences in study methodology challenge the application of radiomics in clinical practice. The purpose of this review is to describe the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors, discuss the potential clinical applications that may impact patients’ management, report limitations of current radiomics studies, and future directions.
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Affiliation(s)
- Roberto Cannella
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Ludovico La Grutta
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Massimo Midiri
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
| | - Tommaso Vincenzo Bartolotta
- Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
- Department of Radiology, Fondazione Istituto Giuseppe Giglio, Ct.da Pietrapollastra, Cefalù (Palermo) 90015, Italy
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Zhang QW, Zhou XX, Zhang RY, Chen SL, Liu Q, Wang J, Zhang Y, Lin J, Xu JR, Gao YJ, Ge ZZ. Comparison of malignancy-prediction efficiency between contrast and non-contract CT-based radiomics features in gastrointestinal stromal tumors: A multicenter study. Clin Transl Med 2020; 10:e291. [PMID: 32634272 PMCID: PMC7418807 DOI: 10.1002/ctm2.91] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 05/17/2020] [Accepted: 05/17/2020] [Indexed: 12/12/2022] Open
Abstract
This work seeks the development and validation of radiomics signatures from nonenhanced computed tomography (CT, NE-RS) to preoperatively predict the malignancy degree of gastrointestinal stromal tumors (GISTs) and the comparison of these signatures with those from contrast-enhanced CT. A dataset for 370 GIST patients was collected from four centers. This dataset was divided into cohorts for training, as well as internal and external validation. The minimum-redundancy maximum-relevance algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm were used to filter unstable features. (a) NE-RS and radiomics signature from contrast-enhanced CT (CE-RS) were built and compared for the prediction of malignancy potential of GIST based on the area under the receiver operating characteristic curve (AUC). (b) The radiomics model was also developed with both the tumor size and NE-RS. The AUC values were comparable between NE-RS and CE-RS in the training (.965 vs .936; P = .251), internal validation (.967 vs .960; P = .801), and external validation (.941 vs .899; P = .173) cohorts in diagnosis of high malignancy potential of GISTs. We next focused on the NE-RS. With 0.185 selected as the cutoff of NE-RS for diagnosis of the malignancy potential of GISTs, accuracy, sensitivity, and specificity for diagnosis high-malignancy potential GIST was 90.0%, 88.2%, and 92.3%, respectively, in the training cohort. For the internal validation set, the corresponding metrics are 89.1%, 94.9%, and 80.0%, respectively. The corresponding metrics for the external cohort are 84.6%, 76.1%, and 91.0%, respectively. Compared with only NE-RS, the radiomics model increased the sensitivity in the diagnosis of GIST with high-malignancy potential by 5.9% (P = .025), 2.5% (P = .317), 10.5% (P = .008) for the training set, internal validation set, and external validation set, respectively. The NE-RS had comparable prediction efficiency in the diagnosis of high-risk GISTs to CE-RS. The NE-RS and radiomics model both had excellent accuracy in predicting malignancy potential of GISTs.
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Affiliation(s)
- Qing-Wei Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Xiao-Xuan Zhou
- Department of Radiology, Sir Run Run Shaw Hospital (SRRSH), School of Medicine, Zhejiang University, Hangzhou, China
| | - Ran-Ying Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Fenglin Road 180, Shanghai, 200032, China
| | - Shuang-Li Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiang Liu
- Department of Pathology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Wang
- Department of radiology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Yan Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Fenglin Road 180, Shanghai, 200032, China
| | - Jian-Rong Xu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yun-Jie Gao
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Zhi-Zheng Ge
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
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Yang CW, Liu XJ, Liu SY, Wan S, Ye Z, Song B. Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2020; 2020:6058159. [PMID: 33304203 PMCID: PMC7714601 DOI: 10.1155/2020/6058159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 10/18/2020] [Accepted: 10/31/2020] [Indexed: 02/05/2023]
Abstract
The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk staging, therapy, and follow-up. The recommended imaging method for staging and follow-up is computed tomography (CT) according to current guidelines. Artificial intelligence (AI) applies and elaborates theses, procedures, modes, and utilization systems for simulating, enlarging, and stretching the intellectual capacity of humans. Recently, researchers have done a few studies to explore AI applications in GIST imaging. This article reviews the present AI studies in GISTs imaging, including preoperative diagnosis, risk stratification and prediction of prognosis, gene mutation, and targeted therapy response.
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Affiliation(s)
- Cai-Wei Yang
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xi-Jiao Liu
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Si-Yun Liu
- 2GE Healthcare (China), Beijing 100176, China
| | - Shang Wan
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- 1 Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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