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Liang Y, Wei Y, Xu F, Wei X. MRI-based radiomic models for the preoperative prediction of extramural venous invasion in rectal cancer: A systematic review and meta-analysis. Clin Imaging 2024; 110:110146. [PMID: 38697000 DOI: 10.1016/j.clinimag.2024.110146] [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: 01/02/2024] [Revised: 03/19/2024] [Accepted: 04/03/2024] [Indexed: 05/04/2024]
Abstract
AIM To estimate the diagnostic value of magnetic resonance imaging (MRI)-based radiomic models in detecting the extramural venous invasion (EMVI) of rectal cancer. MATERIALS AND METHODS Appropriate studies in multiple electronic databases were systematically retrieved. The Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score (RQS) were used to evaluate the eligible studies' methodology quality. Summary accuracy metrics were calculated, and the publication bias was detected using Deek's funnel plot. The sensitivity and meta-regression analysis were performed to investigate the causes of heterogeneity. RESULTS For the seven eligible studies, which included 1175 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.80 (95 % CI, 0.70-0.88), 0.89 (95 % CI, 0.84-0.92), 7.0 (95 % CI, 4.7, 10.4), 0.22 (95 % CI, 0.14, 0.34), and 32 (95 % CI, 16, 65), respectively. The area under the receiver operating characteristic curve (AUC) was 0.91 (95 % CI, 0.88, 0.93). Moderate heterogeneity was found due to I2 values of 38.63 % and 32.29 % in sensitivity and specificity, respectively. Meta-regression analysis suggested that the patient enrollment, number of patients, segmentation method, and RQS score were the source of the heterogeneity. The head-to-head analysis suggested that radiomics model had a higher sensitivity for detection of EMVI than subjective evaluation by radiologist (0.47 vs. 0.73, p ≤ 0.001). CONCLUSION Our study suggests that MRI-based radiomic models have good diagnostic value in detecting EMVI for rectal cancer patients. Nevertheless, more prospective and high-quality studies with larger sample sizes are needed in the future to validate these results.
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Affiliation(s)
- Yingying Liang
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China; Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, Guangdong Province 510180, China
| | - Yaxuan Wei
- Guangzhou Medical University, 195 Dongfengxi road, Guangzhou, Guangdong Province 510180, China
| | - Fan Xu
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, 396 Tongfu road, Guangzhou, Guangdong Province 510220, China
| | - Xinhua Wei
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China; Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, Guangdong Province 510180, China.
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Gao D, Tan BG, Chen XQ, Zhou C, Ou J, Guo WW, Zhou HY, Li R, Zhang XM, Chen TW. Contrast-enhanced CT radiomics features to preoperatively identify differences between tumor and proximal tumor-adjacent and tumor-distant tissues of resectable esophageal squamous cell carcinoma. Cancer Imaging 2024; 24:11. [PMID: 38243339 PMCID: PMC10797955 DOI: 10.1186/s40644-024-00656-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND Esophagectomy is the main treatment for esophageal squamous cell carcinoma (ESCC), and patients with histopathologically negative margins still have a relatively higher recurrence rate. Contrast-enhanced CT (CECT) radiomics might noninvasively obtain potential information about the internal heterogeneity of ESCC and its adjacent tissues. This study aimed to develop CECT radiomics models to preoperatively identify the differences between tumor and proximal tumor-adjacent and tumor-distant tissues in ESCC to potentially reduce tumor recurrence. METHODS A total of 529 consecutive patients with ESCC from Centers A (n = 447) and B (n = 82) undergoing preoperative CECT were retrospectively enrolled in this study. Radiomics features of the tumor, proximal tumor-adjacent (PTA) and proximal tumor-distant (PTD) tissues were individually extracted by delineating the corresponding region of interest (ROI) on CECT and applying the 3D-Slicer radiomics module. Patients with pairwise tissues (ESCC vs. PTA, ESCC vs. PTD, and PTA vs. PTD) from Center A were randomly assigned to the training cohort (TC, n = 313) and internal validation cohort (IVC, n = 134). Univariate analysis and the least absolute shrinkage and selection operator were used to select the core radiomics features, and logistic regression was performed to develop radiomics models to differentiate individual pairwise tissues in TC, validated in IVC and the external validation cohort (EVC) from Center B. Diagnostic performance was assessed using area under the receiver operating characteristics curve (AUC) and accuracy. RESULTS With the chosen 20, 19 and 5 core radiomics features in TC, 3 individual radiomics models were developed, which exhibited excellent ability to differentiate the tumor from PTA tissue (AUC: 0.965; accuracy: 0.965), the tumor from PTD tissue (AUC: 0.991; accuracy: 0.958), and PTA from PTD tissue (AUC: 0.870; accuracy: 0.848), respectively. In IVC and EVC, the models also showed good performance in differentiating the tumor from PTA tissue (AUCs: 0.956 and 0.962; accuracy: 0.956 and 0.937), the tumor from PTD tissue (AUCs: 0.990 and 0.974; accuracy: 0.952 and 0.970), and PTA from PTD tissue (AUCs: 0.806 and 0.786; accuracy: 0.760 and 0.786), respectively. CONCLUSION CECT radiomics models could differentiate the tumor from PTA tissue, the tumor from PTD tissue, and PTA from PTD tissue in ESCC.
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Affiliation(s)
- Dan Gao
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
- Department of Radiology, Medical Center Hospital of Qionglai City, 172# Xinglin Road, Linqiong District, Chengdu, 611530, Sichuan, China
| | - Bang-Guo Tan
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
- Department of Radiology, Panzhihua Central Hospital, 34# Yikang Street, East District, Panzhihua, 617067, Sichuan, China
| | - Xiao-Qian Chen
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
| | - Chuanqinyuan Zhou
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
| | - Jing Ou
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
| | - Wen-Wen Guo
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
| | - Hai-Ying Zhou
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
| | - Rui Li
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1# Maoyuan South Road, Shunqing District, Nanchong, 637000, Sichuan, China
| | - Tian-Wu Chen
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, 74# Linjiang Rd, Yuzhong District, Chongqing, 400010, China.
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