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He D, Wang X, Fu C, Wei X, Bao J, Ji X, Bai H, Xia W, Gao X, Huang Y, Hou J. MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging 2021; 21:46. [PMID: 34225808 PMCID: PMC8259026 DOI: 10.1186/s40644-021-00414-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/10/2021] [Indexed: 01/01/2023] Open
Abstract
Purpose To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. Results The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. Conclusions The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-021-00414-6.
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Affiliation(s)
- Dong He
- Department of Urology, The First Affiliated Hospital of SooChow University, No.188, Shizi St, Canglang District, 215006, Suzhou, Jiangsu, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of SooChow University, No.188, Shizi St, Canglang District, 215006, Suzhou, Jiangsu, China
| | - Chenchao Fu
- Department of Urology, The First Affiliated Hospital of SooChow University, No.188, Shizi St, Canglang District, 215006, Suzhou, Jiangsu, China
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of SooChow University, No.188, Shizi St, Canglang District, 215006, Suzhou, Jiangsu, China
| | - Jie Bao
- Department of Radiology, The First Affiliated Hospital of SooChow University, No.188, Shizi St, Canglang District, 215006, Suzhou, Jiangsu, China
| | - Xuefu Ji
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou New District, 215163, Jiangsu, China.,The School of Electro-Optical Engineering, Changchun University of Science and Technology, 130013, Changchun, China
| | - Honglin Bai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou New District, 215163, Jiangsu, China
| | - Wei Xia
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou New District, 215163, Jiangsu, China
| | - Xin Gao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou New District, 215163, Jiangsu, China
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of SooChow University, No.188, Shizi St, Canglang District, 215006, Suzhou, Jiangsu, China.
| | - Jianquan Hou
- Department of Urology, The First Affiliated Hospital of SooChow University, No.188, Shizi St, Canglang District, 215006, Suzhou, Jiangsu, China. .,Department of Urology, Dushu Lake Hospital affiliated to SooChow University, No.9, Chongwen Road, Suzhou Industrial Park District, Suzhou, Jiangsu, 215000, China.
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