Associating Peritoneal Metastasis With T2‐Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study

• Published in: Journal of magnetic resonance imaging Vol. 59; no. 1; pp. 122 - 131
• Main Authors: Wei, Mingxiang, Zhang, Yu, Ding, Cong, Jia, Jianye, Xu, Haimin, Dai, Yao, Feng, Guannan, Qin, Cai, Bai, Genji, Chen, Shuangqing, Wang, Hong
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2024; Wiley Subscription Services, Inc
• Subjects: Cancer; Carcinoma, Ovarian Epithelial - diagnostic imaging; Decision making; Deep Learning; Female; Field strength; Humans; Magnetic Resonance Imaging; Mathematical models; Metastases; Metastasis; Ovarian cancer; Ovarian Neoplasms - diagnostic imaging; peritoneal metastasis; Peritoneal Neoplasms; Peritoneum; Population studies; Radiology; Radiomics; Retrospective Studies; Sensitivity; Statistical analysis; Statistical tests; deep learning; radiomics; magnetic resonance imaging; ovarian cancer; peritoneal metastasis
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.28761

Background The preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision‐making. Purpose To investigate the performance of T2‐weighted (T2W) MRI‐based deep learning (DL) and radiomics methods for PM evaluation in EOC patients. Study Type Retrospective. Population Four hundred seventy‐nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]). Field Strength/Sequence 1.5 or 3 T/fat‐suppression T2W fast or turbo spin‐echo sequence. Assessment ResNet‐50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision‐level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated. Statistical Tests Receiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two‐tailed P < 0.05 was considered significant. Results The ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789). Data Conclusions T2W MRI‐based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision‐making. Evidence Level 4 Technical Efficacy Stage 2