MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study

• Published in: European radiology 2023-11, Vol.33 (11), p.7561-7572
• Main Authors: Li, Hang, Chen, Xiao-li, Liu, Huan, Liu, Yi-Sha, Li, Zhen-lin, Pang, Ming-hui, Pu, Hong
• Format: Article
• Language: English
• Subjects: Cancer; Colorectal cancer; Diagnostic Radiology; Extranodal Extension; Gastrointestinal; Health risks; Humans; Imaging; Internal Medicine; Interventional Radiology; Lymph nodes; Lymphatic Metastasis; Magnetic Resonance Imaging; Medical prognosis; Medicine; Medicine & Public Health; Metastases; Metastasis; Neuroradiology; Nomograms; Patients; Performance prediction; Prediction models; Prognosis; Radiology; Radiomics; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - surgery; Rectum; Retrospective Studies; Training; Tumors; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-09723-9

Objective To build T2WI-based multiregional radiomics for predicting tumor deposit (TD) and prognosis in patients with resectable rectal cancer. Materials and methods A total of 208 patients with pathologically confirmed rectal cancer from two hospitals were prospectively enrolled. Intra- and peritumoral features were extracted separately from T2WI images and the least absolute shrinkage and selection operator was used to screen the most valuable radiomics features. Clinical-radiomics nomogram was developed by radiomics signatures and the most predictive clinical parameters. Prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis. Results For TD, the area under the receiver operating characteristic curve (AUC) for intratumoral radiomics model was 0.956, 0.823, and 0.860 in the training cohort, test cohort, and external validation cohort, respectively. AUC for the peritumoral radiomics model was 0.929, 0.906, and 0.773 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for combined intra- and peritumoral radiomics model was 0.976, 0.918, and 0.874 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for clinical-radiomics nomogram was 0.989, 0.777, and 0.870 in the training cohort, test cohort, and external validation cohort, respectively. The prognostic model constructed by combining intra- and peritumoral radiomics signature score (radscore)–based TD and MRI-reported lymph nodes metastasis (LNM) indicated good performance for predicting 3-year RFS, with AUC of 0.824, 0.865, and 0.738 in the training cohort, test cohort and external validation cohort, respectively. Conclusion Combined intra- and peritumoral radiomics model showed good performance for predicting TD. Combining intra- and peritumoral radscore-based TD and MRI-reported LNM indicated the recurrence risk. Clinical relevance statement Combined intra- and peritumoral radiomics model could help accurately predict tumor deposits. Combining this predictive model-based tumor deposits with MRI-reported lymph node metastasis was associated with relapse risk of rectal cancer after surgery. Key Points • Combined intra- and peritumoral radiomics model provided better diagnostic performance than that of intratumoral and peritumoral radiomics model alone for predicting TD in rectal cancer. • The predictive performance of the clinical-radiomics nomogram was not improv