MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer

• Published in: Abdominal imaging Vol. 50; no. 6; pp. 2388 - 2400
• Main Authors: Liu, Jun, Liu, Ke, Cao, Fang, Hu, Pingsheng, Bi, Feng, Liu, Siye, Jian, Lian, Zhou, Jumei, Nie, Shaolin, Lu, Qiang, Yu, Xiaoping, Wen, Lu
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2025; Springer Nature B.V
• Subjects: Adult; Aged; Algorithms; Calibration; Cancer; Cancer therapies; Carcinoembryonic antigen; Chemoradiotherapy; Chemotherapy; Colorectal cancer; Confidence intervals; Correlation analysis; Decision making; Disease-Free Survival; Female; Gastroenterology; Health services; Hepatology; Humans; Imaging; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical prognosis; Medicine; Medicine & Public Health; Middle Aged; Neoadjuvant Therapy; Neoplasm Staging; Nomograms; Optimization; Patients; Predictive Value of Tests; Prognosis; Radiation therapy; Radiology; Radiomics; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - pathology; Rectal Neoplasms - therapy; Rectum; Regression analysis; Retrospective Studies; Risk groups; Statistical analysis; Surgery; Surveillance; Survival; Variance analysis; Radiomic; Locally advanced rectal cancer; MRI; Disease-free survival
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-024-04710-0

Purpose Individual prognosis assessment is of paramount importance for treatment decision-making and active surveillance in cancer patients. We aimed to propose a radiomic model based on pre- and post-therapy MRI features for predicting disease-free survival (DFS) in locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) and subsequent surgical resection. Methods This retrospective study included a total of 126 LARC patients, which were randomly assigned to a training set (n = 84) and a validation set (n = 42). All patients underwent pre- and post-nCRT MRI scans. Radiomic features were extracted from higher resolution T2-weighted images. Pearson correlation analysis and ANOVA or Relief were utilized for identifying radiomic features associated with DFS. Pre-treatment, post-treatment, and delta radscores were constructed by machine learning algorithms. An individualized nomogram was developed based on significant radscores and clinical variables using multivariate Cox regression analysis. Predictive performance was evaluated by the C-index, calibration curve, and decision curve analysis. Results The results demonstrated that in the validation set, the clinical model including pre-surgery carcinoembryonic antigen (CEA), chemotherapy after radiotherapy, and pathological stage yielded a C-index of 0.755 (95% confidence interval [CI]: 0.739–0.771). While the optimal pre-, post-, and delta-radscores achieved C-indices of 0.724 (95%CI: 0.701–0.747), 0.701 (95%CI: 0.671–0.731), and 0.625 (95%CI: 0.589–0.661), respectively. The nomogram integrating pre-surgery CEA, pathological stage, alongside pre- and post-nCRT radscore, obtained the highest C-index of 0.833 (95%CI: 0.815–0.851). The calibration curve and decision curves exhibited good calibration and clinical usefulness of the nomogram. Furthermore, the nomogram categorized patients into high- and low-risk groups exhibiting distinct DFS (both P  < 0.0001). Conclusions The nomogram incorporating pre- and post-therapy radscores and clinical factors could predict DFS in patients with LARC, which helps clinicians in optimizing decision-making and surveillance in real-world settings.