Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy

• Published in: Abdominal imaging 2021-05, Vol.46 (5), p.1805-1815
• Main Authors: Wang, Jia, Liu, Xuejun, Hu, Bin, Gao, Yuanxiang, Chen, Jingjing, Li, Jie
• Format: Article
• Language: English
• Subjects: Cancer; Chemoradiotherapy; Chemotherapy; Classifiers; Colorectal cancer; Confidence intervals; Feature extraction; Gastroenterology; Hepatology; Hollow Organ GI; Imaging; Learning algorithms; Machine learning; Magnetic resonance imaging; Medicine; Medicine & Public Health; Nomograms; Performance evaluation; Performance prediction; Prediction models; Radiation therapy; Radiology; Radiomics; Rectum; Regression analysis; Risk analysis; Risk factors; Statistical analysis; Training
• ISSN: 2366-004X; 2366-0058
• DOI: 10.1007/s00261-020-02846-3

Purpose In the clinical management of patients with locally advanced rectal cancer (LARC), the early identification of poor and good responders after neoadjuvant chemoradiotherapy (N-CRT) is essential. Therefore, we developed and validated predictive models including MRI findings from the structured report template, clinical and radiomics parameters to differentiate between poor and good responders in patients with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy. Methods Preoperative multiparametric MRI from 183 patients with locally advanced rectal cancer (122 in the training cohort, 61 in the validation cohort) was included in this retrospective study. After preprocessing, radiomic features were extracted and two methods of feature selection was applied to reduce the number of radiomics features. Logistic regression (LR) and random forest (RF) machine learning classifiers were trained to identify good responders from poor responders. Multivariable logistic regression analysis was used to incorporate the radiomic signature and clinical risk factors into a nomogram. Classifier performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results For the differentiation of poor and good responders, the radiomics model with an LR classifier achieved AUCs of 0.869 and 0.842 for the training and validation cohorts, respectively. The nomogram showed the highest reproducibility and prognostic ability in the training and validation cohorts, with AUCs of 0.923 (95% confidence interval, 0.872–0.975) and 0.898 (0.819–0.978), respectively. Additionally, the nomogram achieved significant risk stratification of patients in respect to progression free survival (PFS). Conclusions The nomogram accurately differentiated good and poor responders in patients with LARC undergoing N-CRT, and showed significant performance for predicting PFS.