High resolution MRI-based radiomic nomogram in predicting perineural invasion in rectal cancer

• Published in: Cancer imaging Vol. 21; no. 1; pp. 1 - 10
• Main Authors: Yang, Yan-song, Qiu, Yong-juan, Zheng, Gui-hua, Gong, Hai-peng, Ge, Ya-qiong, Zhang, Yi-fei, Feng, Feng, Wang, Yue-tao
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.05.2021; BioMed Central Ltd; BMC
• Subjects: Antigens; Cancer; Cancer Research; Cancer therapies; Chemotherapy; Clinical decision making; Clinical medicine; Colorectal cancer; Confidence intervals; Decision analysis; Feature extraction; High resolution; Image resolution; Imaging; Lymphatic system; Magnetic resonance imaging; Medical imaging equipment; Medicine; Medicine & Public Health; Nomogram; Nomograms; Nuclear Medicine; Oncology; Oncology, Experimental; Pathology; Patients; Perineural invasion; Radiology; Radiomics; Rectal cancer; Rectum; Research Article; Risk analysis; Risk factors; Software; Statistical analysis; Surgery; Training; Rectal cancer; Magnetic resonance imaging; Perineural invasion; Nomogram; Radiomics
• ISSN: 1470-7330; 1740-5025; 1470-7330
• DOI: 10.1186/s40644-021-00408-4

Background To establish and validate a high-resolution magnetic resonance imaging (HRMRI)-based radiomic nomogram for prediction of preoperative perineural invasion (PNI) of rectal cancer (RC). Methods Our retrospective study included 140 subjects with RC (99 in the training cohort and 41 in the validation cohort) who underwent a preoperative HRMRI scan between December 2016 and December 2019. All subjects underwent radical surgery, and then PNI status was evaluated by a qualified pathologist. A total of 396 radiomic features were extracted from oblique axial T2 weighted images, and optimal features were selected to construct a radiomic signature. A combined nomogram was established by incorporating the radiomic signature, HRMRI findings, and clinical risk factors selected by using multivariable logistic regression. Results The predictive nomogram of PNI included a radiomic signature, and MRI-reported tumor stage (mT-stage). Clinical risk factors failed to increase the predictive value. Favorable discrimination was achieved between PNI-positive and PNI-negative groups using the radiomic nomogram. The area under the curve (AUC) was 0.81 (95% confidence interval [CI], 0.71–0.91) in the training cohort and 0.75 (95% CI, 0.58–0.92) in the validation cohort. Moreover, our result highlighted that the radiomic nomogram was clinically beneficial, as evidenced by a decision curve analysis. Conclusions HRMRI-based radiomic nomogram could be helpful in the prediction of preoperative PNI in RC patients.