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

Cancer Imaging

Hai-peng Gong¹  Yue-tao Wang¹  Yi-fei Zhang¹  Yong-juan Qiu¹  Gui-hua Zheng²  Feng Feng³  Yan-song Yang⁴  Ya-qiong Ge⁵ 

[1] Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, No.185, Juqian Street, 213003, Changzhou, Jiangsu Province, China;Department of Pathology, Affiliated Tumor Hospital of Nantong University, 226001, Nantong, Jiangsu Province, China;Department of Radiology, Affiliated Tumor Hospital of Nantong University, 226001, Nantong, Jiangsu Province, China;Department of Radiology, Affiliated Tumor Hospital of Nantong University, 226001, Nantong, Jiangsu Province, China;Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, No.185, Juqian Street, 213003, Changzhou, Jiangsu Province, China;GE Healthcare, 210000, Shanghai, China;

关键词: Rectal cancer;    Magnetic resonance imaging;    Radiomics;    Nomogram;    Perineural invasion;   

DOI  :  10.1186/s40644-021-00408-4

来源: Springer

【 摘 要 】

BackgroundTo establish and validate a high-resolution magnetic resonance imaging (HRMRI)-based radiomic nomogram for prediction of preoperative perineural invasion (PNI) of rectal cancer (RC).MethodsOur 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.ResultsThe 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.ConclusionsHRMRI-based radiomic nomogram could be helpful in the prediction of preoperative PNI in RC patients.

【 授权许可】

CC BY