Published online Sep 7, 2021. doi: 10.3748/wjg.v27.i33.5610
Peer-review started: June 2, 2021
First decision: June 25, 2021
Revised: July 3, 2021
Accepted: August 11, 2021
Article in press: August 11, 2021
Published online: September 7, 2021
Processing time: 93 Days and 7.9 Hours
Perineural invasion (PNI) has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative prediction of PNI status is beneficial for individualized treatment and improved prognosis.
Nowadays, preoperative assessment of PNI status is still challenging.
To build a radiomics prediction model for evaluating PNI status preoperatively in RC patients.
We enrolled 303 RC patients in a single institution from March 2018 to October 2019. These patients were classified as the training cohort (n = 242) and validation cohort (n = 61). A large number of intra- and peritumoral radiomics features were extracted to build the Rad-score and combined model.
Our study enrolled more patients (144 PNI+ and 159 PNI-) than previous studies[6,17,18]. Rad-score was built by logistic regression analysis. The combined model was developed by combining Rad-score with computed tomography (CT)-reported T stage and N stage, and carcinoembryonic antigen. The combined model showed good performance to predict PNI status, with an area under the receiver operating characteristic curve of 0.828 [95% confidence interval (CI): 0.774-0.873] in the training cohort and 0.801 (95%CI: 0.679-0.892) in the validation cohort.
The combined model incorporating Rad-score and clinical factors helps to provide an individualized PNI status evaluation.
Other biological characteristics besides PNI are also related to the prognosis of RC patients; for instance, intramural lymphovascular invasion (LVI). Intramural LVI cannot be determined by magnetic resonance imaging and CT. Therefore, using radiomics or deep learning to predict intramural LVI of RC is valuable in the future.