Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 28, 2024; 30(16): 2233-2248
Published online Apr 28, 2024. doi: 10.3748/wjg.v30.i16.2233
Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study
Yan Liu, Bai-Jin-Tao Sun, Chuan Zhang, Bing Li, Xiao-Xuan Yu, Yong Du
Yan Liu, Bai-Jin-Tao Sun, Chuan Zhang, Bing Li, Xiao-Xuan Yu, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Yong Du, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China. duyong@nsmc.edu.cn
Author contributions: Liu Y data acquisition and analysis, drafting and writing of the manuscript; Sun BJT data collection and data analysis; Zhang C, Li B and Yu XX language editing and revisions to the manuscript; Du Y work concept or design and important revisions to the manuscript; all authors have read and approve the final manuscript.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of North Sichuan Medical College.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Corresponding author: Yong Du, MD, Professor, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuannan Road, Nanchong 637000, Sichuan Province, China. duyong@nsmc.edu.cn
Received: January 2, 2024
Peer-review started: January 2, 2024
First decision: January 31, 2024
Revised: February 8, 2024
Accepted: March 20, 2024
Article in press: March 20, 2024
Published online: April 28, 2024
Processing time: 114 Days and 17.6 Hours
Abstract
BACKGROUND

Perineural invasion (PNI) has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer (RC). Preoperative prediction of PNI status is helpful for individualized treatment of RC. Recently, several radiomics studies have been used to predict the PNI status in RC, demonstrating a good predictive effect, but the results lacked generalizability. The preoperative prediction of PNI status is still challenging and needs further study.

AIM

To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.

METHODS

This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers. The patients underwent pre-operative high-resolution magnetic resonance imaging (MRI) between May 2019 and August 2022. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI) and contrast-enhanced T1WI (T1CE) sequences. The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared (T2WI, T1CE and T2WI + T1CE fusion sequences). A clinical-radiomics (CR) model was established by combining the radiomics features and clinical risk factors. The internal and external validation groups were used to validate the proposed models. The area under the receiver operating characteristic curve (AUC), DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the model performance.

RESULTS

Among the radiomics models, the T2WI + T1CE fusion sequences model showed the best predictive performance, in the training and internal validation groups, the AUCs of the fusion sequence model were 0.839 [95% confidence interval (CI): 0.757-0.921] and 0.787 (95%CI: 0.650-0.923), which were higher than those of the T2WI and T1CE sequence models. The CR model constructed by combining clinical risk factors had the best predictive performance. In the training and internal and external validation groups, the AUCs of the CR model were 0.889 (95%CI: 0.824-0.954), 0.889 (95%CI: 0.803-0.976) and 0.894 (95%CI: 0.814-0.974). Delong test, NRI, and IDI showed that the CR model had significant differences from other models (P < 0.05). Calibration curves demonstrated good agreement, and DCA revealed significant benefits of the CR model.

CONCLUSION

The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively, which facilitates individualized treatment of RC patients.

Keywords: Rectal cancer; Perineural invasion; Magnetic resonance imaging; Radiomics; Nomogram

Core Tip: We constructed radiomics predictive models, clinical predictive model and clinical-radiomics (CR) model based on preoperative magnetic resonance imaging images of rectal cancer (RC), and independent clinical risk factors, to predict the preoperative perineural invasion (PNI) status of RC patients. The reliability and repeatability of the established predictive models were analyzed using internal and external validation groups. The CR model had the best stable neutral performance in both the internal and external validation groups. Therefore, the CR model was able to predict the PNI status of RC noninvasively before surgery, thereby providing support for the individualized treatment of RC patients.