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Radiomics for predicting perineural invasion status in rectal cancer

  • Li, Mou
  • Jin, Yu-Mei
  • Zhang, Yong-Chang
  • Zhao, Ya-Li
  • Huang, Chen-Cui
  • Liu, Sheng-Mei
  • Song, Bin
Published Article
World Journal of Gastroenterology
Baishideng Publishing Group Inc
Publication Date
Sep 07, 2021
DOI: 10.3748/wjg.v27.i33.5610
PMID: 34588755
PMCID: PMC8433618
PubMed Central
  • Retrospective Study


BACKGROUND Perineural invasion (PNI), as a key pathological feature of tumor spread, has emerged as an independent prognostic factor in patients with rectal cancer (RC). The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis. However, the preoperative evaluation of PNI status is still challenging. AIM To establish a radiomics model for evaluating PNI status preoperatively in RC patients. METHODS This retrospective study 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) at a ratio of 8:2. A large number of intra- and peritumoral radiomics features were extracted from portal venous phase images of computed tomography (CT). After deleting redundant features, we tested different feature selection ( n = 6) and machine-learning ( n = 14) methods to form 84 classifiers. The best performing classifier was then selected to establish Rad-score. Finally, the clinicoradiological model (combined model) was developed by combining Rad-score with clinical factors. These models for predicting PNI were compared using receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC). RESULTS One hundred and forty-four of the 303 patients were eventually found to be PNI-positive. Clinical factors including CT-reported T stage (cT), N stage (cN), and carcinoembryonic antigen (CEA) level were independent risk factors for predicting PNI preoperatively. We established Rad-score by logistic regression analysis after selecting features with the L1-based method. The combined model was developed by combining Rad-score with cT, cN, and CEA. The combined model showed good performance to predict PNI status, with an AUC 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. For comparison of the models, the combined model achieved a higher AUC than the clinical model (cT + cN + CEA) achieved ( P < 0.001 in the training cohort, and P = 0.045 in the validation cohort). CONCLUSION The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.

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