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SYSTEMATIC REVIEW article

Front. Oncol.
Sec. Gastrointestinal Cancers: Colorectal Cancer
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1425665

The Accuracy of Radiomics in Diagnosing Tumor Deposits and Perineural Invasion in Rectal Cancer: A Systematic Review and Meta-Analysis

Provisionally accepted
Xuewu Liu Xuewu Liu Feng Lin Feng Lin Danni Li Danni Li Nan Lei Nan Lei *
  • The People's Hospital of Lezhi, Ziyang, China

The final, formatted version of the article will be published soon.

    Background: Radiomics has emerged as a promising approach for diagnosing, treating, and evaluating the prognosis of various diseases in recent years. Some investigators have utilized radiomics to create preoperative diagnostic models for tumor deposits (TDs) and perineural invasion (PNI) in rectal cancer (RC). However, there is currently a lack of comprehensive, evidence-based support for the diagnostic performance of these models. Thus, the accuracy of radiomic models was assessed in diagnosing preoperative RC TDs and PNI in this study. Methods: PubMed, EMBASE, Web of Science, and Cochrane Library were searched for relevant articles from their establishment up to December 11, 2023. The radiomics quality score (RQS) was used to evaluate the risk of bias in the methodological quality and research level of the included studies. Results: This meta-analysis included 15 eligible studies, most of which employed logistic regression models (LRMs). For diagnosing TDs, the c-index, sensitivity, and specificity of models based on radiomic features (RFs) alone were 0.85 (95% CI: 0.79 - 0.90), 0.85 (95% CI: 0.75 - 0.91), and 0.82 (95% CI: 0.70 - 0.89); in the validation set, the c-index, sensitivity, and specificity of models based on both RFs and interpretable CFs were 0.87 (95% CI: 0.83 - 0.91), 0.91 (95% CI: 0.72 - 0.99), and 0.65 (95% CI: 0.53 - 0.76), respectively. For diagnosing PNI, the c-index, sensitivity, and specificity of models based on RFs alone were 0.80 (95% CI: 0.74 - 0.86), 0.64 (95% CI: 0.44 - 0.80), and 0.79 (95% CI: 0.68 - 0.87) in the validation set; in the validation set, the c-index, sensitivity, and specificity of models based on both RFs and interpretable CFs were 0.83 (95% CI: 0.77 - 0.89), 0.60 (95% CI: 0.48 - 0.71), and 0.90 (95% CI: 0.84 - 0.94), respectively. Conclusions: Diagnostic models based on both RFs and CFs have proven effective in preoperatively diagnosing TDs and PNI in RC. This non-invasive method shows promise as a new approach.

    Keywords: machine learning, rectal cancer, Perineural invasion, tumor deposits, Systematic review, Meta-analysis

    Received: 30 Apr 2024; Accepted: 18 Dec 2024.

    Copyright: © 2024 Liu, Lin, Li and Lei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Nan Lei, The People's Hospital of Lezhi, Ziyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.