MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.

Authors

Patanè V,Atripaldi U,Sansone M,Marinelli L,Del Tufo S,Arrichiello G,Ciardiello D,Selvaggi F,Martinelli E,Reginelli A

Affiliations (6)

  • Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia 2, 80138, Naples, Italy. [email protected].
  • Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia 2, 80138, Naples, Italy.
  • Department of Electrical Engineering and Information Technology, University of Naples "Federico II", 80125, Naples, Italy.
  • Oncology Unit, AORN "S. Anna e S. Sebastiano", Caserta, Italy.
  • Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology (IEO), IRCCS, Milan, Italy.
  • Department of Advanced Medical and Surgical Sciences, Università Degli Studi Della Campania "Luigi Vanvitelli", Naples, Italy.

Abstract

Preoperative T-staging in rectal cancer is essential for treatment planning, yet conventional MRI shows limited accuracy (~ 60-78). Our study investigates whether radiomic analysis of high-resolution T2-weighted MRI can non-invasively improve staging accuracy through a retrospective evaluation in a real-world surgical cohort. This single-center retrospective study included 200 patients (January 2024-April 2025) with pathologically confirmed rectal cancer, all undergoing preoperative high-resolution T2-weighted MRI within one week prior to curative surgery and no neoadjuvant therapy. Manual segmentation was performed using ITK‑SNAP, followed by extraction of 107 radiomic features via PyRadiomics. Feature selection employed mRMR and LASSO logistic regression, culminating in a Rad-score predictive model. Statistical performance was evaluated using ROC curves (AUC), accuracy, sensitivity, specificity, and Delong's test. Among 200 patients, 95 were pathologically staged as T2 and 105 as T3-T4 (55 T3, 50 T4). After preprocessing, 26 radiomic features were retained; key features including ngtdm_contrast and ngtdm_coarseness showed AUC values > 0.70. The LASSO-based model achieved an AUC of 0.82 (95% CI: 0.75-0.89), with overall accuracy of 81%, sensitivity of 78%, and specificity of 84%. Radiomic analysis of standard preoperative T2-weighted MRI provides a reliable, non-invasive method to predict rectal cancer T-stage. This approach has the potential to enhance staging accuracy and inform personalized surgical planning. Prospective multicenter validation is required for broader clinical implementation.

Topics

Rectal NeoplasmsMagnetic Resonance ImagingJournal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.