Plenary Session
AB089. SOH25_AB_124. Magnetic resonance-derived radiomics to predict post-exenteration disease recurrence in advanced rectal cancer: a multi-institutional analysis
Eimear Kyle1, Niall O’Sullivan1, Michael Kelly2, James Meaney1, Hugo Temperley1, Alison Corr1, Fariba Tohidinezhad3, Mirac Ajredini4, Bedirye Koyuncu Sokmen4, Rumeysa Atabey4, Leyla Ozer4, Erman Aytac5, Alberto Traverso5
1Department of Radiology, St. James’ Hospital, Dublin, Ireland;
2Trinity St. James’ Cancer Institute, St James’ Hospital, Dublin, Ireland;
3Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Centre, Maastricht, The Netherlands;
4Gastrointestinal Oncology Unit, Acibadem University, Atakent Hospital Turkey, Istanbul, Turkey;
5School of Medicine, Libera Università Vita-Salute San Raffaele, Milan, Italy
Background: Local recurrence and distant metastasis remain a concern in advanced rectal cancer with up to 10% and 20–30% of patients suffering local and distal progression respectively. Radiomics refers to a novel technology which extracts and analyses quantitative imaging features from images, which can be subsequently used to develop and tests clinical models predictive of outcomes. We aim to develop and test a magnetic resonance imaging (MRI)-based radiomics nomogram predictive of disease recurrence in patients with T4 rectal cancer.
Methods: We conducted a multi-institutional retrospective analysis of 55 patients with T4 rectal cancer treated with neoadjuvant chemoradiotherapy followed by exenterative surgery. Radiomic features were extracted from pre-treatment T2-weighted MRI scans and used to construct predictive models. The top performing radiomic signatures were identified and internal validation with 1,000 bootstrap samples was performed to calculate optimism-corrected performance measures.
Results: Two radiomic signatures were identified as strong predictors of post-operative disease recurrence. The best-performing model achieved an optimism-corrected area under the curve (AUC) of 0.75, demonstrating good discriminative ability. Calibration plots showed satisfactory fit of the predictions to the actual rates and decision curve analyses confirmed the positive net benefit of the models.
Conclusions: The MRI-based radiomics nomogram provides a promising tool for predicting disease recurrence in T4 rectal cancer patients post-exenteration. This model could improve risk stratification and guide more personalised treatment strategies. Further studies with larger cohorts and external validation are needed to confirm these findings and enhance the model’s generalisability.
Keywords: Advanced rectal cancer; magnetic resonance imaging (MRI); oncology; radiomics; recurrence
Acknowledgments
None.
Funding: None.
Conflicts of Interest: The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
doi: 10.21037/map-25-ab089
Cite this abstract as: Kyle E, O’Sullivan N, Kelly M, Meaney J, Temperley H, Corr A, Tohidinezhad F, Ajredini M, Sokmen BK, Atabey R, Ozer L, Aytac E, Traverso A. AB089. SOH25_AB_124. Magnetic resonance-derived radiomics to predict post-exenteration disease recurrence in advanced rectal cancer: a multi-institutional analysis. Mesentery Peritoneum 2025;9:AB089.
