O'Sullivan Niall J, Tohidinezhad Fariba, Temperley Hugo C, Ajredini Mirac, Sokmen Bedirye Koyuncu, Atabey Rumeysa, Ozer Leyla, Aytac Erman, Corr Alison, Traverso Alberto, Meaney James F, Kelly Michael E
Department of Radiology, St. James's Hospital, Dublin, Ireland.
School of Medicine, Trinity College Dublin, Dublin, Ireland.
Cancer Med. 2025 Feb;14(4):e70699. doi: 10.1002/cam4.70699.
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 that extracts and analyses quantitative imaging features from images, which can be subsequently used to develop and test clinical models predictive of outcomes. We aim to develop and test an MRI-based radiomics nomogram predictive of disease recurrence in patients with T4 rectal cancer.
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 1000 bootstrap samples was performed to calculate optimism-corrected performance measures.
Two radiomic signatures were identified as strong predictors of post-operative disease recurrence. The best-performing model achieved an optimism-corrected AUC of 0.75, demonstrating good discriminative ability. Calibration plots showed a satisfactory fit of the predictions to the actual rates, and decision curve analyses confirmed the positive net benefit of the models.
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 personalized treatment strategies. Further studies with larger cohorts and external validation are needed to confirm these findings and enhance the model's generalizability.
局部复发和远处转移仍是晚期直肠癌患者关注的问题，分别有高达10%和20%-30%的患者出现局部和远处进展。放射组学是一种从图像中提取和分析定量影像特征的新技术，这些特征随后可用于开发和测试预测预后的临床模型。我们旨在开发并测试一种基于MRI的放射组学列线图，以预测T4期直肠癌患者的疾病复发情况。
我们对55例接受新辅助放化疗后行扩大根治术的T4期直肠癌患者进行了多机构回顾性分析。从治疗前的T2加权MRI扫描中提取放射组学特征，并用于构建预测模型。确定表现最佳的放射组学特征，并使用1000次自抽样进行内部验证，以计算经乐观估计校正后的性能指标。
两个放射组学特征被确定为术后疾病复发的强预测指标。表现最佳的模型经乐观估计校正后的AUC为0.75，显示出良好的判别能力。校准图显示预测结果与实际发生率拟合良好，决策曲线分析证实了模型的净效益为正。
基于MRI的放射组学列线图为预测T4期直肠癌患者扩大根治术后的疾病复发提供了一种有前景的工具。该模型可改善风险分层并指导更个性化的治疗策略。需要进一步开展更大样本量的研究和外部验证，以证实这些发现并提高模型的通用性。
