用于检测局部晚期癌症的病理完全反应的重启MRI的放射组学分析
Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.
摘要(中文译文)
基于磁共振成像（MRI）的影像组学已成为无创预测直肠癌治疗反应的一种有前景的方法。本研究旨在开发并验证一种基于影像组学特征的机器学习模型，该特征提取自局部晚期直肠癌（LARC）患者新辅助治疗后再分期MRI，用于识别达到病理完全缓解（pCR）的患者。在这项回顾性单中心研究中，纳入2017年至2022年间经组织学确诊并接受治疗的直肠癌患者，纳入条件为接受新辅助治疗、分期及再分期MRI以及手术。肿瘤分割在斜轴位T2加权图像上进行。影像组学特征在图像重采样和强度离散化后使用PyRadiomics提取。采用最小绝对收缩和选择算子（LASSO）进行特征选择，并训练了带弹性网络正则化的逻辑回归模型。模型性能采用重复分层5折交叉验证进行评估。性能指标包括受试者工作特征曲线下面积（AUC-ROC）、准确率、敏感性和特异性。最终队列纳入86例患者（平均年龄63±12岁；65%为男性），其中33例达到pCR（38.37%），53例未达到（61.63%）。模型表现出中等 discriminative 性能，平均AUC-ROC为0.74，准确率为67.4%，敏感性为60.1%，特异性为71.0%。识别非应答者的性能高于识别达到pCR患者的性能。基于再分期MRI的影像组学显示出预测LARC治疗反应的潜力，尤其是在识别新辅助治疗非应答者方面。尽管性能仍为中等，该方法可能支持治疗分层和个性化管理。尚需在更大规模的多中心队列中进一步验证。
Abstract
magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rectal cancer. This study aimed to develop and validate a machine learning model based on radiomic features extracted from restaging MRI after neoadjuvant therapy in patients with locally advanced rectal cancer (LARC), to identify those achieving pathological complete response (pCR). In this retrospective single-center study, patients with histologically confirmed rectal cancer treated between 2017 and 2022 were included if they underwent neoadjuvant therapy, staging and restaging MRI, and surgery. Tumor segmentation was performed on oblique axial T2-weighted images. Radiomic features were extracted using PyRadiomics following image resampling and intensity discretization. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO), and a logistic regression model with Elastic Net regularization was trained. Model performance was evaluated using repeated stratified 5-fold cross-validation. Performance metrics included area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and specificity. The final cohort included 86 patients (mean age 63 ± 12 years; 65% male) with 33 patients achieving pCR (38.37%) and 53 who did not (61.63%). The model demonstrated moderate discriminative performance, with a mean AUC-ROC of 0.74, accuracy of 67.4%, sensitivity of 60.1%, and specificity of 71.0%. Performance was higher for identifying non-responders compared with patients achieving pCR. MRI-based radiomics from restaging MRI shows potential to predict treatment response in LARC, particularly in identifying non-responders to neoadjuvant therapy. Although performance remains moderate, this approach may support treatment stratification and personalized management. Further validation in larger, multicenter cohorts is warranted.
如何引用
AMA
Ilaria Ambrosini, Roberto Francischello, Salvatore Claudio Fanni, Lorenzo Faggioni, Giacomo Aringhieri, Rachele Fruzza, et al. Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.. European journal of radiology open. 2026; doi:10.1016/j.ejro.2026.100762.
APA
Ilaria Ambrosini, Roberto Francischello, Salvatore Claudio Fanni, Lorenzo Faggioni, Giacomo Aringhieri, Rachele Fruzza, et al (2026). Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.. European journal of radiology open. https://doi.org/10.1016/j.ejro.2026.100762
GB/T 7714
Ilaria Ambrosini, Roberto Francischello, Salvatore Claudio Fanni, Lorenzo Faggioni, Giacomo Aringhieri, Rachele Fruzza, et al. Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.[J]. European journal of radiology open, 2026 doi:10.1016/j.ejro.2026.100762.
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