DataSheet_2_MRI-Based Radiomics Features to Predict Treatment Response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer: A Single Center, Prospective Study.pdf

This is a prospective, single center study aimed to evaluate the predictive power of peritumor and intratumor radiomics features assessed using T2 weight image (T2WI) of baseline magnetic resonance imaging (MRI) in evaluating pathological good response to NAC in patients with LARC (including Tany N+ or T3/4a Nany but not T4b). In total, 137 patients with LARC received NAC between April 2014 and August 2020. All patients were undergoing contrast-enhanced MRI and 129 patients contained small field of view (sFOV) sequence which were performed prior to treatment. The tumor regression grade standard was based on pathological response. The training and validation sets (n=91 vs. n=46) were established by random allocation of the patients. Receiver operating characteristic curve (ROC) analysis was applied to estimate the performance of different models based on clinical characteristics and radiomics features obtained from MRI, including peritumor and intratumor features, in predicting treatment response; these effects were calculated using the area under the curve (AUC). The performance and agreement of the nomogram were estimated using calibration plots. In total, 24 patients (17.52%) achieved a complete or near-complete response. For the individual radiomics model in the validation set, the performance of peritumor radiomics model in predicting treatment response yield an AUC of 0.838, while that of intratumor radiomics model is 0.805, which show no statically significant difference between then(P>0.05). The traditional and selective clinical features model shows a poor predictive ability in treatment response (AUC=0.596 and 0.521) in validation set. The AUC of combined radiomics model was improved compared to that of the individual radiomics models in the validation sets (AUC=0.844). The combined clinic-radiomics model yield the highest AUC (0.871) in the validation set, although it did not improve the performance of the radiomics model for predicting treatment response statically (P>0.05). Good agreement and discrimination were observed in the nomogram predictions. Both peritumor and intratumor radiomics features performed similarly in predicting a good response to NAC in patients with LARC. The clinic-radiomics model showed the best performance in predicting treatment response.

本项前瞻性、单中心研究旨在评估利用基线磁共振成像（MRI）T2加权图像（T2WI）所测定的肿瘤周围及肿瘤内部放射组学特征对评估局部晚期直肠癌（LARC，包括Tany N+或T3/4a Nany，但不包括T4b）患者对新辅助化疗（NAC）病理良好反应的预测能力。总计137名LARC患者于2014年4月至2020年8月期间接受了NAC治疗。所有患者均进行了对比增强MRI扫描，其中129名患者在接受治疗之前进行了小视野（sFOV）序列扫描。肿瘤退缩分级标准基于病理反应。通过随机分配患者建立了训练集和验证集（n=91 vs. n=46）。采用受试者工作特征曲线（ROC）分析来评估基于临床特征和从MRI中获得的放射组学特征（包括肿瘤周围和肿瘤内部特征）的不同模型在预测治疗反应方面的性能；这些效果通过曲线下面积（AUC）进行计算。使用校准图来评估列线图的性能和一致性。总计24名患者（17.52%）实现了完全或近乎完全的反应。对于验证集中的个体放射组学模型，预测治疗反应的肿瘤周围放射组学模型的AUC为0.838，而肿瘤内部放射组学模型的AUC为0.805，两者之间无统计学上的显著差异（P>0.05）。在验证集中，传统和选择性临床特征模型在预测治疗反应方面的预测能力较差（AUC=0.596和0.521）。与个体放射组学模型相比，联合放射组学模型的AUC在验证集中有所提高（AUC=0.844）。联合临床-放射组学模型在验证集中产生了最高的AUC（0.871），尽管它并没有在统计学上改善放射组学模型预测治疗反应的性能（P>0.05）。列线图预测显示了良好的一致性和区分度。肿瘤周围和肿瘤内部的放射组学特征在预测LARC患者对NAC的良好反应方面表现相似。临床-放射组学模型在预测治疗反应方面表现出最佳性能。