DataSheet_1_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基于基线磁共振成像（MRI）T2加权成像（T2WI）所测定的肿瘤周围和肿瘤内部放射组学特征对LARC（包括Tany N+或T3/4a Nany，但不包括T4b）患者病理良好反应对NAC预后的预测能力。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。所有患者均接受了对比增强MRI检查，其中129名患者在接受治疗前进行了小视野序列（sFOV）。肿瘤退缩分级标准基于病理反应。通过患者的随机分配，建立了训练集和验证集（n=91 vs. n=46）。应用受试者工作特征曲线（ROC）分析，评估了基于临床特征和从MRI获取的放射组学特征（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。使用校准图评估了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。在验证集中的单个放射组学模型中，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表现出较差的预测能力（AUC=0.596和0.521）。与单个放射组学模型相比，联合放射组学模型在验证集中的AUC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），尽管它并未在统计学上提高放射组学模型预测治疗反应的性能（P>0.05）。在列线图预测中观察到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。