基于MRI的放射线模型在预测局部晚期直肠癌中对新辅助化学疗法的病理完全反应方面优于放射线医生
MRI-Based Radiomic Models Outperform Radiologists in Predicting Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
影响因子:3.90000
分区:医学2区 / 核医学2区
发表日期:2023 Sep
作者:
Lu Wen, Jun Liu, Pingsheng Hu, Feng Bi, Siye Liu, Lian Jian, Suyu Zhu, Shaolin Nie, Fang Cao, Qiang Lu, Xiaoping Yu, Ke Liu
摘要
15％-27％的局部晚期直肠癌患者（LARC）对新辅助化学疗法（NCRT）实现了病理完全反应（PCR），并且可以避免肠道切除术。我们旨在研究使用NCRT治疗的LARC患者使用基于MRI的前，后，后和三角核特征的治疗反应预测的有效性，并将这些放射线模型与放射学家的视觉评估进行比较。总共包括126名LARC患者，在手术前接受了NCRT的LARC，并随机分配了NCRT，并随机分为训练集（N = 84 = 84 = 84）（N = 84）。从NCRT前和后MRI中提取了250个放射素特征。 Pearson相关分析和AONVA或浮雕用于鉴定与PCR相关的放射线描述符。比较了五个机器学习分类器，以构建放射线模型。放射线命名图是通过多元逻辑回归分析构建的。两名高级放射科医生独立评估了肿瘤回归等级，并与放射线模型进行了比较。 Area under the curve (AUC) of the models and pooled observers were compared by using the DeLong test.The optimal pre-, post-, and delta-radiomic models yielded an AUC of 0.717 (95% CI: 0.639-0.795), 0.805 (95%CI: 0.736-0.874), and 0.724 (95%CI:分别为0.648-0.800）。基于NCRT前CN阶段，NCRT前RADSCORE和NCRT RADSCORE的放射素列图的AUC为0.852（95％CI：0.774-0.930），该AUC高于单个放射线型和合并的读取器和汇集的单个读取器（所有P <0.05）。胜过放射科医生。
Abstract
The 15%-27% of patients with locally advanced rectal cancer (LARC) achieved pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) and could avoid proctectomy. We aimed to investigate the effectiveness of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for LARC patients treated with nCRT and to compare these radiomic models with radiologists' visual assessment.A total of 126 patients with LARC who received nCRT before surgery were included and randomly divided into a training set (n = 84) and a validation set (n = 42). 250 radiomic features were extracted from T2-weighted images from pre- and post-nCRT MRI. Pearson correlation analysis and AONVA or Relief were used to identify radiomic descriptors associated with pCR. Five machine-learning classifiers were compared to construct radiomic models. The radiomic nomogram was built via multivariate logistic regression analysis. Two senior radiologists independently rated tumor regression grades and compared with radiomic models. Area under the curve (AUC) of the models and pooled observers were compared by using the DeLong test.The optimal pre-, post-, and delta-radiomic models yielded an AUC of 0.717 (95% CI: 0.639-0.795), 0.805 (95%CI: 0.736-0.874), and 0.724 (95%CI: 0.648-0.800), respectively. The radiomic nomogram based on pre-nCRT cN stage, pre-nCRT radscore, and post-nCRT radscore achieved an AUC of 0.852 (95%CI: 0.774-0.930), which was higher than the single radiomic models and pooled readers (all p < 0.05).The radiomic nomogram is an effective and invasive tool to predict pCR in LARC patients after nCRT, which outperforms radiologists.