Gui Benedetta, Autorino Rosa, Miccò Maura, Nardangeli Alessia, Pesce Adele, Lenkowicz Jacopo, Cusumano Davide, Russo Luca, Persiani Salvatore, Boldrini Luca, Dinapoli Nicola, Macchia Gabriella, Sallustio Giuseppina, Gambacorta Maria Antonietta, Ferrandina Gabriella, Manfredi Riccardo, Valentini Vincenzo, Scambia Giovanni
Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, 00168 Roma, Italy.
Istituto di Radiologia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy.
Diagnostics (Basel). 2021 Mar 31;11(4):631. doi: 10.3390/diagnostics11040631.
The aim of this study was to create a radiomics model for Locally Advanced Cervical Cancer (LACC) patients to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (NACRT) analysing T2-weighted 1.5 T magnetic resonance imaging (MRI) acquired before treatment start. Patients with LACC and an International Federation of Gynecology and Obstetrics stage from IB2 to IVA at diagnosis were retrospectively enrolled for this study. All patients underwent NACRT, followed by radical surgery; pCR-assessed on surgical specimen-was defined as absence of any residual tumour. Finally, 1889 features were extracted from MR images; features showing statistical significance in predicting pCR at the univariate analysis were selected following an iterative method, which was ad-hoc developed for this study. Based on this method, 15 different classifiers were trained considering the most significant features selected. Model selection was carried out using the area under the receiver operating characteristic curve (AUC) as target metrics. One hundred eighty-three patients from two institutions were analysed. The model, showing the highest performance with an AUC of 0.80, was the random forest method initialised with default parameters. Radiomics appeared to be a reliable tool in pCR prediction for LACC patients undergoing NACRT, supporting the identification of patient risk groups, which paves treatment pathways tailored according to the predicted outcome.
本研究的目的是为局部晚期宫颈癌（LACC）患者创建一个放射组学模型，通过分析治疗开始前采集的1.5 T T2加权磁共振成像（MRI）来预测新辅助放化疗（NACRT）后的病理完全缓解（pCR）。本研究回顾性纳入了诊断为LACC且国际妇产科联盟分期为IB2至IVA期的患者。所有患者均接受了NACRT，随后进行了根治性手术；手术标本评估的pCR定义为无任何残留肿瘤。最后，从MR图像中提取了1889个特征；在单变量分析中显示出对预测pCR具有统计学意义的特征通过一种为本研究专门开发的迭代方法进行选择。基于该方法，考虑所选的最显著特征训练了15种不同的分类器。使用受试者操作特征曲线下面积（AUC）作为目标指标进行模型选择。分析了来自两个机构的183名患者。表现最佳、AUC为0.80的模型是使用默认参数初始化的随机森林方法。放射组学似乎是预测接受NACRT的LACC患者pCR的可靠工具，有助于识别患者风险组，从而根据预测结果制定量身定制的治疗方案。
