Xu Xiaojun, Sun Xun, Ma Ling, Zhang Huangqi, Ji Wenbin, Xia Xiaotian, Lan Xiaoli
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
Front Oncol. 2023 Mar 10;13:1149791. doi: 10.3389/fonc.2023.1149791. eCollection 2023.
This study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters.
Breast cancer patients who underwent F-FDG PET/CT imaging before treatment from January 2012 to December 2020 were eligible for study inclusion. Eighty-seven patients were randomly divided into training (n = 61) and internal test sets (n = 26) and an additional 25 patients were used as the external validation set. Clinical parameters, including age, tumor size, molecular subtype, clinical TNM stage, and laboratory findings were collected. Radiomics features were extracted from preoperative PET/CT images. Least absolute shrinkage and selection operators were applied to shrink feature size and build a predictive radiomics signature. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to assess the association of rad-score and clinical parameter with PFS. Nomograms were constructed to visualize survival prediction. C-index and calibration curve were used to evaluate nomogram performance.
Eleven radiomics features were selected to generate rad-score. The clinical model comprised three parameters: clinical M stage, CA125, and pathological N stage. Rad-score and clinical-model were significantly associated with PFS in the training set (< 0.01) but not the test set. The integrated clinical-radiomics (ICR) model was significantly associated with PFS in both the training and test sets (< 0.01). The ICR model nomogram had a significantly higher C-index than the clinical model and rad-score in the training and test sets. The C-index of the ICR model in the external validation set was 0.754 (95% confidence interval, 0.726-0.812). PFS significantly differed between the low- and high-risk groups stratified by the nomogram ( = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit.
The ICR model, which combined clinical parameters and preoperative F-FDG PET/CT imaging, was able to independently predict PFS in breast cancer patients and was superior to the clinical model alone and rad-score alone.
本研究旨在探讨使用治疗前氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（FDG PET/CT）影像组学特征和临床参数预测乳腺癌患者无进展生存期（PFS）的可行性。
2012年1月至2020年12月期间在治疗前接受F-FDG PET/CT成像的乳腺癌患者符合纳入研究标准。87例患者被随机分为训练集（n = 61）和内部测试集（n = 26），另外25例患者用作外部验证集。收集临床参数，包括年龄、肿瘤大小、分子亚型、临床TNM分期和实验室检查结果。从术前PET/CT图像中提取影像组学特征。应用最小绝对收缩和选择算子来缩小特征规模并构建预测性影像组学特征。使用单变量和多变量Cox比例风险模型以及Kaplan-Meier分析来评估rad评分和临床参数与PFS的相关性。构建列线图以直观显示生存预测。使用C指数和校准曲线评估列线图性能。
选择了11个影像组学特征来生成rad评分。临床模型包含三个参数：临床M分期、CA125和病理N分期。在训练集中，rad评分和临床模型与PFS显著相关（<0.01），但在测试集中并非如此。综合临床-影像组学（ICR）模型在训练集和测试集中均与PFS显著相关（<0.01）。在训练集和测试集中，ICR模型列线图的C指数显著高于临床模型和rad评分。ICR模型在外部验证集中的C指数为0.754（95%置信区间，0.726 - 0.812）。根据列线图分层的低风险和高风险组之间的PFS有显著差异（=0.009）。校准曲线表明ICR模型提供了最大的临床益处。
结合临床参数和术前F-FDG PET/CT成像的ICR模型能够独立预测乳腺癌患者的PFS，并且优于单独的临床模型和单独的rad评分。
