PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters.
UNASSIGNED: Breast cancer patients who underwent 18F-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.
UNASSIGNED: 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 (P< 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 (P< 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 (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit.
UNASSIGNED: The ICR model, which combined clinical parameters and preoperative 18F-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.
UNASSIGNED: Breast cancer patients who underwent 18F-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.
UNASSIGNED: 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 (P< 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 (P< 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 (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit.
UNASSIGNED: The ICR model, which combined clinical parameters and preoperative 18F-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.
摘要：
■本研究旨在研究使用预处理18F-脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（FDGPET/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月在治疗前接受18F-FDGPET/CT显像的乳腺癌患者符合纳入研究的条件。87名患者被随机分为训练集（n=61）和内部测试集（n=26），另外25名患者被用作外部验证集。临床参数,包括年龄,肿瘤大小,分子亚型,临床TNM分期,并收集了实验室发现。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运算符来收缩特征大小并构建预测性放射组学签名。使用单变量和多变量Cox比例风险模型和Kaplan-Meier分析来评估rad评分和临床参数与PFS的关联。构建列线图以可视化生存预测。C指数和校准曲线用于评估列线图性能。
■选择11个放射组学特征来产生rad-score。临床模型包括三个参数：临床M期，CA125和病理N分期。Rad评分和临床模型在训练集中与PFS显着相关（P<0.01），而在测试集中则没有。综合临床-影像组学(ICR)模型在训练集和测试集均与PFS显著相关(P<0.01)。ICR模型列线图在训练和测试集中具有明显高于临床模型和rad评分的C指数。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）。通过列线图分层的低危组和高风险组之间的PFS显着差异（P=0.009）。校准曲线表明ICR模型提供了最大的临床益处。
■ICR模型，结合临床参数和术前18F-FDGPET/CT成像，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rad评分。
2012年1月至2020年12月在治疗前接受18F-FDGPET/CT显像的乳腺癌患者符合纳入研究的条件。87名患者被随机分为训练集（n=61）和内部测试集（n=26），另外25名患者被用作外部验证集。临床参数,包括年龄,肿瘤大小,分子亚型,临床TNM分期,并收集了实验室发现。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运算符来收缩特征大小并构建预测性放射组学签名。使用单变量和多变量Cox比例风险模型和Kaplan-Meier分析来评估rad评分和临床参数与PFS的关联。构建列线图以可视化生存预测。C指数和校准曲线用于评估列线图性能。
■选择11个放射组学特征来产生rad-score。临床模型包括三个参数：临床M期，CA125和病理N分期。Rad评分和临床模型在训练集中与PFS显着相关（P<0.01），而在测试集中则没有。综合临床-影像组学(ICR)模型在训练集和测试集均与PFS显著相关(P<0.01)。ICR模型列线图在训练和测试集中具有明显高于临床模型和rad评分的C指数。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）。通过列线图分层的低危组和高风险组之间的PFS显着差异（P=0.009）。校准曲线表明ICR模型提供了最大的临床益处。
■ICR模型，结合临床参数和术前18F-FDGPET/CT成像，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rad评分。
