Wang Chunhua, Chen Xiaoyu, Luo Hongbing, Liu Yuanyuan, Meng Ruirui, Wang Min, Liu Siyun, Xu Guohui, Ren Jing, Zhou Peng
Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Pharmaceutical Diagnostics, General Electric (GE) Company (Healthcare), Beijing, China.
Front Oncol. 2021 Nov 8;11:754843. doi: 10.3389/fonc.2021.754843. eCollection 2021.
To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients.
This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive ( = 93) and negative ( = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement.
The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all < 0.05).
FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.
基于药代动力学动态对比增强磁共振成像（DCE-MRI）和临床因素，开发并内部验证一种列线图，该列线图结合原发性肿瘤和纤维腺组织（FGT）的放射组学特征，用于术前预测乳腺癌患者前哨淋巴结（SLN）状态。
本研究回顾性纳入了186例接受术前药代动力学DCE-MRI检查的乳腺癌患者，其中SLN阳性（=93）和阴性（=93）各93例。在进行特征提取和选择后，构建肿瘤和FGT的逻辑回归模型及放射组学特征。将放射组学特征与临床因素的独立预测因子进一步结合，构建联合模型。通过受试者操作特征（ROC）、校准和决策曲线分析评估预测性能。模型的ROC曲线下面积（AUC）采用1000次自抽样法校正，并通过德龙检验进行比较。每个独立模型或其组合的增加值也通过净重新分类改善（NRI）和综合判别改善（IDI）指数进行评估。本报告参考了“个体预后或诊断多变量预测模型的透明报告”（TRIPOD）声明。
肿瘤放射组学模型（8个特征）和FGT放射组学模型（3个特征）的AUC分别为0.783（95%置信区间[CI]，0.717-0.849）和0.680（95%CI，0.604-0.757）。结合肿瘤和FGT放射组学特征可获得更高的AUC，为0.799（95%CI，0.737-0.862）。通过将肿瘤和FGT放射组学特征与孕激素受体（PR）状态进一步结合，开发出一种列线图，其对SLN状态具有更好的判别能力[AUC为0.839（95%CI，0.783-0.895）]。与每个独立模型或其中任意两个的组合相比，联合肿瘤、FGT和PR时，IDI和NRI指数也有显著改善（均<0.05）。
FGT和临床因素可提高乳腺癌SLN状态的预测性能。整合肿瘤和FGT的DCE-MRI放射组学特征及PR表达的列线图在预测SLN状态方面表现良好，为临床治疗决策提供了潜在的生物标志物。
