Nijiati Mayidili, Aihaiti Diliaremu, Huojia Aisikaerjiang, Abulizi Abudukeyoumujiang, Mutailifu Sailidan, Rouzi Nueramina, Dai Guozhao, Maimaiti Patiman
Department of Radiology, The First People's Hospital of Kashgar, Xinjiang, China.
Front Oncol. 2022 Jun 6;12:876624. doi: 10.3389/fonc.2022.876624. eCollection 2022.
Preoperative identification of lymphovascular invasion (LVI) in patients with invasive breast cancer is challenging due to absence of reliable biomarkers or tools in clinical settings. We aimed to establish and validate multiparametric magnetic resonance imaging (MRI)-based radiomic models to predict the risk of lymphovascular invasion (LVI) in patients with invasive breast cancer.
This retrospective study included a total of 175 patients with confirmed invasive breast cancer who had known LVI status and preoperative MRI from two tertiary centers. The patients from center 1 was randomly divided into a training set (n=99) and a validation set (n = 26), while the patients from center 2 was used as a test set (n=50). A total of 1409 radiomic features were extracted from the T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), respectively. A three-step feature selection including SelectKBest, interclass correlation coefficients (ICC), and least absolute shrinkage and selection operator (LASSO) was performed to identify the features most associated with LVI. Subsequently, a Support Vector Machine (SVM) classifier was trained to develop single-layer radiomic models and fusion radiomic models. Model performance was evaluated and compared by the area under the curve (AUC), sensitivity, and specificity.
Based on one feature of wavelet-HLH_gldm_GrayLevelVariance, the ADC radiomic model achieved an AUC of 0.87 (95% confidence interval [CI]: 0.80-0.94) in the training set, 0.87 (0.70-1.00) in the validation set, and 0.77 (95%CI: 0.64-0.86) in the test set. However, the combination of radiomic features derived from other MR sequences failed to yield incremental value.
ADC-based radiomic model demonstrated a favorable performance in predicting LVI prior to surgery in patients with invasive breast cancer. Such model holds the potential for improving clinical decision-making regarding treatment for breast cancer.
由于临床环境中缺乏可靠的生物标志物或工具，术前识别浸润性乳腺癌患者的淋巴管侵犯（LVI）具有挑战性。我们旨在建立并验证基于多参数磁共振成像（MRI）的放射组学模型，以预测浸润性乳腺癌患者的淋巴管侵犯（LVI）风险。
这项回顾性研究共纳入了175例确诊为浸润性乳腺癌且已知LVI状态并进行了术前MRI检查的患者，这些患者来自两个三级中心。中心1的患者被随机分为训练集（n = 99）和验证集（n = 26），而中心2的患者用作测试集（n = 50）。分别从T2加权成像（T2WI）、动态对比增强（DCE）成像、扩散加权成像（DWI）和表观扩散系数（ADC）中提取了总共1409个放射组学特征。进行了包括SelectKBest、组内相关系数（ICC）和最小绝对收缩和选择算子（LASSO）在内的三步特征选择，以识别与LVI最相关的特征。随后，训练支持向量机（SVM）分类器以开发单层放射组学模型和融合放射组学模型。通过曲线下面积（AUC）、敏感性和特异性评估并比较模型性能。
基于小波-HLH_gldm_GrayLevelVariance这一特征，ADC放射组学模型在训练集中的AUC为0.87（95%置信区间[CI]：0.80 - 0.94），在验证集中为0.87（0.70 - 1.00），在测试集中为0.77（95%CI：0.64 - 0.86）。然而，源自其他MR序列的放射组学特征组合未能产生额外价值。
基于ADC的放射组学模型在预测浸润性乳腺癌患者术前LVI方面表现出良好性能。这种模型具有改善乳腺癌治疗临床决策的潜力。
