DataSheet_1_Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma.docx

IntroductionThe molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC.
MethodsA total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis.
ResultsIn all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram.
ConclusionBoth the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC.

引言
分子亚型在乳腺癌（BC）中发挥着重要作用，其是指导治疗的核心指标，且与预后密切相关。本研究旨在探讨基于超声的放射组学列线图在术前鉴别乳腺癌患者腔面型与非腔面型的可行性与效能。

方法
本研究共纳入264例接受常规超声检查的乳腺癌患者，其中184例纳入训练集，80例纳入测试集。研究人员在超声图像上手动勾勒乳腺肿瘤，随后提取放射组学特征。在训练集中，采用t检验与最小绝对收缩和选择算子（LASSO）筛选特征，并计算每位患者的放射组学评分（Rad-score）。基于临床风险特征、放射组学评分，以及二者联合的特征，分别构建了三种预测模型。采用受试者工作特征（ROC）曲线与决策曲线分析对模型性能进行验证。

结果
本研究从每例患者的超声图像中提取了788个放射组学特征。经放射组学特征筛选后，最终选取11个特征构建放射组学评分。该评分预测腔面型乳腺癌的ROC曲线下面积（AUC）在训练集为0.828，测试集为0.786。联合放射组学评分与超声报告肿瘤大小的列线图，其训练集与测试集的AUC分别为0.832和0.767，显著高于临床模型在训练集（0.691）与测试集（0.526）的AUC。但放射组学评分与列线图的预测性能无显著差异。

结论
放射组学评分与列线图均可作为有效的无创工具，用于术前鉴别乳腺癌患者的腔面型与非腔面型。本研究或可为乳腺癌分子亚型的术前评估提供一种全新的技术手段。