Santucci Domiziana, Faiella Eliodoro, Cordelli Ermanno, Sicilia Rosa, de Felice Carlo, Zobel Bruno Beomonte, Iannello Giulio, Soda Paolo
Department of Radiology, University of Rome "Campus Bio-medico", Via Alvaro del Portillo, 21, 00128 Rome, Italy.
Unit of Computer Systems and Bioinformatics, Department of Engineering, University of Rome "Campus Bio-medico", Via Alvaro del Portillo, 21, 00128 Rome, Italy.
Cancers (Basel). 2021 May 6;13(9):2228. doi: 10.3390/cancers13092228.
axillary lymph node (LN) status is one of the main breast cancer prognostic factors and it is currently defined by invasive procedures. The aim of this study is to predict LN metastasis combining MRI radiomics features with primary breast tumor histological features and patients' clinical data.
99 lesions on pre-treatment contrasted 3T-MRI (DCE). All patients had a histologically proven invasive breast cancer and defined LN status. Patients' clinical data and tumor histological analysis were previously collected. For each tumor lesion, a semi-automatic segmentation was performed, using the second phase of DCE-MRI. Each segmentation was optimized using a convex-hull algorithm. In addition to the 14 semantics features and a feature ROI volume/convex-hull volume, 242 other quantitative features were extracted. A wrapper selection method selected the 15 most prognostic features (14 quantitative, 1 semantic), used to train the final learning model. The classifier used was the Random Forest.
the AUC-classifier was 0.856 (label = positive or negative). The contribution of each feature group was lower performance than the full signature.
the combination of patient clinical, histological and radiomics features of primary breast cancer can accurately predict LN status in a non-invasive way.
腋窝淋巴结（LN）状态是乳腺癌主要预后因素之一，目前通过侵入性手术来确定。本研究的目的是将MRI影像组学特征与原发性乳腺肿瘤组织学特征及患者临床数据相结合，以预测LN转移情况。
99个预处理后的3T-MRI（DCE）病变。所有患者均经组织学证实患有浸润性乳腺癌，并确定了LN状态。患者的临床数据和肿瘤组织学分析已预先收集。对于每个肿瘤病变，使用DCE-MRI的第二阶段进行半自动分割。每个分割使用凸包算法进行优化。除了14个语义特征和一个特征ROI体积/凸包体积外，还提取了242个其他定量特征。一种包装选择方法选择了15个最具预后性的特征（14个定量特征，1个语义特征），用于训练最终的学习模型。所使用的分类器是随机森林。
分类器的AUC为0.856（标签=阳性或阴性）。每个特征组的贡献表现低于完整特征集。
原发性乳腺癌患者的临床、组织学和影像组学特征相结合，可以以非侵入性方式准确预测LN状态。
