Intra‐ and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer

医学 列线图 淋巴血管侵犯 无线电技术 有效扩散系数 乳腺癌 接收机工作特性 磁共振成像 乳房磁振造影 放射科 磁共振弥散成像 核医学 癌症 肿瘤科 转移 乳腺摄影术 内科学
作者
Wenyan Jiang,Ruiqing Meng,Yuan Cheng,Haotian Wang,Tingting Han,Ning Qu,Tao Yu,Yang Hou,Shu Xu
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:59 (2): 613-625 被引量:29
标识
DOI:10.1002/jmri.28776
摘要

Background Radiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated. Purpose To investigate the value of intra‐ and peritumoral radiomics for assessing LVI, and to develop a nomogram to assist in making treatment decisions. Study Type Retrospective. Population Three hundred and sixteen patients were enrolled from two centers and divided into training ( N = 165), internal validation ( N = 83), and external validation ( N = 68) cohorts. Field Strength/Sequence 1.5 T and 3.0 T/dynamic contrast‐enhanced (DCE) and diffusion‐weighted imaging (DWI). Assessment Radiomics features were extracted and selected based on intra‐ and peritumoral breast regions in two magnetic resonance imaging (MRI) sequences to create the multiparametric MRI combined radiomics signature (RS‐DCE plus DWI). The clinical model was built with MRI‐axillary lymph nodes (MRI ALN), MRI‐reported peritumoral edema (MPE), and apparent diffusion coefficient (ADC). The nomogram was constructed with RS‐DCE plus DWI, MRI ALN, MPE, and ADC. Statistical Tests Intra‐ and interclass correlation coefficient analysis, Mann–Whitney U test, and least absolute shrinkage and selection operator regression were used for feature selection. Receiver operating characteristic and decision curve analyses were applied to compare performance of the RS‐DCE plus DWI, clinical model, and nomogram. Results A total of 10 features were found to be associated with LVI, 3 from intra‐ and 7 from peritumoral areas. The nomogram showed good performance in the training (AUCs, nomogram vs. clinical model vs. RS‐DCE plus DWI, 0.884 vs. 0.695 vs. 0.870), internal validation (AUCs, nomogram vs. clinical model vs. RS‐DCE plus DWI, 0.813 vs. 0.695 vs. 0.794), and external validation (AUCs, nomogram vs. clinical model vs. RS‐DCE plus DWI, 0.862 vs. 0.601 vs. 0.849) cohorts. Data Conclusion The constructed preoperative nomogram might effectively assess LVI. Level of Evidence 3 Technical Efficacy Stage 2
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