Habitat Radiomics Based on Dynamic Contrast‐Enhanced Magnetic Resonance Imaging for Assessing Axillary Lymph Node Burden in Clinical T1–T2 Stage Breast Cancer: A Multicenter and Interpretable Study

乳腺癌 医学 淋巴结 肿瘤科 磁共振成像 逻辑回归 阶段(地层学) 内科学 放射科 癌症 生物 古生物学
作者
Si‐Yi Chen,Yue Zhang,Ying Su,Jie Tian,Yongxin Chen,Wenjie Tang,Yaheng Fan,Chen Jin,Yangcheng He,Yongzhou Xu,Hong Hu,Yuan Guo,Junping Li
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
标识
DOI:10.1002/jmri.29796
摘要

ABSTRACT Background Axillary lymph node burden(ALNB) is a critical factor in determining treatment strategies for clinical T 1 –T 2 (cT 1 –T 2 ) stage breast cancer. However, as ALNB assessment relies on invasive procedures, exploring non‐invasive methods is essential. Purpose To develop and validate a habitat radiomics model for assessing ALNB in cT 1 –T 2 breast cancer, incorporating radiogenomic data to improve interpretability. Study Type Retrospective. Population 468 patients with cT 1 –T 2 stage breast cancer from two institutions and The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA)‐Breast Invasive Carcinoma (BRCA) were included. The cohort was divided into training ( n = 173), internal validation ( n = 58), external validation ( n = 130), and TCGA‐BRCA sets ( n = 107). Patients were categorized into high nodal burden (HNB; > 3 positive lymph nodes) and non‐HNB (≤ 3 positive lymph nodes) groups. Field Strength/Sequence 1.5‐T MRI and 3.0‐T MRI, and three‐dimensional dynamic contrast‐enhanced T1‐weighted gradient‐echo sequences. Assessment Two logistic regression models were developed using habitat‐based and clinical features. Model performance was evaluated using the AUC. SHapley Additive exPlanations (SHAP) analysis was employed to identify key features. Radiogenomic analysis, including gene set enrichment and drug sensitivity assessments, was conducted using transcriptomic data from the TCGA‐BRCA set. Statistical Tests Pearson correlation, Mann–Whitney U , genetic algorithm, logistic regression, AUC analysis, delong test, and SHAP analysis. A p ‐value < 0.05 was considered statistically significant. Results The Habitat model outperformed the Clinical model (AUCs: 0.840–0.932 vs. 0.558–0.673). The SHAP analysis was used to rank feature importance, with subregion 3 showing the highest average SHAP value. Radiogenomic analysis indicated upregulation of the KEGG ribosome pathway in the HNB group and identified differential drug sensitivity profiles among risk groups. Data Conclusion The Habitat model has the potential to assess ALNB in cT 1 –T 2 breast cancer and assist radiologists in axillary diagnosis, which may help reduce the need for unnecessary ALN dissection. Evidence Level: 3. Technical Efficacy: Stage 2.
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