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However, as ALNB assessment relies on invasive procedures, exploring non\u2010invasive methods is essential.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      To develop and validate a habitat radiomics model for assessing ALNB in cT\n                      <jats:sub>1<\/jats:sub>\n                      \u2013T\n                      <jats:sub>2<\/jats:sub>\n                      breast cancer, incorporating radiogenomic data to improve interpretability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>\n                      468 patients with cT\n                      <jats:sub>1<\/jats:sub>\n                      \u2013T\n                      <jats:sub>2<\/jats:sub>\n                      stage breast cancer from two institutions and The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA)\u2010Breast Invasive Carcinoma (BRCA) were included. The cohort was divided into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009173), internal validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200958), external validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009130), and TCGA\u2010BRCA sets (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009107). Patients were categorized into high nodal burden (HNB; &gt;\u20093 positive lymph nodes) and non\u2010HNB (\u2264\u20093 positive lymph nodes) groups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>1.5\u2010T MRI and 3.0\u2010T MRI, and three\u2010dimensional dynamic contrast\u2010enhanced T1\u2010weighted gradient\u2010echo sequences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Two logistic regression models were developed using habitat\u2010based 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\u2010BRCA set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Pearson correlation, Mann\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      , genetic algorithm, logistic regression, AUC analysis, delong test, and SHAP analysis. A\n                      <jats:italic>p<\/jats:italic>\n                      \u2010value &lt;\u20090.05 was considered statistically significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Habitat model outperformed the Clinical model (AUCs: 0.840\u20130.932 vs. 0.558\u20130.673). 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>\n                      The Habitat model has the potential to assess ALNB in cT\n                      <jats:sub>1<\/jats:sub>\n                      \u2013T\n                      <jats:sub>2<\/jats:sub>\n                      breast cancer and assist radiologists in axillary diagnosis, which may help reduce the need for unnecessary ALN dissection.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Evidence Level:<\/jats:bold>\n                      3.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Technical Efficacy:<\/jats:bold>\n                      Stage 2.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.29796","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T05:29:12Z","timestamp":1745213352000},"page":"751-764","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Habitat Radiomics Based on Dynamic Contrast\u2010Enhanced Magnetic Resonance Imaging for Assessing Axillary Lymph Node Burden in Clinical\n                    <scp>T1<\/scp>\n                    \u2013\n                    <scp>T2<\/scp>\n                    Stage Breast Cancer: A Multicenter and Interpretable Study"],"prefix":"10.1002","volume":"62","author":[{"given":"Siyi","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Radiology, Xiangyang No. 1 People's Hospital Hubei University of Medicine  Xiangyang 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