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

Journal of Magnetic Resonance Imaging, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

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

Language: Английский

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Editor's Notebook: January 2025

American Journal of Roentgenology, Journal Year: 2025, Volume and Issue: 224(1)

Published: Jan. 1, 2025

Language: Английский

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Dual-Modality Virtual Biopsy System Integrating MRI and MG for Noninvasive Predicting HER2 Status in Breast Cancer

Academic Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine learning: a multicenter cohort study

Journal of Orthopaedic Surgery and Research, Journal Year: 2025, Volume and Issue: 20(1)

Published: May 24, 2025

To develop and validate an interpretable machine learning model based on clinicoradiological features radiomic magnetic resonance imaging (MRI) to predict the failure of conservative treatment in lateral epicondylitis (LE). This retrospective study included 420 patients with LE from three hospitals, divided into a training cohort (n = 245), internal validation 115), external 60). Patients were categorized 133) success 287) groups outcome treatment. We developed two predictive models: one utilizing features, another integrating features. Seven algorithms evaluated determine optimal for predicting Model performance was assessed using ROC, interpretability examined SHapley Additive exPlanations (SHAP). The LightGBM algorithm selected as because its superior performance. combined demonstrated enhanced accuracy area under ROC curve (AUC) 0.96 (95% CI: 0.91, 0.99) cohort. SHAP analysis identified radiological feature "CET coronal tear size" "AX_log-sigma-1-0-mm-3D_glszm_SmallAreaEmphasis" key predictors failure. validated that integrates LE. demonstrates high offers valuable insights prognostic factors.

Language: Английский

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Editorial Comment: MRI Radiomics and Machine Learning to Advance the Precision of HER2 Classification

American Journal of Roentgenology, Journal Year: 2024, Volume and Issue: 224(1)

Published: Oct. 30, 2024

Language: Английский

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