ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026
Integrating machine learning with histopathological, immunohistochemical, and PET parameters in the study of invasive breast carcinoma, no special type.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThis study investigates the relationship between histopathological (HP) features, immunohistochemical (IHC) markers,
methods384 patients were included in the study.
resultsSignificant correlations were observed between PET-derived parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) and HP/IHC characteristics, including Nottingham grade, molecular subtypes, and Ki-67 proliferation index. Machine learning models (XGBoost, Random Forest, LightGBM, CatBoost) demonstrated moderate success in identifying high-risk pathological and phenotypic features, with SUVmean identified as the most influential predictor via SHAP analysis. The coefficient of variation revealed low to moderate variability across most performance metrics, with CV values predominantly between 0.01 and 0.09, indicating stable and reliable model outputs.
conclusionThese findings suggest that integrating 18 F-FDG PET/CT metabolic parameters and machine learning algorithms can enhance non-invasive risk stratification and guide personalized treatment strategies in breast cancer management.
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