ArticleThe ultrasound journal2026
A hybrid radiomics framework integrating genetic algorithm-optimized random forest for preoperative identification of Luminal B breast cancer and Ki-67 prediction: A multicenter study.
Article in The ultrasound journal, 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
backgroundPreoperative identification of Luminal B breast cancer remains a clinical challenge. This study aimed to develop an ultrasound radiomics framework integrating tumoral and peritumoral information for preoperative identification of Luminal B subtype and prediction of Ki-67 status.
methodsWe retrospectively analyzed 1,944 patients from three centers. The development cohort from Centers One and Two was divided by stratified sampling into a training set (n = 1,434) and an internal test set (n = 253), and an independent cohort from Center Three (n = 257) was used for external validation. Lesion-containing ROIs were processed using deep learning-assisted segmentation and standardized for downstream analysis. Radiomic features were extracted, and a genetic algorithm (GA) was coupled with a random forest (RF) classifier to construct two models: one for Luminal B classification and another for predicting Ki-67 expression.
resultsThe combined tumor-peritumoral model achieved the highest performance, with the Luminal B classifier showing AUCs of 0.876 (training), 0.693 (test), and 0.786 (external validation). The Ki-67 prediction model yielded AUCs of 0.890 (training) and 0.858 (test), though external validation (AUC=0.661) was limited by dataset distribution. The Delong test confirmed that combined ROIs significantly outperformed tumor-only models, with NRI and IDI tests further validating the added value of peritumoral features.
conclusionsUltrasound radiomics integrating tumoral and peritumoral regions can support the preoperative identification of Luminal B breast cancer, and peritumoral region analysis significantly enhances predictive performance. The framework also shows potential for predicting Ki-67 status within this subtype.
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