Integrating tumor habitat heterogeneity with a hybrid deep learning architecture for ultrasound radiomics: a dual-center study on non-invasive prediction of PD-L1 expression in triple-negative breast cancer.

objectiveWe sought to create a non-invasive method for predicting programmed death-ligand 1 (PD-L1) expression in triple-negative breast cancer (TNBC) by combining ultrasound radiomics with tumor habitat analysis and a Transformer-ResNet hybrid deep learning approach. MATERIALS AND

methodsPathologically confirmed TNBC patients treated from January 2020 through December 2024 at two centers were retrospectively analyzed. Pretreatment ultrasound images and PD-L1 immunohistochemistry results were collected, with positivity defined as a combined positive score ≥ 10. We applied K-means clustering to partition tumor regions into three habitat zones and extracted radiomic features from each zone separately. Transformer and ResNet networks provided additional deep learning features. A multi-stage selection process—including intraclass correlation coefficient testing, univariate screening, correlation filtering, and LASSO regression—was used to build Habitat, Transformer, and ResNet models individually. These were then merged into a Combined nomogram. Model performance was examined through ROC curves, calibration plots, and decision curve analysis.

resultsSix hundred fifty-four patients were enrolled (252 with PD-L1 positivity; 402 without). Training used 457 cases from Fujian Medical University Union Hospital; external validation involved 197 cases from the First Affiliated Hospital of Xiamen University. Zone 3 yielded the most predictive features (n = 18). Training AUCs reached 0.843, 0.869, 0.854, and 0.945 for Habitat, Transformer, ResNet, and Combined models respectively. External validation AUCs were 0.812, 0.842, 0.827, and 0.946 respectively. The Combined approach exceeded individual models by 10.4–13.4% and showed superior net benefit at threshold probabilities from 0.2 to 0.7.

conclusionOur Combined model accurately predicts PD-L1 status in TNBC using integrated habitat and deep learning features while offering a practical imaging biomarker for immunotherapy candidate selection.