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Spectral-habitat analysis and perfusion fingerprinting on contrast-enhanced ultrasound for characterizing tumor perfusion in breast lesions
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DOI：10.1186/s40644-026-01099-5.png)
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Abstract
En 中文
To develop a short-time Fourier transform (STFT)-based perfusion fingerprinting method combined with spectral-habitat analysis for characterizing pixel-wise perfusion heterogeneity on contrast-enhanced ultrasound (CEUS) and to evaluate its ability to differentiate benign and malignant breast lesions. Pixel-wise CEUS time-series data were transformed into the time-frequency domain using STFT. Principal component analysis (PCA) was then applied to reduce the dimensionality of STFT-derived features and generate perfusion fingerprinting maps that preserved spatial and temporal perfusion information. Spectral-habitat maps were generated using K-means clustering to identify perfusion-related tumor subregions. A triple-branch multi-layer fusion model was constructed to integrate full-region and habitat-derived features for lesion classification. To provide an exploratory conventional CEUS quantitative baseline, TIC-derived perfusion features were additionally extracted and evaluated using machine learning classifiers. Model performance was evaluated using receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and confusion matrix. The proposed STFT-based perfusion fingerprinting method enabled a more comprehensive characterization of tumor perfusion heterogeneity by preserving pixel-wise spatiotemporal dynamics of CEUS signals. CEUS-derived spectral-habitat maps provided visual representations of intratumoral perfusion heterogeneity and improved the interpretability of the model outputs. In this cohort, the triple-branch multi-layer fusion model achieved an AUC of 0.953, with accuracy of 0.909, precision of 0.907, recall of 0.925, and F1 score of 0.912. Exploratory TIC-based conventional CEUS models achieved AUCs ranging from 0.7677 to 0.8186. DCA suggested potential clinical utility of the proposed model across a range of threshold probabilities, and confusion matrix analysis showed favorable classification performance. The STFT-based perfusion fingerprinting method combined with spectral-habitat analysis provides a promising CEUS-based framework for characterizing perfusion heterogeneity in breast lesions. By preserving pixel-wise spatiotemporal information, this approach may provide complementary quantitative information for more interpretable differentiation between benign and malignant lesions.
Keywords:
Breast cancer
Contrast-enhanced ultrasound
Spectral-habitat analysis
Perfusion fingerprinting
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