Spectral-habitat analysis and perfusion fingerprinting on contrast-enhanced ultrasound for characterizing tumor perfusion in breast lesions
7 affiliations
- 1Department of Ultrasound, Department of Medical Imaging, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
- 2Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
- 3Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
- 4Institute for Future Sciences, University of South China, Changsha, Hunan 410008 China
- 5The Seventh Affiliated Hospital, Hengyang Medical School, University of South China (Hunan Provincial Veterans Administration Hospital), Changsha, Hunan China
- 6School of Computer, University of South China, Hengyang, Hunan 421000 China
- 7Department of Thyroid Breast Surgery, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan China
Abstract
Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s40644-026-01099-5.
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BibTeX
@article{liu2026spectralhabitat,
title = {{Spectral-habitat analysis and perfusion fingerprinting on contrast-enhanced ultrasound for characterizing tumor perfusion in breast lesions}},
author = {Shun Liu and Jie Lan and Jun Li and Yanfen Zhang and Yuewen Qiu and Xinchun Liu and Linyuan Jin and Meng Du and Zhiyi Chen},
journal = {Cancer Imaging},
year = {2026},
month = aug,
doi = {10.1186/s40644-026-01099-5},
url = {https://doi.org/10.1186/s40644-026-01099-5},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Spectral-habitat analysis and perfusion fingerprinting on contrast-enhanced ultrasound for characterizing tumor perfusion in breast lesions
AU - Shun Liu
AU - Jie Lan
AU - Jun Li
AU - Yanfen Zhang
AU - Yuewen Qiu
AU - Xinchun Liu
AU - Linyuan Jin
AU - Meng Du
AU - Zhiyi Chen
PY - 2026
DA - 2026/08/10
JO - Cancer Imaging
DO - 10.1186/s40644-026-01099-5
UR - https://doi.org/10.1186/s40644-026-01099-5
AB - Objective: 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. Methods: 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. Results: 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. Conclusion: 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. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s40644-026-01099-5.
SN - 1740-5025
SN - 1470-7330
ER -