Abstract
Breast cancer diagnosis is crucial due to the high prevalence and mortality rate associated with the disease. However, mammography involves ionizing radiation and has compromised sensitivity in radiographically dense breasts, ultrasonography lacks specificity and has operator-dependent image quality, and magnetic resonance imaging faces high cost and patient exclusion. Photoacoustic computed tomography (PACT) offers a promising solution by combining light and ultrasound for high-resolution imaging that detects tumour-related vasculature changes. Here we introduce a workflow using panoramic PACT for breast lesion characterization, offering detailed visualization of vasculature irrespective of breast density. Analysing PACT features of 78 breasts in 39 patients, we develop learning-based classifiers to distinguish between normal and suspicious tissue, achieving a maximum area under the receiver operating characteristic curve of 0.89, which is comparable with that of conventional imaging standards. We further differentiate malignant and benign lesions using 13 features. Finally, we developed a learning-based model to segment breast lesions. Our study identifies PACT as a non-invasive and sensitive imaging tool for breast lesion evaluation.
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Data availability
The calculated features for all patients and the classification results are available on Figshare at https://doi.org/10.6084/m9.figshare.28675031 (ref. 81). The rest of the main data supporting the results in this study is available within the article and its Supplementary Information. The PA data are available for research purposes from the corresponding author on reasonable request.
Code availability
The code for data analysis is available on Figshare at https://doi.org/10.6084/m9.figshare.28675031 (ref. 81). The original code for MedSAM is available on GitHub82. We applied this code to our dataset with the customized settings described in Methods. We have opted not to make reconstruction and post-processing codes (described in detail in Methods and ref. 74) publicly available because the code is proprietary and used for other projects.
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Acknowledgements
We thank Y. Aborahama and G. Zhao for machine-learning-related discussion, and R. Nelson, D. Schmolze and L. Vora for their interpretation of the radiology findings. This project has been made possible in part by National Institutes of Health grants R35 CA220436 (Outstanding Investigator Award), R01 CA282505, U01 EB029823 and R01 EB028277. Y.Z. was sponsored by the National Institutes of Health grant K99 EB035645. C.Z.L. was sponsored by the National Institutes of Health grants NIGMS T32 GM008042 and NIGMS T32 GM152342.
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L.V.W., L.D.Y., L.L.L., L.L. and X.T. conceived and designed the study. X.T., L.L. and R.C. constructed the hardware system. Y.Z. constructed the control program. P.H. and X.T. developed the software system and the reconstruction algorithm. X.T., C.Z.L., Y.L. and L.L. performed the experiments. X.T., C.Z.L., Y.L., R.C. and J.Z. analysed the data. L.D.Y., L.L.L., M.I., J.D., S.D.S., J.T. and A.K. guided the patient study. L.V.W. supervised the project. All authors contributed to writing the paper.
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L.V.W. has a financial interest in Microphotoacoustics Inc., CalPACT LLC and Union Photoacoustic Technologies Ltd., which, however, did not support this work. The other authors declare no competing interests.
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Tong, X., Liu, C.Z., Luo, Y. et al. Panoramic photoacoustic computed tomography with learning-based classification enhances breast lesion characterization. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01435-3
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DOI: https://doi.org/10.1038/s41551-025-01435-3
