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<p>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.</p>

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Panoramic photoacoustic computed tomography with learning-based classification enhances breast lesion characterization

  • 期刊论文

  • 发表日期：

    2025年6月24日

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  • Xin Tong,
  • Cindy Z. Liu,
  • Yilin Luo,
  • Li Lin,
  • Jessica Dzubnar,
  • Marta Invernizzi,
  • Stephanie Delos Santos,
  • Yide Zhang,
  • Rui Cao,
  • Peng Hu,
  • Junfu Zheng,
  • Jaclene Torres,
  • Armine Kasabyan,
  • Lily L. Lai,
  • Lisa D. Yee,
  • Lihong V. Wang

摘要

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.