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Objective <p>Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical Diagnosis Model (PTs-HDM) for preoperative identification and grading.</p> Methods <p>Ultrasound images from five hospitals were retrospectively collected, with all patients having undergone surgical pathological confirmation of either PTs or fibroadenomas (FAs). PTs-HDM follows a two-stage classification: first distinguishing PTs from FAs, then grading PTs into benign or borderline/malignant. Model performance metrics including AUC and accuracy were quantitatively evaluated. A comparative analysis was conducted between the algorithm’s diagnostic capabilities and those of radiologists with varying clinical experience within an external validation cohort. Through the provision of PTs-HDM’s automated classification outputs and associated thermal activation mapping guidance, we systematically assessed the enhancement in radiologists’ diagnostic concordance and classification accuracy.</p> Results <p>A total of 712 patients were included. On the external test set, PTs-HDM achieved an AUC of 0.883, accuracy of 87.3% for PT vs. FA classification. Subgroup analysis showed high accuracy for tumors &lt; 2&#xa0;cm (90.9%). In hierarchical classification, the model obtained an AUC of 0.856 and accuracy of 80.9%. Radiologists’ performance improved with PTs-HDM assistance, with binary classification accuracy increasing from 82.7%, 67.7%, and 64.2–87.6%, 76.6%, and 82.1% for senior, attending, and resident radiologists, respectively. Their hierarchical classification AUCs improved from 0.566 to 0.827 to 0.725–0.837. PTs-HDM also enhanced inter-radiologist consistency, increasing Kappa values from − 0.05 to 0.41 to 0.12 to 0.65, and the intraclass correlation coefficient from 0.19 to 0.45.</p> Conclusion <p>PTs-HDM shows strong diagnostic performance, especially for small lesions, and improves radiologists’ accuracy across all experience levels, bridging diagnostic gaps and providing reliable support for PTs’ hierarchical diagnosis.</p>

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Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月8日

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  • Yuqi Yan,
  • Yuanzhen Liu,
  • Yao Wang,
  • Tian Jiang,
  • Jiayu Xie,
  • Yahan Zhou,
  • Xin Liu,
  • Meiying Yan,
  • Qiuqing Zheng,
  • Haifei Xu,
  • Jinxiao Chen,
  • Lin Sui,
  • Chen Chen,
  • RongRong Ru,
  • Kai Wang,
  • Anli Zhao,
  • Shiyan Li,
  • Ying Zhu,
  • Yang Zhang,
  • Vicky Yang Wang,
  • Dong Xu

摘要

Objective

Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical Diagnosis Model (PTs-HDM) for preoperative identification and grading.

Methods

Ultrasound images from five hospitals were retrospectively collected, with all patients having undergone surgical pathological confirmation of either PTs or fibroadenomas (FAs). PTs-HDM follows a two-stage classification: first distinguishing PTs from FAs, then grading PTs into benign or borderline/malignant. Model performance metrics including AUC and accuracy were quantitatively evaluated. A comparative analysis was conducted between the algorithm’s diagnostic capabilities and those of radiologists with varying clinical experience within an external validation cohort. Through the provision of PTs-HDM’s automated classification outputs and associated thermal activation mapping guidance, we systematically assessed the enhancement in radiologists’ diagnostic concordance and classification accuracy.

Results

A total of 712 patients were included. On the external test set, PTs-HDM achieved an AUC of 0.883, accuracy of 87.3% for PT vs. FA classification. Subgroup analysis showed high accuracy for tumors < 2 cm (90.9%). In hierarchical classification, the model obtained an AUC of 0.856 and accuracy of 80.9%. Radiologists’ performance improved with PTs-HDM assistance, with binary classification accuracy increasing from 82.7%, 67.7%, and 64.2–87.6%, 76.6%, and 82.1% for senior, attending, and resident radiologists, respectively. Their hierarchical classification AUCs improved from 0.566 to 0.827 to 0.725–0.837. PTs-HDM also enhanced inter-radiologist consistency, increasing Kappa values from − 0.05 to 0.41 to 0.12 to 0.65, and the intraclass correlation coefficient from 0.19 to 0.45.

Conclusion

PTs-HDM shows strong diagnostic performance, especially for small lesions, and improves radiologists’ accuracy across all experience levels, bridging diagnostic gaps and providing reliable support for PTs’ hierarchical diagnosis.