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<p>The Ki67 proliferation index (PI) serves as a crucial prognostic indicator in clinical settings, widely utilized for evaluating breast cancer progression and forecasting chemotherapy efficacy. Nonetheless, conventional manual PI estimation methods are plagued by high subjectivity, time inefficiency, and limited reproducibility. Despite notable advancements in deep learning for Ki67 PI computation, current approaches face challenges in concurrently capturing local cellular morphological features and global tumor proliferation data, particularly when addressing issues like complex backgrounds, staining variability, tumor heterogeneity, and noise in low-resolution images. In response, we introduce Kpi-Net, a multi scale deep learning framework built on U-Net, designed to precisely quantify the Ki67 index in breast cancer pathology images. Initially, we develop a Residual Dilated Multi Scale Module (RDMS Module) that utilizes multi-branch dilated convolutions and residual connections to capture both local and global information, while integrating a Transformer Block to bolster global modeling, effectively mitigating missed and erroneous detections arising from irregular cell distribution. Next, we introduce the High-level Screening-Convolutional Block Attention Module Feature Pyramid Networks (HS-CBAM-FPN), incorporating channel and spatial attention mechanisms alongside the Selective Feature Fusion with CBAM (SFF-CBAM) to facilitate effective integration of multi-level features. Lastly, we apply the watershed algorithm to feature maps, refining cell cluster segmentation via distance transformation and local maximum detection, which improves the precision of Ki67 index computation. Comprehensive experimental results indicate that Kpi-Net surpasses current leading methods in metrics like F1 score and root mean square error (RMSE), highlighting its potential for accurate Ki67 index computation and precise cell detection, thereby offering a dependable tool for accurate breast cancer diagnosis and therapeutic decision-making.</p>

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Multi scale deep learning quantifies Ki67 index in breast cancer histopathology images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Qi Liu,
  • Zhenfeng Zhao,
  • Lei Lou,
  • Yuehong Li,
  • Shenwen Wang

摘要

The Ki67 proliferation index (PI) serves as a crucial prognostic indicator in clinical settings, widely utilized for evaluating breast cancer progression and forecasting chemotherapy efficacy. Nonetheless, conventional manual PI estimation methods are plagued by high subjectivity, time inefficiency, and limited reproducibility. Despite notable advancements in deep learning for Ki67 PI computation, current approaches face challenges in concurrently capturing local cellular morphological features and global tumor proliferation data, particularly when addressing issues like complex backgrounds, staining variability, tumor heterogeneity, and noise in low-resolution images. In response, we introduce Kpi-Net, a multi scale deep learning framework built on U-Net, designed to precisely quantify the Ki67 index in breast cancer pathology images. Initially, we develop a Residual Dilated Multi Scale Module (RDMS Module) that utilizes multi-branch dilated convolutions and residual connections to capture both local and global information, while integrating a Transformer Block to bolster global modeling, effectively mitigating missed and erroneous detections arising from irregular cell distribution. Next, we introduce the High-level Screening-Convolutional Block Attention Module Feature Pyramid Networks (HS-CBAM-FPN), incorporating channel and spatial attention mechanisms alongside the Selective Feature Fusion with CBAM (SFF-CBAM) to facilitate effective integration of multi-level features. Lastly, we apply the watershed algorithm to feature maps, refining cell cluster segmentation via distance transformation and local maximum detection, which improves the precision of Ki67 index computation. Comprehensive experimental results indicate that Kpi-Net surpasses current leading methods in metrics like F1 score and root mean square error (RMSE), highlighting its potential for accurate Ki67 index computation and precise cell detection, thereby offering a dependable tool for accurate breast cancer diagnosis and therapeutic decision-making.