用病理知识指导超声模型，无须配对图像即可提升前列腺癌分级准确率。
GUIDE-US: Grade-Informed Unpaired Distillation of Encoder Knowledge from Histopathology to Micro-UltraSound
- 不依赖图像配对，通过分级条件蒸馏病理知识到超声编码器。
- 在60%特异性下，对高危癌症敏感度提升3.5%，整体敏感度提高1.2%。
- 适合临床早期癌症风险分层，尤其适用于缺乏配对数据的场景。
目的：从微超声（micro-US）无创评估前列腺癌（PCa）分级可加快分诊并引导活检至更具侵袭性的区域，但现有模型在低分辨率下难以推断组织微观结构。方法：提出一种无配对的组织病理学知识蒸馏策略，训练微超声编码器以模拟预训练组织病理学基础模型的嵌入分布，且条件依赖于国际泌尿病理学会（ISUP）分级。训练无需患者级配对或图像配准，且推理时不再使用病理学输入。结果：相比当前最优方法，在60%特异性下，本方法对临床显著前列腺癌（csPCa）的敏感度提升3.5%，整体敏感度提升1.2%。结论：该方法仅基于影像即可实现更早、更可靠的癌症风险分层，显著提升临床可行性。源代码将在发表后公开。
原文摘要 · Abstract (English)
Purpose: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggressive regions, yet current models struggle to infer tissue micro-structure at coarse imaging resolutions. Methods: We introduce an unpaired histopathology knowledge-distillation strategy that trains a micro-US encoder to emulate the embedding distribution of a pretrained histopathology foundation model, conditioned on International Society of Urological Pathology (ISUP) grades. Training requires no patient-level pairing or image registration, and histopathology inputs are not used at inference. Results: Compared to the current state of the art, our approach increases sensitivity to clinically significant PCa (csPCa) at 60% specificity by 3.5% and improves overall sensitivity at 60% specificity by 1.2%. Conclusion: By enabling earlier and more dependable cancer risk stratification solely from imaging, our method advances clinical feasibility. Source code will be publicly released upon publication.
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