AI可精准识别前列腺癌难辨组织，减少40%以上免疫组化使用。
Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry
- 用AI分析H&E染色切片，自动判读可疑腺体形态。
- 在三个队列中降低42%-44%的IHC需求，无漏诊。
- 适合病理诊断资源紧张的医院快速决策。
前列腺癌诊断依赖组织病理学评估，存在主观差异。虽然免疫组化（IHC）有助于区分良恶性组织，但增加工作量、成本和诊断延迟。人工智能（AI）有望通过准确分类苏木精-伊红（H&E）染色切片中的非典型腺体和边界性形态，减少对IHC的依赖。本研究回顾性分析了三家病理机构的常规诊断中难以判断的前列腺核心针活检样本。这些队列仅包含需IHC才能确诊的疑难病例。AI模型在检测癌症时的曲线下面积（AUC）为0.951–0.993。采用以敏感性优先的诊断阈值后，在三个队列中分别减少了44.4%、42.0%和20.7%的IHC使用，且未出现任何假阴性预测。该模型显示了优化IHC使用、提升前列腺病理决策效率和减轻资源负担的潜力。
原文摘要 · Abstract (English)
Prostate cancer diagnosis heavily relies on histopathological evaluation, which is subject to variability. While immunohistochemical staining (IHC) assists in distinguishing benign from malignant tissue, it involves increased work, higher costs, and diagnostic delays. Artificial intelligence (AI) presents a promising solution to reduce reliance on IHC by accurately classifying atypical glands and borderline morphologies in hematoxylin & eosin (H&E) stained tissue sections. In this study, we evaluated an AI model's ability to minimize IHC use without compromising diagnostic accuracy by retrospectively analyzing prostate core needle biopsies from routine diagnostics at three different pathology sites. These cohorts were composed exclusively of difficult cases where the diagnosing pathologists required IHC to finalize the diagnosis. The AI model demonstrated area under the curve values of 0.951-0.993 for detecting cancer in routine H&E-stained slides. Applying sensitivity-prioritized diagnostic thresholds reduced the need for IHC staining by 44.4%, 42.0%, and 20.7% in the three cohorts investigated, without a single false negative prediction. This AI model shows potential for optimizing IHC use, streamlining decision-making in prostate pathology, and alleviating resource burdens.
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