用AI从普通病理切片生成特定免疫染色图像，让医生一眼看懂癌变特征。
Deep Learning for Generating Computational PIN-4 Immunohistochemistry Staining from Prostate Biopsy H&E Images
- 用条件GAN模型从H&E切片直接生成PIN-4免疫染色图
- 合成图像在172例患者数据上达到平均PSNR 21.88dB、SSIM 0.667
- 可还原关键蛋白表达模式，适合临床病理辅助诊断
免疫组化（IHC）常用于解析前列腺活检中血苏木精-伊红（H&E）染色的诊断模糊区域。然而，PIN-4 IHC通常在邻近组织切片上进行，限制了与H&E形态的空间对应比较。本研究基于93名患者的常规临床前列腺活检全切片图像（WSIs），构建了配对注册的H&E/PIN-4数据集，并训练了一个条件生成对抗网络（cGAN），直接从原始H&E图像块合成PIN-4染色模式。最终数据集包含172对WSIs和27,298对1024×1024像素图像块，涵盖腺癌阳性及良性病例，覆盖不同年龄、种族和族裔群体。模型在17张全切片共1,814个图像块的独立测试集上评估，平均峰值信噪比（PSNR）为21.88 dB，结构相似性指数（SSIM）为0.667，皮尔逊相关系数（PCC）为0.684，学习感知图像块相似度（LPIPS）为0.417。一位认证病理科医师的定性评审显示，生成图像成功捕捉到诊断相关的PIN-4染色特征，包括AMACR/消旋酶表达和基底细胞相关染色，同时保持与源H&E形态的空间一致性。合成精度在高分级癌和导管内癌等复杂区域有所下降。结果表明，基于监督学习的PIN-4染色合成在常规明亮场H&E前列腺活检图像上是可行的，该方法使预测的PIN-4标记模式可直接结合原生H&E架构解读，解决了传统邻近切片IHC存在的空间局限性。
原文摘要 · Abstract (English)
Immunohistochemistry (IHC)is frequently used to resolve diagnostically ambiguous prostate cancer biopsy findings on hematoxylin and eosin (H&E)-stained tissue. However, PIN-4 IHC staining is typically performed on adjacent tissue sections, limiting direct spatial comparison between the H&E morphology and the corresponding immunophenotypic signal. A paired, registered H&E/PIN-4 dataset was constructed from routine clinical prostate biopsy whole-slide images (WSIs), and a conditional generative adversarial network (cGAN) was trained to synthesize PIN-4 staining patterns directly from native H&E image patches. The final dataset comprised 172 paired WSIs from 93 patients and 27,298 registered 1024x1024 patch pairs, spanning adenocarcinoma-positive and benign cases with representation across age, race, and ethnicity groups. The model was evaluated on a held-out test set of 1,814 patch pairs from 17 WSIs, achieving a mean peak signal-to-noise ratio (PSNR) of 21.88 dB, structural similarity index measure (SSIM) of 0.667, Pearson correlation coefficient (PCC) of 0.684, and learned perceptual image patch similarity (LPIPS) of 0.417. Qualitative review by a board-certified pathologist showed that generated images captured diagnostically relevant PIN-4 staining patterns, including AMACR/racemase expression and basal-cell-associated staining, while preserving spatial correspondence with the source H&E morphology. Accuracy of synthesis varied across morphologically complex regions, including high-grade carcinoma and intraductal carcinoma. These results support the feasibility of supervised PIN-4 synthesis from routinely acquired brightfield H&E prostate biopsy images. The approach enables direct interpretation of predicted PIN-4 marker patterns in the context of the source prostate H&E architecture, addressing a current spatial limitation of conventional adjacent-section IHC.
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