PDF(Pubmed)
Abstract：
Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.
摘要：
前列腺癌病理在临床管理中起着至关重要的作用，但耗时。人工智能（AI）在检测前列腺癌和分级模式方面显示出希望。我们测试了病理学家的基于AI的数字双胞胎，vPatho,用苏木精和伊红染色的前列腺组织的2603张组织学图像。我们分析了影响vPatho系统与六名人类病理学家之间肿瘤分级不一致的各种因素。我们的结果表明，vPatho在前列腺癌检测和肿瘤体积估计方面取得了可比的性能，正如文献报道的那样。检查了vPatho与人类病理学家之间的一致性水平。值得注意的是,在确定互补的组织学特征如导管，cribriform,神经,血管,和淋巴细胞浸润。然而,与活检核心（κ=0.70）相比，应用于前列腺切除术标本（κ=0.44）时，肿瘤分级的一致性降低。将次要Gleason模式的决策阈值从5％调整到10％，可提高病理学家与vPatho之间在前列腺切除术标本上肿瘤分级的一致性水平（κ从0.44到0.64）。等级不一致的潜在原因包括肿瘤朝向前列腺边界的垂直范围以及具有前列腺癌的载玻片的比例。格里森模式4与该人群特别相关。值得注意的是,根据vPatho的分级并不特异于参与常规临床分级的6名病理学家.总之,我们的研究强调了AI在为病理学家开发数字双胞胎方面的潜在效用.这种方法可以帮助发现AI采用的局限性以及当前前列腺癌病理分级系统的实际应用。
