The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason grading system that does not require extensive region-level manual annotations by experts and/or complex algorithms for the automatic generation of region-level annotations. A total of 6664 and 936 prostate needle biopsy single-core slides (689 and 99 cases) from two institutions were used for system discovery and validation, respectively. Pathological diagnoses were converted into grade groups and used as the reference standard. The grade group prediction accuracy of the system was 77.5% (95% confidence interval (CI): 72.3–82.7%), the Cohen’s kappa score (κ) was 0.650 (95% CI: 0.570–0.730), and the quadratic-weighted kappa score (κ_quad) was 0.897 (95% CI: 0.815–0.979). When trained on 621 cases from one institution and validated on 167 cases from the other institution, the system’s accuracy reached 67.4% (95% CI: 63.2–71.6%), κ 0.553 (95% CI: 0.495–0.610), and the κ_quad 0.880 (95% CI: 0.822–0.938). In order to evaluate the impact of the proposed method, performance comparison with several baseline methods was also performed. While limited by case volume and a few more factors, the results of this study can contribute to the potential development of an artificial intelligence system to diagnose other cancers without extensive region-level annotations.

格里森评分在预测前列腺癌结果和选择合适的治疗方案方面有显着贡献，这受到众所周知的观察者间差异的影响。我们提出了一种新颖的基于深度学习的自动化格里森分级系统，它不需要专家进行大量区域级手动注释和/或用于自动生成区域级注释的复杂算法。来自两个机构的总共6664和936个前列腺穿刺活检单核载玻片（689和99例）分别用于系统发现和验证。将病理诊断转化为等级组作为参考标准。该系统的年级组预测准确率为 77.5%（95% 置信区间（CI）：72.3-82.7%），Cohen's kappa 分数（κ) 为 0.650 (95% CI: 0.570–0.730)，二次加权 kappa 分数 ( κ _quad ) 为 0.897 (95% CI: 0.815–0.979)。当对一个机构的 621 个案例进行训练并在另一机构的 167 个案例上进行验证时，系统的准确率达到 67.4%（95% CI：63.2–71.6%）、κ 0.553（95% CI：0.495–0.610），κ _quad 0.880（95% CI：0.822–0.938）。为了评估所提出方法的影响，还进行了与几种基线方法的性能比较。虽然受病例数量和其他一些因素的限制，但这项研究的结果可以促进人工智能系统的潜在发展，以诊断其他癌症，而无需广泛的区域级注释。