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期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02915-w

摘要内容如下：

未知原发部位癌症（CUP）由于其难以捉摸的性质，给诊断带来了挑战。许多CUP病例表现为胸膜和腹膜浆膜腔积液。利用来自四家三级医院的57，220例患者的细胞学图像，我们开发了一种使用细胞学组织学（TORCH）进行肿瘤起源鉴别的深度学习方法，该方法可以在胸水和腹水中识别恶性肿瘤并预测肿瘤起源。我们在三个内部（N=12，799）和两个外部（N=14，538）测试集上检查了其性能。在内部和外部测试组中，TORCH实现了用于癌症诊断的0.953至0.991和用于肿瘤起源定位的0.953至0.979的受试者工作曲线下面积值。TORCH准确预测原发肿瘤来源，TOP-1准确率为82.6%，TOP-3准确率为98.9%。与病理学家的结果相比，TORCH显示出更好的预测效能（1.677比1.265，P<0.001），显著提高了初级病理学家的诊断评分（1.326比1.101，P<0.001）。与接受不一致治疗的患者相比，初始治疗方案与TORCH预测来源一致的CUP患者的总生存率更高（27个月对17个月，P=0.006）。我们的研究强调了TORCH在临床实践中作为一种有价值的辅助工具的潜力，尽管需要在随机试验中进一步验证。

英文原文如下：

Abstracts

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.

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