背景:为了降低胃癌(GC)的高发病率和高死亡率,我们的目标是开发基于深度学习的模型,以帮助使用病理图像预测GC患者的诊断和总生存期(OS)。
方法:从两个队列中收集了1037例GC患者的2333张苏木精和伊红染色的病理照片,以开发我们的算法,武汉大学人民医院(RHWU)和癌症基因组图谱(TCGA)。此外,我们从国家人类遗传资源共享服务平台(NHGRP)获得了91名GC患者的175张数码照片,作为独立的外部验证集。使用人工智能(AI)开发了两个模型,一个名为GastroMIL的诊断GC,另一个名为MIL-GC,用于预测GC的结果。
结果:GastroMIL的辨别能力在外部验证集中达到了0.920的准确度,优于初级病理学家,与专家病理学家相当。在预后模型中,内部和外部验证集生存预测的C指数分别为0.671和0.657。此外,在单变量(HR=2.414,P<0.0001)和多变量(HR=1.803,P=0.043)分析中,外部验证集中MIL-GC输出的风险评分被证明是OS的强预测因子.预测过程可在在线网站(https://baigao。github.io/病理预后分析/)。
结论:我们的研究开发了AI模型,并有助于预测GC患者的精确诊断和预后。这将为选择合适的治疗方法提供帮助,以改善GC患者的生存状况。
背景:不适用。
BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.
METHODS: 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC.
RESULTS: The discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, C-indices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/).
CONCLUSIONS: Our
study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients.
BACKGROUND: Not applicable.