基于人工智能的前列腺分析系统，无需人工监督训练，可根据组织样本预测患者预后。
AI-based prostate analysis system trained without human supervision to predict patient outcome from tissue samples.
作者信息
Walhagen Peter, Bengtsson Ewert, Lennartz Maximilian, Sauter Guido, Busch Christer
机构信息
Spearpoint Analytics AB, Stockholm, Sweden.
Centre for Image Analysis, Dept. of Information technology, Uppsala University, Uppsala, Sweden.
出版信息
J Pathol Inform. 2022 Sep 8;13:100137. doi: 10.1016/j.jpi.2022.100137. eCollection 2022.
In order to plan the best treatment for prostate cancer patients, the aggressiveness of the tumor is graded based on visual assessment of tissue biopsies according to the Gleason scale. Recently, a number of AI models have been developed that can be trained to do this grading as well as human pathologists. But the accuracy of the AI grading will be limited by the accuracy of the subjective "ground truth" Gleason grades used for the training. We have trained an AI to predict patient outcome directly based on image analysis of a large biobank of tissue samples with known outcome without input of any human knowledge about cancer grading. The model has shown similar and in some cases better ability to predict patient outcome on an independent test-set than expert pathologists doing the conventional grading.
为了为前列腺癌患者制定最佳治疗方案，根据 Gleason 评分系统，通过对组织活检进行视觉评估来对肿瘤的侵袭性进行分级。最近，已经开发出了一些人工智能模型，这些模型可以像人类病理学家一样接受训练来进行这种分级。但是，人工智能分级的准确性将受到用于训练的主观“真实”Gleason 分级准确性的限制。我们训练了一种人工智能，直接基于对大量已知结果的组织样本生物库的图像分析来预测患者的预后，而无需输入任何有关癌症分级的人类知识。在独立测试集上，该模型在预测患者预后方面表现出与进行传统分级的专家病理学家相似甚至在某些情况下更好的能力。
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