软组织肉瘤（STS）需要及时准确的组织病理学诊断，这仍然具有挑战性。此外，人工智能（AI）的进步以及病理切片数字化的发展可能会增加对 STS 行为预测的需求。在本文中，我们探讨了深度学习在 STS 患者组织病理学图像预测预后中的应用。我们的回顾性研究共包括来自 STS 患者的 35 张组织病理学切片。我们训练了 Inception v3，它是基于卷积神经网络的生存性估计的提出方法。 F1 评分确定了受试者工作特征曲线 (AUC) 的准确性和面积，作为 4 倍验证的主要结果指标。该队列包括 35 名患者，平均年龄为 64 岁，平均随访时间为34 个月（2-66 个月）。我们的深度学习方法在预测总生存期方面的 AUC 为 0.974，准确率为 91.9%。对于无转移生存的预测，准确率为84.2%，AUC为0.852。AI可以帮助病理学家准确预测预后。这项研究可以大大改善 STS 患者的临床管理。© 2024 作者。约翰·威利出版的癌症医学

Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitization may empower the demand for the prediction of behavior of STSs. In this article, we explored the application of deep learning for prediction of prognosis from histopathological images in patients with STS.Our retrospective study included a total of 35 histopathological slides from patients with STS. We trained Inception v3 which is proposed method of convolutional neural network based survivability estimation. F1 score which identify the accuracy and area under the receiver operating characteristic curve (AUC) served as main outcome measures from a 4-fold validation.The cohort included 35 patients with a mean age of 64 years, and the mean follow-up period was 34 months (2-66 months). Our deep learning method achieved AUC of 0.974 and an accuracy of 91.9% in predicting overall survival. Concerning with the prediction of metastasis-free survival, the accuracy was 84.2% with the AUC of 0.852.AI might be used to help pathologists with accurate prognosis prediction. This study could substantially improve the clinical management of patients with STS.© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.