早期生存预测对于癌症患者的临床管理至关重要，因为通过个性化治疗计划可以更好地控制肿瘤。传统的生存预测方法基于放射组学特征工程和/或临床指标（例如癌症分期）。最近，随着深度学习技术的进步，生存预测模型通过利用医学图像的深层特征，在端到端生存预测方面取得了最先进的性能。然而，现有模型严重依赖原发肿瘤内的预后信息，无法有效利用表征局部肿瘤转移和邻近组织侵袭的肿瘤外预后信息。此外，现有模型在利用多模态医学图像方面并不是最优的，因为它们依赖于经验设计的融合策略来集成多模态信息，其中融合策略是基于特定领域的人类先验知识预先定义的，并且本质上受限于适应性。在这里，我们提出了一种自适应多模态分割生存模型（AdaMSS），用于根据多模态医学图像进行生存预测。 AdaMSS 可以根据训练数据自适应其融合策略，还可以调整其焦点区域以捕获原发肿瘤之外的预后信息。对两个大型癌症数据集（来自 9 个医疗中心的 1380 名患者）进行的广泛实验表明，我们的 AdaMSS 超越了最先进的生存预测性能（C 指数：0.804 和 0.757），展示了促进个性化治疗计划的潜力。 © 2024。作者。

Early survival prediction is vital for the clinical management of cancer patients, as tumors can be better controlled with personalized treatment planning. Traditional survival prediction methods are based on radiomics feature engineering and/or clinical indicators (e.g., cancer staging). Recently, survival prediction models with advances in deep learning techniques have achieved state-of-the-art performance in end-to-end survival prediction by exploiting deep features derived from medical images. However, existing models are heavily reliant on the prognostic information within primary tumors and cannot effectively leverage out-of-tumor prognostic information characterizing local tumor metastasis and adjacent tissue invasion. Also, existing models are sub-optimal in leveraging multi-modality medical images as they rely on empirically designed fusion strategies to integrate multi-modality information, where the fusion strategies are pre-defined based on domain-specific human prior knowledge and inherently limited in adaptability. Here, we present an Adaptive Multi-modality Segmentation-to-Survival model (AdaMSS) for survival prediction from multi-modality medical images. The AdaMSS can self-adapt its fusion strategy based on training data and also can adapt its focus regions to capture the prognostic information outside the primary tumors. Extensive experiments with two large cancer datasets (1380 patients from nine medical centers) show that our AdaMSS surmounts the state-of-the-art survival prediction performance (C-index: 0.804 and 0.757), demonstrating the potential to facilitate personalized treatment planning.© 2024. The Author(s).