urvival prediction for cancer patients is critical for optimal treatment selection and patient management. Current patient survival prediction methods typically extract survival information from patients' clinical record data or biological and imaging data. In practice, experienced clinicians can have a preliminary assessment of patients' health status based on patients' observable physical appearances, which are mainly facial features. However, such assessment is highly subjective. In this work, the efficacy of objectively capturing and using prognostic information contained in conventional portrait photographs using deep learning for survival predication purposes is investigated for the first time. A pre-trained StyleGAN2 model is fine-tuned on a custom dataset of our cancer patients' photos to empower its generator with generative ability suitable for patients' photos. The StyleGAN2 is then used to embed the photographs to its highly expressive latent space. Utilizing the state-of-the-art survival analysis models and based on StyleGAN's latent space photo embeddings, this approach achieved a C-index of 0.677, which is notably higher than chance and evidencing the prognostic value embedded in simple 2D facial images. In addition, thanks to StyleGAN's interpretable latent space, our survival prediction model can be validated for relying on essential facial features, eliminating any biases from extraneous information like clothing or background. Moreover, a health attribute is obtained from regression coefficients, which has important potential value for patient care.

翻译：癌症患者的生存预测对于优化治疗方案选择和患者管理至关重要。当前的患者生存预测方法通常从临床记录数据、生物学数据或影像数据中提取生存信息。在实践中，经验丰富的临床医生可根据患者可观测的外貌特征（主要为面部特征）对其健康状况进行初步评估，但这种评估具有高度主观性。本研究首次探讨了利用深度学习客观捕捉常规人像照片中蕴含的预后信息并用于生存预测的有效性。我们针对癌症患者照片定制数据集对预训练StyleGAN2模型进行微调，使其生成器具备适配患者照片的生成能力，进而利用StyleGAN2将照片嵌入其高表达潜空间。基于StyleGAN潜空间照片嵌入，结合最新生存分析模型，该方法实现了0.677的C指数，显著高于随机水平，证实了简单二维面部图像中蕴含的预后价值。此外，得益于StyleGAN可解释的潜空间特性，本生存预测模型可通过依赖核心面部特征进行验证，有效消除服装、背景等无关信息带来的偏差。同时，通过回归系数获得健康属性指标，这对患者护理具有重要潜在价值。