Survival 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患者的临床记录数据或生物与影像数据中提取生存信息。在实践中，经验丰富的临床医生能够根据患者可观察的体貌特征（主要是面部特征）对患者的健康状况进行初步评估。然而，这种评估具有很强的主观性。本研究首次探讨了利用深度学习客观捕获并利用常规肖像照片中所含预后信息进行生存预测的有效性。我们在自定义的癌症患者照片数据集上对预训练的StyleGAN2模型进行微调，使其生成器具备适用于患者照片的生成能力。随后，使用StyleGAN2将照片嵌入其高表达性的潜在空间。基于StyleGAN潜在空间的照片嵌入，并利用最先进的生存分析模型，该方法取得了0.677的C指数，显著高于随机水平，证明了简单二维面部图像中蕴含的预后价值。此外，得益于StyleGAN可解释的潜在空间，我们的生存预测模型能够验证其依赖于关键面部特征，消除了服装或背景等无关信息带来的偏差。同时，通过回归系数获得了一项健康属性，这对患者护理具有重要的潜在价值。