标题： 一种针对肾癌精准肿瘤学的疾病导向视觉-语言基础模型 显示英文标题

标题： A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer

摘要： 非侵入性评估日益发现的肾部肿块是泌尿肿瘤学中的一个关键挑战，其中诊断不确定性经常导致对良性或惰性肿瘤的过度治疗。 在本研究中，我们使用来自九家中国医疗中心和公共TCIA队列的8,809名患者的27,866张CT扫描数据集，开发并验证了RenalCLIP，这是一个用于肾部肿块表征、诊断和预后的视觉-语言基础模型。 该模型通过一种两阶段的预训练策略进行开发，首先通过领域特定知识增强图像和文本编码器，然后通过对比学习目标对齐它们，以创建具有强大泛化能力和诊断精度的表示。 与其它最先进的通用CT基础模型相比，RenalCLIP在涵盖肾脏癌完整临床工作流程的10个核心任务中表现出更好的性能和优越的泛化能力，包括解剖评估、诊断分类和生存预测。 特别是对于TCIA队列中复杂的任务，如无复发生存预测，RenalCLIP达到了0.726的C指数，比领先的基线模型提高了约20%。 此外，RenalCLIP的预训练显著提高了数据效率；在诊断分类任务中，它仅需要20%的训练数据就能达到所有基线模型的峰值性能，即使这些模型在100%的数据上进行了充分微调。 此外，它在报告生成、图像-文本检索和零样本诊断任务中也表现出优越的性能。 我们的研究结果表明，RenalCLIP提供了一个强大的工具，有望提高诊断准确性，优化预后分层，并个性化管理肾癌患者。 显示英文摘要

摘要： The non-invasive assessment of increasingly incidentally discovered renal masses is a critical challenge in urologic oncology, where diagnostic uncertainty frequently leads to the overtreatment of benign or indolent tumors. In this study, we developed and validated RenalCLIP using a dataset of 27,866 CT scans from 8,809 patients across nine Chinese medical centers and the public TCIA cohort, a visual-language foundation model for characterization, diagnosis and prognosis of renal mass. The model was developed via a two-stage pre-training strategy that first enhances the image and text encoders with domain-specific knowledge before aligning them through a contrastive learning objective, to create robust representations for superior generalization and diagnostic precision. RenalCLIP achieved better performance and superior generalizability across 10 core tasks spanning the full clinical workflow of kidney cancer, including anatomical assessment, diagnostic classification, and survival prediction, compared with other state-of-the-art general-purpose CT foundation models. Especially, for complicated task like recurrence-free survival prediction in the TCIA cohort, RenalCLIP achieved a C-index of 0.726, representing a substantial improvement of approximately 20% over the leading baselines. Furthermore, RenalCLIP's pre-training imparted remarkable data efficiency; in the diagnostic classification task, it only needs 20% training data to achieve the peak performance of all baseline models even after they were fully fine-tuned on 100% of the data. Additionally, it achieved superior performance in report generation, image-text retrieval and zero-shot diagnosis tasks. Our findings establish that RenalCLIP provides a robust tool with the potential to enhance diagnostic accuracy, refine prognostic stratification, and personalize the management of patients with kidney cancer.