Survival prediction post-cystectomy is essential for the follow-up care of bladder cancer patients. This study aimed to evaluate artificial intelligence (AI)-large language models (LLMs) for extracting clinical information and improving image analysis, with an initial application involving predicting five-year survival rates of patients after radical cystectomy for bladder cancer. Data were retrospectively collected from medical records and CT urograms (CTUs) of bladder cancer patients between 2001 and 2020. Of 781 patients, 163 underwent chemotherapy, had pre- and post-chemotherapy CTUs, underwent radical cystectomy, and had an available post-surgery five-year survival follow-up. Five AI-LLMs (Dolly-v2, Vicuna-13b, Llama-2.0-13b, GPT-3.5, and GPT-4.0) were used to extract clinical descriptors from each patient’s medical records. As a reference standard, clinical descriptors were also extracted manually. Radiomics and deep learning descriptors were extracted from CTU images. The developed multi-modal predictive model, CRD, was based on the clinical (C), radiomics (R), and deep learning (D) descriptors. The LLM retrieval accuracy was assessed. The performances of the survival predictive models were evaluated using AUC and Kaplan–Meier analysis. For the 163 patients (mean age 64 ± 9 years; M:F 131:32), the LLMs achieved extraction accuracies of 74%~87% (Dolly), 76%~83% (Vicuna), 82%~93% (Llama), 85%~91% (GPT-3.5), and 94%~97% (GPT-4.0). For a test dataset of 64 patients, the CRD model achieved AUCs of 0.89 ± 0.04 (manually extracted information), 0.87 ± 0.05 (Dolly), 0.83 ± 0.06~0.84 ± 0.05 (Vicuna), 0.81 ± 0.06~0.86 ± 0.05 (Llama), 0.85 ± 0.05~0.88 ± 0.05 (GPT-3.5), and 0.87 ± 0.05~0.88 ± 0.05 (GPT-4.0). This study demonstrates the use of LLM model-extracted clinical information, in conjunction with imaging analysis, to improve the prediction of clinical outcomes, with bladder cancer as an initial example.
膀胱癌患者根治性膀胱切除术后生存预测对其后续护理至关重要。本研究旨在评估人工智能大语言模型在提取临床信息和改进影像分析方面的应用，并以膀胱癌根治性膀胱切除术后患者五年生存率预测作为初步应用场景。研究回顾性收集了2001年至2020年间膀胱癌患者的医疗记录和CT尿路造影数据。在781例患者中，163例符合以下条件：接受过化疗、拥有化疗前后CT尿路造影数据、接受根治性膀胱切除术、且具备术后五年生存随访记录。研究采用五种大语言模型（Dolly-v2、Vicuna-13b、Llama-2.0-13b、GPT-3.5和GPT-4.0）从患者医疗记录中提取临床描述符，同时以人工提取作为参考标准。从CT尿路造影图像中提取放射组学和深度学习描述符。基于临床描述符、放射组学描述符和深度学习描述符构建了多模态预测模型CRD。评估了大语言模型的信息提取准确率，并采用受试者工作特征曲线下面积和Kaplan-Meier分析法评估生存预测模型的性能。在163例患者（平均年龄64±9岁；男女比例131:32）中，大语言模型的提取准确率分别为：Dolly 74%~87%、Vicuna 76%~83%、Llama 82%~93%、GPT-3.5 85%~91%、GPT-4.0 94%~97%。在包含64例患者的测试数据集中，CRD模型取得的曲线下面积为：人工提取信息0.89±0.04、Dolly提取0.87±0.05、Vicuna提取0.83±0.06~0.84±0.05、Llama提取0.81±0.06~0.86±0.05、GPT-3.5提取0.85±0.05~0.88±0.05、GPT-4.0提取0.87±0.05~0.88±0.05。本研究以膀胱癌为例，证明了大语言模型提取的临床信息结合影像分析能够有效改善临床结局预测。
肿瘤(癌症)患者之家