Development and validation of a deep learning radiomics model based on ultrasound and clinical features to predict prognosis in elderly patients with advanced pancreatic cancer after HIFU therapy

作者：Yumei Liu, Yongshuo Ji, Jun-qiu Zhu, Ling-lin Zhu, Yan-fei Zhu, Hong Zhao, Zhijun Bao · 发表于：BMC Gastroenterology · 年份：2026 · DOI：10.1186/s12876-026-04716-6 · 研究领域：Medicine

Background This study aimed to construct an artificial intelligence model based on ultrasound radiomics and deep learning, integrating clinical features to develop a fusion model for individualized prediction of survival outcomes in elderly patients with advanced pancreatic cancer receiving high-intensity focused ultrasound (HIFU) treatment. Methods This retrospective study enrolled elderly patients with advanced pancreatic cancer admitted to Huadong Hospital Affiliated to Fudan University from March 2015 to March 2024, randomly divided into training and validation cohorts in a 7:3 ratio. Patients were categorized into four groups based on treatment modality: HIFU alone, HIFU combined with 125I seed implantation, HIFU combined with chemotherapy, and triple therapy (HIFU + 125I + chemotherapy). Traditional radiomics features and deep learning features based on the ResNet architecture were extracted from pre-treatment ultrasound images. After rigorous feature selection, clinical, radiomics, deep learning, and multi-modal fusion Cox proportional hazards models were constructed. Predictive performance for overall survival and clinical utility were evaluated comprehensively. Results This study included 250 elderly pancreatic cancer patients. Multivariate analysis identified liver metastasis, TNM stage, body weight, number of HIFU sessions, and treatment regimen as independent prognostic factors (all p < 0.05). Predictive models were constructed using selected clinical features, tr...