机构：[1]Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.[2]Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.医技片磁共振科云南省第一人民医院[3]Department of Radiology, Yunnan Cancer Hospital, the Third Affiliated Hospital of KunmingMedical University, Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China.[4]Fudan University, Shanghai, China.[5]Department of Pathology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China.[6]Department of Pathology, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.医技片病理科云南省第一人民医院[7]Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.重庆医科大学附属第一医院[8]Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.[9]Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.[10]Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China[11]Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
High-grade serous ovarian cancer (HGSOC) presents challenges in prognostic prediction. This study aimed to develop a universal foundation model-driven multimodal model (FoMu model) to assess the prognosis of HGSOC patients. We conducted a retrospective cohort study involving 712 eligible patients across four centers, collecting clinical, MRI, and hematoxylin and eosin (H&E)-stained whole slide images (WSIs) data. Pre-trained radiological and pathological foundation models were employed for feature precoding. Subsequently, we introduced unimodal and cross-modal adaptive aggregation networks to comprehensively model the features derived from each modality. Our findings revealed that both unimodal and cross-modal FoMu models exhibited superior and stable predictive capabilities for overall survival (OS) and progression-free survival (PFS). In summary, our study successfully developed a FoMu model that effectively integrates multimodal data to assess the prognoses of HGSOC patients, highlighting its potential for improving individualized patient management and clinical decision-making in future applications.
基金：
the National Natural Science Foundations of China [82460340, 82471943, 82471932, 82271940, 82160524]; National Natural Science Foundations of China [H-2024080]; Yunnan Health Training Project of High Level Talents [KUST-KH2022027Y]; Kunming University of Science and Technology & the First People's Hospital of Yunnan Province Joint Special Project on Medical Research [202301AY070001-084]; Basic Research on Application of Joint Special Funding of Science and Technology Department of Yunnan Province-Kunming Medical University [SZK2023A02]; Shanghai Jinshan District Health Committee [22ZR1412500]; Natural Science Foundation of Shanghai
第一作者机构：[1]Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.[2]Department of MRI, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
共同第一作者：
推荐引用方式(GB/T 7714)：
Bi Qiu,Ai Conghui,Qu Linhao,et al.Foundation model-driven multimodal prognostic prediction in patients undergoing primary surgery for high-grade serous ovarian cancer[J].NPJ PRECISION ONCOLOGY.2025,9(1):doi:10.1038/s41698-025-00900-1.
APA：
Bi, Qiu,Ai, Conghui,Qu, Linhao,Meng, Qingyin,Wang, Qinqing...&Qiang, Jinwei.(2025).Foundation model-driven multimodal prognostic prediction in patients undergoing primary surgery for high-grade serous ovarian cancer.NPJ PRECISION ONCOLOGY,9,(1)
MLA：
Bi, Qiu,et al."Foundation model-driven multimodal prognostic prediction in patients undergoing primary surgery for high-grade serous ovarian cancer".NPJ PRECISION ONCOLOGY 9..1(2025)
医学