A multi-modal model integrating MRI habitat and clinicopathology to predict platinum sensitivity in patients with high-grade serous ovarian cancer: a diagnostic study.
Authors
Affiliations (10)
Affiliations (10)
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
- Department of MRI, the First People's Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, YN, China.
- Department of Radiology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical.
- Department of Pathology, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical.
- Department of Pathology, the First People's Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, YN, China.
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, JS, China.
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, JS, China.
- MR Research Collaboration Team, Siemens Healthineers Ltd, Shanghai, China.
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Abstract
Platinum resistance of high-grade serous ovarian cancer (HGSOC) cannot currently be recognized by specific molecular biomarkers. We aimed to compare the predictive capacity of various models integrating MRI habitat, whole slide images (WSIs), and clinical parameters to predict platinum sensitivity in HGSOC patients. A retrospective study involving 998 eligible patients from four hospitals was conducted. MRI habitats were clustered using K-means algorithm on multi-parametric MRI. Following feature extraction and selection, a Habitat model was developed. Vision Transformer (ViT) and multi-instance learning were trained to derive the patch-level prediction and WSI-level prediction on hematoxylin and eosin (H&E)-stained WSIs, respectively, forming a Pathology model. Logistic regression (LR) was used to create a Clinic model. A multi-modal model integrating Clinic, Habitat, and Pathology (CHP) was constructed using Multi-Head Attention (MHA) and compared with the unimodal models and Ensemble multi-modal models. The area under the curve (AUC) and integrated discrimination improvement (IDI) value were used to assess model performance and gains. In the internal validation cohort and the external test cohort, the Habitat model showed the highest AUCs (0.722 and 0.685) compared to the Clinic model (0.683 and 0.681) and the Pathology model (0.533 and 0.565), respectively. The AUCs (0.789 and 0.807) of the multi-modal model interating CHP based on MHA were highest than those of any unimodal models and Ensemble multi-modal models, with positive IDI values. MRI-based habitat imaging showed potentials to predict platinum sensitivity in HGSOC patients. Multi-modal integration of CHP based on MHA was helpful to improve prediction performance.