Multi-modal gradual fusion transformer-based model for predicting immunotherapy response in patients with hepatocellular carcinoma.
Authors
Affiliations (11)
Affiliations (11)
- Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
- Department of Medical Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China.
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
- Department of Medical Oncology, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Nanchang 330029, China.
- Jiangxi Provincial Key Laboratory of Prevention and Treatment of Infectious Diseases, Jiangxi Medical Center for Critical Public Health Events, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330029, China.
- Department of Thoracic Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo University, Ningbo, Zhejiang 315040, China.
- Department of Medical Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili 556000, China.
- The First Clinical Medical School, Southern Medical University, Guangzhou 510515, China.
- Department of Oncology, Shunde Hospital, Southern Medical University (The First People' s Hospital of Shunde, Foshan), Foshan 528308, China; Department of Hepatobiliary Pancreatic Oncology, Cancer Center, Southern Medical University Hospital of Integrated Chinese and Western Medicine, Guangzhou 510515, China. Electronic address: [email protected].
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: [email protected].
- Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: [email protected].
Abstract
Immunotherapy effectively extends survival in hepatocellular carcinoma (HCC) patients. Predicting immunotherapy responses can inform treatment strategies for HCC. This study aimed to develop multi-modal transformer-based models to predict the immunotherapy response and to validate their performance in an independent cohort. Patients with HCC from five medical centers were retrospectively included. Clinical features were selected using Least Absolute Shrinkage and Selection Operator method. Multi-modal gradual fusion transformer-based models were trained using clinical features and intra- and peritumoral patches from arterial and portal venous phase computed tomography images in the training cohort. These models were tested on internal validation and external test cohorts. Models' performance and generalization across different modalities were compared. Patients from the Hospital 1 were partitioned into a training (n = 209) and an internal validation cohort (n = 90) at a 7:3 ratio. And patients from the other four centers formed an independent external test cohort (n = 85). The number of progressive disease (PD) patients in the training, internal validation, and external test cohorts was 44 (21.1%), 20 (22.2%), and 20 (23.5%), respectively. The model using clinical data, intratumoral imaging, and peritumoral imaging modalities (GIFT-CIP) demonstrated strong predictive performance, achieving an area under the curve (AUC) values of 0.926 (95% CI: 0.892-0.962), 0.911 (95% CI: 0.878-0.946), and 0.883 (95% CI: 0.835-0.935) for training cohort, internal validation cohort, and external test cohort, respectively. Crucially, the GIFT-CIP model effectively stratified patients into low- and high-risk groups, showing significant differences in progression-free survival and overall survival in external test cohort (p < 0.01). The GIFT-CIP model is a non-invasive method for predicting immunotherapy responses in patients with HCC. This model may be clinically useful for assisting clinicians in guiding surveillance follow-up and identifying optimal immunotherapy strategies.