ArticleFrontiers in oncology2023
Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 16 citations in OpenAlex.
- A multi-task masked autoencoder with GAN-based augmentation for PD-L1 prediction from chest CT images.Scientific reports · 2026Article
- Decision-Making Biomarkers Guiding Therapeutic Strategies in Hepatocellular Carcinoma: From Prediction to Personalized Care.Cancers · 2025Review
- Associations between the SMARS score derived from CT and MRI with histopathological features in HCC.Scientific reports · 2025Article
- Deep learning progressive distill for predicting clinical response to conversion therapy from preoperative CT images of advanced gastric cancer patients.Scientific reports · 2025Article
- SiameseNet based on multiple instance learning for accurate identification of the histological grade of ICC tumors.Frontiers in oncology · 2025Article
- A radiomic model for noninvasive prediction of PD-L1 and VETC expression in hepatocellular carcinoma using enhanced abdominal CT.Frontiers in oncology · 2025Article
- Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies.Frontiers in oncology · 2024Review
- A self-supervised classification model for endometrial diseases.Journal of cancer research and clinical oncology · 2023Article
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: Programmed cell death protein-1 (PD-1) and programmed cell death-ligand-1 (PD-L1) expression status, determined by immunohistochemistry (IHC) of specimens, can discriminate patients with hepatocellular carcinoma (HCC) who can derive the most benefits from immune checkpoint inhibitor (ICI) therapy. A non-invasive method of measuring PD-1/PD-L1 expression is urgently needed for clinical decision support. Materials and methods: We included a cohort of 87 patients with HCC from the West China Hospital and analyzed 3094 CT images to develop and validate our prediction model. We propose a novel deep learning-based predictor, Contrastive Learning Network (CLNet), which is trained with self-supervised contrastive learning to better extract deep representations of computed tomography (CT) images for the prediction of PD-1 and PD-L1 expression. Results: Our results show that CLNet exhibited an AUC of 86.56% for PD-1 expression and an AUC of 83.93% for PD-L1 expression, outperforming other deep learning and machine learning models. Conclusions: We demonstrated that a non-invasive deep learning-based model trained with self-supervised contrastive learning could accurately predict the PD-1 and PD-L1 expression status, and might assist the precision treatment of patients withHCC, in particular the use of immune checkpoint inhibitors.
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