ArticleDiagnostics (Basel, Switzerland)2021
Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
- A Computed Tomography Radiomics Approach for Assessing the T Cell-Inflamed Gene Expression Profile in Hepatocellular Carcinoma and Its Association With Immunotherapy Response.Health care science · 2026Article
- Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.Abdominal radiology (New York) · 2025Review
- Associations between the SMARS score derived from CT and MRI with histopathological features in HCC.Scientific reports · 2025Article
- MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma.BMC medical imaging · 2025Article
- A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Review
- Evaluation of programmed cell death ligand-1 expression in primary central nervous system lymphoma using whole-tumor histogram analysis of multiparametric MRI: implications for immunotherapy selection.Frontiers in immunology · 2025Article
- Radiomics predicting immunohistochemical markers in primary hepatic carcinoma: Current status and challenges.Heliyon · 2024Review
- CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer.BMC medical imaging · 2024Article
- Article
- Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence.Korean journal of radiology · 2024Review
- Multiparametric MRI for characterization of the tumour microenvironment.Nature reviews. Clinical oncology · 2024Review
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors.Biomedicines · 2023Review
- A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma.Phenomics (Cham, Switzerland) · 2023Article
- Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT.Journal of cancer research and clinical oncology · 2023Article
- Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma.Scientific reports · 2023Article
- Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
To assess if quantitative integrated deep learning and radiomics features can predict the PD-L1 expression level in preoperative MRI of hepatocellular carcinoma (HCC) patients. The data in this study consist of 103 hepatocellular carcinoma patients who received immunotherapy in a single center. These patients were divided into a high PD-L1 expression group (30 patients) and a low PD-L1 expression group (73 patients). Both radiomics and deep learning features were extracted from their MRI sequence of T2-WI, which were merged into an integrative feature space for machine learning for the prediction of PD-L1 expression. The five-fold cross-validation was adopted to validate the performance of the model, while the AUC was used to assess the predictive ability of the model. Based on the five-fold cross-validation, the integrated model achieved the best prediction performance, with an AUC score of 0.897 ± 0.084, followed by the deep learning-based model with an AUC of 0.852 ± 0.043 then the radiomics-based model with AUC of 0.794 ± 0.035. The feature set integrating radiomics and deep learning features is more effective in predicting PD-L1 expression level than only one feature type. The integrated model can achieve fast and accurate prediction of PD-L1 expression status in preoperative MRI of HCC patients.
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