ArticlePrecision radiation oncology2025
Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.
Article in Precision radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
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- A multicenter study identifies an immune inflammatory and nutritional prognostic signature in intrahepatic cholangiocarcinoma.Discover oncology · 2026Article
- Impact of Annotation Level on Multisequence MRI Models for Preoperative Microvascular Invasion Prediction in Hepatocellular Carcinoma.Radiology. Imaging cancer · 2026Article
- Clinically deployable AI to predict objective response to radiotherapy-intensified immunotherapy in advanced hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Interpretable AI for treatment decision-making in immunoradiotherapy of locally advanced nasopharyngeal carcinoma.Frontiers in oncology · 2026Article
- Artificial intelligence-based personalized treatment strategies for unresectable hepatocellular carcinoma: integrating HSP90α for prognosis and survival prediction.NPJ digital medicine · 2025Article
- Predicting axillary lymph node metastasis in breast cancer patients using CNN-GCN on DCE-MRI: a multicenter study.BMC medical imaging · 2025Article
- A nomogram for predicting overall survival in advanced hepatocellular carcinoma patients receiving radiotherapy combined with targeted therapy: a multicenter retrospective study.Translational cancer research · 2025Article
- Elevated CircYthdc2 expression is correlated with aggressive features and poor progression-free survival in hepatocellular carcinoma.BMC gastroenterology · 2025Article
- Combined preoperative platelet-albumin ratio and cancer inflammation prognostic index predicts prognosis in colorectal cancer: a retrospective study.Scientific reports · 2025Article
- Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.BMC medical imaging · 2025Article
- Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.Precision radiation oncology · 2025Article
- Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Cohort Study.Journal of hepatocellular carcinoma · 2025Article
- LASSO-Cox model in the prognostic evaluation of radiochemotherapy efficacy for lymph node metastatic nasopharyngeal carcinoma.Frontiers in oncology · 2025Article
- Lymph node metastasis in patients with hepatocellular carcinoma using machine learning: a population-based study.Frontiers in oncology · 2025Article
- Radiotherapy for glioma in the AI era: current applications and future prospects.Frontiers in oncology · 2025Review
- Prediction of prognosis in T4 or N3 locally advanced nasopharyngeal carcinoma receiving chemoradiotherapy using machine learning methods.Frontiers in oncology · 2025Article
- Artificial intelligence-based evaluation of prognostic benefits from immunotherapy plus targeted therapy with or without radiotherapy or TACE in advanced hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Performance of AI-based machine learning models for overall survival prediction in advanced hepatocellular carcinoma patients receiving immunoradiotherapy.Frontiers in pharmacology · 2025Article
- Exploring HSP90α and hs-CRP using AI models to predict prognosis in advanced hepatocellular carcinoma treated with PD-1 inhibitors and targeted therapy.Frontiers in pharmacology · 2025Article
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Abstract
Background: We hypothesize generative adversarial networks (GAN) combined with self-attention (SA) and aggregated residual transformations (ResNeXt) perform better than conventional deep learning models in differentiating hepatocellular carcinoma (HCC). Attention modules facilitate concentrating on salient features and suppressing redundancies, while residual transformations can reuse relevant features. Therefore, we aim to propose a GAN+SA+ResNeXt deep learning model to improve HCC prediction accuracy. Methods: 228 multiphase CTs from 57 patients were retrospectively analyzed with local IRB's approval, where 30 patients were pathologically confirmed with HCC and the rest 27 were non-HCC. Pre-processing of automatic liver segmentation and Hounsfield unit (HU) normalization was performed, followed by deep learning training with five-fold cross validation in a conventional 3D GAN, a 3D GAN+A, and a 3D GAN+A+ ResNeXt, respectively (training: testing ∼ 4:1). Area under receiver operating characteristics curves (AUROC), accuracy, sensitivity and specificity of HCC prediction were evaluated. Results: Results showed the proposed method had larger AUROC (95%), better accuracy (91%) and sensitivity (93%) with acceptable specificity (88%) and prediction time (0.04s). Deep GAN with attentions and residual transformations for HCC diagnosis using multiphase CT is feasible and favorable with improved accuracy and efficiency, which harbors clinical potentials in differentiating HCC from other benign or malignant liver lesions.
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