ArticleEuropean journal of radiology open2025
Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images.
Article in European journal of radiology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Hybrid Fusion of Time-Intensity Curve, Deep Learning, and Fractional Zernike-Caputo Features for Accurate Liver Lesion Classification in DCE-MRI.Journal of imaging · 2026Article
- Automated Segmentation of Liver and Liver Tumors with SwinUNETR and UNET Neural Networks onMolecular imaging and radionuclide therapy · 2026Article
- Insertion Network for Image Sequence Correspondence Building.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Deep learning algorithm for pathological grading of renal cell carcinoma based on multi-phase enhanced CT.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
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Authors and funding
6 authors.
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Abstract
Objectives: To develop and validate a deep learning model using multimodal PET/CT imaging for detecting and classifying focal liver lesions (FLL). Methods: This study included 185 patients who underwent Results: This study finally included 150 patients, comprising 46 patients with benign liver nodules, 51 patients with malignant liver nodules, and 53 patients with no FLLs. Significant differences were observed among groups for age, AST, ALP, GGT, AFP, CA19-9and CEA. On the validation set, the Dice coefficient of the model was 0.740. For the normal group, the recall was 0.918, precision was 0.904, F1-score was 0.909, and AUC was 0.976. For the benign group, the recall was 0.869, precision was 0.862, F1-score was 0.863, and AUC was 0.928. For the malignant group, the recall was 0.858, precision was 0.914, F1-score was 0.883, and AUC was 0.979. The model's overall diagnostic performance was between that of junior and senior physician. Conclusion: This deep learning model demonstrated high sensitivity in detecting FLLs and effectively differentiated between benign and malignant lesions.
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