Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images
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 18F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serologic...
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Elsevier
2025-06-01
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| Series: | European Journal of Radiology Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047724000790 |
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| author | Yingqi Luo Qingqi Yang Jinglang Hu Xiaowen Qin Shengnan Jiang Ying Liu |
| author_facet | Yingqi Luo Qingqi Yang Jinglang Hu Xiaowen Qin Shengnan Jiang Ying Liu |
| author_sort | Yingqi Luo |
| collection | DOAJ |
| description | 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 18F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serological data and imaging. Liver lesions were segmented on PET and CT, serving as the ''reference standard''. Deep learning models were trained using PET and CT images to generate predicted segmentations and classify lesion nature. Model performance was evaluated by comparing the predicted segmentations with the reference segmentations, using metrics such as Dice, Precision, Recall, F1-score, ROC, and AUC, and compared it with physician diagnoses. 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. |
| format | Article |
| id | doaj-art-4c4a541df5944367be14fb67a32a317e |
| institution | DOAJ |
| issn | 2352-0477 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Radiology Open |
| spelling | doaj-art-4c4a541df5944367be14fb67a32a317e2025-08-20T02:49:44ZengElsevierEuropean Journal of Radiology Open2352-04772025-06-011410062410.1016/j.ejro.2024.100624Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT imagesYingqi Luo0Qingqi Yang1Jinglang Hu2Xiaowen Qin3Shengnan Jiang4Ying Liu5Department of Nuclear medicine, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, ChinaDepartment of Vascular Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, ChinaSchool of Medicine, Sun Yat-Sen University, Guangzhou, ChinaDepartment of Nuclear medicine, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, ChinaDepartment of Nuclear medicine, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, ChinaDepartment of Nuclear medicine, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China; Correspondence to: No. 250 Changgang East Road, Haizhu District, Guangzhou City, Guangdong Province, China.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 18F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serological data and imaging. Liver lesions were segmented on PET and CT, serving as the ''reference standard''. Deep learning models were trained using PET and CT images to generate predicted segmentations and classify lesion nature. Model performance was evaluated by comparing the predicted segmentations with the reference segmentations, using metrics such as Dice, Precision, Recall, F1-score, ROC, and AUC, and compared it with physician diagnoses. 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.http://www.sciencedirect.com/science/article/pii/S2352047724000790Deep learningFocal liver lesionPET/CT |
| spellingShingle | Yingqi Luo Qingqi Yang Jinglang Hu Xiaowen Qin Shengnan Jiang Ying Liu Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images European Journal of Radiology Open Deep learning Focal liver lesion PET/CT |
| title | Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images |
| title_full | Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images |
| title_fullStr | Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images |
| title_full_unstemmed | Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images |
| title_short | Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images |
| title_sort | preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal pet ct images |
| topic | Deep learning Focal liver lesion PET/CT |
| url | http://www.sciencedirect.com/science/article/pii/S2352047724000790 |
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