LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning
Abstract Background Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interpretation but is rather complex and time-co...
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BMC
2025-03-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-025-00844-6 |
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| author | Róbert Stollmayer Selda Güven Christian Marcel Heidt Kai Schlamp Pál Novák Kaposi Oyunbileg von Stackelberg Hans-Ulrich Kauczor Miriam Klauss Philipp Mayer |
| author_facet | Róbert Stollmayer Selda Güven Christian Marcel Heidt Kai Schlamp Pál Novák Kaposi Oyunbileg von Stackelberg Hans-Ulrich Kauczor Miriam Klauss Philipp Mayer |
| author_sort | Róbert Stollmayer |
| collection | DOAJ |
| description | Abstract Background Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interpretation but is rather complex and time-consuming. These limitations could potentially be alleviated using recent deep learning-based segmentation and classification methods such as nnU-Net. The study aims to create and evaluate an automatic segmentation model for HCC risk assessment, according to LI-RADS v2018 using nnU-Net. Methods For this single-center retrospective study, 602 patients at risk for HCC were included, who had dynamic EOB-MRI examinations between 05/2005 and 09/2022, containing ≥ LR-3 lesion(s). Manual lesion segmentations in semantic segmentation masks as LR-3, LR-4, LR-5 or LR-M served as ground truth. A set of U-Net models with 14 input channels was trained using the nnU-Net framework for automatic segmentation. Lesion detection, LI-RADS classification, and instance segmentation metrics were calculated by post-processing the semantic segmentation outputs of the final model ensemble. For the external evaluation, a modified version of the LiverHccSeg dataset was used. Results The final training/internal test/external test cohorts included 383/219/16 patients. In the three cohorts, LI-RADS lesions (≥ LR-3 and LR-M) ≥ 10 mm were detected with sensitivities of 0.41–0.85/0.40–0.90/0.83 (LR-5: 0.85/0.90/0.83) and positive predictive values of 0.70–0.94/0.67–0.88/0.90 (LR-5: 0.94/0.88/0.90). F1 scores for LI-RADS classification of detected lesions ranged between 0.48–0.69/0.47–0.74/0.84 (LR-5: 0.69/0.74/0.84). Median per lesion Sørensen–Dice coefficients were between 0.61–0.74/0.52–0.77/0.84 (LR-5: 0.74/0.77/0.84). Conclusion Deep learning-based HCC risk assessment according to LI-RADS can be implemented as automatically generated tumor risk maps using out-of-the-box image segmentation tools with high detection performance for LR-5 lesions. Before translation into clinical practice, further improvements in automatic LI-RADS classification, for example through large multi-center studies, would be desirable. |
| format | Article |
| id | doaj-art-3af042495bbb4ccbb8ea9a4e42552065 |
| institution | DOAJ |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
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| series | Cancer Imaging |
| spelling | doaj-art-3af042495bbb4ccbb8ea9a4e425520652025-08-20T02:41:34ZengBMCCancer Imaging1470-73302025-03-0125111710.1186/s40644-025-00844-6LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learningRóbert Stollmayer0Selda Güven1Christian Marcel Heidt2Kai Schlamp3Pál Novák Kaposi4Oyunbileg von Stackelberg5Hans-Ulrich Kauczor6Miriam Klauss7Philipp Mayer8Clinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University HospitalDepartment of Radiology, Diskapi Yildirim Beyazit Training and Research Hospital, University of Health SciencesClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University HospitalDepartment of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of HeidelbergDepartment of Radiology, Medical Imaging Centre, Semmelweis UniversityClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University HospitalClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University HospitalClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University HospitalClinic for Diagnostic and Interventional Radiology (DIR), Heidelberg University HospitalAbstract Background Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interpretation but is rather complex and time-consuming. These limitations could potentially be alleviated using recent deep learning-based segmentation and classification methods such as nnU-Net. The study aims to create and evaluate an automatic segmentation model for HCC risk assessment, according to LI-RADS v2018 using nnU-Net. Methods For this single-center retrospective study, 602 patients at risk for HCC were included, who had dynamic EOB-MRI examinations between 05/2005 and 09/2022, containing ≥ LR-3 lesion(s). Manual lesion segmentations in semantic segmentation masks as LR-3, LR-4, LR-5 or LR-M served as ground truth. A set of U-Net models with 14 input channels was trained using the nnU-Net framework for automatic segmentation. Lesion detection, LI-RADS classification, and instance segmentation metrics were calculated by post-processing the semantic segmentation outputs of the final model ensemble. For the external evaluation, a modified version of the LiverHccSeg dataset was used. Results The final training/internal test/external test cohorts included 383/219/16 patients. In the three cohorts, LI-RADS lesions (≥ LR-3 and LR-M) ≥ 10 mm were detected with sensitivities of 0.41–0.85/0.40–0.90/0.83 (LR-5: 0.85/0.90/0.83) and positive predictive values of 0.70–0.94/0.67–0.88/0.90 (LR-5: 0.94/0.88/0.90). F1 scores for LI-RADS classification of detected lesions ranged between 0.48–0.69/0.47–0.74/0.84 (LR-5: 0.69/0.74/0.84). Median per lesion Sørensen–Dice coefficients were between 0.61–0.74/0.52–0.77/0.84 (LR-5: 0.74/0.77/0.84). Conclusion Deep learning-based HCC risk assessment according to LI-RADS can be implemented as automatically generated tumor risk maps using out-of-the-box image segmentation tools with high detection performance for LR-5 lesions. Before translation into clinical practice, further improvements in automatic LI-RADS classification, for example through large multi-center studies, would be desirable.https://doi.org/10.1186/s40644-025-00844-6Hepatocellular carcinomaMultiparametric MRIDeep learningClinical guidelines |
| spellingShingle | Róbert Stollmayer Selda Güven Christian Marcel Heidt Kai Schlamp Pál Novák Kaposi Oyunbileg von Stackelberg Hans-Ulrich Kauczor Miriam Klauss Philipp Mayer LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning Cancer Imaging Hepatocellular carcinoma Multiparametric MRI Deep learning Clinical guidelines |
| title | LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning |
| title_full | LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning |
| title_fullStr | LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning |
| title_full_unstemmed | LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning |
| title_short | LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning |
| title_sort | li rads based hepatocellular carcinoma risk mapping using contrast enhanced mri and self configuring deep learning |
| topic | Hepatocellular carcinoma Multiparametric MRI Deep learning Clinical guidelines |
| url | https://doi.org/10.1186/s40644-025-00844-6 |
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