Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery
Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to p...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353923001748 |
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| author | Benoit Schmauch Sarah S. Elsoukkary Amika Moro Roma Raj Chase J. Wehrle Kazunari Sasaki Julien Calderaro Patrick Sin-Chan Federico Aucejo Daniel E. Roberts |
| author_facet | Benoit Schmauch Sarah S. Elsoukkary Amika Moro Roma Raj Chase J. Wehrle Kazunari Sasaki Julien Calderaro Patrick Sin-Chan Federico Aucejo Daniel E. Roberts |
| author_sort | Benoit Schmauch |
| collection | DOAJ |
| description | Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently. |
| format | Article |
| id | doaj-art-9df2723580f74a68805a47b36e4ecca2 |
| institution | OA Journals |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-9df2723580f74a68805a47b36e4ecca22025-08-20T02:35:39ZengElsevierJournal of Pathology Informatics2153-35392024-12-011510036010.1016/j.jpi.2023.100360Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgeryBenoit Schmauch0Sarah S. Elsoukkary1Amika Moro2Roma Raj3Chase J. Wehrle4Kazunari Sasaki5Julien Calderaro6Patrick Sin-Chan7Federico Aucejo8Daniel E. Roberts9Owkin Lab, Owkin, Inc., New York, NY, USAOwkin Lab, Owkin, Inc., New York, NY, USA; Department of Pathology, Cleveland Clinic, Cleveland, OH, USADepartment of Surgery, Cleveland Clinic, Cleveland, OH, USADepartment of Surgery, Cleveland Clinic, Cleveland, OH, USADepartment of Surgery, Cleveland Clinic, Cleveland, OH, USADepartment of Surgery, Stanford University, Palo Alto, CA, USADepartment of Pathology, Henri Mondor University Hospital, Créteil, FranceOwkin Lab, Owkin, Inc., New York, NY, USADepartment of Surgery, Cleveland Clinic, Cleveland, OH, USADepartment of Pathology, Cleveland Clinic, Cleveland, OH, USA; Corresponding author at: Cleveland Clinic, Department of Pathology, 2119 E 93rd St, Cleveland, OH 44106, USA.Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.http://www.sciencedirect.com/science/article/pii/S2153353923001748Hepatocellular carcinomaArtificial intelligenceMachine learningDeep learningPrognostic modeling |
| spellingShingle | Benoit Schmauch Sarah S. Elsoukkary Amika Moro Roma Raj Chase J. Wehrle Kazunari Sasaki Julien Calderaro Patrick Sin-Chan Federico Aucejo Daniel E. Roberts Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery Journal of Pathology Informatics Hepatocellular carcinoma Artificial intelligence Machine learning Deep learning Prognostic modeling |
| title | Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery |
| title_full | Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery |
| title_fullStr | Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery |
| title_full_unstemmed | Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery |
| title_short | Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery |
| title_sort | combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery |
| topic | Hepatocellular carcinoma Artificial intelligence Machine learning Deep learning Prognostic modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2153353923001748 |
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