Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma
BackgroundComputer-aided histopathological image analysis is increasingly used for image evaluation and decision-making in cancer patients. This study extracted quantitative histopathological image features to predict molecular features, and combined them with omics data to predict prognosis of hepa...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1591165/full |
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| author | Linyan Chen Yang Li Zhiyuan Zhang Tongshu Yang Hao Zeng |
| author_facet | Linyan Chen Yang Li Zhiyuan Zhang Tongshu Yang Hao Zeng |
| author_sort | Linyan Chen |
| collection | DOAJ |
| description | BackgroundComputer-aided histopathological image analysis is increasingly used for image evaluation and decision-making in cancer patients. This study extracted quantitative histopathological image features to predict molecular features, and combined them with omics data to predict prognosis of hepatocellular carcinoma (HCC) patients.MethodsTotally 334 patients from The Cancer Genome Atlas were divided equally into the training and testing sets. Histopathological image features and multiple omics data (somatic mutation, mRNA expression, and protein expression) were used alone or in combination to build prediction models through machine learning. Areas under receiver operating characteristic curves (AUCs) were assessed for 1-year, 3-year, and 5-year overall survival (OS).ResultsHistopathological image features were able to predict somatic mutations: TERT promoter (AUC = 0.926), TP53 (AUC = 0.893), CTNNB1 (AUC = 0.885), ALB (AUC = 0.879), molecular subtypes (AUCs from 0.905 to 0.932), and OS (5-year AUC = 0.819) in the testing set, which also had good performances for OS in the external validation sets of tissue microarrays from 263 patients (5-year AUCs from 0.682 to 0.761). Furthermore, the integrated models of histopathological image features and omics data increased the accuracy of prognosis prediction, especially the multi-platform model that combined all types of features (5-year AUC = 0.904). The risk score based on the multi-platform model was a significant predictor for OS in the testing set (HR = 15.09, p < 0.0001). Additionally, the multi-platform model achieved a higher net benefit in decision curve analysis.ConclusionHistopathological image features had the potential to predict molecular features and survival outcomes, and could be integrated with multiple omics data as a practical tool for prognosis prediction and risk stratification, facilitating personalized medicine for HCC patients. |
| format | Article |
| id | doaj-art-e1be245ae2814f6c8c1eff6e4e0e4de1 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-e1be245ae2814f6c8c1eff6e4e0e4de12025-08-20T03:51:53ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-07-011510.3389/fonc.2025.15911651591165Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinomaLinyan Chen0Yang Li1Zhiyuan Zhang2Tongshu Yang3Hao Zeng4Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, ChinaDivision of Gastrointestinginal Surgery Ward, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, ChinaBackgroundComputer-aided histopathological image analysis is increasingly used for image evaluation and decision-making in cancer patients. This study extracted quantitative histopathological image features to predict molecular features, and combined them with omics data to predict prognosis of hepatocellular carcinoma (HCC) patients.MethodsTotally 334 patients from The Cancer Genome Atlas were divided equally into the training and testing sets. Histopathological image features and multiple omics data (somatic mutation, mRNA expression, and protein expression) were used alone or in combination to build prediction models through machine learning. Areas under receiver operating characteristic curves (AUCs) were assessed for 1-year, 3-year, and 5-year overall survival (OS).ResultsHistopathological image features were able to predict somatic mutations: TERT promoter (AUC = 0.926), TP53 (AUC = 0.893), CTNNB1 (AUC = 0.885), ALB (AUC = 0.879), molecular subtypes (AUCs from 0.905 to 0.932), and OS (5-year AUC = 0.819) in the testing set, which also had good performances for OS in the external validation sets of tissue microarrays from 263 patients (5-year AUCs from 0.682 to 0.761). Furthermore, the integrated models of histopathological image features and omics data increased the accuracy of prognosis prediction, especially the multi-platform model that combined all types of features (5-year AUC = 0.904). The risk score based on the multi-platform model was a significant predictor for OS in the testing set (HR = 15.09, p < 0.0001). Additionally, the multi-platform model achieved a higher net benefit in decision curve analysis.ConclusionHistopathological image features had the potential to predict molecular features and survival outcomes, and could be integrated with multiple omics data as a practical tool for prognosis prediction and risk stratification, facilitating personalized medicine for HCC patients.https://www.frontiersin.org/articles/10.3389/fonc.2025.1591165/fullliver cancerhistopathologygenomicstranscriptomicsproteomics |
| spellingShingle | Linyan Chen Yang Li Zhiyuan Zhang Tongshu Yang Hao Zeng Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma Frontiers in Oncology liver cancer histopathology genomics transcriptomics proteomics |
| title | Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma |
| title_full | Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma |
| title_fullStr | Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma |
| title_full_unstemmed | Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma |
| title_short | Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma |
| title_sort | multi platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma |
| topic | liver cancer histopathology genomics transcriptomics proteomics |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1591165/full |
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