Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma
Abstract Objectives The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study aimed to investigate the value of the ITH-based deep learning model for preoperative prediction of histopathologic grade in hepatocellular carci...
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BMC
2025-03-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-13781-1 |
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| author | Shaoming Song Gong Zhang Zhiyuan Yao Ruiqiu Chen Kai Liu Tianchen Zhang Guineng Zeng Zizheng Wang Rong Liu |
| author_facet | Shaoming Song Gong Zhang Zhiyuan Yao Ruiqiu Chen Kai Liu Tianchen Zhang Guineng Zeng Zizheng Wang Rong Liu |
| author_sort | Shaoming Song |
| collection | DOAJ |
| description | Abstract Objectives The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study aimed to investigate the value of the ITH-based deep learning model for preoperative prediction of histopathologic grade in hepatocellular carcinoma (HCC). Materials and methods A total of 858 patients from primary cohort and two external cohorts were included. 3.0T or 1.5T axial portal venous phase MRI images were collected. We conducted radiomics feature-driven K-means clustering for automatic partition to reveal ITH. 2.5D and 3D deep learning models based on ResNet architecture were trained to extract deep learning hidden features of each subregion. The selected features were used to train Random Forest classifier, which constructed the feature-fusion model. Results The extracted voxel-level radiomics features were unsupervised clustered by K-means to generate three subregions. In the 2.5D deep learning, the feature-fusion model based on ITH had superior predictive efficacy than the whole-tumor model (AUC: 0.82 vs. 0.72; p = 0.004). Even in the validation and external test sets, this model maintained a high AUC of 0.78–0.83, and net reclassification indices indicated that it could improve prediction by 25–28%. Regarding the prognostic value, overall survival (OS) and recurrence-free survival (RFS) could be significantly stratified by the 2.5D feature-fusion model, and multivariable Cox regressions indicated its signature was identified as a risk predictor for OS and RFS (p < 0.05). Conclusion The ITH-based feature-fusion model provided a non-invasive method for classifying tumor differentiation in HCC, which may serve as a promising strategy for stratification management. |
| format | Article |
| id | doaj-art-198d693df4c8434b9546cbfe75bd1a1a |
| institution | DOAJ |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-198d693df4c8434b9546cbfe75bd1a1a2025-08-20T02:41:32ZengBMCBMC Cancer1471-24072025-03-0125111310.1186/s12885-025-13781-1Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinomaShaoming Song0Gong Zhang1Zhiyuan Yao2Ruiqiu Chen3Kai Liu4Tianchen Zhang5Guineng Zeng6Zizheng Wang7Rong Liu8The First School of Clinical Medicine, Lanzhou UniversityFaculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General HospitalFaculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General HospitalThe First School of Clinical Medicine, Lanzhou UniversityFaculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General HospitalThe First School of Clinical Medicine, Lanzhou UniversityFaculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General HospitalDepartment of Hepatobiliary Surgery, Senior Department of Hepatology, The Fifth Medical Center of Chinese PLA General HospitalThe First School of Clinical Medicine, Lanzhou UniversityAbstract Objectives The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study aimed to investigate the value of the ITH-based deep learning model for preoperative prediction of histopathologic grade in hepatocellular carcinoma (HCC). Materials and methods A total of 858 patients from primary cohort and two external cohorts were included. 3.0T or 1.5T axial portal venous phase MRI images were collected. We conducted radiomics feature-driven K-means clustering for automatic partition to reveal ITH. 2.5D and 3D deep learning models based on ResNet architecture were trained to extract deep learning hidden features of each subregion. The selected features were used to train Random Forest classifier, which constructed the feature-fusion model. Results The extracted voxel-level radiomics features were unsupervised clustered by K-means to generate three subregions. In the 2.5D deep learning, the feature-fusion model based on ITH had superior predictive efficacy than the whole-tumor model (AUC: 0.82 vs. 0.72; p = 0.004). Even in the validation and external test sets, this model maintained a high AUC of 0.78–0.83, and net reclassification indices indicated that it could improve prediction by 25–28%. Regarding the prognostic value, overall survival (OS) and recurrence-free survival (RFS) could be significantly stratified by the 2.5D feature-fusion model, and multivariable Cox regressions indicated its signature was identified as a risk predictor for OS and RFS (p < 0.05). Conclusion The ITH-based feature-fusion model provided a non-invasive method for classifying tumor differentiation in HCC, which may serve as a promising strategy for stratification management.https://doi.org/10.1186/s12885-025-13781-1Intratumoral heterogeneityDeep learningHistopathologic gradeMagnetic resonance imagingHepatocellular carcinoma |
| spellingShingle | Shaoming Song Gong Zhang Zhiyuan Yao Ruiqiu Chen Kai Liu Tianchen Zhang Guineng Zeng Zizheng Wang Rong Liu Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma BMC Cancer Intratumoral heterogeneity Deep learning Histopathologic grade Magnetic resonance imaging Hepatocellular carcinoma |
| title | Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma |
| title_full | Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma |
| title_fullStr | Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma |
| title_full_unstemmed | Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma |
| title_short | Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma |
| title_sort | deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma |
| topic | Intratumoral heterogeneity Deep learning Histopathologic grade Magnetic resonance imaging Hepatocellular carcinoma |
| url | https://doi.org/10.1186/s12885-025-13781-1 |
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