Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study
Background and objective: Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral...
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | Translational Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523324003863 |
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| author | Kun Chen Chunxiao Sui Ziyang Wang Zifan Liu Lisha Qi Xiaofeng Li |
| author_facet | Kun Chen Chunxiao Sui Ziyang Wang Zifan Liu Lisha Qi Xiaofeng Li |
| author_sort | Kun Chen |
| collection | DOAJ |
| description | Background and objective: Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC). Methods: A total of 232 HCC patients were retrospectively included, including a training/validation cohort and two external testing cohorts from 4 centers. For habitat radiomics, intratumoral habitat partitioning based on CT images was first performed by using Otsu thresholding method. Second, a total of 350 habitat radiomic models were constructed to select the optimal model. Then, both ROC curve analyses and Kaplan-Meier survival curve analyses were applied to assess the predictive performances. Ultimately, an immune status profiling was conducted based on bioinformatic analyses and multiplex immunohistochemistry (mIHC) assays to reveal the potential mechanisms. Results: A total of 4 habitats were segmented, and the corresponding habitat radiomic models were constructed based on each habitat and an integration of all the four habitats. Generally, habitat radiomic models outperformed traditional radiomic models in stratifying prognosis for HCC. The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUCmean of 0.806. Distinct resting natural killer (NK) cell infiltrations significantly contributed to the prognosis stratification of HCC by the optimal habitat radiomic model. Conclusions: The habitat radiomic model based on CT images was potentially predictive of overall survival for HCC, with a superiority over the traditional radiomic model. The prognostic power of the habitat radiomic model was partly attributed to the distinct immune status captured in the CT images. |
| format | Article |
| id | doaj-art-5c5412e2361a4d8ab0bd648ef7005948 |
| institution | Kabale University |
| issn | 1936-5233 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj-art-5c5412e2361a4d8ab0bd648ef70059482025-01-22T05:41:31ZengElsevierTranslational Oncology1936-52332025-02-0152102260Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation studyKun Chen0Chunxiao Sui1Ziyang Wang2Zifan Liu3Lisha Qi4Xiaofeng Li5Department of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, ChinaDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, ChinaDepartment of Nuclear medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin 300304, ChinaDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, ChinaDepartment of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China; Corresponding authors at: Huanhuxi Road, Hexi Distinct, Tianjin, 300060, China.Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China; Corresponding authors at: Huanhuxi Road, Hexi Distinct, Tianjin, 300060, China.Background and objective: Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC). Methods: A total of 232 HCC patients were retrospectively included, including a training/validation cohort and two external testing cohorts from 4 centers. For habitat radiomics, intratumoral habitat partitioning based on CT images was first performed by using Otsu thresholding method. Second, a total of 350 habitat radiomic models were constructed to select the optimal model. Then, both ROC curve analyses and Kaplan-Meier survival curve analyses were applied to assess the predictive performances. Ultimately, an immune status profiling was conducted based on bioinformatic analyses and multiplex immunohistochemistry (mIHC) assays to reveal the potential mechanisms. Results: A total of 4 habitats were segmented, and the corresponding habitat radiomic models were constructed based on each habitat and an integration of all the four habitats. Generally, habitat radiomic models outperformed traditional radiomic models in stratifying prognosis for HCC. The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUCmean of 0.806. Distinct resting natural killer (NK) cell infiltrations significantly contributed to the prognosis stratification of HCC by the optimal habitat radiomic model. Conclusions: The habitat radiomic model based on CT images was potentially predictive of overall survival for HCC, with a superiority over the traditional radiomic model. The prognostic power of the habitat radiomic model was partly attributed to the distinct immune status captured in the CT images.http://www.sciencedirect.com/science/article/pii/S1936523324003863Hepatocellular carcinomaComputer tomographyHabitat radiomicsPrognosisImmune status |
| spellingShingle | Kun Chen Chunxiao Sui Ziyang Wang Zifan Liu Lisha Qi Xiaofeng Li Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study Translational Oncology Hepatocellular carcinoma Computer tomography Habitat radiomics Prognosis Immune status |
| title | Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study |
| title_full | Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study |
| title_fullStr | Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study |
| title_full_unstemmed | Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study |
| title_short | Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study |
| title_sort | habitat radiomics based on ct images to predict survival and immune status in hepatocellular carcinoma a multi cohort validation study |
| topic | Hepatocellular carcinoma Computer tomography Habitat radiomics Prognosis Immune status |
| url | http://www.sciencedirect.com/science/article/pii/S1936523324003863 |
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