Evidence map›Paper›PMID 40511597›Full record

ArticleThe journal of pathology. Clinical research2025

Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology.

Huijuan Hu, Tianhua Tan, Yerong Liu, Wei Liang, Wei Zhang, Jinsong Zhang, Ju Cui, Jinghai Song, Xuefei Li

Abstract read
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Article in The journal of pathology. Clinical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Huijuan Hu *State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China.
Tianhua Tan *Department of General Surgery, Department of Hepatopancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
Yerong LiuState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China.
Wei LiangSchool of Mathematical Sciences, Xiamen University, Xiamen, PR China.
Wei ZhangDepartment of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
Jinsong ZhangDepartment of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
Ju CuiThe Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Science, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, PR China.
Jinghai SongDepartment of General Surgery, Department of Hepatopancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
Xuefei LiState Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China.ORCID 0000-0002-6085-0838

Funding

Guangdong Basic and Applied Basic Research Foundation 2021A1515012461National High Level Hospital Clinical Research Funding BJ-2023-083National High Level Hospital Clinical Research Funding BJ-2024-219Shenzhen Institute of Synthetic Biology Scientific Research Program DWKF20210009Shenzhen Science and Technology Program ZDSYS20220606100606013Strategic Priority Research Program of the Chinese Academy of Sciences XDB0480000the Guangdong Grants 2021QN02Y554the National Key R&D Program of China 2021YFA0911100the National Natural Science Foundation of China 32000886the National Natural Science Foundation of China 32170672the National Natural Science Foundation of China 82073264
6 · The paper itself

Abstract

The spatial structure of various cell types in the tumour microenvironment (TME) can provide valuable insights into disease progression. However, identifying the spatial organization of diverse cell types that significantly correlates with patient prognosis remains challenging. In this study, enabled by deep learning-based cell segmentation and recognition, we developed a computational pipeline to systematically quantify the spatial distribution features of tumour cells, stromal cells, and lymphocytes in haematoxylin and eosin (H&E)-stained pathological images of hepatocellular carcinoma (HCC). We identified six cellular spatial features that consistently and significantly correlated with the overall survival of patients in two independent HCC patient cohorts, The Cancer Genome Atlas Program cohort and the Beijing Hospital cohort. Each threshold for patient stratification was the same for both cohorts, and the six features independently served as prognostic indicators when individually analysed alongside clinical variables. Furthermore, the combination of features such as the mean value of cellular diversity around stromal cells (StrDiv-M), the median distance between all cells (CellDis-MED), and the median value of variation coefficient of the distance around stromal cells and their neighbours (CvStrDis-MED) could further stratify the patient prognosis. In addition, incorporating cell spatial features with another clinical feature, microvascular invasion improved prognostic stratification efficacy for patients from both cohorts. In conclusion, by quantifying the cellular spatial organization features in the HCC TME, we discovered novel biomarkers for evaluating tumour prognosis. These findings could promote mechanistic studies of the cellular spatial organization within the HCC TME and potentially guide future clinical treatment.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularDeep LearningLiver NeoplasmsFemaleHumansMaleMiddle AgedPrognosisStromal CellsTumor MicroenvironmentBiomarkers, Tumorcell classificationcell segmentationcellular diversitycoefficient of variationDelaunay network

Identifiers

PMID40511597
PMCPMC12163547

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.