Identification of topological features in renal tumor microenvironment associated with patient survival
Open accessAbstract
Motivation: As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results: We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers. Availability and implementation: https://github.com/chengjun583/KIRP-topological-features Supplementary information: Supplementary data are available at Bioinformatics online.
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These datasets can be correlated to molecular tumor characteristics and can be used to quantitatively characterize tissue at multiple spatial scales to create biomarkers that predict outcome and treatment response.
“These datasets can be correlated to molecular tumor characteristics and can be used to quantitatively characterize tissue at multiple spatial scales to create biomarkers that predict outcome and treatment response [150], [152]–[154].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [153]
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AI in Medical Imaging Informatics: Current Challenges and Future Directions
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- Authors
- Cheng, J., Mo, X., Wang, X., Parwani, A., Feng, Q., & Huang, K.
- Affiliations
- Southern Medical University (CN)
- The Ohio State University Wexner Medical Center (US)
- The Ohio State University (US)
- Indiana University School of Medicine (US)
- Corresponding author
- Feng, Qianjin
- Huang, Kun
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-11-09
- Volume
- 34
- Issue
- 6
- Pages
- 1024-1030
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7263397
- OpenAlex
- W2767330859
- Cited by
- 79
- References
- 29
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 9 · 2023: 20 · 2022: 14 · 2021: 11 · 2020: 9 · 2019: 5 · 2018: 1
- FWCI
- 2.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion)
- Funders
- NIH
- NCI ITCR (U01CA188547)
- Leidos (15x040)
- Shenzhen Peacock Plan (KQTD2016053112051497)
- Science and Technology Project of Guangdong Province, China (2015B010131011)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- papillary renal cell carcinoma, topological properties, digital pathology, histopathology image analysis, patient survival, tumor staging
- MeSH terms
- Machine Learning, Adult, Aged, Aged, 80 and over, Carcinoma, Papillary, Female, Humans, Image Processing, Computer-Assisted, Kidney Neoplasms, Male, Middle Aged, Prognosis, Tumor Microenvironment
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