ArticleBioinformatics (Oxford, England)2018
Identification of topological features in renal tumor microenvironment associated with patient survival.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Automated multi-regional IHC scoring enhances prognostication in colorectal cancer.The journal of pathology. Clinical research · 2025Article
- Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Patients With Glioblastoma.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models.Nature biomedical engineering · 2025Article
- Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma.Frontiers in medicine · 2025Article
- Nuclei-level prior knowledge constrained multiple instance learning for breast histopathology whole slide image classification.iScience · 2024Article
- Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding.Frontiers in neuroscience · 2024Article
- Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.Biomedicines · 2023Review
- Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models.Cell reports methods · 2023Article
- A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2023Article
- Quantile Index Biomarkers Based on Single-Cell Expression Data.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma.Cancer research · 2023Article
- Enhanced Pathology Image Quality with Restore-Generative Adversarial Network.The American journal of pathology · 2023Article
- Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.The journal of pathology. Clinical research · 2023Article
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- Multimodal data analysis reveals that pancreatobiliary-type ampullary adenocarcinoma resembles pancreatic adenocarcinoma and differs from cholangiocarcinoma.Journal of translational medicine · 2022Article
- Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials.NPJ precision oncology · 2022Article
- Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications.Cancers · 2022Review
Corrections and comments
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Authors and funding
6 authors.
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Abstract
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. Contact: 1271992826@qq.com or kunhuang@iu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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.