ArticleScientific reports2015
Quantitative histology analysis of the ovarian tumour microenvironment.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Decoding inflammatory regulation in ovarian cancer at single-cell resolution.Frontiers in immunology · 2025Review
- The endothelin-1-driven tumor-stroma feed-forward loops in high-grade serous ovarian cancer.Clinical science (London, England : 1979) · 2024Article
- Spatiotemporal architecture of immune cells and cancer-associated fibroblasts in high-grade serous ovarian carcinoma.Science advances · 2024Article
- The prognostic value of tumor-stroma ratio and a newly developed computer-aided quantitative analysis of routine H&E slides in high-grade serous ovarian cancer.Research square · 2023Article
- Profiling ovarian cancer tumor and microenvironment during disease progression for cell-based immunotherapy design.iScience · 2023Article
- Spatial Transcriptomics Depict Ligand-Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors.Cancer research · 2023Article
- Injectable three-dimensional tumor microenvironments to study mechanobiology in ovarian cancer.Acta biomaterialia · 2022Article
- The impact of the spatial heterogeneity of resistant cells and fibroblasts on treatment response.PLoS computational biology · 2022Article
- Computational pathology in ovarian cancer.Frontiers in oncology · 2022Review
- Clinical Implication of Systemic Immune-Inflammation Index and Prognostic Nutritional Index in Skull Base Chordoma Patients.Frontiers in oncology · 2021Article
- Digital Pathology-based Study of Cell- and Tissue-level Morphologic Features in Serous Borderline Ovarian Tumor and High-grade Serous Ovarian Cancer.Journal of pathology informatics · 2021Article
- Why is cancer not more common? A changing microenvironment may help to explain why, and suggests strategies for anti-cancer therapy.Open biology · 2020Article
- Organotypic breast tumor model elucidates dynamic remodeling of tumor microenvironment.Biomaterials · 2020Article
- Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy.Cancer medicine · 2020Article
- A four-factor immune risk score signature predicts the clinical outcome of patients with spinal chordoma.Clinical and translational medicine · 2020Article
- Analysis of tumour ecological balance reveals resource-dependent adaptive strategies of ovarian cancer.EBioMedicine · 2019Article
- Stromal cell ratio based on automated image analysis as a predictor for platinum-resistant recurrent ovarian cancer.BMC cancer · 2019Article
- Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity.Nature communications · 2018Article
- A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome.Scientific reports · 2017Article
- Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Concerted efforts in genomic studies examining RNA transcription and DNA methylation patterns have revealed profound insights in prognostic ovarian cancer subtypes. On the other hand, abundant histology slides have been generated to date, yet their uses remain very limited and largely qualitative. Our goal is to develop automated histology analysis as an alternative subtyping technology for ovarian cancer that is cost-efficient and does not rely on DNA quality. We developed an automated system for scoring primary tumour sections of 91 late-stage ovarian cancer to identify single cells. We demonstrated high accuracy of our system based on expert pathologists' scores (cancer = 97.1%, stromal = 89.1%) as well as compared to immunohistochemistry scoring (correlation = 0.87). The percentage of stromal cells in all cells is significantly associated with poor overall survival after controlling for clinical parameters including debulking status and age (multivariate analysis p = 0.0021, HR = 2.54, CI = 1.40-4.60) and progression-free survival (multivariate analysis p = 0.022, HR = 1.75, CI = 1.09-2.82). We demonstrate how automated image analysis enables objective quantification of microenvironmental composition of ovarian tumours. Our analysis reveals a strong effect of the tumour microenvironment on ovarian cancer progression and highlights the potential of therapeutic interventions that target the stromal compartment or cancer-stroma signalling in the stroma-high, late-stage ovarian cancer subset.
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