ArticleProceedings. IEEE International Symposium on Biomedical Imaging2017
BREAST CANCER HISTOPATHOLOGY IMAGE ANALYSIS PIPELINE FOR TUMOR PURITY ESTIMATION.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.The Journal of pathology · 2025Article
- Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence.Cancers · 2024Article
- Artificial intelligence algorithm for neoplastic cell percentage estimation and its application to copy number variation in urinary tract cancer.Journal of pathology and translational medicine · 2024Article
- Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- Correlation analysis of tumor purity with clinicopathological, molecular, and imaging features in high-grade gliomas.Neurosurgical review · 2022Article
- Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.Patterns (New York, N.Y.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
The translation of genomic sequencing technology to the clinic has greatly advanced personalized medicine. However, the presence of normal cells in tumors is a confounding factor in genome sequence analysis. Tumor purity, or the percentage of cancerous cells in whole tissue section, is a correction factor that can be used to improve the clinical utility of genomic sequencing. Currently, tumor purity is estimated visually by expert pathologists; however, it has been shown that there exist vast inter-observer discrepancies in tumor purity scoring. In this paper, we propose a quantitative image analysis pipeline for tumor purity estimation and provide a systematic comparison between pathologists' scores and our image-based tumor purity estimation.
Indexed as
Identifiers
What OpenQuestion holds
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.