ArticleScientific data2017
A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients.
Article in Scientific data, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 32 citations in OpenAlex.
- The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population.Scientific data · 2026Article
- Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer.Nature communications · 2025Article
- Enhanced hierarchical attention mechanism for mixed MIL in automatic Gleason grading and scoring.Scientific reports · 2025Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Proteomic-based stratification of intermediate-risk prostate cancer patients.Life science alliance · 2024Article
- Aberrations of DNA Repair Pathways in Prostate Cancer-The State of the Art.International journal of molecular sciences · 2023Article
- Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management.Clinical proteomics · 2020Review
- Inferring clonal composition from multiple tumor biopsies.NPJ systems biology and applications · 2020Article
- Intratumoral Heterogeneity Determines the Expression of mTOR-pathway Proteins in Prostate Cancer.Disease markers · 2019Article
- Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.NPJ digital medicine · 2019Article
- Automated Gleason grading of prostate cancer tissue microarrays via deep learning.Scientific reports · 2018Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microscopy image data of human cancers provide detailed phenotypes of spatially and morphologically intact tissues at single-cell resolution, thus complementing large-scale molecular analyses, e.g., next generation sequencing or proteomic profiling. Here we describe a high-resolution tissue microarray (TMA) image dataset from a cohort of 71 prostate tissue samples, which was hybridized with bright-field dual colour chromogenic and silver in situ hybridization probes for the tumour suppressor gene PTEN. These tissue samples were digitized and supplemented with expert annotations, clinical information, statistical models of PTEN genetic status, and computer source codes. For validation, we constructed an additional TMA dataset for 424 prostate tissues, hybridized with FISH probes for PTEN, and performed survival analysis on a subset of 339 radical prostatectomy specimens with overall, disease-specific and recurrence-free survival (maximum 167 months). For application, we further produced 6,036 image patches derived from two whole slides. Our curated collection of prostate cancer data sets provides reuse potential for both biomedical and computational studies.
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.