ReviewLaboratory investigation; a journal of technical methods and pathology2015
Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images.
Review in Laboratory investigation; a journal of technical methods and pathology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Meta-analysis of Survival Prediction in Glioblastoma Patients using Advanced MRI Techniques.Current medical imaging · 2026Pooled it
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- NLTD 2.0: A Nonlinear Framework for Robust and Customizable Color Deconvolution in Histopathology.bioRxiv : the preprint server for biology · 2025Article
- Compensator-based small animal IMRT enables conformal preclinical dose painting: application to tumor hypoxia.Scientific reports · 2025Article
- Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.Breast cancer research : BCR · 2024Article
- Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.Cancers · 2023Article
- CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study.Journal of translational medicine · 2022Article
- Hierarchical cancer heterogeneity analysis based on histopathological imaging features.Biometrics · 2022Article
- A novel transfer-learning based physician-level general and subtype classifier for non-small cell lung cancer.Heliyon · 2022Article
- Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology.Scientific reports · 2022Article
- The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.Frontiers in oncology · 2022Article
- The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers.Computational and structural biotechnology journal · 2022Review
- Evolution of Artificial Intelligence-Powered Technologies in Biomedical Research and Healthcare.Advances in biochemical engineering/biotechnology · 2022Review
- JOINT AND INDIVIDUAL ANALYSIS OF BREAST CANCER HISTOLOGIC IMAGES AND GENOMIC COVARIATES.The annals of applied statistics · 2021Article
- Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2021Article
- Article
- Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma.Frontiers in oncology · 2021Article
- Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma.Frontiers in oncology · 2021Article
- Role of artificial intelligence in integrated analysis of multi-omics and imaging data in cancer research.Translational cancer research · 2019Article
- Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.Nature reviews. Clinical oncology · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Technological advances in computing, imaging, and genomics have created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. Slide scanning devices are now capable of rapidly producing massive digital image archives that capture histological details in high resolution. Commensurate advances in computing and image analysis algorithms enable mining of archives to extract descriptions of histology, ranging from basic human annotations to automatic and precisely quantitative morphometric characterization of hundreds of millions of cells. These imaging capabilities represent a new dimension in tissue-based studies, and when combined with genomic and clinical endpoints, can be used to explore biologic characteristics of the tumor microenvironment and to discover new morphologic biomarkers of genetic alterations and patient outcomes. In this paper, we review developments in quantitative imaging technology and illustrate how image features can be integrated with clinical and genomic data to investigate fundamental problems in cancer. Using motivating examples from the study of glioblastomas (GBMs), we demonstrate how public data from The Cancer Genome Atlas (TCGA) can serve as an open platform to conduct in silico tissue-based studies that integrate existing data resources. We show how these approaches can be used to explore the relation of the tumor microenvironment to genomic alterations and gene expression patterns and to define nuclear morphometric features that are predictive of genetic alterations and clinical outcomes. Challenges, limitations, and emerging opportunities in the area of quantitative imaging and integrative analyses are also discussed.
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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.