ArticleOncotarget2015
Automated tumor analysis for molecular profiling in lung cancer.
Article in Oncotarget, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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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.
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Who cites it
30 citing papers in PubMed.
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- Deep Learning Model-Based Architectures for Lung Tumor Mutation Profiling: A Systematic Review.Cancers · 2025Review
- NLTD 2.0: A Nonlinear Framework for Robust and Customizable Color Deconvolution in Histopathology.bioRxiv : the preprint server for biology · 2025Article
- Utility of a Novel High-Sensitivity Multiplex Companion Diagnostic Test Using Formalin-Fixed Paraffin-Embedded Cell Block Materials of Non-small Cell Lung Cancer.Cancer medicine · 2025Article
- Digital counting of tissue cells for molecular analysis: the QuANTUM pipeline.Virchows Archiv : an international journal of pathology · 2025Article
- Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis.The Journal of pathology · 2025Article
- MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models.Pigment cell & melanoma research · 2025Article
- MLCDL: A Critical Practice and Implementation of Multi-tissue Classification and Diagnosis Using Deep Learning Algorithm.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer.British journal of cancer · 2024Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology.Scientific reports · 2024Article
- The crucial role of bioimage analysts in scientific research and publication.Journal of cell science · 2024Article
- Low densities of immune cells indicate unfavourable overall survival in patients suffering from squamous cell carcinoma of the lung.Scientific reports · 2024Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- 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
- Developing image analysis methods for digital pathology.The Journal of pathology · 2022Review
- Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.Patterns (New York, N.Y.) · 2022Article
- Identification of technology frontiers of artificial intelligence-assisted pathology based on patent citation network.PloS one · 2022Review
- A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer.Cancers · 2021Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery and clinical application of molecular biomarkers in solid tumors, increasingly relies on nucleic acid extraction from FFPE tissue sections and subsequent molecular profiling. This in turn requires the pathological review of haematoxylin & eosin (H&E) stained slides, to ensure sample quality, tumor DNA sufficiency by visually estimating the percentage tumor nuclei and tumor annotation for manual macrodissection. In this study on NSCLC, we demonstrate considerable variation in tumor nuclei percentage between pathologists, potentially undermining the precision of NSCLC molecular evaluation and emphasising the need for quantitative tumor evaluation. We subsequently describe the development and validation of a system called TissueMark for automated tumor annotation and percentage tumor nuclei measurement in NSCLC using computerized image analysis. Evaluation of 245 NSCLC slides showed precise automated tumor annotation of cases using Tissuemark, strong concordance with manually drawn boundaries and identical EGFR mutational status, following manual macrodissection from the image analysis generated tumor boundaries. Automated analysis of cell counts for % tumor measurements by Tissuemark showed reduced variability and significant correlation (p < 0.001) with benchmark tumor cell counts. This study demonstrates a robust image analysis technology that can facilitate the automated quantitative analysis of tissue samples for molecular profiling in discovery and diagnostics.
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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.