AIMC Topic: Tissue Array Analysis
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A Tc1- and Th1-T-lymphocyte-rich tumor microenvironment is a hallmark of MSI colorectal cancer.
The Journal of pathology
Apr 3, 2025
Microsatellite instability is a strong predictor of response to immune checkpoint therapy and patient outcome in colorectal cancer. Although enrichment of distinct T-cell subpopulations has been determined to impact the response to immune checkpoint ...
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set.
Scientific data
May 20, 2021
Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. Though histologically DLBCL shows varying morphologies, no morphologic features have been consistently demonstrated to correlate with prognosis. We present a morphologic a...
Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification.
BMC medical imaging
May 8, 2021
BACKGROUND: One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated da...
Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods.
Scientific reports
Jan 27, 2021
Tissue microarray (TMA) core images are a treasure trove for artificial intelligence applications. However, a common problem of TMAs is multiple sectioning, which can change the content of the intended tissue core and requires re-labelling. Here, we ...
High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Sep 3, 2020
Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated det...
Hypothesis-free deep survival learning applied to the tumour microenvironment in gastric cancer.
The journal of pathology. Clinical research
Jun 27, 2020
The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we p...
Immunohistochemistry
Antigens, CD20
Ki-67 Antigen
Tissue Array Analysis
Antigens, Differentiation, Myelomonocytic
CD8 Antigens
Predictive Value of Tests
Antigens, CD
Risk Assessment
Neoplasm Staging
Deep Learning
Image Interpretation, Computer-Assisted
Time Factors
Cell Proliferation
Stomach Neoplasms
Tumor Microenvironment
Humans
Biomarkers, Tumor
Risk Factors
Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images.
Scientific reports
Apr 29, 2020
Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorith...
Computational analysis of morphological and molecular features in gastric cancer tissues.
Cancer medicine
Feb 3, 2020
Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies...
Artificial Intelligence Algorithms to Assess Hormonal Status From Tissue Microarrays in Patients With Breast Cancer.
JAMA network open
Jul 3, 2019
IMPORTANCE: Immunohistochemistry (IHC) is the most widely used assay for identification of molecular biomarkers. However, IHC is time consuming and costly, depends on tissue-handling protocols, and relies on pathologists' subjective interpretation. I...
Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms.
Computer methods and programs in biomedicine
Apr 10, 2019
OBJECTIVE: A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth. This study presents an exhaustive comparative ...
Models, Statistical
Regression Analysis
Normal Distribution
Decision Trees
Oncogenes
Area Under Curve
Tissue Array Analysis
Colon
Humans
Bayes Theorem
Gene Expression Profiling
Neural Networks, Computer
Discriminant Analysis
Logistic Models
Machine Learning
Support Vector Machine
Colonic Neoplasms
Sensitivity and Specificity
Risk
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