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Immunohistochemistry

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Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry.

Applied immunohistochemistry & molecular morphology : AIMM
Invasive breast carcinomas are routinely tested for HER2 using immunohistochemistry (IHC), with reflex in situ hybridization (ISH) for those scored as equivocal (2+). ISH testing is expensive, time-consuming, and not universally available. In this st...

[Artificial intelligence: a solution for the lack of pathologists?].

Der Pathologe
Given the rapid developments, there is no doubt that artificial intelligence (AI) will substantially impact pathological diagnostics. However, it remains an open question if AI will primarily be another diagnostic tool, such as immunohistochemistry, ...

Deep learning based on hematoxylin-eosin staining outperforms immunohistochemistry in predicting molecular subtypes of gastric adenocarcinoma.

The Journal of pathology
In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratorie...

Objective quantification of nerves in immunohistochemistry specimens of thyroid cancer utilising deep learning.

PLoS computational biology
Accurate quantification of nerves in cancer specimens is important to understand cancer behaviour. Typically, nerves are manually detected and counted in digitised images of thin tissue sections from excised tumours using immunohistochemistry. Howeve...

Quantitative Assessment of Epithelial Proliferation in Rat Mammary Gland Using Artificial Intelligence Independent of Choice of Proliferation Marker.

The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society
Epithelial proliferation in the rat mammary gland is recommended in regulatory guidelines as an endpoint for assessment of the in vivo carcinogenic potential of insulin analogues. Epithelial proliferation is traditionally assessed by immunohistochemi...

Digital Technology in Diagnostic Breast Pathology and Immunohistochemistry.

Pathobiology : journal of immunopathology, molecular and cellular biology
Digital technology has been used in the field of diagnostic breast pathology and immunohistochemistry (IHC) for decades. Examples include automated tissue processing and staining, digital data processing, storing and management, voice recognition sys...

Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry.

Biomolecules
Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the ...

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images.

Scientific reports
Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the...

Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning.

Scientific reports
Immune checkpoint inhibitor (ICI) therapy is widely used but effective only in a subset of gastric cancers. Epstein-Barr virus (EBV)-positive and microsatellite instability (MSI) / mismatch repair deficient (dMMR) tumors have been reported to be high...

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

EBioMedicine
BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.