AIMC Topic: Colonic Neoplasms
Clear Filters Showing 121 to 130 of 186 articles
Molecular docking and machine learning analysis of Abemaciclib in colon cancer.
BMC molecular and cell biology
Jul 8, 2020
BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design ...
Cell Line, Tumor
Gene Expression Regulation, Neoplastic
Adenocarcinoma
Databases, Protein
Algorithms
Prognosis
Molecular Docking Simulation
Gene Expression Profiling
Benzimidazoles
Fatty Acid-Binding Proteins
Gastrointestinal Hormones
Epistasis, Genetic
Aminopyridines
Drug Repositioning
Neoplasm Staging
Colonic Neoplasms
Survival Analysis
Humans
Machine Learning
A machine learning-based prognostic predictor for stage III colon cancer.
Scientific reports
Jun 25, 2020
Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided approach which combing convolutional neural network with machine classifier to predict the prognosis ...
Colectomy
Kaplan-Meier Estimate
Risk Assessment
Colon
Follow-Up Studies
Neoplasm Staging
Neoplasm Recurrence, Local
Colonic Neoplasms
Disease-Free Survival
Image Interpretation, Computer-Assisted
Young Adult
Adult
Male
Middle Aged
Female
Risk Factors
Aged
Aged, 80 and over
Humans
Machine Learning
Prognosis
Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.
Gastroenterology
Jun 17, 2020
BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Comput...
Female
Reproducibility of Results
Aged
Young Adult
Humans
Prospective Studies
Diagnosis, Computer-Assisted
Deep Learning
China
Adolescent
Predictive Value of Tests
Adult
Male
Middle Aged
Colonoscopy
Missed Diagnosis
Colonic Neoplasms
Colonic Polyps
Image Interpretation, Computer-Assisted
Adenomatous Polyps
A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.
mBio
Jun 9, 2020
Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward dev...
Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis.
Methods (San Diego, Calif.)
May 19, 2020
Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-aided tissue examination using machine learning techniques, especially convolutional neural networks. A number of convolutional neural network-based methodolog...
The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis.
Journal of gastroenterology and hepatology
Apr 26, 2020
BACKGROUND AND AIM: The utility of artificial intelligence (AI) in colonoscopy has gained popularity in current times. Recent trials have evaluated the efficacy of deep convolutional neural network (DCNN)-based AI system in colonoscopy for improving ...
Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study.
Cancer imaging : the official publication of the International Cancer Imaging Society
Apr 25, 2020
BACKGROUND: Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In thi...
Stable gene selection by self-representation method in fuzzy sample classification.
Medical & biological engineering & computing
Mar 24, 2020
In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challen...
An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detection.
Expert review of gastroenterology & hepatology
Nov 30, 2019
: Among the Gastrointestinal (GI) Endoscopy Editorial Board top 10 topics in advances in endoscopy in 2018, water exchange colonoscopy and artificial intelligence were both considered important advances. Artificial intelligence holds the potential to...
Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.
Gut
Nov 28, 2019
OBJECTIVE: Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.
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