AIMC Topic: Colorectal Neoplasms

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Computed Tomography-Based Radiomic Features Could Potentially Predict Microsatellite Instability Status in Stage II Colorectal Cancer: A Preliminary Study.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate whether quantitative radiomics features extracted from computed tomography (CT) can predict microsatellite instability (MSI) status in an Asian cohort of patients with stage Ⅱ colorectal cancer (CRC).

Quality assurance of computer-aided detection and diagnosis in colonoscopy.

Gastrointestinal endoscopy
Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-aided detection and diagnosis (CADe and/or CADx) for colonoscopy. AI is expected to have at least 2 m...

Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populations. Before it can be widely applied, significant research priorities need to be addressed. We presen...

Artificial intelligence and colonoscopy: Current status and future perspectives.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is s...

Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis.

Journal of biomedical informatics
Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples...

Semi-supervised learning to improve generalizability of risk prediction models.

Journal of biomedical informatics
The utility of a prediction model depends on its generalizability to patients drawn from different but related populations. We explored whether a semi-supervised learning model could improve the generalizability of colorectal cancer (CRC) risk predic...

Potential roles of artificial intelligence learning and faecal immunochemical testing for prioritisation of colonoscopy in anaemia.

British journal of haematology
Iron deficiency anaemia (IDA) is the most common cause of anaemia and a frequent indication for colonoscopy, although the prevalence of colorectal cancer (CRC) in IDA is low. Measurement of faecal haemoglobin by immunochemical techniques (FIT) is use...

Automated Segmentation of Colorectal Tumor in 3D MRI Using 3D Multiscale Densely Connected Convolutional Neural Network.

Journal of healthcare engineering
The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. ...

Synthesis of chitosan nanoparticles, chitosan-bulk, chitosan nanoparticles conjugated with glutaraldehyde with strong anti-cancer proliferative capabilities.

Artificial cells, nanomedicine, and biotechnology
In recent years, natural and synthetic polymers have attracted much attention due to their great potentials in medical science. In the present study, we have investigated the effect of chitosan-bulk (Ch-bulk), chitosan nanoparticles (ChNP), chitosan ...