AIMC Topic: Colorectal Neoplasms

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Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment.

International journal of surgery (London, England)
BACKGROUND: Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and...

The deconstructed procedural description in robotic colorectal surgery.

Journal of robotic surgery
Increasing robotic surgical utilisation in colorectal surgery internationally has strengthened the need for standardised training. Deconstructed procedural descriptions identify components of an operation that can be integrated into proficiency-based...

Multi-scale nested UNet with transformer for colorectal polyp segmentation.

Journal of applied clinical medical physics
BACKGROUND: Polyp detection and localization are essential tasks for colonoscopy. U-shape network based convolutional neural networks have achieved remarkable segmentation performance for biomedical images, but lack of long-range dependencies modelin...

Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm.

Chinese journal of integrative medicine
OBJECTIVE: To establish the dynamic treatment strategy of Chinese medicine (CM) for metastatic colorectal cancer (mCRC) by machine learning algorithm, in order to provide a reference for the selection of CM treatment strategies for mCRC.

Image-based profiling and deep learning reveal morphological heterogeneity of colorectal cancer organoids.

Computers in biology and medicine
Patient-derived organoids have proven to be a highly relevant model for evaluating of disease mechanisms and drug efficacies, as they closely recapitulate in vivo physiology. Colorectal cancer organoids, specifically, exhibit a diverse range of morph...

Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.

Scientific reports
Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-ai...

A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes.

Computers in biology and medicine
The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for...

Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining.

International journal of molecular sciences
Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intell...

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. How...