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Colorectal Neoplasms

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Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Personalized therapy planning remains a significant challenge in advanced colorectal cancer care, despite extensive research on prognostic and predictive markers. A strong correlation of sarcopenia or overall body composition and survival...

Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence.

PloS one
BACKGROUND: Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce...

Improving colorectal cancer screening - consumer-centred technological interventions to enhance engagement and participation amongst diverse cohorts.

Clinics and research in hepatology and gastroenterology
The current "Gold Standard" colorectal cancer (CRC) screening approach of faecal occult blood test (FOBT) with follow-up colonoscopy has been shown to significantly improve morbidity and mortality, by enabling the early detection of disease. However,...

Computer-aided classification of colorectal segments during colonoscopy: a deep learning approach based on images of a magnetic endoscopic positioning device.

Scandinavian journal of gastroenterology
OBJECTIVE: Assessment of the anatomical colorectal segment of polyps during colonoscopy is important for treatment and follow-up strategies, but is largely operator dependent. This feasibility study aimed to assess whether, using images of a magnetic...

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.

Sensors (Basel, Switzerland)
The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade...

Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning.

International journal of colorectal disease
PURPOSE: Develop a prediction model to determine the probability of no lymph node metastasis (pN0) in patients with colorectal cancer.

Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis.

European radiology
OBJECTIVES: To develop and validate a deep learning (DL) model based on quantitative analysis of contrast-enhanced ultrasound (CEUS) images that predicts early recurrence (ER) after thermal ablation (TA) of colorectal cancer liver metastasis (CRLM).

The success rate of robotic natural orifice intracorporeal anastomosis and transrectal extraction (NICE procedure) in a large cohort of consecutive unselected patients.

Surgical endoscopy
BACKGROUND: The Robotic NICE procedure is a total intracorporeal natural orifice approach in which specimen extraction and anastomosis is accomplished without an abdominal wall incision other than the port sites themselves. We aim to present the succ...

Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis.

IEEE journal of translational engineering in health and medicine
Deep learning facilitates complex medical data analysis and is increasingly being explored in colorectal cancer diagnostics. However, the training cost of the deep learning model limits its real-world medical utility. In this study, we present a comp...