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Colorectal Neoplasms

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Robotic colorectal resection in combination with a multimodal enhanced recovery program - results of the first 100 cases.

International journal of colorectal disease
PURPOSE: In Germany, colorectal robot-assisted surgery (RAS) has found its way and is currently used as primary technique in colorectal resections at our clinic. We investigated whether RAS can be extensively combined with enhanced recovery after sur...

Whole slide image-based prediction of lymph node metastasis in T1 colorectal cancer using unsupervised artificial intelligence.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: Lymph node metastasis (LNM) prediction for T1 colorectal cancer (CRC) is critical for determining the need for surgery after endoscopic resection because LNM occurs in 10%. We aimed to develop a novel artificial intelligence (AI) system u...

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study.

Cell reports. Medicine
Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions genera...

CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study.

Journal of translational medicine
BACKGROUND: Stratification of DNA mismatch repair (MMR) status in patients with colorectal cancer (CRC) enables individual clinical treatment decision making. The present study aimed to develop and validate a deep learning (DL) model based on the pre...

Robot-assisted general surgery is safe during the learning curve: a 5-year Australian experience.

Journal of robotic surgery
Robot-assisted general surgery has become increasingly common in the Australian public sector since 2003. It provides significant technical advantages compared to laparoscopic surgery. Currently, it is estimated that the learning curve for surgeons s...

Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image.

Abdominal radiology (New York)
OBJECTIVES: Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. Th...

Artificial intelligence based personalized predictive survival among colorectal cancer patients.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Colorectal cancer is a major health concern. It is now the third most common cancer and the fourth leading cause of cancer mortality worldwide. The aim of this study was to evaluate the performance of machine learning algori...

Evaluation of the advantages of robotic versus laparoscopic surgery in elderly patients with colorectal cancer.

BMC geriatrics
BACKGROUND: The incidence of colorectal cancer increases with aging. Curative-intent surgery based on a minimally invasive concept is expected to bring survival benefits to elderly patients (aged over 80 years) with colorectal cancer who are frequent...

EBHI: A new Enteroscope Biopsy Histopathological H&E Image Dataset for image classification evaluation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND AND PURPOSE: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathol...

Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features.