Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultim...
PURPOSE: Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC...
BACKGROUND: Robot-assisted surgery is increasingly implemented for the resection of colorectal cancer, although the scientific evidence for adopting this technique is still limited. This study's main objective was to compare short-term complication r...
Accepting surgical complications, especially those related to the learning curve, as unavoidable events in colorectal procedures, is like accepting to fly onboard an aircraft with a 10% to 20% chance of not arriving at final destination. Under this c...
BACKGROUND: Tumor mutational burden-high (TMB-H), which is detected with gene panel testing, is a promising biomarker for immune checkpoint inhibitors (ICIs) in colorectal cancer (CRC). However, in clinical practice, not every patient is tested for T...
High levels of microsatellite instability (MSI-H) occurs in about 15% of sporadic colorectal cancer (CRC) and is an important predictive marker for response to immune checkpoint inhibitors. To test the feasibility of a deep learning (DL)-based classi...
Clinical cancer research : an official journal of the American Association for Cancer Research
Apr 22, 2021
PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastatic colorectal cancer (mCRC). However, ligand RNA assays have not been adopted into routine practice ...
Best practice & research. Clinical gastroenterology
Apr 20, 2021
OBJECTIVES: This study review focuses on a deep learning method for the detection of colorectal lesions in colonoscopy and AI support for detecting colorectal neoplasia, especially in flat lesions.
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