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Colorectal Neoplasms

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Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning.

Journal of cancer research and clinical oncology
PURPOSE: Rapid diagnosis and risk stratification can provide timely treatment for colorectal cancer (CRC) patients. Deep learning (DL) is not only used to identify tumor regions in histopathological images, but also applied to predict survival and ac...

Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.

Journal of surgical oncology
BACKGROUND AND OBJECTIVES: Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon c...

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.

Journal of translational medicine
BACKGROUND: We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer.

Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer.

Frontiers in public health
BACKGROUND: This study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients.

PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recent studies have shown that colorectal cancer (CRC) patients with microsatellite instability high (MSI-H) are more likely to benefit from immunotherapy. However, current MSI testing methods are not available for all patie...

Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer.

AJR. American journal of roentgenology
CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiautomated tools to evaluate body composition in patients with colorectal cancer (CRC). The purpose of ...

mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning.

Frontiers in immunology
Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains...

Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation.

Journal of gastroenterology
BACKGROUND: Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the ...