AIMC Topic: Colorectal Neoplasms

Clear Filters Showing 141 to 150 of 794 articles

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

Molecular imaging and biology
PURPOSE: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learning strategies, aiming to enhance clinical decision-making and improve patient outcomes in oncology.

High throughput analysis of rare nanoparticles with deep-enhanced sensitivity via unsupervised denoising.

Nature communications
The large-scale multiparametric analysis of individual nanoparticles is increasingly vital in the diverse fields of biology, medicine, and materials science. However, the current methods struggle with the tradeoff between measurement scalability and ...

Identification of a gene signature and prediction of overall survival of patients with stage IV colorectal cancer using a novel machine learning approach.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
OBJECTIVE: We sought to characterize unique gene signature patterns associated with worse overall survival (OS) among patients with stage IV colorectal cancer (CRC) using a machine learning (ML) approach.

Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.

Scientific reports
Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap between the current state of CCE in clinical practice, and the state of its counterpart optical colonosc...

Integration of 101 machine learning algorithm combinations to unveil m6A/m1A/m5C/m7G-associated prognostic signature in colorectal cancer.

Scientific reports
Colorectal cancer (CRC) is the most common malignancy in the digestive system, with a lower 5-year overall survival rate. There is increasing evidence showing that RNA modification regulators such as m1A, m5C, m6A, and m7G play crucial roles in tumor...

Multimodal deep learning: tumor and visceral fat impact on colorectal cancer occult peritoneal metastasis.

European radiology
OBJECTIVES: This study proposes a multimodal deep learning (DL) approach to investigate the impact of tumors and visceral fat on occult peritoneal metastasis in colorectal cancer (CRC) patients.

Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.

AJR. American journal of roentgenology
Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. The purpose of this study was to develop an artificial intelligence (AI) model to detect CRC on ro...

Machine Learning-Enabled Non-Invasive Screening of Tumor-Associated Circulating Transcripts for Early Detection of Colorectal Cancer.

International journal of molecular sciences
Colorectal cancer (CRC) is a major cause of cancer-related mortality, highlighting the need for accurate and non-invasive diagnostics. This study assessed the utility of tumor-associated circulating transcripts (TACTs) as biomarkers for CRC detection...

A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected Colorectal Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography-based radiomics nomogram for cancer-specific survival (CSS) prediction in curatively resected colorectal cancer (CRC), and its performance was compared with the American Joint Co...