AIMC Topic: Colonic Neoplasms

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Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.

International journal of colorectal disease
BACKGROUND: Colon adenocarcinoma (COAD) remains a leading cause of cancer-related mortality worldwide. Although tumor deposits (TDs) are established prognostic indicators, their molecular characteristics and potential for improving risk stratificatio...

Entropy-driven signal amplification integrated with machine learning in multiplex lateral flow immunoassay for sensitive Point-of-Care colon cancer diagnosis.

Journal of nanobiotechnology
Investigations on epithelial-mesenchymal transition (EMT) events occurring on circulating tumor cells (CTCs) are poised to significantly advance nanoliquid biopsy methodologies. This study presented a colorimetric multiplex lateral flow immunoassay s...

Comparison of Machine Learning Models for Colon Cancer Survival: Predictive Modeling Approach.

JMIR cancer
BACKGROUND: Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used ...

Fusion of classical and deep learning features with incremental learning for improved classification of lung and colon cancer.

Scientific reports
Correct histopathological image classification of lung and colon cancer is a stringent challenge for clinical pathology. This work introduces a hybrid deep learning network by combining traditional handcrafted features of LBP, GLCM, wavelet, color, a...

Machine learning-based prediction model for omental metastasis in right-sided colon cancer patients: a retrospective multicenter study.

International journal of colorectal disease
PURPOSE: Current diagnostic modalities lack sufficient sensitivity for detecting omental metastasis (OM), often underestimating metastatic burden. Unlike traditional statistical model, machine learning (ML) model is designed to detect subtle variable...

MX1 is a novel crucial prognostic and therapeutic target inducing chemoresistance in right-sided colon cancer: insights from machine learning-based multi-omics analysis.

Human genomics
BACKGROUND: Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aim...

MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI.

Scientific reports
Cancer is a life-threatening disease that affects several human lives all over the world. The classification of cancer severities utilizing histopathological images is vital for effective and timely diagnosis. This always creates a demandable require...

Enhancing explainability of random survival forests in predicting stent patency risk for malignant colonic obstruction.

BMC gastroenterology
BACKGROUND: This study aims to enhance the explainability and predictive accuracy of the Random Survival Forest (RSF) algorithm in predicting stent patency risk for patients with malignant colonic obstruction.

CRLS1 influences liver metastasis in colon cancer by regulating lipid metabolism pathways.

Functional & integrative genomics
Colon cancer is one of the leading causes of cancer-related mortality, with liver metastasis commonly complicating its progression and significantly worsening patient prognosis. This study aims to explore the relationship between liver metastasis in ...