AIMC Topic: Adenocarcinoma

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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...

A machine-learning informed circulating microbial DNA signature for early diagnosis of esophageal adenocarcinoma.

Gut microbes
Esophageal adenocarcinoma (EAC) has seen a dramatic rise in incidence in developed countries over the past three decades. Early detection of its precursors-gastroesophageal reflux disease (GERD), Barrett's esophagus (BE), and high-grade dysplasia (HG...

Targeted inhibition of gastric adenocarcinoma by nano-curcumin liposomes: Insights from combined machine learning and experimental analyses into the mechanisms of cuproptosis and metabolic reprogramming.

International journal of pharmaceutics
PURPOSE: Gastric adenocarcinoma is a highly aggressive malignancy characterized by a complex tumor microenvironment. Nano-curcumin liposomes hold great potential in inhibiting tumor growth and survival, as well as inducing cuproptosis and oxidative s...

The diagnostic value of serum cysteine protease inhibitor (CST4) in colorectal cancer: a preliminary study.

BMC gastroenterology
BACKGROUND: CST4 is associated with various cancers but its diagnostic value in colorectal cancer (CRC) has not been clearly established. This study aims to further validate the diagnostic value of CST4 in colorectal cancer using random forest models...

Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.

Journal of cancer research and clinical oncology
PURPOSE: The integration of artificial intelligence (AI) with hyperspectral imaging (HSI) offers a promising avenue for improving pre-therapeutic prognosis, a key factor in optimizing cancer treatment strategies. This study explores the potential of ...

Multi-omics analysis of parthanatos related molecular subgroup and prognostic model development in stomach adenocarcinoma.

PloS one
Stomach adenocarcinoma (STAD), the most prevalent histological subtype of gastric cancer, exhibits high heterogeneity and poor prognosis, posing significant therapeutic challenges. Parthanatos, a distinct form of regulated cell death mediated by poly...

Detection and score grading for prostate adenocarcinoma using semantic segmentation.

PloS one
Prostate cancer is a major global health challenge. In this study, we present an approach for the detection and grading of prostate cancer through the semantic segmentation of adenocarcinoma tissues, specifically focusing on distinguishing between Gl...

Exploring the survival benefits of surgical treatment for pancreatic adenocarcinoma using the DeepSurv neural network model.

Computer assisted surgery (Abingdon, England)
To develop a DeepSurv model for predicting survival in pancreatic adenocarcinoma patients, evaluating the benefit of surgical versus non-surgical treatment across different stages, including stage IV subcategories. Clinical data were extracted from t...

Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma.

PloS one
Chloride channel accessory 1 (CLCA1) is considered a potential prognostic biomarker for colon adenocarcinoma (COAD). The objective of this research was to develop two pathomics models to predict CLCA1 expression from hematoxylin-eosin (H&E) stained p...