Colon Cancer

RRBP1 promotes osimertinib resistance by activating fatty acid oxidation-dependent metabolic and immune reprogramming in EGFR-Mutant lung adenocarcinoma.

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that improves clinical outcomes in patients with EGFR-mutant lung adenocarcinoma (LUAD); however, acquired resistance remains a major limitation. Increasing evidence suggests that metabolic reprogramming within the tumor microenvironment contributes to drug resistance, whereas the molecular mechanis...

An exploratory multimodal pipeline for recurrence prediction in CRC with XELOX therapy.

Colorectal cancer(CRC) recurrence remains a challenge despite curative surgery and adjuvant therapy. Integrating clinicopathological and liquid biopsy data may improve risk stratification, but evidence in small‑sample settings is lacking. This exploratory study assessed the feasibility of a multimodal machine learning pipeline for recurrence prediction in a well‑defined XELOX‑treated cohort. We an...

Comments on "Artificial Intelligence in Colonoscopy Surveillance for Lynch Syndrome: Emerging Evidence, Lessons Learned From Average-Risk Populations, and Future Directions".

Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.

BACKGROUND: Developing a rational model for precise preoperative staging of rectal cancer using magnetic resonance imaging is crucial for improving th...

A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.

Early detection of lung cancer remains one of the most effective strategies for improving survival; however, diagnostic performance is limited by vari...

CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with high early recurrence rates after curative resection. Current predictio...

A Neural Network-Enabled, Enzymatic cfDNA Methylation Assay for Colorectal Cancer Early Detection.

Early detection of colorectal cancer (CRC) remains critical for reducing disease-specific mortality, yet current noninvasive screening approaches have...

Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personali...

Gut fungal signatures in colorectal cancer and their potential for supporting diagnosis: a multi-cohort metagenomic analysis.

BACKGROUND: Colorectal cancer (CRC) is influenced by host factors and environmental exposures that shape gut microbial ecosystems. Although bacterial ...

Machine learning for predicting liver metastasis in colorectal cancer.

AIM: To evaluate the performance of machine learning models in predicting liver metastasis in colorectal cancer (CRC) patients using the SEER database...

A CLIP-based framework for multiclass lung histopathology classification with prompt engineering and class-imbalance-aware focal optimization.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and accurate histopathological classification is essential for ti...

Differentiating malignancy from liver parenchyma in Ex-Vivo OCT images using anomaly detection.

Primary liver cancer and colorectal liver metastases (CRLM) pose significant challenges, because of limited early diagnosis and the reliance on time-c...

Bulk and single-cell transcriptomics reveal prognostic signatures of phosphoinositide metabolism in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most severe malignant tumors. Phosphoinositides metabolism (PIM) plays an important role in maintaining the n...

Efficient colorectal cancer classification from histopathological images utilizing lightweight convolutional neural networks.

As one of the most fatal malignancies worldwide, colorectal cancer demands accurate histopathological image analysis to support timely diagnosis and e...

Targeting colorectal cancer stem-like cells: Overcoming therapeutic resistance and reprogramming the immunosuppressive niche.

Colorectal cancer stem-like cells (CRC-SCs) represent a tumor subpopulation with enhanced tumor-initiating capacity and have been implicated in metast...

Deciphering the potential molecular links between ochratoxin A and colorectal cancer: an integrated computational toxicological and single-cell transcriptomic study.

BACKGROUND: Ochratoxin A (OTA), a common foodborne mycotoxin, is classified as a potential human carcinogen. However, the specific molecular mechanism...

A Novel Blood-Based Screening Test for Colorectal Cancer Using Raman Spectroscopy.

BACKGROUND: Label-free vibrational spectroscopic techniques (Raman spectroscopy) combined with machine learning (ML) methodologies have huge potential...

Circulating Amino Acid Network Remodeling Reveals Systemic Metabolic Reprogramming Predictive of Colorectal Cancer Recurrence and Metastasis.

Characterizing the dynamics of systemic metabolic pathways during cancer progression could enable the development of prognostic tools and therapeutic ...

Age-stratified risk profiles for emergency colorectal cancer resection: A machine-learning analysis.

BACKGROUND: Emergency colorectal cancer resection (ECCR) is associated with worse perioperative and oncologic outcomes than elective surgery. As the i...

Predicting 5-Year Mortality in Non-Small-Cell Lung Cancer Using the Korean Central Cancer Registry: Model Development and Validation Study.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is one of the most common cancers and a leading cause of cancer-related mortality, making prognostic pr...