Colon Cancer

[Artificial intelligence prediction of surgical difficulty in mid-low rectal cancer: a single-center cohort study].

Objective: This study processes and analyzes rectal MRI images of patients with mid-to-low rectal cancer using deep learning technology, and integrates these data with clinical baseline information to construct a fully automated end-to-end prediction model. The model is designed to assist colorectal surgeons in preoperatively assessing surgical difficulty and selecting the optimal surgical approac...

[Diagnosis and treatment of colorectal cancer in the era of artificial intelligence: review and prospect].

In recent years, artificial intelligence (AI) has achieved groundbreaking progress in the field of medicine, particularly in the diagnosis and treatment of colorectal cancer (CRC). In terms of data analysis, AI-assisted diagnosis and treatment has significantly improved the sensitivity of colonoscopy and the accuracy of pathological diagnosis, thereby providing robust support for CRC diagnosis. Re...

Interpretable machine learning model for predicting refeeding syndrome after colorectal cancer surgery.

OBJECTIVE: Refeeding syndrome (RFS) is a common yet frequently overlooked complication during postoperative nutritional support in patients undergoing...

Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.

BACKGROUND: Lung cancer is one of the major cancers worldwide, and rapid, accurate diagnosis is crucial for subsequent treatment and management. Curre...

FlareDB: A Database of Significant Flares in Solar Cycles 24 and 25 with SDO/HMI and SDO/AIA Observations.

We present FlareDB, a database that provides comprehensive magnetic field information, ultraviolet/extreme ultraviolet (UV/EUV) emissions, and white l...

ProteoBoostR: an interactive framework for supervised machine learning in clinical proteomics.

BACKGROUND: Mass spectrometry-based proteomics enables high-throughput quantification of thousands of proteins in clinical samples, fueling biomarker ...

Diagnostic Performance of Preoperative Imaging-based Radiomics Models for Predicting Liver Metastases in Colorectal Cancer: A Systematic Review and Meta-analysis.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of preoperative computed tomography (CT) and magnetic resonance imaging (MRI)-based r...

Integrated network toxicology, machine learning, and bioinformatics analysis reveals sodium dehydroacetate-induced coagulation dysfunction in colorectal cancer.

Coagulation dysfunction, a common hematologic disorder with unclear pathogenesis, is influenced by environmental factors. Sodium dehydroacetate (SDA),...

Integrating thermal liquid biopsy, clinical data, and mass spectrometry for early diagnosis and biomarker discovery in colorectal cancer.

Early detection of colorectal cancer is essential to improving survival, where yet current diagnostic tools show limited performance. This study aimed...

An integrative multiomics random forest framework for robust biomarker discovery.

BACKGROUND: High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic ...

Real-Time Characterization of Colonic Polyps: A Multicenter Prospective Study Evaluating the CAD-EYE System in Screening Colonoscopies.

BACKGROUND AND STUDY AIMS: Polypectomy-related costs could potentially be reduced through optical diagnosis strategies, such as 'diagnose-and-leave' a...

CT-based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information.

BACKGROUND: Accurate preoperative prediction of visceral pleural invasion (VPI) in lung adenocarcinoma is essential for guiding surgical decision-maki...

Digital horizons: Exploring the future of AI-enabled gastroenterology in the Kingdom of Saudi Arabia.

Artificial intelligence is rapidly reshaping gastroenterology through demonstrable gains in diagnostic precision, procedural quality, and operational ...

Machine learning using entropy-based texture features from MRI to differentiate histological subtypes of non-small cell lung cancer identified as metabolically active on PET/MRI.

Texture analysis is a foundational approach in imaging studies and demonstrates excellent diagnostic performance, with radiomic analysis being the mos...

Development and validation of an Interpretable Machine learning model for Discriminating between benign and malignant breast cancer.

OBJECTIVE: Breast cancer prognosis depends on early detection. We developed and externally validated a model using routine, readily available clinical...

Personalized surveillance in giant congenital melanocytic nevus, including the role of AI, histopathology and MC1R genotyping: A case report.

Giant congenital melanocytic nevi (GCMN) are rare pigmented skin lesions present at birth that carry an increased risk of malignant melanoma and neuro...

Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers.

Non-small cell lung cancer (NSCLC) patient management relies on molecular analysis to determine eligibility for targeted therapy. Furthermore, neoadju...

Foundation Model-Enabled Multimodal Deep Learning for Prognostic Prediction in Colorectal Cancer with Incomplete Modalities: A Multi-Institutional Retrospective Study.

Accurate prognostic prediction for colorectal cancer is essential for optimizing personalized treatment strategies and improving patient outcomes. Cur...

Pathomics Signature for Prognosis and CA19-9 Interception in Pancreatic Ductal Adenocarcinoma: A Real-Life, Multi-Center Study.

Histopathological hematoxylin and eosin (H&E) slides contain valuable prognostic information for pancreatic ductal adenocarcinoma (PDAC), yet systemat...

Improved visualization of pancreas and tumor boundaries using high-frequency kernels with deep-learning image reconstruction at high-strength level.

PURPOSE: This study aimed to investigate the feasibility of combining high-frequency reconstruction kernels and deep-learning image reconstruction at ...