Colon Cancer

A novel flexible near-infrared endoscopic device that enables real-time artificial intelligence fluorescence tissue characterization.

Real-time endoscopic rectal lesion characterization employing artificial intelligence (AI) and near-infrared (NIR) imaging of the fluorescence perfusion indicator agent Indocyanine Green (ICG) has demonstrated promise. However, commercially available fluorescence endoscopes do not possess the flexibility and anatomical reach capabilities of colonoscopy while commercial flexible scopes do not yet p...

Machine learning prediction of overall survival in prostate adenocarcinoma using ensemble techniques.

Prostate adenocarcinoma (PAC) is a complex and common cancer in males and is one of the leading causes of cancer-related death globally. PAC is a multifaceted disease that encompasses different subtypes, including acinar and ductal adenocarcinoma, small cell carcinoma, neuroendocrine tumors, and transitional cell carcinoma with each subtype presenting distinct prognostic difficulties. Therefore, p...

Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.

BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patient...

Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool.

BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly patients who face a high risk of inadequate prepara...

A deep-learning model to predict the completeness of cytoreductive surgery in colorectal cancer with peritoneal metastasis☆.

BACKGROUND: Colorectal cancer (CRC) with peritoneal metastasis (PM) is associated with poor prognosis. The Peritoneal Cancer Index (PCI) is used to ev...

Enhancing pancreatic cancer diagnostics: Ensemble-based model for automated urine biomarker classification.

This research addresses the critical challenge of early detection in pancreatic ductal adenocarcinoma (PDAC) by exploring urinary biomarkers and integ...

Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer.

BACKGROUND: The presence of tumour-infiltrating lymphocytes (TILs) is a well-established prognostic biomarker across multiple cancer types, with highe...

Artificial intelligence and whole slide imaging, a new tool for the microsatellite instability prediction in colorectal cancer: Friend or foe?

Colorectal cancer (CRC) is the third most common and second most deadly cancer worldwide. Despite advances in screening and treatment, CRC is heteroge...

Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography.

OBJECTIVES: Computed tomography (CT) colonography is increasingly recognized as a valuable modality for diagnosing colorectal lesions, however, the in...

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole s...

Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.

BACKGROUND: To evaluate the clinical applicability of deep learning (DL) models based on automatic segmentation in preoperatively predicting tumor spr...

Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study.

BACKGROUND AND OBJECTIVE: Spread through air spaces (STAS) is an important factor in determining the aggressiveness and recurrence risk of lung cancer...

A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths.

The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learni...

Use of large language models as clinical decision support tools for management pancreatic adenocarcinoma using National Comprehensive Cancer Network guidelines.

BACKGROUND: Large language models may form the basis of clinical decision support tools to improve rates of guideline concordant care for pancreatic d...

Machine Learning-Aided Identification of Fecal Extracellular Vesicle microRNA Signatures for Noninvasive Detection of Colorectal Cancer.

Colorectal cancer (CRC) remains a formidable threat to human health, with considerable challenges persisting in its diagnosis, particularly during the...

Performance of Machine Learning Models in Predicting BRAF Alterations Using Imaging Data in Low-Grade Glioma: A Systematic Review and Meta-Analysis.

BACKGROUND: Understanding the BRAF alterations preoperatively could remarkably assist in predicting tumor behavior, which leads to a more precise prog...

Development and external validation of a machine learning-based model to predict postoperative recurrence in patients with duodenal adenocarcinoma: a multicenter, retrospective cohort study.

BACKGROUND: Duodenal adenocarcinoma (DA) has a high recurrence rate, making the prediction of recurrence after surgery critically important.

Artificial intelligence-based model to predict recurrence after local excision in T1 rectal cancer.

BACKGROUND: According to current guideline, patients with resected specimens showing high-risk features are recommended additional surgery after local...

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, th...

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

PURPOSE: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learn...