Colon Cancer

Integrative proteomic profiling of tumor and plasma extracellular vesicles identifies a diagnostic biomarker panel for colorectal cancer.

The lack of reliable non-invasive biomarkers for early colorectal cancer (CRC) diagnosis underscores the need for improved diagnostic tools. Extracellular vesicles (EVs) have emerged as promising candidates for liquid-biopsy-based cancer monitoring. Here, we propose a comprehensive workflow that integrates staged mass spectrometry (MS)-based discovery and verification with ELISA-based validation t...

Microbiome and fragmentation pattern of blood cell-free DNA and fecal metagenome enhance colorectal cancer micro-dysbiosis and diagnosis analysis: a proof-of-concept study.

Colorectal cancer (CRC) is the third most common cancer, and it can be prevented by performing early screening. As a hallmark of cancer, the human microbiome plays important roles in the occurrence and development of CRC. Recently, the blood microbiome has been proposed as an effective diagnostic tool for various diseases, yet its performance on CRC deserves further exploration. In this study, 133...

Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features.

BACKGROUND: The appropriate ligation level of the inferior mesenteric artery (IMA) in left-sided colorectal cancer (CRC) surgery is debated, with meta...

Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-m...

A practical approach for colorectal cancer diagnosis based on machine learning.

In this paper, we present the results of applying machine learning models to build a Colorectal Cancer Diagnosis system. The methodology encompasses s...

Artificial intelligence alert system based on intraluminal view for colonoscopy intubation.

Mucosal contact of the tip of colonoscopy causes red-out views, and more pressure may result in perforation. There is still a lack of quantitative ana...

F-FDG PET/CT-based deep learning models and a clinical-metabolic nomogram for predicting high-grade patterns in lung adenocarcinoma.

BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasivel...

Machine Learning to Predict Early Death Despite Pancreaticoduodenectomy.

INTRODUCTION: About 25% of patients undergoing pancreaticoduodenectomy (PD) for right-sided pancreatic ductal adenocarcinoma (PDAC) die within 1 y of ...

Prescription data and demographics: An explainable machine learning exploration of colorectal cancer risk factors based on data from Danish national registries.

OBJECTIVES: Despite substantial advancements in both treatment and prevention, colorectal cancer continues to be a leading cause of global morbidity a...

Bioactive components of Jiedu Sangen decoction against colorectal cancer: A novel and comprehensive research strategy for natural drug development.

BACKGROUND: Jiedu Sangen Decoction (JSD) is widely used in the treatment of colorectal cancer (CRC) patients in southern China due to its good clinica...

Artificial intelligence prediction of carcinoembryonic antigen structure and interactions relevant for colorectal cancer.

Carcinoembryonic antigen (CEA) is used as a biomarker for colorectal cancer. It is expressed during fetal development but in healthy adult cells the e...

Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types.

Cancer remains the second leading cause of death worldwide, representing a substantial challenge to global health. Although traditional risk predictio...

Coptidis rhizoma and berberine as anti-cancer drugs: A 10-year updates and future perspectives.

Cancer continues to be among the most substantial health challenges globally. Among various natural compounds, berberine, an isoquinoline alkaloid obt...

Explainable machine learning for predicting lung metastasis of colorectal cancer.

Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model...

In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis.

BACKGROUND AND OBJECTIVE: In search of a key driver for the invasive growth of cancer metastasis, AKT2 is found to be exceptionally expressed in color...

Deep learning model based on ultrasound images predicts BRAF V600E mutation in papillary thyroid carcinoma.

BRAF V600E mutation status detection facilitates prognosis prediction in papillary thyroid carcinoma (PTC). We developed a deep-learning model to dete...

Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.

BACKGROUND AND AIM: Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal ca...

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model.

Lymph node status is a critical prognostic predictor for patients; however, the prognosis of colorectal signet-ring cell carcinoma (SRCC) has garnered...

VMKLA-UNet: vision Mamba with KAN linear attention U-Net.

In the domain of medical image segmentation, while convolutional neural networks (CNNs) and Transformer-based architectures have attained notable succ...

Conversion of Mixed-Language Free-Text CT Reports of Pancreatic Cancer to National Comprehensive Cancer Network Structured Reporting Templates by Using GPT-4.

OBJECTIVE: To evaluate the feasibility of generative pre-trained transformer-4 (GPT-4) in generating structured reports (SRs) from mixed-language (Eng...