Colon Cancer

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-o...

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD...

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach.

INTRODUCTION: In this study, we aimed to evaluate the predictive value of circulating lymphocyte subsets and inflammatory indexes in response to neoad...

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approach...

Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data.

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a n...

Contrastive Pre-Training and Multiple Instance Learning for Predicting Tumor Microsatellite Instability.

Accurate classification between tumor MicroSatellite Stability (MSS) and Instability (MSI) is crucial in gastrointestinal (GI) cancer prognosis and tr...

Assessing the Impact of Federated Learning and Differential Privacy on Multi-centre Polyp Segmentation.

Federated Learning (FL) is emerging in the medical field to address the need for diverse datasets while complying with data protection regulations. Th...

EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder.

This work introduces EffiSegNet, a novel segmentation framework leveraging transfer learning with a pre-trained Convolutional Neural Network (CNN) cla...

Towards the development of a FAIR-compliant biomedical ontology for colorectal cancer.

Despite the widespread development of ontologies in many domains of healthcare, the field of colorectal cancer (CRC) presents a notable gap considerin...

C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading.

Graph-based learning approaches, due to their ability to encode tissue/organ structure information, are increasingly favored for grading colorectal ca...

Evolutionary learning-derived lncRNA signature with biomarker discovery for predicting stage of colon adenocarcinoma.

In recent years, long non-coding RNAs (lncRNAs) have emerged as potential regulators of biological processes and genes, with the potential to serve as...

Prompting Whole Slide Image Based Genetic Biomarker Prediction

Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF in colorectal cancer is crucial for clinical decision making. In this pa...

[Application value of artificial intelligence in surgical precision diagnosis and treatment of rectal cancer].

Colorectal cancer is the most common malignant tumor of digestive tract, and the incidence of colorectal cancer in China is especially characterized b...

Effectiveness of ChatGPT in explaining complex medical reports to patients

Electronic health records contain detailed information about the medical condition of patients, but they are difficult for patients to understand ev...

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes...

Personalized prediction of survival rate with combination of penalized Cox models in patients with colorectal cancer.

The investigation into individual survival rates within the patient population was typically conducted using the Cox proportional hazards model. This ...

Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence.

UNLABELLED: Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morph...

ctGAN: combined transformation of gene expression and survival data with generative adversarial network.

Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and su...

Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demons...

[Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning].

Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrenc...