Other Cancers

Raman Spectroscopy and Exosome-Based Machine Learning Predicts the Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer.

Early prediction of the neoadjuvant therapy efficacy for HER2-positive breast cancer is crucial for personalizing treatment and enhancing patient outcomes. Exosomes, which play a role in tumor development and treatment response, are emerging as potential biomarkers for cancer diagnosis and efficacy prediction. Despite their promise, current exosome detection and isolation methods are cumbersome an...

Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer.

BACKGROUND/AIMS: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC.

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell...

Boosting skin cancer diagnosis accuracy with ensemble approach.

Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the s...

The role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis.

BACKGROUND: Endothelial cells are integral components of the tumor microenvironment and play a multifaceted role in tumor immunotherapy. Targeting end...

Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.

Cancer's heterogeneity presents significant challenges in accurate diagnosis and effective treatment, including the complexity of identifying tumor su...

Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.

OBJECTIVES: The pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By...

Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly aggressive tumor with one of the highest morbidity and mortality rates in the world. Nucleotide met...

Hepatoid adenocarcinoma of the stomach with ideal response to neoadjuvant chemo-immunotherapy: a case report.

Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer characterized by histological features resembling hepatocellular carc...

Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein ...

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.

In the present scenario, cancerous tumours are common in humans due to major changes in nearby environments. Skin cancer is a considerable disease det...

Automated Quantification of HER2 Amplification Levels Using Deep Learning.

HER2 assessment is necessary for patient selection in anti-HER2 targeted treatment. However, manual assessment of HER2 amplification is time-costly, l...

Improved health outcomes of nasopharyngeal carcinoma patients 3 years after treatment by the AI-assisted home enteral nutrition management.

OBJECTIVES: Patients with nasopharyngeal carcinoma (NPC) are prone to malnutrition, which leads to deterioration of health. This study is to clarify t...

Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data.

Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during...

Understanding the role of the gut microbiome in solid tumor responses to immune checkpoint inhibitors for personalized therapeutic strategies: a review.

Immunotherapy, especially immune checkpoint inhibitor (ICI) therapy, has yielded remarkable outcomes for some patients with solid cancers, but others ...

Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy.

BACKGROUND: Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effe...

Application progress of artificial intelligence in tumor diagnosis and treatment.

The rapid advancement of artificial intelligence (AI) has introduced transformative opportunities in oncology, enhancing the precision and efficiency ...

Interpretable machine learning models for predicting skip metastasis in cN0 papillary thyroid cancer based on clinicopathological and elastography radiomics features.

BACKGROUND: Skip lymph node metastasis (SLNM) in papillary thyroid cancer (PTC) involves cancer cells bypassing central nodes to directly metastasize ...

Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.

This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified ...

Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines.

Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, inclu...