Oncology/Hematology

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

BACKGROUND: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time-consuming and expensive high-throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significan...

A Neural Network-Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer.

Breast cancer is a prevalent malignancy affecting women worldwide. Currently, there are no precise molecular biomarkers with immense potential for accurately predicting breast cancer development, which limits clinical management options. Recent evidence has highlighted the importance of metastatic and tumor-infiltrating immune cells in modulating the antitumor therapy response. However, the progno...

Accuracy of an Artificial Intelligence System for Interval Breast Cancer Detection at Screening Mammography.

Background Artificial intelligence (AI) systems can be used to identify interval breast cancers, although the localizations are not always accurate. P...

Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer.

PURPOSE: Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in pr...

Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.

BACKGROUND: Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastri...

CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.

OBJECTIVE: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metasta...

Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study.

PURPOSE: Most research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-...

Artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer: an evidence synthesis early value assessment.

BACKGROUND: Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung...

Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.

OBJECTIVE: If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the rec...

Exploiting Metabolic Defects in Glioma with Nanoparticle-Encapsulated NAMPT Inhibitors.

The treatment of primary central nervous system tumors is challenging due to the blood-brain barrier and complex mutational profiles, which is associa...

UnPaSt: unsupervised patient stratification by differentially expressed biclusters in omics data

Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical appr...

Are gene-by-environment interactions leveraged in multi-modality neural networks for breast cancer prediction?

Polygenic risk scores (PRSs) can significantly enhance breast cancer risk prediction when combined with clinical risk factor data. While many studie...

[Research Progress of Artificial Intelligence in Prostate Cancer Diagnosis Application].

With the continuous advancement of artificial intelligence in the field of prostate cancer research, numerous studies have shown that AI performance c...

[Diagnostic Value of Micropure Imaging Combined with Strain Elastography in Correcting Artificial Intelligence S-Detect Technology for Benign and Malignant Breast Complex Cystic and Solid Masses].

OBJECTIVE: To explore the diagnostic value of micropure imaging (MI) combined with strain elastography (SE) in correcting artificial intelligence (AI)...

Predicting T-Cell Receptor Specificity

Researching the specificity of TCR contributes to the development of immunotherapy and provides new opportunities and strategies for personalized ca...

Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperka...

Analyzing Brain Tumor Connectomics using Graphs and Persistent Homology

Recent advances in molecular and genetic research have identified a diverse range of brain tumor sub-types, shedding light on differences in their m...

Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.

With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent ...

BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire.

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to for...

scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring ...