Oncology/Hematology

Bibliometric and LDA analysis of acute rejection in liver transplantation: Emerging trends, immunotherapy challenges, and the role of artificial intelligence.

With the rising demand for liver transplantation (LT), research on acute rejection (AR) has become increasingly diverse, yet no consensus has been reached. This study presents a bibliometric and latent Dirichlet allocation (LDA) topic modeling analysis of AR research in LT, encompassing 1399 articles. The United States, Zhejiang University, and the University of California, San Francisco emerged a...

Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma.

BACKGROUND: Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associated signature in melanoma, which had never been elucidated yet.

Interpretable lung cancer risk prediction using ensemble learning and XAI based on lifestyle and demographic data.

Lung cancer is a leading cause of cancer-related death worldwide. The early and accurate detection of lung cancer is crucial for improving patient out...

LA-ResUNet: Attention-based network for longitudinal liver tumor segmentation from CT images.

Longitudinal liver tumor segmentation plays a fundamental role in studying and monitoring the progression of associated diseases. The correlation and ...

Control of dental calculus Prevents severe Radiation-Induced oral mucositis in patients undergoing radiotherapy for nasopharyngeal carcinoma.

PURPOSE: This study aims to develop an artificial intelligence model to predict severe radiation-induced oral mucositis (RIOM) in patients with locall...

Efficient annotation bootstrapping for cell identification in follicular lymphoma.

BACKGROUND AND OBJECTIVE: In the medical field of digital pathology, many tasks rely on visual assessments of tissue patterns or cells, presenting an ...

The growing field of liquid biopsy and its Snowball effect on reshaping cancer management.

Liquid biopsy (LB) has emerged as a transformative tool in oncology, providing a minimally invasive approach for tumor detection, molecular characteri...

Cancer Cell Line Classification Using Raman Spectroscopy of Cancer-Derived Exosomes and Machine Learning.

Liquid biopsies are an emerging, noninvasive tool for cancer diagnostics, utilizing biological fluids for molecular profiling. Nevertheless, the curre...

Deep learning techniques for proton dose prediction across multiple anatomical sites and variable beam configurations.

To evaluate the impact of beam mask implementation and data aggregation on artificial intelligence-based dose prediction accuracy in proton therapy, w...

Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a var...

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data.

Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing...

scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains c...

Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.

BACKGROUND: Catheter-related thrombosis (CRT) is a serious complication in cancer patients undergoing chemotherapy, yet existing risk prediction model...

The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer.

Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limit...

Predicting quality of life of patients after treatment for spinal metastatic disease: development and internal evaluation.

BACKGROUND CONTEXT: When treating spinal metastases in a palliative setting, maintaining or enhancing quality of life (QoL) is the primary therapeutic...

Discovery and characterization of novel FAK inhibitors for breast cancer therapy via hybrid virtual screening, biological evaluation and molecular dynamics simulations.

Focal adhesion kinase (FAK) is a critical drug target implicated in various disease pathways, including hematological malignancies and breast cancer. ...

SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.

Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth and tumor formation. Understanding the intricate in...

Breast cancer detection and classification: A study on the specification and implementation of multilayer perceptron analog artificial neural networks.

Breast cancer is a leading cause of mortality worldwide. Screening therefore remains the best defense against this disease, highlighting the need for ...

Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.

Breast cancer classification using gene expression data presents significant challenges due to high dimensionality and complexity. This study introduc...

Multimodal multi-instance evidence fusion neural networks for cancer survival prediction.

Accurate cancer survival prediction plays a crucial role in assisting clinicians in formulating treatment plans. Multimodal data, such as histopatholo...