Oncology/Hematology

Spatial recognition and semi-quantification of epigenetic events in pancreatic cancer subtypes with multiplexed molecular imaging and machine learning.

Genomic alterations are the driving force behind pancreatic cancer (PC) tumorigenesis, but they do not fully account for its diverse phenotypes. Investigating the epigenetic landscapes of PC offers a more comprehensive understanding and could identify targeted therapies that enhance patient survival. In this study, we have developed a new promising methodology of spatial epigenomics that integrate...

Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.

BACKGROUND/OBJECTIVE: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor growth and characterizing the tumors as well.

Inhibition of CDC27 O-GlcNAcylation coordinates the antitumor efficacy in multiple myeloma through the autophagy-lysosome pathway.

Multiple myeloma (MM) is a prevalent hematologic malignancy characterized by abnormal proliferation of cloned plasma cells. Given the aggressive natur...

Explainable label guided lightweight network with axial transformer encoder for early detection of oral cancer.

Oral cavity cancer exhibits high morbidity and mortality rates. Therefore, it is essential to diagnose the disease at an early stage. Machine learning...

Generalizable deep neural networks for image quality classification of cervical images.

Successful translation of artificial intelligence (AI) models into clinical practice, across clinical domains, is frequently hindered by the lack of i...

Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.

BACKGROUND: This scoping review systematically maps externally validated machine learning (ML)-based models in cancer patient care, quantifying their ...

Deep learning models for differentiating three sinonasal malignancies using multi-sequence MRI.

PURPOSE: To develop MRI-based deep learning (DL) models for distinguishing sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC) and...

Development and external validation of a machine learning-based model to predict postoperative recurrence in patients with duodenal adenocarcinoma: a multicenter, retrospective cohort study.

BACKGROUND: Duodenal adenocarcinoma (DA) has a high recurrence rate, making the prediction of recurrence after surgery critically important.

Multi-cancer early detection based on serum surface-enhanced Raman spectroscopy with deep learning: a large-scale case-control study.

BACKGROUND: Early detection of cancer can help patients with more effective treatments and result in better prognosis. Unfortunately, established canc...

Plasma Cytokine and Chemokine Profiles Predict Efficacy and Toxicity of Anti-CD19 CAR-T Cell Therapy in Large B-Cell Lymphoma.

BACKGROUND: Anti-CD19 chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a promising treatment for large B-cell lymphoma (LBCL); however,...

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, th...

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

PURPOSE: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learn...

Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.

We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wi...

A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.

Skin cancer is widespread and can be potentially fatal. According to the World Health Organisation (WHO), it has been identified as a leading cause of...

Predicting hepatocellular carcinoma survival with artificial intelligence.

Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfa...

The Role of Artificial Intelligence in Gynecologic Oncology Decision-Making: A Feasibility Study.

OBJECTIVES: The objective of this study was to examine the potential of artificial intelligence (AI) in gynecologic oncology decision-making.

Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography.

PURPOSE: Primary barriers to application of immune checkpoint inhibitor (ICI) therapy for cancer include severe side effects (such as potentially life...

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.

Mathematical modelling has proven to be a valuable tool in predicting the delivery and efficacy of molecular, antibody-based, nano and cellular therap...

Detection of Ocular Surface Squamous Neoplasia Using Artificial Intelligence With Anterior Segment Optical Coherence Tomography.

PURPOSE: To develop and validate a deep learning (DL) model to differentiate ocular surface squamous neoplasia (OSSN) from pterygium and pinguecula us...

A novel generative model for brain tumor detection using magnetic resonance imaging.

Brain tumors are a disease that kills thousands of people worldwide each year. Early identification through diagnosis is essential for monitoring and ...