Oncology/Hematology

Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment.

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects. Despite advancements in surgery and chemoradiotherapy, the prognosis for glioblastoma multiforme (GBM) and brain metastases remains poor, underscoring the need for innovative diagnostic strate...

Deep Learning for Pediatric Sleep Staging From Photoplethysmography: A Transfer Learning Approach From Adults to Children.

BACKGROUND: Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have shown promise in automating sleep staging in adults. However, for children, algorithm development is hindered by the limited availability of datasets, w...

Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.

OBJECTIVE: To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free s...

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.

BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in bra...

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network.

Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can deve...

Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics.

OBJECTIVE: Breast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behavi...

Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data.

Doxorubicin (DOXO) is a primary treatment for breast cancer but can cause cardiotoxicity in over 25% of patients within the first year post-chemothera...

Do people prefer AI-generated patient educational materials over traditional ones?

OBJECTIVE: This study aimed to assess people's preference between traditional and Artificial Intelligence (AI)-generated colon cancer staging Patient ...

Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy.

This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and ...

Convolutional neural networks for accurate real-time diagnosis of oral epithelial dysplasia and oral squamous cell carcinoma using high-resolution in vivo confocal microscopy.

Oral cancer detection is based on biopsy histopathology, however with digital microscopy imaging technology there is real potential for rapid multi-si...

Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.

Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targe...

Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach.

The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bri...

The Role of Artificial Intelligence for Advanced Endoscopy.

Artificial intelligence (AI) application in gastroenterology has grown in the last decade and continues to evolve very rapidly. Early promising result...

Implementation of artificial intelligence approaches in oncology clinical trials: A systematic review.

INTRODUCTION: There is a growing interest in leveraging artificial intelligence (AI) technologies to enhance various aspects of clinical trials. The g...

Artificial Neural Network-Based Validation, DFT, Thermal and Biological Evaluation of 4-Aminoantipyrine-Derived Ru(III) Complexes.

New methodologies have been evaluated for validating analytical characterization with artificial neural networks (ANNs). Compared to previous machine ...

VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).

The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a ...

Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment.

Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis ...

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.

Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translat...

Exploring the anticancer activities of Sulfur and magnesium oxide through integration of deep learning and fuzzy rough set analyses based on the features of Vidarabine alkaloid.

Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC 6.97 µg∕mL, 25.78...

Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods.

Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinica...