Oncology/Hematology

Harnessing computational intelligence for synthetic lethality: A roadmap from network biology to interpretable deep learning in precision oncology.

Synthetic lethality (SL) is an emerging therapeutic paradigm in precision oncology that enables the selective targeting of cancer cells based on their specific genetic vulnerabilities. Despite its clinical promise, the enormous combinatorial search space of gene-gene interactions poses a major challenge for experimental screening. This review provides a critical survey of computational SL discover...

Observational study of predictors and outcomes of lung cancer in never-smokers in the UK (OLIVE): study protocol.

INTRODUCTION: Lung cancer is commonly associated with smoking. However, if considered separately, lung cancer in never-smokers (LCINS) is the seventh most common cause of cancer-related death worldwide. Expanding the limited understanding of LCINS, especially in the UK, is crucial to improving diagnosis. METHODS AND ANALYSIS: We will establish a retrospective and prospective UK multicentre observa...

Simulation-Enhanced Learning in Nuclear Medicine: Counterpoint.

Simulation-based education has become widely embedded in health professional training and is increasingly positioned as a solution to clinical placeme...

Simulation-Enhanced Learning in Nuclear Medicine: Theory, Modalities, and Applications Across the Training Continuum.

Simulation-based education has evolved into a foundational component of nuclear medicine technologist training, driven by increasing procedural comple...

Multiscale Prediction of Tumor Micronecrosis and Progression in Clear Cell Renal Cell Carcinoma Based on Artificial Intelligence: A Multicenter Cohort Study.

RATIONALE AND OBJECTIVES: Tumor micronecrosis (TM) represents a critical pathological feature influencing the prognosis and surgical management of cle...

Influence of gravity variations on the activity of neuronal spheroids in an acoustic trap.

Among biological models, cell culture constitutes an important paradigm that allows rapid examination of cell phenotype and behavior. While cell cultu...

Performance enhancement of THz antenna with dielectric resonator and prediction bandwidth using machine learning approach for 6G applications.

The need for ultra-low latency and ultra-wideband in 6G applications requires efficient solutions for dielectric resonator antenna design. This paper ...

Deep-learning based image reconstruction enables reduced dose CT pulmonary angiography with non-inferior image quality.

To investigate whether a CT pulmonary angiography (CTPA) protocol with reduced radiation dose and deep-learning based image reconstruction (DLIR) is n...

BCAT1 mediates the carcinogenic effects of environmental bisphenol exposure: mechanistic discoveries in osteosarcoma and pan-cancer analysis.

Bisphenols (including BPA, BPF, BPS, BPAF and TBBPA) are ubiquitous environmental endocrine disruptors linked to increased risk of multiple malignanci...

mFLIP: metabolic flux interval prediction.

BACKGROUND: Understanding cellular metabolism often involves an accurate estimation of metabolic fluxes-the rates at which metabolites are converted i...

Tumor organoids as a revolutionary platform for advancing cancer nanomedicine.

Nanomedicine has revolutionized oncology through targeted drug delivery and theranostic applications. However, its clinical translation remains a form...

A Novel Blood-Based Screening Test for Colorectal Cancer Using Raman Spectroscopy.

BACKGROUND: Label-free vibrational spectroscopic techniques (Raman spectroscopy) combined with machine learning (ML) methodologies have huge potential...

Machine learning-driven conditional survival prediction model for atypical teratoid/rhabdoid tumor.

PURPOSE: Atypical teratoid/rhabdoid tumor (AT/RT) is a rare and highly malignant pediatric brain tumor with dismal prognosis. We aimed to characterize...

Multi-omics and machine learning integration of diverse cell death pathways optimize risk stratification and inform drug therapy in Wilms tumor.

INTRODUCTION: Despite significant improvements in the overall survival of Wilms tumor (WT), a subset of patients still experiences poor outcomes. Prog...

Hybrid-CMLP: Hybrid CNN-MLP Networks for Low-to-standard-dose PET Synthesis.

Positron Emission Tomography (PET) is a critical modality in medical imaging for detecting abnormalities and diagnosing diseases. However, the radiati...

Breast Cancer Diagnosis and HER2+ Versus Triple Negative Discrimination by Infrared Spectral Histopathology.

Breast cancer is a heterogeneous disease comprising distinct molecular subtypes that require accurate diagnosis for effective treatment. Conventional ...

Circulating Amino Acid Network Remodeling Reveals Systemic Metabolic Reprogramming Predictive of Colorectal Cancer Recurrence and Metastasis.

Characterizing the dynamics of systemic metabolic pathways during cancer progression could enable the development of prognostic tools and therapeutic ...

Age-stratified risk profiles for emergency colorectal cancer resection: A machine-learning analysis.

BACKGROUND: Emergency colorectal cancer resection (ECCR) is associated with worse perioperative and oncologic outcomes than elective surgery. As the i...

A multidimensional strategy for identifying quality markers of Radix ginseng-Schisandra chinensis in the disruption of the inflammation-cancer transformation process of hepatocellular carcinoma based on metabolic regulation and chemical properties.

Comprehensive quality control is essential for ensuring the efficacy and safety of traditional Chinese medicines (TCMs). However, current quality cont...

Screening of Demethylation-related Biomarkers and Exploration of Regulatory Mechanisms in Esophageal Cancer Patients Based on Machine Learning and Mendelian Randomization.

INTRODUCTION: The aggressive cancer known as Esophageal Squamous Cell Carcinoma (ESCC) has a dismal prognosis. Epigenetic changes such as demethylatio...