Oncology/Hematology

Order-Aware Deep Learning for Drug Combination Benefit Prediction in Cancer Cell Lines.

Drug combination therapy has exhibited favorable effects in treating cancer patients, with less toxicity and adverse reactions compared to monotherapy. To accelerate the discovery of therapeutic drug combinations, numerous computational methods have been developed to predict drug synergy in cancer cell lines, typically modeling the task as binary classification (synergistic vs non-synergistic) or ...

Using Latent Dirichlet Allocation Topic Modeling to Uncover Latent Research Topics and Trends in Renal Cell Carcinoma: Bibliometric Review.

BACKGROUND: Renal cell carcinoma (RCC) is a common, often lethal kidney cancer that originates in the renal cortex. Its incidence is rising, and major factors include smoking, obesity, and hypertension, though its etiology is uncertain. While surgery is effective for localized RCC, treatments for metastatic RCC have advanced significantly due to better diagnostic, prognostic, and predictive tools....

Barriers and facilitators to implementing the living guideline development framework in oncology: a mixed methods study.

OBJECTIVE: To explore stakeholder experiences with implementing the living guideline (LG) development framework in oncology, and to identify barriers,...

DrugBank mining with machine learning reveals novel candidates for BCL-2 inhibition.

Apoptosis, also known as programmed cell death, is a fundamental biological process essential for development and cellular homeostasis. An imbalance i...

Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH.

The progression from metabolic dysfunction-associated steatotic liver disease (MASLD) to metabolic dysfunction-associated steatohepatitis (MASH) is a ...

Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.

Transcriptomic assays such as the PAM50-based ROR-P score guide recurrence risk stratification in non-metastatic, ER-positive, HER2-negative breast ca...

Survival and Surrogate Biomarkers in Interventional Oncology Trials: Pitfalls, Challenges, and Future Directions.

INTRODUCTION: Interventional oncology (IO) is a key component of multidisciplinary cancer care, delivering minimally invasive therapies with safety an...

Artificial intelligence in bladder cancer management: a narrative review of diagnostic and surgical advances and current limitations.

INTRODUCTION: Bladder cancer (BC) is associated with high recurrence and progression rates that require intensive surveillance. In this context, Artif...

Unlocking the potential of nailfold videocapillaroscopy in diagnosing and staging wild-type transthyretin amyloidosis: A preliminary approach.

BACKGROUND: Wild-type transthyretin amyloidosis (ATTRwt) is a serious condition. At early stages, symptoms resemble those of heart failure with preser...

Potential link between dioxin induced progression of diffuse large B-cell lymphoma: New insights from machine learning and in vitro experiments.

BACKGROUND: Dioxin has emerged as a major and modifiable determinant of tumor burden worldwide. Building on this premise, we investigated whether diox...

Generalizable Deep Learning for Prostate Cancer Risk Stratification: Multicenter Study Integrating 18F-PSMA-1007 PET/CT and mpMRI.

BACKGROUND: Prostate cancer is the second most common cancer in men, with rising mortality rates necessitating precise risk stratification. High-invas...

Deep Learning Analysis Based on Dual-energy CT-Derived Iodine Map for Predicting PD-L1 Expression in Gastric Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: PD-L1 expression is a critical biomarker in guiding immunotherapy for gastric cancer (GC). This study aims to investigate th...

Radiomics profiling combined with clinical risk factors for preoperative Lymphatic Metastasis prediction in Colorectal cancer: A multicenter study.

PURPOSE: Accurate preoperative assessment of regional lymphatic metastases (LNM) is essential for effective surgical selection of patients with colore...

BraTioUS: A multicenter dataset of baseline intraoperative brain tumor ultrasound images.

The BraTioUS (Brain Tumor Intraoperative Ultrasound) dataset [1] is a large-scale, multicenter, and publicly available collection of intraoperative ul...

Correlation of Automated in Vivo Image Quality With Radiologist's Performance in Abdomen Computed Tomography Across Conventional and Deep Learning Reconstructions.

OBJECTIVE: Image quality evaluation in radiology is most relevant when reflects radiologists' performance. This study assessed how image quality measu...

Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.

Pleural diseases pose a significant burden on healthcare systems due to diagnostic challenges and high costs. Artificial intelligence (AI) has the pot...

TomoRay cranial: synthesis of cranial CT imaging from biplanar radiographs using a generative adversarial network.

OBJECTIVES: Besides clinical examination, cranial CT plays a critical role in diagnostics in neurosurgery. In trauma cases or perioperatively, having ...

Automatic radiation-free evaluation of Cobb angle for spinal curvature based on fringe projection profilometry and deep learning technology.

OBJECTIVE: Pediatric scoliosis is the most prevalent spinal disorder, often leading to abnormal curvature and deformation of the spine. Early detectio...

Biologically explicable multimodal model predicting local tumor progression and tumor invasiveness of hepatocellular carcinoma.

PURPOSE: Local tumor progression (LTP) of hepatocellular carcinoma (HCC) after thermal ablation (TA) is related to tumor invasiveness and threaten the...

Caries Microbiome: time to move from blame to balance.

BACKGROUND: Advances in next-generation sequencing(NGS) and multi-omics approaches reinforced the concept of functional diversity within biofilm commu...