Oncology/Hematology

dMMR prediction from colorectal cancer histopathology: Leveraging non-tumor and low-magnification regions.

BACKGROUND AND OBJECTIVE: Colorectal cancer is the second leading cause of cancer-related mortality worldwide, posing a substantial burden on healthcare systems. Identifying DNA mismatch repair deficiency (dMMR) is critical for guiding treatment, yet conventional methods rely on labor-intensive DNA analysis. While deep-learning approaches have shown promise for predicting dMMR from histopathologic...

LymphUs: A multicenter open-access database of lymph node ultrasound images in patients with papillary thyroid carcinoma for clinical and artificial intelligence research.

Approximately 30-50% of Papillary thyroid carcinoma (PTC) patients develop cervical lymph nodes (LNs) metastasis, significantly increasing the risk of disease recurrence and impacting long-term outcomes. We introduced an open-access multicenter lymph node ultrasound image database (LymphUs) specifically designed to advance research in LN assessment for PTC. Ultrasound imaging was performed on PTC ...

The role of clinical competency-based education in training in radiation science: A scoping review.

BACKGROUND: Clinical competency-based education (CBE) has emerged as a critical strategy to enhance workforce readiness in radiation sciences. Despite...

A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans.

Lung cancer is an extremely fatal kind of cancer, resulting in the deaths of almost 7.6 million individuals annually around the globe. Nevertheless, a...

A generative explainable model for antimicrobial peptide prediction using bidirectional temporal convolutional neural network.

Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, im...

Artificial intelligence in neurology practice: promise, perils, and a roadmap for responsible integration.

Artificial intelligence (AI) has emerged as a transformative force in neurology, offering unprecedented potential to enhance diagnostic precision, str...

BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.

BACKGROUND: Cancer heterogeneity results in patients with the same diagnosis responding differently to drugs, making treatments extremely challenging....

Signs and symptoms indicating the transition to the palliative phase in patients with COPD and heart failure in primary healthcare: a mixed-methods study.

The pre-metastatic niche: mechanisms, heterogeneity, and therapeutic opportunities.

Metastasis remains the leading cause of cancer-related mortality. The concept of the pre-metastatic niche (PMN) has provided a new framework for under...

Unraveling the diagnostic and prognostic signatures of oral microbiota in head and neck cancer.

BACKGROUND: Head and neck cancer, predominantly squamous cell carcinoma, has emerged as a significant global health concern. Growing evidence has esta...

AI-enhanced precision: boosting pathologist confidence in HER2 ultra-low diagnosis through intelligent re-screening.

Accurate assessment of Human Epidermal Growth Factor Receptor 2 (HER2) immunohistochemistry (IHC) expression, particularly the precise identification ...

A genetically optimized physics-informed neural network for multiphysics modeling of magnetic hyperthermia in brain cancer.

Magnetic hyperthermia therapy (MHT) in glioblastoma requires accurate modeling of nanoparticle transport and heat deposition across highly heterogeneo...

Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes.

Identifying drugs that reverse disease-associated transcriptomic features has been widely explored for drug repurposing, but its potential for de novo...

Decoding the immune-tumor synapse for data-driven design of next-generation immunotherapies.

The immunological synapse (IS) formed between cytotoxic CD8+ T lymphocytes (CTLs) and tumor cells represents the critical interface where many immunot...

Tumor organoids on-a-chip and the role of AI in predictive oncology and personalized cancer medicine.

The drug development process in cancer faces significant challenges due to high failure rates in translational studies despite promisingin vitroresult...

Immune checkpoint molecules beyond PD-1 and CTLA-4: emerging targets in autoimmune diseases and cancer immunotherapy.

BACKGROUND: Immune checkpoint molecules are pivotal regulators of immune activation and tolerance, playing critical roles in cancer, autoimmunity, inf...

Unsupervised clustering analysis unravels the role of systemic inflammatory indices in the prognosis of patients with locally advanced cervical cancer treated with chemoradiation.

BACKGROUND AND PURPOSE: This study aimed to predict the treatment outcomes and survival of patients with locally advanced cervical cancer (LACC) recei...

Application value of gastric contrast ultrasonography in gastric tumors.

In recent years, gastric oral contrast ultrasonography (OCUS) and double contrast-enhanced ultrasonography (DCEUS) have emerged as promising imaging t...

Molecular and Pharmacogenetic Marker Evaluation in Relation to the Toxicity and Clinical Response of Acute Lymphoblastic Leukemia Treatment in Indian Children (MPGx-INDALL): Protocol for a Prospective Observational Cohort Study.

BACKGROUND: Understanding interindividual variability in treatment response and toxicity is essential for optimizing outcomes in pediatric acute lymph...

Automated daily adaptation for breast cancer radiotherapy: benefits of deep learning-driven dose prediction workflow for CBCT-based adaptation.

Objective Daily anatomical variations can jeopardize the quality of modern radiotherapy (RT). Adaptive radiotherapy (ART) aims to address this by ...