Skin Cancer

Concentration-dependent effects of immunomodulatory cocktails on the generation of leukemia-derived dendritic cells, DC mediated T-cell activation and on-target/off-tumor toxicity.

Acute myeloid leukemia (AML) remains a devastating diagnosis in clear need of therapeutic advances. Both targeted dendritic cells (DC) and particularly leukemia-derived dendritic cells (DC) can exert potent anti-leukemic activity. By converting AML blasts into immune activating and leukemia-antigen presenting cells, DC/DC-generating protocols can induce immune responses against AML blasts. Such pr...

User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study.

BACKGROUND: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulat...

Machine learning-based lactate-related genes signature predicts clinical outcomes and unveils novel therapeutic targets in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC), a predominant subtype of esophageal cancer, typically presents with poor prognosis. Lactate is a crucial me...

Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist.

The rapid development of artificial intelligence (AI) based tools in pathology laboratories has brought forward unlimited opportunities for pathologis...

Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning.

BACKGROUND: Immune checkpoint inhibitors have proven efficacy against hepatitis B-virus positive hepatocellular. However, Immunotherapy-related advers...

Intestinal Microbiome Modulation of Therapeutic Efficacy of Cancer Immunotherapy.

Bacteria are associated with certain cancers and may induce genetic instability and cancer progression. The gut microbiome modulates the response to c...

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.

To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing and prognosis prediction in lung adenocarcin...

KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics.

KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-c...

Feature selection enhances peptide binding predictions for TCR-specific interactions.

INTRODUCTION: T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocomp...

Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles.

Lung cancer is a devastating public health threat and a leading cause of cancer-related deaths. Therefore, it is imperative to develop sophisticated t...

Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.

This white paper examines the potential of pioneering technologies and artificial intelligence-driven solutions in advancing clinical trials involving...

Minimal sourced and lightweight federated transfer learning models for skin cancer detection.

One of the most fatal diseases that affect people is skin cancer. Because nevus and melanoma lesions are so similar and there is a high likelihood of ...

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...

Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection.

The most deadly type of skin cancer is melanoma. A visual examination does not provide an accurate diagnosis of melanoma during its early to middle st...

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...

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...

Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.

Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell c...

Leveraging Deep Learning for Immune Cell Quantification and Prognostic Evaluation in Radiotherapy-Treated Oropharyngeal Squamous Cell Carcinomas.

The tumor microenvironment plays a critical role in cancer progression and therapeutic responsiveness, with the tumor immune microenvironment (TIME) b...

Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.

PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomar...

Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.

BACKGROUND: Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-...