Skin Cancer

An integrative machine learning model for the identification of tumor T-cell antigens.

The escalating global incidence of cancer poses significant health challenges, underscoring the need for innovative and more efficacious treatments. Cancer immunotherapy, a promising approach leveraging the body's immune system against cancer, emerges as a compelling solution. Consequently, the identification and characterization of tumor T-cell antigens (TTCAs) have become pivotal for exploration...

Echoes of images: multi-loss network for image retrieval in vision transformers.

This paper introduces a novel approach to enhance content-based image retrieval, validated on two benchmark datasets: ISIC-2017 and ISIC-2018. These datasets comprise skin lesion images that are crucial for innovations in skin cancer diagnosis and treatment. We advocate the use of pre-trained Vision Transformer (ViT), a relatively uncharted concept in the realm of image retrieval, particularly in ...

Combining artificial intelligence and human expertise for more accurate dermoscopic melanoma diagnosis: A 2-session retrospective reader study.

Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study.

BACKGROUND: Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but r...

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features.

Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pa...

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was lim...

Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in skin cutaneous melanoma.

Intratumor heterogeneity (ITH) is defined as differences in molecular and phenotypic profiles between different tumor cells and immune cells within a ...

Artificial intelligence in immunotherapy PET/SPECT imaging.

OBJECTIVE: Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are lik...

A Machine Learning Computational Framework Develops a Multiple Programmed Cell Death Index for Improving Clinical Outcomes in Bladder Cancer.

Comprehensive action patterns of programmed cell death (PCD) in bladder cancer (BLCA) have not yet been thoroughly investigated. Here, we collected 19...

[Feeling analysis on allergen immunotherapy on using an unsupervised machine learning model].

OBJECTIVE: Analyze feelings about allergen-specific immunotherapy on using the VADER model VADER () model.

Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy.

OBJECTIVE: Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoin...

Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions' Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection.

Melanoma is still a major health problem worldwide. Early diagnosis is the first step toward reducing its mortality, but it remains a challenge even f...

Can ChatGPT vision diagnose melanoma? An exploratory diagnostic accuracy study.

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to...

An Overview of Advances in Rare Cancer Diagnosis and Treatment.

Cancer stands as the leading global cause of mortality, with rare cancer comprising 230 distinct subtypes characterized by infrequent incidence. Despi...

Integration of multi-omics and clinical treatment data reveals bladder cancer therapeutic vulnerability gene combinations and prognostic risks.

BACKGROUND: Bladder cancer (BCa) is a common malignancy of the urinary tract. Due to the high heterogeneity of BCa, patients have poor prognosis and t...

A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches.

Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based o...

CanVaxKB: a web-based cancer vaccine knowledgebase.

Cancer vaccines have been increasingly studied and developed to prevent or treat various types of cancers. To systematically survey and analyze differ...

Using multiple real-world dermoscopic photographs of one lesion improves melanoma classification via deep learning.

Historical perspective and future directions: computational science in immuno-oncology.

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastati...