Oncology/Hematology

Skin Cancer

Latest AI and machine learning research in skin cancer for healthcare professionals.

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Showing 190-210 of 9,298 articles
Development of an external quality assurance (EQA) structure to evaluate the quality of genetic pathology reporting.

A standard for reporting genetic pathology results currently does not exist as a consensus. While ef...

A tumor-infiltrating B lymphocytes -related index based on machine-learning predicts prognosis and immunotherapy response in lung adenocarcinoma.

INTRODUCTION: Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune mic...

Machine Learning and Mendelian Randomization Reveal a Tumor Immune Cell Profile for Predicting Bladder Cancer Risk and Immunotherapy Outcomes.

This study's objective was to develop predictive models for bladder cancer (BLCA) using tumor infilt...

Evaluating the Diagnostic Accuracy of ChatGPT-4 Omni and ChatGPT-4 Turbo in Identifying Melanoma: Comparative Study.

ChatGPT is increasingly used in healthcare. Fields like dermatology and radiology could benefit from...

Artificial Intelligence and Convolutional Neural Networks-Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes.

BACKGROUND: Lymph node (LN) assessment is a critical component in the staging and management of cuta...

Multimodal treatment of colorectal liver metastases: Where are we? Current strategies and future perspectives.

Despite the continued high prevalence of colorectal cancer in the Western world, recent years have w...

Aggregation induced emission luminogen bacteria hybrid bionic robot for multimodal phototheranostics and immunotherapy.

Multimodal phototheranostics utilizing single molecules offer a "one-and-done" approach, presenting ...

Surface-Induced Unfolding Reveals Unique Structural Features and Enhances Machine Learning Classification Models.

Native ion mobility-mass spectrometry combined with collision-induced unfolding (CIU) is a powerful ...

Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.

Sepsis represents a significant global health challenge, necessitating early detection and effective...

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.

Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in...

Transcriptome analysis reveals the potential role of neural factor EN1 for long-terms survival in estrogen receptor-independent breast cancer.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative ...

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.

Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor...

GRATCR: Epitope-Specific T Cell Receptor Sequence Generation With Data-Efficient Pre-Trained Models.

T cell receptors (TCRs) play a crucial role in numerous immunotherapies targeting tumor cells. Howev...

Machine learning analysis identified NNMT as a potential therapeutic target for hepatocellular carcinoma based on PCD-related genes.

Programmed cell death (PCD) plays a critical role in cancer biology, influencing tumor progression a...

Pretrained transformers applied to clinical studies improve predictions of treatment efficacy and associated biomarkers.

Cancer treatment has made significant advancements in recent decades, however many patients still ex...

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