Skin Cancer

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.

PURPOSE: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unresectable or metastatic uveal melanoma (mUM). Notably, a survival benefit has been observed even in the absence of radiographic response. This study aims to investigate the feasibility and prognostic value of artificial intelligence (AI)-assisted quanti...

A deep learning-clinical nomogram hybrid for predicting sentinel lymph node metastasis in melanoma.

BACKGROUND: Melanoma is a life-threatening skin malignancy, with sentinel lymph node metastasis (SLNM) serving as a critical prognostic factor. While machine and deep learning models using histopathology have focused on melanoma diagnosis, limited efforts have aimed to predict SLNM. OBJECTIVES: This study aimed to develop a collaborative machine and deep learning model that integrates histopatholo...

LYnet: Computational identification of tumor T cell antigens using convolutional and recurrent neural networks.

BACKGROUND: Immunotherapy represents a paradigm shift in oncology, offering advantages in efficacy and specificity over traditional therapies. Key to ...

Histopathological images-based deep-learning for risk stratification of uveal melanoma.

Non-invasive prediction of the secondary enucleation risk in uveal melanoma based on pretreatment CT and MRI prior to stereotactic radiotherapy.

PURPOSE: The aim of this study was to develop a radiomic model to non-invasively predict the risk of secondary enucleation (SE) in patients with uveal...

Cancer vaccines: Discovery, development, and challenges for clinical translation.

Cancer vaccines stimulate antitumor immunity by delivering tumor antigens and, in recent years, have emerged as a promising therapeutic strategy again...

AI Prognostication in Nonsmall Cell Lung Cancer: A Systematic Review.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognosticat...

Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning.

Colorectal cancer (CRC) exhibits substantial heterogeneity in molecular subtypes and clinical outcomes. We performed an integrative analysis of multi-...

Development and validation of a prognostic model for predicting survival and immunotherapy benefits in melanoma based on metabolism-relevant genes.

Skin cutaneous melanoma (SKCM) is a fatal form of skin cancer. Metabolism-related genes (MRGs) comprise a group of genes that possess the ability to m...

SLC3A2 as a key anoikis-related gene for prognosis and tumor microenvironment remodeling in melanoma.

OBJECTIVE: Anoikis, a form of programmed cell death triggered by detachment from the extracellular matrix, plays a crucial role in metastasis and immu...

Identification of neural crest and melanoma cancer cell invasion and migration genes using high-throughput screening and deep attention networks.

BACKGROUND: Cell migration and invasion are well-coordinated in development and disease but remain poorly understood. We previously showed that the ne...

Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.

Melanoma is the most dangerous type of skin cancer. Although it accounts for only about 1% of all skin cancer cases, it is responsible for the majorit...

USP5-Mediated PD-L1 deubiquitination regulates immunotherapy efficacy in melanoma.

BACKGROUND: The role of post-translational modifications(PTMs) in PD-L1-mediated immune resistance and melanoma progression remains poorly understood.

Single-cell spatial transcriptomics reveals immunotherapy-driven bone marrow niche remodeling in AML.

Given the graft-versus-leukemia effect observed with allogeneic hematopoietic stem cell transplantation in refractory or relapsed acute myeloid leukem...

Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.

Neoantigens, which are tumor-specific peptides generated by malignant cells, can be presented to T cells to elicit immune responses. Owing to their tu...

Leveraging machine learning models to evaluate immune infiltration in the ovarian cancer microenvironment: a single-cell analysis approach.

BACKGROUND: The prognosis of ovarian cancer is closely related to the degree of immune cell infiltration within the tumor microenvironment. However, c...

Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning.

Baseline genomic data have not demonstrated significant value for predicting the response duration to MAPK inhibitors (MAPKi) in patients with advance...

Analysis of Single-Cell RNA-Seq Data to Investigate Tumor Cell Heterogeneity in Uroepithelial Bladder Cancer and Predict Immunotherapy Response.

BACKGROUND: Numerous studies have suggested a close association between cancer stem cells (CSCs) and the tumor microenvironment (TME), suggesting that...

Metabolic alterations driven by LDHA in CD8 + T cells promote immune evasion and therapy resistance in NSCLC.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide. Despite advancements in treatment, prognosis for patients wi...

Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.

Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing ...