Skin Cancer

Multi-omics and machine learning framework reveals ABCG2 as a therapeutic target of Eleven Flavored Shenqi Tablets in clear cell renal cell carcinoma.

ETHNOPHARMACOLOGICAL RELEVANCE: Eleven Flavored Shenqi Tablets (EFST) is a classical multi-herbal prescription in traditional Chinese medicine, traditionally prescribed for kidney tonification and systemic regulation. Despite its reported anti-inflammatory and antiproliferative properties, the therapeutic value of EFST in clear cell renal cell carcinoma (ccRCC) remains unclear. METHODS: We integra...

Predicting sentinel lymph node metastasis in melanoma patients: A machine learning-based predictive model.

BACKGROUND: Risk factors for sentinel lymph node (SLN) metastasis in melanoma have been studied. However, there remains a lack of widely applicable models with considerable predictive potential for clinical use. OBJECTIVE: To developed a well-performing machine learning-based model for predicting SLN metastasis in melanoma patients. METHODS: This study collected data on 351 melanoma patients with ...

Response to 'MISSLE in melanoma: Promise, limitations and the need for broader validation'.

Genomic and Transcriptomic Profiling of High-risk Bladder Cancer Reveals Diverse Molecular and Microenvironment Ecosystems.

BACKGROUND AND OBJECTIVE: High-risk bladder cancer recurs in 30% of cases and causes fatal outcomes in 10% within 2 yr despite surgical resection, end...

Reply to 'Risk stratification of cutaneous melanoma using deep learning on whole slide images'.

MISSLE in melanoma: Promise, limitations and the need for broader validation.

MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics.

Melanoma is a highly malignant skin cancer, and identifying its pathogenic genes is crucial for understanding its pathogenesis and developing treatmen...

Exosomal biomarkers in cancer: Insights from Multi-OMIC approaches.

Extracellular vesicles, particularly exosomes, are emerging as powerful tools in cancer research due to their role in intercellular communication and ...

Nivolumab plus ipilimumab for potentially resectable hepatocellular carcinoma: Long-term efficacy and biomarker exploration.

BACKGROUND & AIMS: Immune checkpoint inhibitor-based combination therapy has demonstrated high objective response rates in patients with hepatocellula...

The unmet potential of patient context in melanoma AI: Addressing design and data biases.

A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma.

Melanoma is a highly aggressive cutaneous malignancy characterized by a strong propensity for metastasis and therapy resistance, with its progression ...

Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.

Esophageal cancer (EC) is one of the most serious health issues around the world, ranking seventh among the most lethal types of cancer and eleventh a...

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 unrese...

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 ...

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-...