Skin Cancer

Corrigendum to 'CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy' [Acad Radiol 32/6 (2025) 3397-3409].

Deep Learning Multi Modal Melanoma Detection: Algorithm Development and Validation.

BACKGROUND: The visual similarity of melanoma and seborrheic keratosis has made it difficult for elderly patients with disabilities to know when to seek medical attention, contributing to the metastasis of melanoma.

Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex mo...

Exploring the impact of neutrophils on lung adenocarcinoma using Mendelian randomization and transcriptomic study.

Tumor immune microenvironment plays a crucial role in determining the prognosis of lung adenocarcinoma (LUAD), with the interaction of immune cells wi...

Folate metabolism-associated CYP26A1 is a clinico-immune target in colorectal cancer.

Folic acid plays a crucial role in cellular regulation and metabolism, commonly included in dietary supplements. Despite this, its involvement in colo...

Stereotactic radiotherapy or protons for uveal melanoma patients? An artificial intelligence (AI)-based clinical treatment decision-making tool predicting doses to radiotherapy constraints.

PURPOSE: For ocular melanoma, selecting between stereotactic radiotherapy (SRT) and protons requires a lengthy plan comparison process. The purpose of...

DCAF13 Regulates Cell Proliferation and Immune Escape of Hepatocellular Carcinoma Through Activating the NF-κB Pathway.

Hepatocellular carcinoma (HCC), the third leading cause of global cancer deaths, has a high unmet clinical need due to limited therapeutic efficacy. I...

Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers.

PANoptosis is closely associated with tumorigenesis and therapeutic response, yet its role in multiple myeloma (MM) remains unclear. This study analyz...

A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer.

BACKGROUND: Manganese modulates tumorigenesis and immune regulation. High levels of manganese may promote cancer progression. While manganese toxicity...

Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of most common cancers with high invasiveness and poor prognosis. Obesity and aging are correlated wi...

Impact of tertiary lymphoid structure-associated biomarkers on pancreatic cancer via a dual-disease analysis of psoriasis and pancreatic cancer.

Pancreatic cancer (PC), often referred to as the "king of cancers", has demonstrated limited success with immunotherapies, many of which are still und...

Automatic melanoma detection using an optimized five-stream convolutional neural network.

Melanoma is among the deadliest forms of malignant skin cancer, with the number of cases increasing dramatically worldwide. Its early and accurate dia...

Machine learning developed LKB1-AMPK signaling related signature for prognosis and drug sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, posing a significant threat to the life and health of people globally. LKB1...

Integrated analysis of shared gene expression signatures and immune microenvironment heterogeneity in type 2 diabetes mellitus and colorectal cancer.

Emerging evidence suggests a bidirectional relationship between colorectal cancer (CRC) and type 2 diabetes mellitus (T2DM), yet the shared molecular ...

Consensus artificial intelligence-driven prognostic signature for predicting the prognosis of hepatocellular carcinoma: a multi-center and large-scale study.

Hepatocellular carcinoma (HCC), a leading cause of global cancer mortality, requires molecular stratification to advance precision oncology. This stud...

Machine learning to detect melanoma exploiting nuclei morphology and Spatial organization.

Cutaneous melanoma is one of the most lethal forms of skin cancer, and its incidence is increasing globally. Its diagnosis typically relies on manual ...

Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.

Some studies have developed machine learning (ML) models for the prediction of pneumonitis following immunotherapy and radiotherapy for patients with ...

Regulatory T cells and matrix-producing cancer associated fibroblasts contribute on the immune resistance and progression of prognosis related tumor subtypes in ccRCC.

Clear cell renal cell carcinoma (ccRCC) is a prevalent malignant tumor in the field of urology. The effect of cell heterogeneity on the prognosis and ...

MRI's evolving role in rectal cancer in the era of personalized medicine.

Recent decades have seen great advances in the diagnosis and management of rectal cancer, and magnetic resonance imaging (MRI) has become pivotal for ...

Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles.

Once deployed, medical image analysis methods are often faced with unexpected image corruptions and noise perturbations. These unknown covariate shift...