Skin Cancer

Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients

Accurate prognosis of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy is essential for personalized treatment planning, enabling informed patient decisions, and improving both treatment outcomes and quality of life. However, the lack of large, relevant datasets and effective multi-modal feature fusion strategies pose significant challenges in this domain. To address these ch...

Leveraging Artificial Intelligence for Neoantigen Prediction.

Neoantigens represent a class of antigens within tumor microenvironments that arise from diverse somatic mutations and aberrations specific to tumorigenesis, holding substantial promise for advancing tumor immunotherapy. However, only a subset of neoantigens effectively elicits antitumor immune responses, and the specific neoantigens recognized by individual T-cell receptors (TCR) remain incomplet...

Artificial Intelligence Can Predict Personalized Immunotherapy Outcomes in Cancer.

The rapid advancement of artificial intelligence (AI) technologies has opened new avenues for advancing personalized immunotherapy in cancer treatment...

Advancing T-cell immunotherapy for cellular senescence and disease: Mechanisms, challenges, and clinical prospects.

Cellular senescence is a complex biological process with a dual role in tissue homeostasis and aging-related pathologies. Accumulation of senescent ce...

Establishment of an intelligent analysis system for clinical image features of melanonychia based on deep learning image segmentation.

Melanonychia, a condition that can be indicative of malignant melanoma, presents a significant challenge in early diagnosis due to the invasive nature...

Integrating multi-omics data with artificial intelligence to decipher the role of tumor-infiltrating lymphocytes in tumor immunotherapy.

Tumor-infiltrating lymphocytes (TILs) are capable of recognizing tumor antigens, impacting tumor prognosis, predicting the efficacy of neoadjuvant the...

Comparing the number-needed-to-biopsy ratio for melanoma diagnosis by artificial intelligence as a medical device, teledermatology and face-to-face models of care.

[Progress in neoadjuvant immunotherapy for locally advanced rectal cancer].

Neoadjuvant chemoradiotherapy (NACRT) is the standard treatment for locally advanced rectal cancer (LARC), yet the pathological complete response (pCR...

Nanotechnology-Enhanced siRNA Delivery: Revolutionizing Cancer Therapy.

RNA interference (RNAi) has emerged as a transformative approach for cancer therapy, enabling precise gene silencing through small interfering RNA (si...

Characterization of m6A-Related Genes in Tumor-Associated Macrophages for Prognosis, Immunotherapy, and Drug Prediction in Lung Adenocarcinomas Based on Machine Learning Algorithms.

Tumor-associated macrophages (TAMs) are a vital immune component within the tumor microenvironment (TME) of lung adenocarcinoma (LUAD), exerting signi...

Artificial Intelligence and Machine Learning in the Development of Vaccines and Immunotherapeutics Yesterday, Today, and Tomorrow

In the past, the development of vaccines and immunotherapeutics relied heavily on trial-and-error experimentation and extensive in vivo testing, oft...

ImmunoFOMO: Are Language Models missing what oncologists see?

Language models (LMs) capabilities have grown with a fast pace over the past decade leading researchers in various disciplines, such as biomedical r...

Immunological mechanisms and immunoregulatory strategies in intervertebral disc degeneration

Intervertebral discs are avascular and maintain immune privilege. However, during intervertebral disc degeneration (IDD), this barrier is disrupted,...

Knowledge-guided Contextual Gene Set Analysis Using Large Language Models

Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conv...

Quantum Ensembling Methods for Healthcare and Life Science

Learning on small data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble m...

Deep learning model for predicting immunotherapy response in patients with advanced NSCLC: Study findings demonstrate a strong and independent deep learning-based feature associated with an immune checkpoint inhibitor response in patients with NSCLC across cohorts.

Self-supervised multi-modality learning for multi-label skin lesion classification.

BACKGROUND: The clinical diagnosis of skin lesions involves the analysis of dermoscopic and clinical modalities. Dermoscopic images provide detailed v...

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to c...

Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center.

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handl...

Skin Lesion Phenotyping via Nested Multi-modal Contrastive Learning

We introduce SLIMP (Skin Lesion Image-Metadata Pre-training) for learning rich representations of skin lesions through a novel nested contrastive le...