Other Cancers

Spatial omics of immunity: Mapping cellular landscapes in tissue microenvironments.

The tumor microenvironment (TME) is composed of diverse heterogeneous components and plays a crucial role in immune cell infiltration, immune evasion, and dynamic interactions between tumor cells and the immune system. A precise understanding of the TME is essential for tissue immunology research and the development of effective immunotherapies. Technologies that spatially dissect the TME and anal...

Multimodal Ultrasound Integration Pathways and Paradigm Innovations in Precision Diagnosis and Treatment of Thyroid Cancer.

RATIONALE AND OBJECTIVES: Thyroid cancer, the fastest-growing endocrine malignancy, is shifting from morphological evaluation to molecular-functional imaging. This review systematically evaluates the translational value of multimodal ultrasound technologies-high-frequency ultrasound (HFUS), elastography, contrast-enhanced ultrasound (CEUS), and super-resolution imaging (SRI)-across the entire "scr...

Development and early feasibility testing of machine-learning algorithms to non-invasively assess hemoglobin levels.

The HeMonitor study evaluated the feasibility and accuracy of non-invasive hemoglobin (Hb) assessment using image-based techniques and machine learnin...

Synthetic dataset of PLGA and liposome nanocarrier formulations for brain-cancer-relevant drug delivery, release, blood-brain-barrier transport, and paired cell-viability proxies.

This data article describes an original synthetic/simulated dataset designed to support materials-informatics and comparative formulation analysis of ...

Breast cancer survival prediction using machine learning and multimodal data for personalized care plan.

This paper presents a time-stratified breast cancer survival analysis that incorporates tumor characteristics, disease stage, and patient features, us...

Landscape of T-cell bispecific antibodies in cancer therapy: therapeutic strategies, challenges and future prospection.

T-cell bispecific antibodies (TCBs), a specialized subclass of bispecific antibodies (BsAbs), are engineered to simultaneously engage T cells and tumo...

Deep learning-driven decoding of ubiquitination: from regulatory mechanisms to targeted protein degradation.

BACKGROUND: Ubiquitination is a highly dynamic post-translational modification that plays central roles in protein homeostasis, signal transduction, i...

Development and validation of a machine learning model for predicting distant metastasis in patients with renal cell carcinoma: a population-based study.

BACKGROUND: Distant metastasis is the leading cause of death in renal cell carcinoma (RCC), yet accurate prediction tools remain lacking. We aimed to ...

Precision Medicine Gene Network Analyser: part I-cancer driver gene identification through network topology and ensemble machine learning.

PURPOSE: Precision oncology depends on identifying cancer driver genes and linking them to targeted therapies. Current methods using curated gene sets...

Integration of Raman Spectroscopy and Metabolomics for Early Breast Cancer Detection and Classification.

Breast cancer, now the fourth leading cause of cancer-related mortality worldwide, necessitates early detection for improved clinical outcomes. Conven...

[A nomogram combining ultrasound radiomics and clinical features for predicting pathological invasiveness of papillary thyroid carcinoma].

OBJECTIVES: To develop a nomogram model combining ultrasound radiomics and clinical features and to evaluate its predictive value for pathological inv...

Machine learning assessment of pathologic response in lung cancer resections after neoadjuvant therapy - IASLC MPR Project.

INTRODUCTION: Machine learning algorithms may improve efficiency and accuracy of pathologic response (PR) assessment in surgically resected lung cance...

Ferroptosis in cancer: integrating multi-omics, AI-driven biomarkers, and translational challenges.

Ferroptosis, an iron-dependent regulated cell death driven by lipid peroxidation, has emerged as a potential target in cancers resistant to apoptosis ...

Automated vs. manual segmentation for small renal mass imaging.

INTRODUCTION: Automated segmentation using artificial intelligence (AI) has the potential to rapidly perform three-dimensional (3D) segmentation of sm...

Radiomics-Based Machine Learning versus the MAP Score for Predicting Haematocrit Decline after Laparoscopic Partial Nephrectomy.

OBJECTIVE: To evaluate and compare the ability of the Mayo Adhesive Probability (MAP) score and radiomics-based machine learning approaches to predict...

Organ-Specific Cancer-Associated Fibroblast Subtypes Across the Digestive System Linked to Cancer Hallmarks.

Cancer-associated fibroblasts (CAFs) are major stromal components of the tumor microenvironment (TME) and play diverse roles in gastrointestinal (GI) ...

Artificial intelligence-assisted urine cytology based on the Paris System for Reporting Urothelial Carcinoma.

BACKGROUND: Urine cytology is a noninvasive and valuable tool for detecting urothelial carcinoma but suffers from variable sensitivity and observer de...

The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors.

PURPOSE: Pediatric posterior fossa tumors represent a major subset of childhood central nervous system neoplasms; however, overlapping MRI features of...

Predicting Metastatic Potential from Histopathological Whole Slide Images of Primary Tumors in Head and Neck Squamous Cell Carcinoma Using Attention-Based Deep Learning and Signaling Pathway Alterations.

BACKGROUND: Head and Neck Squamous Cell Carcinoma (HNSCC) ranks as the 6th most prevalent cancer worldwide, imposing a significant burden on global he...

Integrative multi-omics analysis identifies a circadian rhythm-associated gene signature for prognosis and therapeutic stratification in lung adenocarcinoma.

BACKGROUND/OBJECTIVES: Circadian rhythm disruption is increasingly implicated in tumor progression and therapy resistance. However, its prognostic val...