Breast Cancer

Identification and validation of BLK and OSBPL10 as diagnostic and prognostic biomarkers for nasopharyngeal carcinoma through machine learning algorithms.

Despite substantial advances in radiotherapy and chemotherapy for nasopharyngeal carcinoma (NPC), a subset of patients still develops metastasis or recurrence following initial treatment. Additionally, the atypical early symptoms of NPC often lead to clinical misdiagnosis or missed diagnosis, resulting in late diagnosis and unfavorable prognosis. Thus, novel diagnostic biomarkers are urgently requ...

Comparative 10-year performance of mammography artificial intelligence, polygenic, and clinical breast cancer risk models in the Kaiser Permanente Research Bank.

BACKGROUND: We compared performance across 3 breast cancer risk domains-clinical, polygenic, and mammography artificial intelligence-alone and in combination over a 10-year time horizon among women with a negative screening mammogram within a Kaiser Permanente Research Bank (KPRB) prospective cohort. METHODS: The study included 82 957 women (61 962 non-Hispanic White, 7256 Asian, 3414 Black, and 5...

Innovative screening for OSAS in patients with head and neck cancer using the apneal application: Protocol for a prospective single-centre study.

INTRODUCTION: OSAS is a common yet underdiagnosed condition, particularly among patients with head and neck cancers (HNC). Anatomical changes caused b...

Real-World Application of a Machine Learning-Based Early Recurrence Model for Guiding Adjuvant Chemotherapy Use in Patients with Gallbladder Cancer.

BACKGROUND/AIMS: Although recent randomized controlled trials have reported the efficacy of adjuvant chemotherapy for resected biliary tract cancer, d...

Radiation shielding and microstructural characteristics of nano-silica and nano-alumina modified cement composites.

The increasing use of nuclear technology in medicine, industry, and energy requires effective durable radiation shielding. This study aimed to develop...

MammoDenseSegNet: A Context-Aware Deep Learning Model for Dense Tissue Segmentation in Digital Mammograms.

Breast density is a breast cancer risk factor. The accurate quantification of breast density requires reliable segmentation of dense tissue in mammogr...

Beyond surgical risk: Multidimensional predictors of postoperative arm morbidity after breast cancer treatment.

BACKGROUND: Postoperative upper-limb morbidity following breast cancer treatment is common and clinically significant, encompassing pain, functional i...

Fully automated artificial intelligence-based echocardiographic analysis for global longitudinal strain monitoring and cancer therapy-related cardiac dysfunction detection in breast cancer patients.

AIMS: Global longitudinal strain (GLS) is essential for the early detection of cancer therapy-related cardiac dysfunction (CTRCD). A fully automated e...

Feature Transformation Network based on Correlation Distribution Graph for Disease Diagnosis.

Gene expression data play a crucial role in disease diagnosis. Transforming such non-image data into spatially structured image representations enable...

Latent twins: a framework for scene recognition and fast radiative transfer inversion in FORUM all-sky observations.

The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for the first time, systematic far-infrared spectral me...

Based on artificial intelligence-assisted generation and in-depth in-silico evaluation of potential inhibitors targeting Stearoyl-CoA desaturase 1 (SCD-1).

Stearoyl-CoA desaturase 1 (SCD-1), a key rate-limiting enzyme in lipid metabolism, catalyzes the conversion of saturated fatty acids to monounsaturate...

Identifying predictive hematological biomarkers for radiation exposure by machine learning in mouse models.

BACKGROUND: Population-scale radiation exposure assessment during radiological emergencies is hindered by the slow and costly nature of current method...

Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.

OBJECTIVE: Adipose tissue is a highly heterogeneous and plastic endocrine and immune organ whose characteristics have been linked to prognosis in NSCL...

Spatially interpretable artificial intelligence framework to tailored neoadjuvant dual HER2 blockade in HER2-positive breast cancer.

Neoadjuvant dual HER2 blockade with trastuzumab and pertuzumab plus chemotherapy represents the current standard-of-care for HER2-positive breast canc...

Development and validation of an interpretable model for pre-chemotherapy triage of fatigue-pain-sleep disturbance burden in lung cancer.

PURPOSE: The aim of the study is to develop an interpretable machine learning model that can effectively identify patients at risk of high fatigue-pai...

Perspectives on artificial intelligence-generated chronic disease risk predictions in early breast cancer: an international survey among radiation oncologists.

Frontiers in Raman nano-diagnostics: SERS and TERS of extracellular vesicles for cancer detection.

Efforts to create rapid, non-invasive, and reliable cancer diagnostics have increasingly focused on extracellular vesicles (EVs), nanoscale carriers o...

Engineered small extracellular vesicles in hematologic malignancies: mechanisms, therapeutic strategies, and translational challenges.

Hematologic malignancies remain among the most challenging cancers to treat due to genetic heterogeneity, clonal evolution, and therapy resistance. Ex...

Multi-Omics Analysis Identifies Paxillin as a Biomarker of Doxorubicin Resistance via Cytoskeletal Remodeling and Immune Exhaustion.

Chemotherapy resistance remains a primary cause of treatment failure in breast cancer, yet the global proteomic landscape driving this phenotype has n...

[Reirradiation: A new therapeutic paradigm in oncology].

Reirradiation (reRT) has become an essential therapeutic option for selected patients with locoregional recurrences, when surgery or systemic therapie...