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Triple-negative breast cancer (TNBC) is a subtype of breast cancer with strong invasiveness, limited treatment options, and poor prognosis. Its early accurate diagnosis and individualized treatment are major challenges f...

Abstract Background This study aims to explore spatial heterogeneity within tumors, establish and validate an interpretable machine learning model combining habitat radiomics and deep learning, and investigate its predic...

Abstract Objective Given the distinct intratumoral heterogeneity of glioblastoma (GB) and solitary brain metastasis (SBM), habitat-based radiomics derived from neurite orientation dispersion and density imaging (NODDI) m...

Abstract Background Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) is essential for optimizing treatment decisions. This study aimed to deve...

Abstract Background Enchondromas (EC) present cartilaginous tumors that are difficult to differentiate from their intermediate counterpart, atypical cartilaginous tumors (ACT). Histologically, tumor distinction of these ...

Abstract Background Placenta accreta spectrum(PAS)is a major cause of maternal mortality during the perinatal period. This study aims to develop and validate a novel nomogram prediction model integrating clinical charact...

Abstract Background Radiomics analysis of 18F-FDG PET/CT enables extraction of high-dimensional quantitative features that may serve as imaging biomarkers for tumor biology. In particular, in non-small cell lung cancer (...

Benign–malignant classification of pulmonary nodules from chest computed tomography (CT) is essential for early lung cancer screening and for reducing unnecessary biopsies and follow-up examinations, yet it remains cha...

IntroductionWe evaluated whether a generative adversarial network (GAN)-based super-resolution (SR) radiomics pipeline could improve the pre-fine-needle aspiration (FNA) prediction of nondiagnostic (Bethesda I) outcomes ...

Abstract Background Characterisation of CT detected ovarian masses is challenging with overlapping imaging features, unreliable biomarker or clinical presentation. We proposed a two-staged CT-based radiomics model to ide...

Background and purpose: The clinical utility of radiomics in head-and-neck (H&N) cancer is hindered by poor reliability caused by delineation uncertainties from the use of binary mask (BinMask). This study introduced a f...

Abstract Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with limited predictive markers to guide personalized treatment, particularly in human papillomavirus (HPV)-negative cases, whic...

Abstract Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) affects 30–50% of obese children, yet accurate non-invasive quantification remains challenging. While magnetic resonance imaging-proton density ...

Introduction and aims: Oral and maxillofacial surgery (OMFS) produces complex clinical data, while artificial intelligence (AI) applications in this field remain limited despite great potential. This study aimed to map t...

Abstract Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence (AI) models and conducting retrospective clinical research. However, multimodal datasets...

Objective: To explore the imaging biomarkers obtained from baseline multi-modality imaging PET/CT before metastasis-directed therapy (MDT), which could offer early response prediction before MDT treatment, optimizing pat...

Materias / palabras clave: dosimetry, radiomics, radionuclide therapy, radiopharmacy, radiation safety, pet and spect; Medicine: Medicine (General): Medical physics. Medical radiology. Nuclear medicine