Radiology

Multiparametric MRI-based radiomics machine learning nomogram for predicting aggressive histology in endometrial cancer.

OBJECTIVE: To develop and validate a radiomics-based machine learning nomogram using multiparametric MRI for preoperative prediction of aggressive histology in endometrial cancer (EC) patients. METHODS: This dual-center study retrospectively analyzed histologically confirmed EC patients who underwent preoperative MRI. Radiomics features were trained and tested to predict aggressive histology with ...

Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI.

OBJECTIVE: This systematic review critically appraises the current landscape of physics-aware artificial intelligence (AI) in medical imaging for quantitative biomarker mapping in Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive form, MASH. It focuses on deep learning and radiomics applications across ultrasound, CT, and MRI. METHODS: A PRISMA 2020-guided system...

Development of a time-dependent random survival forest model for predicting FGR based on prenatal screening date.

OBJECTIVE: Fetal growth restriction (FGR) is a progressive condition that amplifies risks with advancing gestation. This study develops a time-depende...

Adapt or specialize? A comprehensive evaluation of adapted SAM versus task-specific CNNs for fetal abdominal segmentation.

BACKGROUND: The fetal abdomen is crucial in prenatal screening, offering key insights into fetal growth and congenital anomalies. However, segmenting ...

The added value of radiomic analysis for predicting spontaneous preterm birth in the first trimester.

BACKGROUND AND OBJECTIVE: Preterm birth (PTB) is a public health problem. Researchers have worked to identify ways to detect women at risk for PTB ear...

An accurate, straightforward computer vision algorithm for optimal tumor-feeding visualization in cone-beam computed tomography hepatic arteriography: A preliminary study.

AIM: Although standardized 3D volume rendering techniques (VRT) and embolization guidance visualize and identify tumor-feeding arteries, current vesse...

Automated Recognition and Measurement for Levator Hiatus in 3D Ultrasound: A Clinical Study in Postpartum Women.

OBJECTIVES: To evaluate the performance of a CNN-based (convolutional neural networks-based) AI software for automatic recognition and measurement of ...

Delineating white matter phenotypes of sensori-/psychomotor functioning in large-scale cohorts of healthy individuals and patients with mental disorders across the lifespan (whiteSPAN): rationale and methods of an interdisciplinary bicentric project.

Aberrant sensori-/psychomotor functioning-including muscular hand weakness, sedentary behavior, psychomotor agitation, slowing, agitation, apathy, and...

An innovative AI-based dual segmentation application for head surgery.

Accurate anatomical segmentation in computed tomography (CT) imaging is vital for diagnostics and virtual surgical planning in head and neck surgery, ...

MRI-Based Radiomics Model for Classifying Axillary Lymph Node Burden and Disease-Free Survival in Patients With Early-Stage Breast Cancer.

BACKGROUND: Axillary lymph node (ALN) burden is a key prognostic determinant in breast cancer and plays an important role in diagnosis and treatment p...

Context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in renal cell carcinoma.

The invasiveness prediction in renal cell carcinoma (RCC) is of significant importance for the decision of clinical surgical plans and the patients' p...

A comprehensive review on effects of non-conventional plant food processing technologies on selected bioactive compounds: The current trends and future challenges.

The food industry is continually evolving, driven by the demand for innovative processing technologies capable of preserving the nutritional and funct...

Deep learning for non-invasive detection of steatosis and fibrosis in MASLD: a multicenter study with a new fibroscan-labelled ultrasound dataset.

PURPOSE: This study aimed to develop and validate deep learning models for non-invasive assessment of hepatic steatosis and fibrosis using conventiona...

Early prediction of response to ADT plus ARPIs in mHSPC using interpretable machine learning on PSMA PET/CT: a real-world study.

BACKGROUND: Reliable tools for early prediction of treatment response to androgen deprivation therapy (ADT) plus novel androgen receptor pathway inhib...

Use of Expected Utility to Evaluate Artificial Intelligence-Enabled Rule-out Devices for Mammography Screening.

BackgroundAn artificial intelligence (AI)-enabled rule-out device may autonomously remove patient images unlikely to have cancer from radiologist revi...

Application of Total-Body PET in Pediatric Patients.

This article highlights the advancements in total-body PET (TB PET) imaging, emphasizing its benefits for pediatric patients, including low-dose proto...

Imaging of vestibular function and disorders and its clinical relevance.

PURPOSE OF REVIEW: To discuss recent advances in imaging of the structural organization and functional connectivity of central vestibular disorders wi...

Deep learning-reconstructed hepatobiliary MRI: Enhancing biliary delineation, lesion margin sharpness, and small lesion detection.

OBJECTIVE: To evaluate the comparative performance of a deep learning-reconstructed T1-weighted volumetric interpolated breath-hold examination (DL VI...

Deep learning-based image reconstruction significantly improves image quality of MRI examinations of the orbit at 3 Tesla.

PURPOSE: The purpose of this study was to assess the benefit of a deep learning-based image reconstruction (DLBIR) for improving image quality in orbi...

Artificial intelligence for TNM staging in NSCLC: a critical appraisal of segmentation utility in [1⁸F]FDG PET/CT.

PURPOSE: This study aims to investigate whether a diagnostic AI model can effectively support lesion detection and staging in non-small cell lung canc...