BACKGROUND: Prostate cancer (PCa) in the transition zone (TZ) is uncommon and often poses challenges for early diagnosis, but its genomic determinants and therapeutic vulnerabilities remain poorly characterized.
BACKGROUND: Rotavirus is the leading cause of severe dehydrating diarrhea in children under 5 years worldwide. Timely diagnosis is critical, but access to confirmatory testing is limited in hospital settings. Machine learning (ML) models have shown p...
ObjectiveNonpuerperal mastitis (NPM) is an inflammatory condition, including periductal mastitis (PDM) and granulomatous lobular mastitis (GLM). The clinical manifestations of PDM and GLM are highly similar, posing significant challenges in their dif...
The Journal of hand surgery, European volume
Apr 12, 2025
This study sought to establish and validate a machine learning-based multi-sequence MRI radiomics model for predicting postoperative complications in patients with peripheral nerve sheath tumours. We conducted a retrospective analysis of 303 patients...
Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Apr 12, 2025
PURPOSE: To construct and validate a magnetic resonance imaging (MRI) radiomics combined with delta-radiomics and clinical information (C) model for predicting pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC...
BACKGROUND: Remdesivir (RDV) was the first antiviral approved for mild-to-moderate COVID-19 and for those patients at risk for progression to severe disease after clinical trials supported its association with improved outcomes. Real-world evidence (...
The exploration of deep learning techniques for predicting various biological characteristics of endometrial cancer (EC) is of significant importance. The objective of this study was to develop an optimized radiomics scheme combining multiparametric ...
PURPOSE: To develop machine learning models that are driven by Gd-EOB-DTPA-MRI features for the preoperative prediction of early recurrence in HCC and compare them to the previously proposed ERASL-pre method.
RATIONALE AND OBJECTIVES: To construct a deep learning model (DL) based on high-resolution T2-weighted images for preoperative differentiation between T2 and T3 stage rectal cancer (RC), and to compare its performance with experienced radiologists.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge with a particularly grim prognosis. Accurate prediction of lymph node metastasis (LNM) in ESCC is crucial for optimizing treatment strategies and improv...
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