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Purpose: To assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps based on whole-tumor in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from angiomatous meningioma (AM).
Materials And Methods: Pathologically confirmed intracranial SFT/HPC (n = 15) and AM (n = 20) were retrospectively collected and their clinical and conventional MRI features were analyzed. Diffusion-weighted (DW) images (b = 0 and 1000 s/mm) were processed with the mono-exponential model. Regions of interest covering the whole tumor were drawn on all slices of the ADC maps to obtain histogram parameters, including mean ADC (ADCmean), median ADC (ADCmedian), maximum ADC (ADCmax), minimum ADC (ADCmin), skewness and kurtosis, as well as the 5th, 10th, 25th, 75th, 90th and 95th percentile ADC (ADC5, ADC10, ADC25, ADC75, ADC90 and ADC95). Differences of histogram parameters between SFT/HPC and AM were compared using Mann-Whitney U test. Receiver operating characteristic (ROC) curve was used to determine the diagnostic performance.
Results: The ADCmin (P = 0.001) and ADC5 (P = 0.045) were significantly lower in SFT/HPCs than in AMs, while no significant difference was found in sex, age, conventional MRI features or any other histogram parameters between the two entities (P = 0.051-1.000). ADCmin showed the best diagnostic performance (area under curve [AUC], 0.86; sensitivity, 81.3%; specificity, 83.3%) in differentiating SFT/HPC from AM with optimal cutoff value being 569.00 × 10 mm/s, followed by ADC5 (AUC, 0.72; sensitivity, 68.8%; specificity, 75%) with optimal cutoff value being 781.97 × 10 mm/s.
Conclusion: SFT/HPC and AM share similar conventional MR appearances. Whole-tumor histogram analysis of ADC maps may be a useful tool for differential diagnosis, with ADCmin and ADC5 being potential parameters.
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http://dx.doi.org/10.1016/j.ejrad.2019.01.023 | DOI Listing |
PLoS One
September 2025
Department of Pathology, Hospital Tuanku Fauziah, Jalan Tun Abdul Razak, Kangar, Perlis, Malaysia.
Cervical cancer remains a significant cause of female mortality worldwide, primarily due to abnormal cell growth in the cervix. This study proposes an automated classification method to enhance detection accuracy and efficiency, addressing contrast and noise issues in traditional diagnostic approaches. The impact of image enhancement on classification performance is evaluated by comparing transfer learning-based Convolutional Neural Network (CNN) models trained on both original and enhanced images.
View Article and Find Full Text PDFPhotodiagnosis Photodyn Ther
September 2025
Department of Ophthalmology, People's Hospital of Feng Jie, Chongqing, 404600, China. Electronic address:
Objective: This study aims to develop a robust, multi-task deep learning framework that integrates vessel segmentation and radiomic analysis for the automated classification of four retinal conditions- diabetic retinopathy (DR), hypertensive retinopathy (HR), papilledema, and normal fundus-using fundus images.
Materials: AND.
Methods: A total of 2,165 patients from eight medical centers were enrolled.
Front Plant Sci
August 2025
Chinese Academy of Agriculture Mechanization Sciences Group Co., Ltd., Beijing, China.
Intercropping maize and soybean with distinct plant heights is a typical practice in diversified cropping systems, where shadows cast by taller maize plants onto soybean rows pose significant challenges for image based recognition. This study conducted experiments throughout the entire soybean-maize intercropping period to address illumination variation. Based on the height difference between crops, solar elevation angle, and light intensity at the top of the soybean canopy, an illumination compensation regression model was developed.
View Article and Find Full Text PDFAccurate honey bee subspecies identification is vital for biodiversity conservation and pollination resilience, yet current methods face critical limitations. Classical morphometric techniques, reliant on manual wing vein measurements, suffer from subjectivity and poor scalability across hybrid populations, while deep learning approaches demand extensive labeled datasets and exhibit limited interpretability in noisy field conditions. Crucially, existing methods fail to reconcile scalability with the ability to analyze phenotypic gradients in hybrid specimens.
View Article and Find Full Text PDFActa Oncol
September 2025
Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.
Background And Purpose: Accurate stopping-power ratio (SPR) estimation is crucial for proton therapy planning. In brain cancer patients with metal clips, SPR accuracy may be affected by high-density materials and imaging artefacts. Dual-energy CT (DECT)-based methods have been shown to improve SPR accuracy.
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