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Background: This study evaluated the performance of histogram analysis in the time course of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for differentiating cancerous tissues from benign tissues in the prostate.
Methods: We retrospectively analyzed the histograms of DCE-MRI of 30 patients. Histograms within regions of interest(ROI) in the peripheral zone (PZ) and transitional zone (TZ) were separately analyzed. The maximum difference wash-in slope (MWS) and delay phase slope (DPS) were defined for each voxel. Differences in histogram parameters, namely the mean, standard deviation (SD), the coefficient of variation (CV), kurtosis, skewness, interquartile range (IQR), percentile (P10, P25, P75, P90, and P90P10), Range, and modified full width at half-maximum (mFWHM) between cancerous and benign tissues were assessed.
Results: In the TZ, CV for ROIs of 7.5 and 10mm was the only significantly different parameter of the MWS (P = 0.034 and P = 0.004, respectively), whereas many parameters of the DPS (mean, skewness, P10, P25, P50, P75 and P90) differed significantly (P = <0.001-0.016 and area under the curve [AUC] = 0.73-0.822). In the PZ, all parameters of the MWS exhibited significant differences, except kurtosis and skewness in the ROI of 7.5mm(P = <0.001-0.017 and AUC = 0.865-0.898). SD, IQR, mFWHM, P90P10 and Range were also significant differences in the DPS (P = 0.001-0.035).
Conclusion: The histogram analysis of DCE-MRI is a potentially useful approach for differentiating prostate cancer from normal tissues. Different histogram parameters of the MWS and DPS should be applied in the TZ and PZ.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6372178 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212092 | PLOS |
Photodiagnosis 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.
View Article and Find Full Text PDFMed Phys
September 2025
Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, P.R. China.
Background: Advanced diffusion models have been introduced to improve characterization of tissue microstructure in breast cancer assessment.
Purpose: This study aimed to evaluate the diagnostic utility of monoexponential apparent diffusion coefficient (ADC), time-dependent diffusion magnetic resonance imaging (td-dMRI), and the Continuous-Time Random-Walk (CTRW) diffusion model for differentiating breast lesions and predicting Ki-67 expression levels.
Methods: Fifty-three consecutive patients with suspected breast lesions undergoing preoperative MRI were enrolled in this prospective investigation.