European journal of radiology
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Randomized Controlled Trial
Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging.
To explore an MRI-based radiomics nomogram for preoperatively predicting of pelvic lymph node (PLN) metastasis in patients with early-stage cervical cancer (ECC). ⋯ The radiomics nomogram based on joint T2WI and DWI demonstrated an improved prediction ability for PLN metastasis in ECC. This noninvasive and convenient tool may be used to facilitate preoperative identification of PLN metastasis in patients with ECC.
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Mild traumatic brain injury is known to have frequent cognitive impairment. Accumulating evidence is pointing to the malfunctioning of the substantia nigra (SN) as an important factor for head trauma. However, it remains unknown whether changes in the SN-based resting state functional connectivity following mTBI at acute stage and its relationship with cognitive function. ⋯ The present study indicated that patients with acute mTBI suffer from disruption in their SN resting state networks. Moreover, abnormal functional connectivity significantly correlated with cognitive function. Taking together, these results may better improve our understanding of the neuropathological mechanism underlying the neurocognitive symptoms associated with acute mTBI.
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To investigate which clinical and radiological characteristics can predict clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions. To investigate which clinical and radiological characteristics influence the clinician to biopsy a PI-RADS 3 lesion. ⋯ Prostate volume and the ratio of ADC tumor on ADC of the contralateral prostate have the potential to predict csPCa in PI-RADS 3 lesions with a sensitivity of 59% and specificity of 88%. A suspicious rectal examination and the mentioning of prostatitis on the MRI report influenced the decision of clinicians to biopsy a PI-RADS 3 lesion.
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Multicenter Study
Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases.
The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden. ⋯ The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.
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Randomized Controlled Trial
Histogram analysis combined with morphological characteristics to discriminate adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma appearing as pure ground-glass nodule.
To construct a predictive model to discriminate adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) appearing as pure ground-glass nodules (pGGNs) using computed tomography (CT) histogram analysis combined with morphological characteristics and to evaluate its diagnostic performance. ⋯ Histogram analysis combined with morphological characteristics exhibit a superior diagnostic performance in discriminating AIS-MIA from IAC appearing as pGGNs.