Objective: To optimize a measuring method of osteonecrotic area by analyzing the average osteonecrotic areas and osteonecritic volume using multi layer MR images.
Methods: The MRI images of 87 cases (120 hips) of ONFH(ARCO II) were collected retrospectively from January 2011 to January 2012 in Wangjing Hospital of China Academy of Chinese Medicine Science. PHILIPS Achieval 1.5T MR was used to obtain coronal TSE T1 weighted (T1W) images. The scanning parameters were shown as follows:slice thickness, 3.5 mm; gap, 0.3 mm; images repetition time(TR), 500 ms;echo time(TE), 20 ms;field of view (FOV), 374 mm;total 12 layers. According to the distribution rule of osteonecrotic lesion, the layer of coronal T1-weighted imaging showing most of femoral neck was marked as layer 0(L0). The layers before L0 were marked in sequence L1, L2, L3, L4... , and the layers after L0 were marked in sequence L-1, L-2, L-3, L-4... . Auto CAD 2007 was used to measure the percentage of osteonecrotic area and calcu late the average data, and then decreased the layer from low to high layer successively based on frequency of osteonecrotic occurrences. First, the layer with lowest frequency of osteonecrotic occurrenoses L3 was removed, then calculated the average osteonecrotic area of the ramaining 8 layers. L5, L4, L-2, L-1, L3 layers were gradually removed, resulting in the calculation of avereage osteonecrotic areas in 7, 6, 5, 4, 3 layers. These areas were compared to the osteonecrotic volume in MR imagings, leading to the optimization of the fewest layer measuring method of osteonecrotic area using a statistical analysis.
Results: The percentage of osteonecrotic volume in 120 hips was 0.333±0.151. The average osteonecrotic areas of 9 to 3 layers were 0.321±0.117, 0.317±0.136, 0.312±0.147, 0.333±0.153, 0.348±0.172, 0.365±0.174, 0.377±0.202 respectively. There were no statistical differences of the average osteonecrotic areas and osteonecrotic volume in 9 to 3 layers(>0.05), but when the osteonecrotic layers were reduced to 3, there were statistical differences(<0.05). Total 120 hips were grouped according to osteonecrotic volume based on ARCO staging criteria, among them, 12 hips were grade A, 43 were B, 65 were C. According to average osteonecrotic areas of 4 layers, 10 hips were A grade, 32 were B, 78 were C. There were no statistical differences between two methods(>0.05). There was a high degree of concordance among two methods.
Conclusions: The results of 4(L0, L1, L2, L3) layers measuring method and osteonecrotic volume measuring method are similar. The 4 layers measuring method is an accurate, convenient, valuable method measuring the esteonecrotic area with the fewest layers, which is worth to be popularized in clinical application.
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http://dx.doi.org/10.3969/j.issn.1003-0034.2016.09.016 | DOI Listing |
Brief Bioinform
November 2024
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction.
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Greenslopes Private Hospital, Gallipoli Medical Research, Brisbane, Australia.
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Department of Psychology, Lakehead University, Thunder Bay, ON, Canada.
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Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States.
Background: Readability is important to consider when developing informed consent communications for prospective research participants, but not the most important consideration. Other factors to consider relate to learning preferences and literacy needs of people recruited to participate in research, as these factors can influence understanding of consent communications. To promote understanding among prospective participants, researchers should take a human-centered approach to develop consent communications.
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