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Breast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically investigated the performances of the CBBCT and DBT for different reconstruction algorithms using both BR3D breast phantom and breast images. We acquired clinical breast images and scanned the BR3D phantom for additional breast images. These acquired images were used to simulate projection data using predefined CBBCT and DBT geometries. The simulated projections were reconstructed using five different reconstruction algorithms. To evaluate the reconstruction accuracy, we calculated average image quality assessment (IQA) indices, including peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and others, across different algorithms and modalities. The pooled PSNR, SSIM, and RMSE for DBT and CBBCT images are (31.6265 ± 0.8725), (0.9353 ± 0.0077), and (0.0270 ± 0.0025) and (29.7007 ± 0.9249), (0.9136 ± 0.0130), and (0.0342 ± 0.0040), which implies that the overall IQA indices of DBT are superior to CBBCT; therefore, DBT tends to reveal more BC detectability as the diagnosis outcome would largely depend on good quality images. The results show that DBT gives an improved result for all algorithms compared to CBBCT, although further experimental trials may be needed to establish the findings fully. The findings suggest that using DBT may enhance the accuracy of BC diagnosis compared to CBBCT due to its superior image quality in clinical practice, emphasizing the importance of selecting optimal reconstruction algorithms for improved diagnostic outcomes. [ABSTRACT FROM AUTHOR]

In the process of two‐phase countercurrent flow in the extraction column, the study of the hydrodynamic characteristics of dispersed phase droplets is crucial for the design and scaling up of the extraction column. In a pulsed extraction column with a liquid–liquid system, the four‐sensor optical fiber probe was used to assess the hydrodynamic characteristics of the dispersed phase droplets, including size, mean velocity, and holdup. 1 M HNO3 and 30% TBP/kerosene were used as continuous phase and dispersed phase, respectively. The effects of two‐phase superficial velocities and pulsation intensity were investigated on droplet size, mean velocity, and holdup. The findings show that aside from the local holdup, the pulsation intensity has the greatest impact on the hydrodynamic characteristics. A new and concise algorithm was used to deal with the light signals to determine the droplet size and mean velocity. This algorithm created two Cartesian coordinate systems, each with a separate z‐axis using the leading probe as the origin of the coordinate instead of using the vector approach. Compared to the previous algorithm, both the needed number of the time interval between the voltage peaks and that of the direction angles decrease. The relative error of the droplet size obtained by this improved algorithm is within 7%, and droplet velocity is almost the same, compared to the previous algorithm, which indicates that the new algorithm of the four‐sensor optical fiber signal processing is reliable to the measurement of the hydrodynamic characteristics. [ABSTRACT FROM AUTHOR]

publisher‐imprint‐name Springer volume‐issue‐count 1 issue‐article‐count 0 issue‐toc‐levels 0 issue‐pricelist‐year 2020 issue‐copyright‐holder The Author(s) issue‐copyright‐year 2020 article‐contains‐esm Yes article‐numbering‐style Unnumbered article‐registration‐date‐year 2020 article‐registration‐date‐month 1 article‐registration‐date‐day 20 article‐toc‐levels 0 toc‐levels 0 volume‐type Regular journal‐product ArchiveJournal numbering‐style Unnumbered article‐grants‐type OpenChoice metadata‐grant OpenAccess abstract‐grant OpenAccess bodypdf‐grant OpenAccess bodyhtml‐grant OpenAccess bibliography‐grant OpenAccess esm‐grant OpenAccess online‐first false pdf‐file‐reference BodyRef/PDF/40169_2020_Article_263.pdf pdf‐type Typeset target‐type OnlinePDF issue‐type Regular article‐type OriginalPaper journal‐subject‐primary Medicine & Public Health journal‐subject‐secondary Medicine/Public Health, general journal‐subject‐collection Medicine open‐access true --> Background and Aim: To develop and validate radiomic prediction models using contrast‐enhanced computed tomography (CE‐CT) to preoperatively predict Ki‐67 expression in gastrointestinal stromal tumors (GISTs). Method: A total of 339 GIST patients from four centers were categorized into the training, internal validation, and external validation cohort. By filtering unstable features, minimum redundancy, maximum relevance, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, a radiomic signature was built to predict the malignant potential of GISTs. Individual nomograms of Ki‐67 expression incorporating the radiomic signature or clinical factors were developed using the multivariate logistic model and evaluated regarding its calibration, discrimination, and clinical usefulness. Results: The radiomic signature, consisting of 6 radiomic features had AUC of 0.787 [95% confidence interval (CI) 0.632–0.801], 0.765 (95% CI 0.683–0.847), and 0.754 (95% CI 0.666–0.842) in the prediction of high Ki‐67 expression in the training, internal validation and external validation cohort, respectively. The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with AUC of 0.801 (95% CI 0.726–0.876), 0.828 (95% CI 0.681–0.974), and 0.784 (95% CI 0.701–0.868) in the training, internal validation and external validation cohort respectively. Based on the Decision curve analysis, the radiomics nomogram was found to be clinically significant and useful. Conclusions: The radiomic signature from CE‐CT was significantly associated with Ki‐67 expression in GISTs. A nomogram consisted of radiomic signature, and tumor size had maximum accuracy in the prediction of Ki‐67 expression in GISTs. Results from our study provide vital insight to make important preoperative clinical decisions. [ABSTRACT FROM AUTHOR]