Search

Despite recent advances in surgical techniques and perinatal management in obstetrics for reducing intraoperative bleeding, blood transfusion may occur during a cesarean section (CS). This study aims to identify machine learning models with an optimal diagnostic performance for intraoperative transfusion prediction in parturients undergoing a CS. Additionally, to address model performance degradation due to data imbalance, this study further investigated the variation in predictive model performance depending on the ratio of event to non-event data (1:1, 1:2, 1:3, and 1:4 model datasets and raw data).The area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) were evaluated to compare the predictive accuracy of different machine learning algorithms, including XGBoost, K-nearest neighbor, decision tree, support vector machine, multilayer perceptron, logistic regression, random forest, and deep neural network. We compared the predictive performance of eight prediction algorithms that were applied to five types of datasets. The intraoperative transfusion in maternal CS was 7.2% (1020/14,254). XGBoost showed the highest AUROC (0.8257) and AUPRC (0.4825) among the models. The most significant predictors for transfusion in maternal CS as per machine learning models were placenta previa totalis, haemoglobin, placenta previa partialis, and platelets. In all eight prediction algorithms, the change in predictive performance based on the AUROC and AUPRC according to the resampling ratio was insignificant. The XGBoost algorithm exhibited optimal performance for predicting intraoperative transfusion. Data balancing techniques employed to alter the event data composition ratio of the training data failed to improve the performance of the prediction model. [ABSTRACT FROM AUTHOR]

Objective: To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set., Materials and Methods: Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine., Results: Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal ( p < 0.001) and supramarginal gyrus ( p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%)., Conclusion: Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer's disease.

Objective: To simulate the B 1 -inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Materials and Methods: B 1 -inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast R 1 , contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters (K trans , v e , and v p )., Results: B 1 -inhomogeneity resulted in -23-5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: -16.7-61.8% and -16.7-61.8% for the pre-contrast R 1 , -1.0-0.3% and -5.2-1.3% for the contrast-enhancement ratio, and -14.2-58.1% and -14.1-57.8% for the Gd-concentration, respectively. These resulted in -43.1-48.4% error for K trans , -32.3-48.6% error for the v e , and -43.2-48.6% error for v p . The pre-contrast R 1 was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a -10% FA error led to a 23.6% deviation in the pre-contrast R 1 , -0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a -10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were -23.7% for K trans , -23.7% for v e , and -23.7% for v p ., Conclusion: Even a small degree of B 1 -inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast R 1 calculations to FA deviations is a significant cause of the miscalculation.

Objective: The purpose of this article is to quantitatively assess the rate of resolution of clot burden detected on pulmonary CT angiography (CTA) in patients with acute pulmonary embolism (PE)., Materials and Methods: We evaluated 111 consecutive patients (55 men and 56 women) in a retrospective cohort who were diagnosed with PE by pulmonary CTA and had at least one follow-up pulmonary CTA within 1 year. Two radiologists in consensus measured the volume of each clot using a semiautomated quantification program. Semiquantitative measures of clot burden were also computed. The resolution rates of the total clot volume, as well as clot volumes of the central (main and lobar) and peripheral vessels (segmental and subsegmental), were analyzed., Results: The mean (± SD) clot volume per study was 3403.3 ± 6505.6 mm(3) at baseline and 531.6 ± 2383.5 mm(3) at the follow-up pulmonary CTA. Overall, 85 patients (77% ) showed complete resolution at the follow-up pulmonary CTA. Complete resolution was seen in 17 of 30 patients (56.7%) at a follow-up interval of 1-14 days, in 24 of 31 patients (77.4%) at 29-90 days, and in 32 of 34 patients (94.1%) after 90 days. The total clot volume measurements summed for all patients decreased by 78% (central clot, 69.4%; peripheral clot, 86.0%) at 14 days, by 96.6% (central clot, 93.4%; peripheral clot, 100%) at 90 days, and by 97.7% (central clot, 95.9%; peripheral clot, 100%) after 90 days., Conclusion: Clot burden resolved completely in 77% of patients during the follow-up period. Our analysis showed that clots resolved faster in the peripheral arteries than in the central pulmonary arteries.

Objective: The purpose of our study was to assess the accuracy and reproducibility of clot burden quantification with pulmonary CT angiography (CTA)., Materials and Methods: A semiautomated program was developed for segmentation and volumetric quantification of pulmonary embolus with pulmonary CTA. The accuracy of this measurement method was assessed using two pulmonary embolus phantoms. Reproducibility of the measurement method was assessed using clinical pulmonary CTA in 30 patients (16 women, 14 men; mean age, 62 years) with pulmonary embolism (PE). Two observers segmented and measured the volume of blood clot from pulmonary CTA images twice at two separate sessions. Accuracy was evaluated by the relative volume measurement error. Intra- and interobserver reliability were evaluated using intraclass correlation coefficient (ICC); agreement between measurements within and between the two observers was assessed using Bland-Altman analysis., Results: Mean relative measurement error from the two phantoms was less than 1% for both observers. A total of 60 emboli were measured from the 30 patients. The intraobserver ICC was 0.990 for observer 1 and 0.999 for observer 2; interobserver ICC was 0.994 for session 1 and 0.989 for session 2. ICC for all four clot measurements was 0.988. Mean volume measurement difference for intraobserver agreement was 0.9% for observer 1 and 0.3% for observer 2, and interobserver agreement was -5.1% for session 1 and -5.8% for session 2., Conclusion: Blood clot burden can be quantified with a high degree of accuracy and reproducibility from pulmonary CTA images using a semiautomated segmentation method.