Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation.

BACKGROUND: Prenatal evaluation of fetal lung maturity (FLM) is a challenge, and an effective non-invasive method for prenatal assessment of FLM is needed. The study aimed to establish a normal fetal lung gestational age (GA) grading model based on deep learning (DL) algorithms, validate the effectiveness of the model, and explore the potential value of DL algorithms in assessing FLM. METHODS: A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41 + 6 weeks were analyzed in this study. There were no pregnancy-related complications that affected fetal lung development, and all infants were born without neonatal respiratory diseases. The images were divided into three classes based on the gestational week: class I: 20 to 29 + 6 weeks, class II: 30 to 36 + 6 weeks, and class III: 37 to 41 + 6 weeks. There were 3323, 2142, and 1548 images in each class, respectively. First, we performed a pre-processing algorithm to remove irrelevant information from each image. Then, a convolutional neural network was designed to identify different categories of fetal lung ultrasound images. Finally, we used ten-fold cross-validation to validate the performance of our model. This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA. This was used to establish a grading model. The performance of the grading model was assessed using accuracy, sensitivity, specificity, and receiver operating characteristic curves. RESULTS: A normal fetal lung GA grading model was established and validated. The sensitivity of each class in the independent test set was 91.7%, 69.8%, and 86.4%, respectively. The specificity of each class in the independent test set was 76.8%, 90.0%, and 83.1%, respectively. The total accuracy was 83.8%. The area under the curve (AUC) of each class was 0.982, 0.907, and 0.960, respectively. The micro-average AUC was 0.957, and the macro-average AUC was 0.949. CONCLUSIONS: The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs, which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy. The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.

Model for liver hardness using two-dimensional shear wave elastography, durometer, and preoperative biomarkers.

BACKGROUND: Post-hepatectomy liver failure (PHLF) increases morbidity and mortality after liver resection for patients with advanced liver fibrosis and cirrhosis. Preoperative liver stiffness using two-dimensional shear wave elastography (2D-SWE) is widely used to evaluate the degree of fibrosis. However, the 2D-SWE results were not accurate. A durometer measures hardness by quantifying the ability of a material to locally resist the intrusion of hard objects into its surface. However, the durometer score can only be obtained during surgery. AIM: To measure correlations among 2D-SWE, palpation by surgeons, and durometer-measured objective liver hardness and to construct a liver hardness regression model. METHODS: We enrolled 74 hepatectomy patients with liver hardness in a derivation cohort. Tactile-based liver hardness scores (0-100) were determined through palpation of the liver tissue by surgeons. Additionally, liver hardness was measured using a durometer. Correlation coefficients for durometer-measured hardness and preoperative parameters were calculated. Multiple linear regression models were constructed to select the best predictive durometer scale. Receiver operating characteristic (ROC) curves and univariate and multivariate analyses were used to calculate the best model's prediction of PHLF and risk factors for PHLF, respectively. A separate validation cohort (n = 162) was used to evaluate the model. RESULTS: The stiffness measured using 2D-SWE and palpation scale had good linear correlation with durometer-measured hardness (Pearson rank correlation coefficient 0.704 and 0.729, respectively, P < 0.001). The best model for the durometer scale (hardness scale model) was based on stiffness, hepatitis B virus surface antigen, and albumin level and had an R 2 value of 0.580. The area under the ROC for the durometer and hardness scale for PHLF prediction were 0.807 (P = 0.002) and 0.785 (P = 0.005), respectively. The optimal cutoff value of the durometer and hardness scale was 27.38 (sensitivity = 0.900, specificity = 0.660) and 27.87 (sensitivity = 0.700, specificity = 0.787), respectively. Patients with a hardness scale score of > 27.87 were at a significantly higher risk of PHLF with hazard ratios of 7.835 (P = 0.015). The model's PHLF predictive ability was confirmed in the validation cohort. CONCLUSION: Liver stiffness assessed by 2D-SWE and palpation correlated well with durometer hardness values. The multiple linear regression model predicted durometer hardness values and PHLF.

[MRI scan of striatum volume in healthy adults].

OBJECTIVE: To examine the age, sex, and hemispheric differences in volume of the striatum by MRI in healthy adults. METHODS: The volumes of the bilateral caudate nucleus and putamen were measured on MR images in 100 healthy right-handed adults (18-70 y). RESULTS: The volume of bilateral caudate nucleus and putamen in healthy adults was (8.42 +/-0.88) cm(3) and (8.90 +/-0.89) cm(3), which were decreased with aging (for caudate nucleus r=-0.727, P<0.001; for putamen r=-0.709, P<0.001). The average annual shrinkage rate was 0.52 % in the caudate nucleus and 0.50 % in the putamen. There were no gender differences in the volume of the striatum, however, the age-related shrinkage of the striatum was more evident in men than that in women. The volume of the left caudate nucleus (t=4.43, P<0.001) and the putamen (t=4.88, P<0.001) was greater than that of its right counterpart. CONCLUSION: Bilateral age-related shrinkage of the striatum is found in healthy adults, which is more evident in men than that in women. In both sexes, significant leftward asymmetry in volume of the caudate nucleus and the putamen is found.

[Role of activated brain regions during tactile stimulation in early Parkinson's disease: a preliminary fMRI study].

OBJECTIVE: To investigate the role of activated brain regions in Parkinson's disease (PD) during tactile stimulation. METHODS: Twenty-one patients with early PD[mean age (60.43 +/-9.65)y] and twenty-two age-matched healthy controls [mean age (59.23 +/-11.12)y] were enrolled in the study. All the patients were tested by the United Parkinson Disease Rating Scale (UPDRS) as the evaluation of the disease severity. A block design was used when the finger tactile stimulation was given to the subjects. The hypoactive and hyperactive regions of PD patients were confirmed first, which were identified as regions of interest (ROI). ROI analysis was performed to quantify BOLD signal changes when subjects were under tactile stimulation. The correlations of signal changes with disease severity, and correlations of hyperactive with hypoactive regions were analyzed. RESULTS: Right primary sensory and motor cortex, right supplementary motor area (SMA), bilateral caudates, bilateral precuneus, bilateral occipital visual cortex and left middle temporal gyrus were hypoactivated in PD, while right prefrontal cortex (PFC) and right caudate were hyperactivated. The hypoactivation of right SMA was negatively correlated with disease severity. All the hypoactive and hyperactive regions were positively correlated with activation of caudates. There was a positive correlation between hyperactive PFC and hypoactive regions. CONCLUSIONS: The signal change of SMA is directly related to disease severity in early PD, and caudates may play a significant role in PD tactile processing. The hyperactivation of PFC may be not a compensation but a pathophysiological change related to PD neural dysfunction.

Pre-operative Detection of Liver Fibrosis in Hepatocellular Carcinoma Patients Using 2D Shear Wave Elastography: Where to Measure?

The aim of this study was to pre-operatively investigate the diagnostic performance of 2D shear wave elastography (2D-SWE) for staging liver fibrosis and inflammation in patients with hepatocellular carcinoma (HCC) who then undergo surgery and to determine the optimal locations for measurement. In total, 106 patients were enrolled in this prospective study from March 2017 to May 2018. Two-dimensional SWE was used to measure liver stiffness (LS) in each patient 0-1, 1-2 and 2-5 cm from the tumor border (groups 1, 2 and 3, respectively). Spearman's correlation was used to evaluate the relationships between LS and hepatic fibrosis and between LS and inflammation. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic accuracy of 2D-SWE. The technical success rate of SWE in tissue distant from the tumor (group 3) was significantly higher than that in peri-tumoral tissue (groups 1 and 2) (p < 0.001). Moreover, the area under the ROC for diagnosing cirrhosis (F4) and severe inflammation (A3) was higher for group 3 than for groups 1 and 2. Our results suggest that 2D-SWE is a helpful approach to assessment of hepatic fibrosis in HCC patients before hepatic resection. We found that to achieve a superior success rate and preferable diagnosis accuracy for patients with HCC, LS measurement should be performed 2-5 cm from the tumor margin.

Geometric optimization of a mathematical model of radiofrequency ablation in hepatic carcinoma.

Radio frequency ablation (RFA) is an effective means of achieving local control of liver cancer. It is a particularly suitable mode of therapy for small and favorably located tumors. However, local progression rates are substantially higher for large tumors (>3.0 cm). In the current study, we report on a mathematical model based on geometric optimization to treat large liver tumors. A database of mathematical models relevant to the configuration of liver cancer was also established. The specific placement of electrodes and the frequency of ablation were also optimized. In addition, three types of liver cancer lesion were simulated by computer guidance incorporating mathematical models. This approach can be expected to provide a more effective and rationale mechanism for employing RFA in the therapy of hepatic carcinoma.