Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence.

BACKGROUND: To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy. METHODS: Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction. RESULTS: RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score. CONCLUSIONS: AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

[Geographical Detection of Spatial Heterogeneity and Drivers of PM2.5 in the Yangtze River Economic Belt].

Based on ground monitoring data, we explored the spatiotemporal characteristics and drivers of PM2.5 in the Yangtze River Economic Belt (YREB) in 2018 using spatial autocorrelation analysis and geodetector modeling methods. The results showed that:① the PM2.5 concentration in the YREB posed the obvious characteristics of low values in summer and high values in winter, seasonal variation in spring and autumn, monthly U-shaped variation, and daily pulse variation. The low value area was mainly concentrated in the south bank of the upper reaches, whereas the high value area was located in the north of the middle-lower reaches of the YREB. ② PM2.5 pollution in the YREB had a stable positive spatial correlation, and the local association pattern showed a significant HH and LL spatial convergence. ③ The spatial correlation of PM2.5 in the YREB decreased with the increase in geographical distance, and its spatial autocorrelation threshold was approximately 870 km, within which the spatial agglomeration of PM2.5 pollution was strong. ④ The influences of natural and anthropogenic factors on PM2.5 had significant spatial differences. Altitude, relief, and population density were the high impact factors of PM2.5 pollution in the YREB. The interaction of factors had a far greater explanatory power on PM2.5 pollution than that of single factors. The dominant interaction factor was industrial structure ∩ altitude, which reflected the complexity of the drivers of air pollution in the YREB.

The value of the signal intensity of peritumoral tissue on Gd-EOB-DTPA dynamic enhanced MRI in assessment of microvascular invasion and pathological grade of hepatocellular carcinoma.

ABSTRACT: The aim of the study was to assess the potential role of preoperative gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) dynamic enhanced MR imaging for diagnosing microvascular invasion (MVI) and pathological grade of hepatocellular carcinoma (HCC).A total of 113 consecutive HCC patients confirmed by histopathology underwent preoperative Gd-EOB-DTPA dynamic enhanced MRI were included. Signal intensity (SI) of peritumoral, normal liver tissue and tumor parenchyma during arterial phase and hepatobiliary phase (HBP) were analyzed. The receiver operating characteristic (ROC) curves were performed to assess the potential diagnostic capability for MVI and pathological grade of HCC. Kaplan-Meier method was performed to estimate the recurrence-free survival rate and compared using the log rank test.SI ratio of peritumoral tissue to normal liver in arterial phase (SIAp/Al) was independently associated with MVI [odds ratio (OR) = 3.115, 95% confidence interval (CI): 1.867-5.198] and pathological grades (OR = 1.437, 95% CI: 1.042-1.981). The area under the curve (AUC) of SIAp/Al was equivalent to the SI of tumor parenchyma on arterial phase (SIAt) in distinguishing low and high pathological grades. However, the AUC of SIAp/Al (0.851) was larger than peritumoral hypointensity on HBP (0.668) for distinguishing MVI. The recurrence-free survival rate of HCC patients with SIAp/Al<1.1 was higher than HCC with SIAp/Al≥1.1(P = .025).The SIAp/Al in preoperative Gd-EOB-DTPA dynamic enhanced MR imaging is a potential diagnosis marker for MVI and pathological grade of HCC noninvasively. The higher SIAp/Al may predict the poor prognosis of HCC after surgery.

A Driver's Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model.

Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers' physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers' physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R-R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi'an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies.

[Research on fast detecting tomato seedlings nitrogen content based on NIR characteristic spectrum selection].

In order to improve the accuracy and robustness of detecting tomato seedlings nitrogen content based on near-infrared spectroscopy (NIR), 4 kinds of characteristic spectrum selecting methods were studied in the present paper, i. e. competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variables elimination (MCUVE), backward interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS). There were totally 60 tomato seedlings cultivated at 10 different nitrogen-treatment levels (urea concentration from 0 to 120 mg . L-1), with 6 samples at each nitrogen-treatment level. They are in different degrees of over nitrogen, moderate nitrogen, lack of nitrogen and no nitrogen status. Each sample leaves were collected to scan near-infrared spectroscopy from 12 500 to 3 600 cm-1. The quantitative models based on the above 4 methods were established. According to the experimental result, the calibration model based on CARS and MCUVE selecting methods show better performance than those based on BiPLS and SiPLS selecting methods, but their prediction ability is much lower than that of the latter. Among them, the model built by BiPLS has the best prediction performance. The correlation coefficient (r), root mean square error of prediction (RMSEP) and ratio of performance to standard derivate (RPD) is 0. 952 7, 0. 118 3 and 3. 291, respectively. Therefore, NIR technology combined with characteristic spectrum selecting methods can improve the model performance. But the characteristic spectrum selecting methods are not universal. For the built model based or single wavelength variables selection is more sensitive, it is more suitable for the uniform object. While the anti-interference ability of the model built based on wavelength interval selection is much stronger, it is more suitable for the uneven and poor reproducibility object. Therefore, the characteristic spectrum selection will only play a better role in building model, combined with the consideration of sample state and the model indexes.

[An international comparison of physician control systems].

This study compares physicians' regulations set by the United Kingdom, the United States, Canada and Germany which have typical healthcare systems. Physicians' regulations are defined in this study as four aspects: physicians' training and qualifications, career pathways, payment methods and behavior regulations. Strict access rules, practicing with freedom, different training models between general and special practitioners, health services priced by negotiations and regulations by professional organizations are the common features of physicians' regulations in these four western countries. Three aspects--introducing contract mechanism, enhancing the roles of professional organizations and extending physicians' practice space should be taken into account in China's future reform of physicians' regulations.

[Distribution and ecological risk assessment of polycyclic aromatic hydrocarbons in overlying waters and surface sediments from the Yangtze estuarine and coastal areas].

Polluted levels, temporal and spatial distributions of polycyclic aromatic hydrocarbons (PAHs) were studied in overlying water and surface sediments from nine typical sampling sites in the Yangtze estuarine and coastal areas. The results showed that PAHs concentrations in overlying waters and surface sediments in dry season (1988 ng/L and 1154 ng/g) were both higher than those in flood season (1727 ng/L and 605 ng/g). And phenanthrene (Phe) was dominant among PAH compounds. Temperature was the most important factor that controlled PAHs seasonal variation in overlying waters, while organic carbon and soot carbon influenced PAHs accumulation in surface sediments. Complicated hydrodynamic conditions and pollutant inputs caused by various anthropogenic activities not only affected on PAHs spatial distribution, but also led to complicated sources in the Yangtze estuarine and coastal areas. Ecological risk assessment indicated that PAHs in water-sediment system from the Yangtze estuarine and coastal areas might potentially damage the Yangtze estuary ecosystem to some extent. Some of PAH compounds in overlying waters had exceeded the ecotoxicological assessment standard or EPA National Recommended Water Quality Criteria, and BaP also exceeded the normal concentration of Chinese Environmental Quality Standards for surface water. Some of PAH compounds in surface sediments had exceeded the effects range low (ER-L) levels and ISQV-L values.