Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Comparative Study of Modified Posterior Operation to Treat Kümmell's Disease

The present study aimed at examining the curative effect of modified posterior operation on treatment of Kümmell''s disease.About 30 patients of Kümmell''s disease with complete image and clinical data treated during June 2004 to July 2013 were conducted with anterior and posterior approaches, respectively. Kyphotic Cobb angle, fractured vertebra wedge angle, and the anterior and posterior heights of fractured vertebra were all measured through x-ray before and after operation, and the pain visual analog scale (VAS) was determined for evaluating the effect of operations. The injury and restoration of neurological function were assessed using Frankel classification.Patients in group A were treated with anterior operation, whereas group B was posterior operation. Postoperatively, VAS score, kyphotic Cobb angle, anterior vertebra height, and pathologic vertebra wedge angle were all significantly improved in patients with Kümmell''s disease receiving modified posterior operation (group B). Similar results were also observed in patients with anterior operation. The improvement of VAS and correction rate of kyphotic Cobb angle indicated equivalent effects of posterior and anterior operations. Meanwhile, alleviated neurological function damage was observed in 2 groups. Relevant factor analysis illustrated that there was no significant correlation of the severity and improvement rate of pain symptoms with age, medical history, anterior and posterior vertebra heights, kyphotic Cobb angle, and pathological vertebra wedge angle.Compared with traditional anterior approach, modified posterior operation, adopting transpedicular vertebral body grafting combined with vertebral pedicle screw fixation, could produce equivalent effects on kyphosis correction, pain relief, and improvement of neurological function in patients with Kümmell''s disease.     

Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure

BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894; 95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy.     

甲状腺疾病患者血清 Tg-CIC 中 Tg-Ab 水平检测及相关临床研究

目的　对血清甲状腺球蛋白免疫复合物 (thyroglobulin circulating immune complex, Tg-CIC) 中抗甲状腺球蛋白抗体 (anti-thyroglobulin antibody, Tg-Ab) 含量检测，探讨 Tg-CIC 在甲状腺疾病中的临床以及流行病学意义。方法　收集甲状腺疾病患者血清标本 187 例，每例血清标本分为两组 ( 实验组和空白对照组 )，采用免疫复合物解离专利技术对甲状腺疾病患者血清标本 Tg-CIC 进行分离、解离，通过检测 Tg-CIC 解离后的 Tg-Ab 含量来间接反映甲状腺疾病患者血清中Tg-CIC 水平，按照不同的甲状腺疾病进行分组并对实验结果分析讨论。结果　不同甲状腺患者血清中 Tg-CIC 总阳性率达 92.51%，不同甲状腺疾病血清中 Tg-CIC 的阳性率差异无统计学意义 (χ?=2.917, P>0.05)。不同甲状腺疾病患者间血清游离 Tg-Ab 含量差异无统计学意义 (H=3.882, P>0.05)，不同甲状腺疾病患者间血清 CIC 中 Tg-Ab 含量差异无统计学意义(H=5.5842, P>0.05)。不同甲状腺疾病患者中，甲状腺功能亢进、甲状腺炎以及甲状腺结节患者血清游离 Tg-Ab 的含量与血清 CIC 中的 Tg-Ab 含量呈正相关 (P<0.05)，甲状腺功能减退、甲状腺肿瘤患者血清游离 Tg-Ab 的含量与血清 CIC 中的Tg-Ab 含量不相关 (P>0.05)。结论　在甲状腺疾病患者血清中，大部分可检出 Tg-CIC，与疾病类型无关。而甲状腺疾病患者中，甲状腺功能亢进、甲状腺炎以及甲状腺结节患者血清游离 Tg-Ab 含量与血清 CIC 中 Tg-Ab 含量存在相关性，为我们进一步了解 Tg-CIC 与甲状腺疾病的发生、发展的相关性研究奠定了基础，提供了新的研究视角。     

Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot–savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm³. With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.