Development and validation of a nomogram for predicting the survival of patients with non-metastatic nasopharyngeal carcinoma after curative treatment

Background: The TNM staging system is far from perfect in predicting the survival of individual cancer patients because only the gross anatomy is considered. The survival rates of the patients who have the same TNM stage disease vary across a wide spectrum. This study aimed to develop a nomogram that incorporates other clinicopathologic factors for predicting the overall survival(OS) of non-metastatic nasopharyngeal carcinoma(NPC) patients after curative treatments.Methods: We retrospectively collected the clinical data of 1520 NPC patients who were diagnosed histologically between November 2000 and September 2003. The clinical data of a separate cohort of 464 patients who received intensity-modulated radiation therapy(IMRT) between 2001 and 2010 were also retrieved to examine the extensibility of the model. Cox regression analysis was used to identify the prognostic factors for building the nomogram. The predictive accuracy and discriminative ability were measured using the concordance index(c-index).Results: We identiied and incorporated 12 independent clinical factors into the nomogram. The calibration curves showed that the prediction of OS was in good agreement with the actual observation in the internal validation set and IMRT cohort. The c-index of the nomogram was statistically higher than that of the 7th edition TNM staging system for predicting the survival in both the primary cohort(0.69 vs. 0.62) and the IMRT cohort(0.67 vs. 0.63).Conclusion: We developed and validated a novel nomogram that outperformed the TNM staging system in predicting the OS of non-metastatic NPC patients who underwent curative therapy.

基于内镜图像深度学习的鼻咽恶性肿瘤检测模型的建立与验证

背景与目的由于鼻咽部解剖位置隐匿且腺体增生频发,活检时恶性肿瘤的阳性率较低,从而导致初诊时鼻咽恶性肿瘤确诊延时或漏诊。本文旨在建立一种人工智能工具——基于深度学习的内镜检查,来检测鼻咽恶性肿瘤。方法建立了一种基于内镜图像的鼻咽恶性肿瘤检测模型(endoscopic imagesbased nasopharyngeal malignancies detection model,eNPM-DM),该模型由基于空间结构的全卷积网络构成,采用单独训练集和验证集对分类和分割进行微调。总共收集了28,966张合格图像。其中,自2008年1月1日至2016年12月31日,从7951例个体中获得了27,536张经活检证实的图像,按照7∶1∶2的比例随机分为训练、验证和测试集。此外,将2017年1月1日到2017年3月31日获得的1430张图像纳入预测集,用以对建立模型的性能与肿瘤专家的评价进行比较。以鼻咽镜图像为背景,对自动分割和专家手工分割进行比较,采用dice相似系数(dice similarity coefficient,DSC)评价eNPM-DM从鼻咽部内镜图像的背景中自动分割出恶性肿瘤区域的效率。结果所有图像经过病理组织学验证,包括正常对照5713(19.7%)例、鼻咽癌(nasopharyngeal carcinoma,NPC)19,107(66.0%)例、其他恶性肿瘤335(1.2%)例和3811(13.2%)例良性病变。在测试集中,eNPM-DM检测恶性肿瘤的总准确率达88.7%[95%置信区间(confidence interval,CI):87.8%–89.5%]。在预测比较阶段,eNPM-DM表现优于专家:总准确率分别为88.0%(95%CI:86.1%–89.6%)和80.5%(95%CI:77.0%–84.0%)。eNPM-DM耗时更短(40 s vs. 110.0±5.8 min),且从背景中自动分割出鼻咽恶性肿瘤区域方面表现优秀,测试集和预测集中的平均DSC分别为0.78±0.24和0.75±0.26。结论 eNPM-DM在鼻咽肿块良性/恶性诊断分类方面优于肿瘤学家评估,并且实现了从鼻咽内镜图像背景中对恶性区域自动分割。

Magnetic Resonance Imaging Studies of Neurodegenerative Disease:From Methods to Translational Research

Neurodegenerative diseases(NDs)have become a significant threat to an aging human society.Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers.Magnetic resonance imaging(MRI)is a powerful tool for investigating structural and functional brain alterations in NDs.With the advantages of being non-invasive and non-radioactive,it has been frequently used in both animal research and large-scale clinical investigations.MRI may serve as a bridge connecting micro-and macro-level analysis and promoting bench-to-bed translational research.Nevertheless,due to the abundance and complexity of MRI techniques,exploiting their potential is not always straightforward.This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.

Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt.Here,we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.Methods:An endoscopic images-based nasopharyngeal malignancy detection model(eNPM-DM)consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation.Briefly,a total of 28,966 qualified images were collected.Among these images,27,536 biopsy-proven images from 7951 individuals obtained from January 1st,2008,to December 31st,2016,were split into the training,validation and test sets at a ratio of 7:1:2 using simple randomiza-tion.Additionally,1430 images obtained from January 1st,2017,to March 31st,2017,were used as a prospective test set to compare the performance of the established model against oncologist evaluation.The dice similarity coef-ficient(DSC)was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images,by comparing automatic segmentation with manual segmenta-tion performed by the experts.Results:All images were histopathologically confirmed,and included 5713(19.7%)normal control,19,107(66.0%)nasopharyngeal carcinoma(NPC),335(1.2%)NPC and 3811(13.2%)benign diseases.The eNPM-DM attained an overall accuracy of 88.7%(95%confidence interval(CI)87.8%-89.5%)in detecting malignancies in the test set.In the prospective comparison phase,eNPM-DM outperformed the experts:the overall accuracy was 88.0%(95%CI 86.1%-89.6%)vs.80.5%(95%CI 77.0%-84.0%).The eNPM-DM required less time(40 s vs.110.0±5.8 min)and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background,with an average DSC of 0.78±0.24 and 0.75±0.26 in the test and prospective test sets,respectively.Conclusions:The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant,and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.

Comprehensive profiling of EBV gene expression in nasopharyn- geal carcinoma through paired-end transcriptome sequencing

The latent expression pattern of Epstein-Barr Virus （EBV）igenes in nasopharyngeal carcinoma （NPC） has been extensively investigated, and the expression of several lytic genes in NPC has been reported. However, comprehensive information through EBV transcriptome analysis in NPC is limited. We performed paired-end RNA-seq to systematically and comprehensively characterize the expression of EBV genes in NPC tissue and C666-1 NPC cell line, which consistently carries EBV. In addition to the transcripts restricted to type II latency infection, the type Ⅲ latency EBNA3s genes and a substantial number of lytic genes, such as BZLF1, BRLF1, and BMRF1, were detected through RNA-seq and were further verified in C666-1 cells and NPC tissue through real- time PCR. We also performed clustering analysis to classify NPC patient groups in terms of EBV gene expression, which presented two subtypes of NPC samples. Results revealed interesting patterns of EBV gene expression in NPC patients. This clustering was correlated with many signaling pathways, such as those related to heterotrimeric G-protein signaling, inflammation mediated by chemokine and cytokine signaling, ribosomes, protein metabolism, influenza infection, and ECM-receptor interaction. Our combined findings suggested that the expression of EBV genes in NPC is restricted not only to type II latency genes but also to type Ⅲ latency and lyric genes. This study provided further insights into the potential role of EBV in the development of NPC.

Aberrant functional connectivity network in subjective memory complaint individuals relates to pathological biomarkers

Background:Individuals with subjective memory complaints(SMC)feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease(AD).However,the pathological mechanism underlying SMC remains unclear.We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.Methods:We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography(PET).Based on graph theory approaches,we detected local and global functional connectivity across the whole brain by using degree centrality(DC)and eigenvector centrality(EC)respectively.Additionally,we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid(CSF)data.The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition.Then,we correlated these network metrics with pathological results.Results:The SMC individuals showed higher DC in the bilateral hippocampus(HP)and left fusiform gyrus and lower DC in the inferior parietal region than controls.Across all subjects,the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau181.However,no significant between-group difference existed in EC and cerebral amyloid deposition.Conclusion:We found impaired local,but not global,intrinsic connectivity networks in SMC individuals.Given the relationships between DC value and tau level,we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.

Oscillation-specific nodal alterations in early to middle stages Parkinson’s disease

Background:Different oscillations of brain networks could carry different dimensions of brain integration.We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease(PD)across early stage to middle stage by using graph theory-based analysis.Methods:Eighty-eight PD patients including 39 PD patients in the early stage(EPD)and 49 patients in the middle stage(MPD)and 36 controls were recruited in the present study.Graph theory-based network analyses from three oscillation frequencies(slow-5:0.01–0.027 Hz;slow-4:0.027–0.073 Hz;slow-3:0.073–0.198 Hz)were analyzed.Nodal metrics(e.g.nodal degree centrality,betweenness centrality and nodal efficiency)were calculated.Results:Our results showed that(1)a divergent effect of oscillation frequencies on nodal metrics,especially on nodal degree centrality and nodal efficiency,that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed,which visually showed that network was perturbed in PD;(2)PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities,which was consistently detected within all three oscillation frequencies;(3)the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients;(4)logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level;(5)occipital disruption within high frequency(slow-3)made a significant influence on motor impairment which was dominated by akinesia and rigidity.Conclusions:Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.