基于R语言BP神经网络瓦里安NovalisTx直线加速器MLC系统故障预测模型研究

目的 构建并研究瓦里安NovalisTx直线加速器MLC系统故障预测BP神经网络模型。方法 取加速器临床使用18个月MLC系统故障统计数据为研究对象,以加速器使用总时间、月治疗患者数量、日均开机工作时间、RapidArc计划数量及加速器保养后时间间隔为输入故障因素,以故障频次预测为输出结果,采用R语言AMORE包构建MLC系统故障预测BP神经网络模型并对其进行仿真验证。结果 模型采用3层网络实现输入输出转换,其输入层5个节点、隐层13个节点、输出层1个节点;输入层至隐层、隐层至输出层分别选用tansig、purelin传递函数;模型设定最大训练学习次数150次,实际使用111次,设定误差3%,实际误差2.7%,表明其收敛较好。该模型对18个月临床故障数据仿真验证结果表明预测数据与实际数据较为接近。结论 基于R语言BP神经网络故障预测模型实现了MLC系统故障因素与故障频次间映射关系描述,可为设备故障规律了解和备件库存管理提供参考。     

Topoisomerase II alpha amplification may predict benefit from adjuvant anthracyclines in HER2 positive early breast cancer

The purpose of the study was to evaluate the use of metabolic phenotype, described by high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS), as a tool for prediction of histological grade, hormone status, and axillary lymphatic spread in breast cancer patients. Biopsies from breast cancer (n = 91) and adjacent non-involved tissue (n = 48) were excised from patients (n = 77) during surgery. HR MAS MR spectra of intact samples were acquired. Multivariate models relating spectral data to histological grade, lymphatic spread, and hormone status were designed. The multivariate methods applied were variable reduction by principal component analysis (PCA) or partial least-squares regression-uninformative variable elimination (PLS-UVE), and modelling by PLS, probabilistic neural network (PNN), or cascade correlation neural network. In the end, model verification by prediction of blind samples (n = 12) was performed. Validation of PNN training resulted in sensitivity and specificity ranging from 83 to 100% for all predictions. Verification of models by blind sample testing showed that hormone status was well predicted by both PNN and PLS (11 of 12 correct), lymphatic spread was best predicted by PLS (8 of 12), whereas PLS-UVE PNN was the best approach for predicting grade (9 of 12 correct). MR-determined metabolic phenotype may have a future role as a supplement for clinical decision-making-concerning adjuvant treatment and the adaptation to more individualised treatment protocols.     

Comparing the predictive value of neural network models to logistic regression models on the risk of death for small-cell lung cancer patients

Cancer is one of the leading causes of mortality in the developed world, and prognostic assessment of cancer patients is indispensable in medical care. Medical researchers are accustomed to using regression models to predict patient outcomes. Neural networks have been proposed as an alternative with great potential. Nonetheless, empirical evidence remains lacking to support the application of this technique as the appropriate method to investigate cancer prognosis. Utilizing data on patients from two National Cancer Institute of Canada clinical trials, we compared predictive accuracy of neural network models and logistic regression models on risk of death of limited-stage small-cell lung cancer patients. Our results suggest that neural network and logistic regression models have similar predictive accuracy. The distributions of individual predicted probabilities are very similar. On occasion, however, the prediction pairs are quite different, suggesting that they do not always give the same interpretations of the same variables.     

Artificial neural networks applied to survival prediction in breast cancer

In this study, we evaluated the accuracy of a neural network in predicting 5-, 10- and 15-year breast-cancer-specific survival. A series of 951 breast cancer patients was divided into a training set of 651 and a validation set of 300 patients. Eight variables were entered as input to the network: tumor size, axillary nodal status, histological type, mitotic count, nuclear pleomorphism, tubule formation, tumor necrosis and age. The area under the ROC curve (AUC) was used as a measure of accuracy of the prediction models in generating survival estimates for the patients in the independent validation set. The AUC values of the neural network models for 5-, 10- and 15-year breast-cancer-specific survival were 0.909, 0.886 and 0.883, respectively.The corresponding AUC values for logistic regression were 0.897, 0.862 and 0.858. Axillary lymph node status (N0 vs. N+) predicted 5-year survival with a specificity of 71% and a sensitivity of 77%. The sensitivity of the neural network model was 91% at this specificity level. The rate of false predictions at 5 years was 82/300 for nodal status and 40/300 for the neural network. When nodal status was excluded from the neural network model, the rate of false predictions increased only to 49/300 (AUC 0. 877). An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction. The consistently high accuracy over time and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction. Copyright Copyright 1999 S. Karger AG, Basel     

基因芯片技术在肿瘤研究中的进展

目的:肿瘤的发生发展是多基因、多阶段的复杂过程,目前对肿瘤的研究仅局限于某一个或几个基因和它们之间简单的调控关系,由于试验技术的限制,无法全面认识肿瘤发生发展过程中各基因复杂的相互调控关系。基因芯片技术可同时检测少则几十,多则几万个基因的差异表达状态,并分析它们之间的相互作用关系,为全面了解肿瘤发生、发展提供了一件实验利器。     

Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

PURPOSE: New techniques for the prediction of tumor behavior are needed, because statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. Whereas artificial neural networks (ANN), the best-studied form of AI, have been used successfully, its hidden networks remain an obstacle to its acceptance. Neuro-fuzzy modeling (NFM), another AI method, has a transparent functional layer and is without many of the drawbacks of ANN. We have compared the predictive accuracies of NFM, ANN, and traditional statistical methods, for the behavior of bladder cancer. EXPERIMENTAL DESIGN: Experimental molecular biomarkers, including p53 and the mismatch repair proteins, and conventional clinicopathological data were studied in a cohort of 109 patients with bladder cancer. For all three of the methods, models were produced to predict the presence and timing of a tumor relapse. RESULTS: Both methods of AI predicted relapse with an accuracy ranging from 88% to 95%. This was superior to statistical methods (71-77%; P < 0.0006). NFM appeared better than ANN at predicting the timing of relapse (P = 0.073). CONCLUSIONS: The use of AI can accurately predict cancer behavior. NFM has a similar or superior predictive accuracy to ANN. However, unlike the impenetrable "black-box" of a neural network, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions. This technique could be used widely in a variety of areas of medicine.     

Artificial neural network for prediction of distant metastasis in colorectal cancer

Background and Objectives: Artificial neural networks (ANNs) are flexible and nonlinear models which can be used by clinical oncologists in medical research as decision making tools. This study aimed to predict distant metastasis (DM) of colorectal cancer (CRC) patients using an ANN model. Methods: The data of this study were gathered from 1219 registered CRC patients at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran (January 2002 and October 2007). For prediction of DM in CRC patients, neural network (NN) and logistic regression (LR) models were used. Then, the concordance index (C index) and the area under receiver operating characteristic curve (AUROC) were used for comparison of neural network and logistic regression models. Data analysis was performed with R 2.14.1 software. Results: The C indices of ANN and LR models for colon cancer data were calculated to be 0.812 and 0.779, respectively. Based on testing dataset, the AUROC for ANN and LR models were 0.82 and 0.77, respectively. This means that the accuracy of ANN prediction was better than for LR prediction. Conclusion: The ANN model is a suitable method for predicting DM and in that case is suggested as a good classifier that usefulness to treatment goals.     

The effectiveness of artificial neural networks in evaluating treatment plans for patients requiring external beam radiotherapy

This study was designed to determine the ability of a neural network to use data summarized in artificial generated dose volume histograms (DVH) for the rectum to evaluate and compare different patient treatment plans for the treatment of localized prostate cancer. One radiotherapist evaluated 250 artificially generated DVHs representing the distribution of a dose of ration throughout the rectum during external radiotherapy for patients with prostatic adenocarcinoma (PAC). The data were also analyzed using the Lyman NTCP-model for assessing complication probabilities. A neural network consisting of 10 input nodes and one output node was trained to categorize the plans according to the radiotherapist's score. The volume in each isodose was used as input, and the risk for a severe complication was presented as output. The classifications made by the neural network matched those determined by the radiotherapist and the NTCP-model. All three techniques showed a high correlation between each other. Artificially generated dose volume histograms (DVH) for the rectum can be used for training a neural network for scoring rectal DVHs in treatment plans for localized prostate cancer.     

miR-29在肿瘤中的研究进展*

微小RNA（microRNA,miRNA）是生物体内一种重要的基因调控分子,参与多种疾病的发生过程,与肿瘤关系密切,已成为近年来肿瘤学研究的新方向。研究发现,miRNA-29（miR-29）在多种肿瘤中均有涉及,具有抑癌和促癌双重作用,且其表达水平在肿瘤组织和非肿瘤组织中也存在差异。因此,miR-29有望成为恶性肿瘤诊断及预后的生物标记物或治疗靶点。本文就miR-29家族在恶性肿瘤中的研究进展进行综述。      

Comparison of the Performance of Log-logistic Regression nd Artificial Neural Networks for Predicting Breast Cancer elapse

Background: Breast cancer is the most common cancers in female populations. The exact cause is notknown, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM)is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificialneural network (ANN) models have been increasingly applied to predict survival data. The present research wasconducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer(BC) survival. Materials and Methods: A historical cohort study was established with 104 patients sufferingfrom BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas underthe receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzedusing R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statisticallyhigher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between theperformance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the abilityof prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction ofsurvival in field of breast cancer is suggested.     

Lung Cancer Risk Prediction Method Based on Feature Selection and Artificial Neural Network

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisherand ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer riskprediction. The process featured two steps, firstly choosing the risk factors by combining two feature selectionalgorithms, then providing the predictive value by neural network. Based on the method framework, an algorithmLCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practicalapplications. The proposed method is suitable for health monitoring and self-testing. Experiments showed itcan actually provide satisfactory accuracy under low dimensions of risk factors.     

Artificial neural networks for diagnosis and survival prediction in colon cancer

ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature shows that applications of these networks have improved the accuracy of colon cancer classification and survival prediction when compared to other statistical or clinicopathological methods. Accuracy, however, must be exercised when designing, using and publishing biomedical results employing machine-learning devices such as ANNs in worldwide literature in order to enhance confidence in the quality and reliability of reported data.     

人工神经网络在宫颈癌预后预测中的应用

目的探讨人工神经网络在宫颈癌术后5年生存期预测中的应用。方法收集125例宫颈癌患者的临床病理资料及治疗随访信息,按照4∶1的比例,随机分为训练组(100例)和测试组(25例),分别采用Logistics回归分析,筛选单因素分析有统计学意义的因素建立Logistics回归模型和概率神经网络模型（PNN）,用训练组训练网络模型,用测试组检测网络模型。结果PNN模型的准确性92%,敏感度为75%,特异性为95.23%,Logistics回归模型的准确性为84%,敏感度为50.0%,特异性为82.61%。结论神经网络在生存分析中有很大的灵活性;在模型中可以容纳非线性效应,不需要对数据的随机特征如分布等作出假设,不要求满足H0假定,具有较广泛的应用前景。     

微卫星不稳定性在胃癌预后及疗效预测中的意义

微卫星不稳定性(MSI)是由DNA错配修复(MMR)蛋白功能缺陷导致,与多种肿瘤发生发展、预后及疗效预测密切相关。微卫星高度不稳定(MSI-H)与错配修复蛋白缺乏(dMMR)可能是胃癌患者预后良好的预测因素,也可能是可切除胃癌化疗疗效的负性预测因素;同时MSI-H/dMMR是晚期胃癌免疫检查点抑制剂有效治疗的预测标志物,但对于晚期胃癌姑息化疗的预测作用尚未明确。文章就MSI/MMR状态与胃癌预后及疗效预测方面的相关性研究进展进行阐述。     

Uncertainties in model-based outcome predictions for treatment planning

Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose–volume outcome model predictions.

Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty (“noise”) is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data.

Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment plans can be ranked based on the probability that one plan is superior to another. Thus, reliability of plan ranking could also be assessed.

Conclusions: A comprehensive framework for incorporating uncertainties into treatment-plan-specific outcome predictions is described. Uncertainty histograms for continuous variable endpoint models provide a straightforward method for visual review of the reliability of outcome predictions for each treatment plan.     

肿瘤代谢在胶质瘤诊治中的研究进展

胶质瘤是一种异质性的中枢神经系统肿瘤,尽管包括手术、放化疗以及电场治疗在内的综合手段已经取得了极大的进步,但患者的预后仍然极差。代谢重编程是肿瘤细胞的标志之一,在胶质瘤中,异柠檬酸脱氢酶1的突变已经成为肿瘤分类的依据,同时在肿瘤细胞内存在明显的糖、脂质、氨基酸等代谢异常。此外。利用肿瘤代谢的诊断成像及治疗已经开始改善患者的预后。对胶质瘤的代谢重编程的研究进展进行综述,说明驱动胶质瘤发生发展的代谢变化,并对代谢在诊断及治疗的进展进行总结,以期为胶质瘤的诊断、治疗和预后提供帮助。     

Artificial intelligence for predicting recurrence-free probability of non-invasive high-grade urothelial bladder cell carcinoma

The objective of our study was to define a neural network for predicting recurrence and progression-free probability in patients affected by recurrent pTaG3 urothelial bladder cancer to use in everyday clinical practice. Among all patients who had undergone transurethral resection for bladder tumors, 143 were finally selected and enrolled. Four follow-ups for recurrence, progression or survival were performed at 6, 9, 12 and 108 months. The data were analyzed by using the commercially available software program NeuralWorks Predict. These data were compared with univariate and multivariate analysis results. The use of Artificial Neural Networks (ANN) in recurrent pTaG3 patients showed a sensitivity of 81.67% and specificity of 95.87% in predicting recurrence-free status after transurethral resection of bladder tumor at 12 months follow-up. Statistical and ANN analyses allowed selection of the number of lesions (multiple, HR=3.31, p=0.008) and the previous recurrence rate (>or=2/year, HR=3.14, p=0.003) as the most influential variables affecting the output decision in predicting the natural history of recurrent pTaG3 urothelial bladder cancer. ANN applications also included selection of the previous adjuvant therapy. We demonstrated the feasibility and reliability of ANN applications in everyday clinical practice, reporting a good recurrence predicting performance. The study identified a single subgroup of pTaG3 patients with multiple lesions, >or=2/year recurrence rate and without any response to previous Bacille Calmette-Guérin adjuvant therapy, that seem to be at high risk of recurrence.     

ONECUT2 基因在人胃癌中的表达及其临床意义

目的：探讨ONECUT2（one cut homeobox 2,OC-2）基因在人胃癌组织中的表达水平及其临床意义。方法：基于生物信息学技术全面检索Oncomine、GEPIA、CCLE、EBI 数据库,分析OC-2 在胃癌和其他种类肿瘤中的表达水平,用Kmplot 数据库验证其表达水平与胃癌患者预后的关系,用STRING数据库构建蛋白互作网络（protein protein interaction network,PPI network）,分析与胃癌相关的OC-2 共表达基因。结果：在OC-2 差异表达的不同种类肿瘤中,其表达水平一般上调。在胃癌组织和细胞中OC-2 表达水平均显著升高（均P<0.05）,且可能与组织分型和肿瘤分期无关（均P>0.05）。OC-2 表达水平与胃癌患者的预后有关,OC-2 低表达组胃癌患者的中位总生存期和中位无病生存期均显著高于高表达组（40.0 vs 26.5 个月,26.2 vs 16.1 个月;均P<0.01）。筛查获得了15 个OC-2 的共表达基因;构建的PPI 网络预测了30 个功能蛋白与OC-2 蛋白相互作用,其中有11 个蛋白基因也与胃癌的发生发展有关,Pearson 相关分析后得出4个与OC-2密切正相关的蛋白基因：PDX1（R=0.49）、CREB1（R=0.31）、MAPK1（R=0.26）、CTSS（R=0.25）。结论：OC-2 在胃癌发生发展及侵袭转移过程中可能起重要的作用,有望成为胃癌诊治和判断预后的重要指标和新的筛查靶点。     

Development and validation of a method for using breast core needle biopsies for gene expression microarray analyses.

PURPOSE: Gene expression microarray technologies have the potential to define molecular profiles that may identify specific phenotypes(diagnosis), establish a patient's expected clinical outcome (prognosis), and indicate the likelihood of a beneficial effect of a specific therapy (prediction). We wished to develop optimal tissue acquisition, processing, and analysis procedures for exploring the gene expression profiles of breast core needle biopsies representing cancer and noncancer tissues. EXPERIMENTAL DESIGN: Human breast cancer xenografts were used to evaluate several processing methods for prospectively collecting adequate amounts of high-quality RNA for gene expression microarray studies. Samples were assessed for the preservation of tissue architecture and the quality and quantity of RNA recovered. An optimized protocol was applied to a small study of core needle breast biopsies from patients, in which we compared the molecular profiles from cancer with those from noncancer biopsies. Gene expression data were obtained using Research Genetics, Inc. Named Genes cDNA microarrays. Data were visualized using simple hierarchical clustering and a novel principal component analysis-based multidimensional scaling. Data dimensionality was reduced by simple statistical approaches. Predictive neural networks were built using a multilayer perceptron and evaluated in an independent data set from snap-frozen mastectomy specimens. RESULTS: Processing tissue through RNALater preserves tissue architecture when biopsies are washed for 5 min on ice with ice-cold PBS before histopathological analysis. Cell margins are clear, tissue folding and fragmentation are not observed, and integrity of the cores is maintained, allowing optimal pathological interpretation and preservation of important diagnostic information. Adequate concentrations of high-quality RNA are recovered; 51 of 55 biopsies produced a median of 1.34 microg of total RNA (range, 100 ng to 12.60 microg). Snap-freezing or the use of RNALater does not affect RNA recovery or the molecular profiles obtained from biopsies. The neural network predictors accurately discriminate between predominantly cancer and noncancer breast biopsies. CONCLUSIONS: The approaches generated in these studies provide a simple, safe, and effective method for prospectively acquiring and processing breast core needle biopsies for gene expression studies. Gene expression data from these studies can be used to build accurate predictive models that separate different molecular profiles. The data establish the use and effectiveness of these approaches for future prospective studies.