一个以前列腺特异抗原密度和穿刺评分为基础的中国人群中前列腺癌根治术后病理升级预测模型的建立

目的：分析前列腺癌根治术后病理得分较穿刺得分增加的原因，并建立一个可以预测中国人群中前列腺癌根治术后病理升 级的模型。方法：以2008 年8 月至2013 年12 月在我院泌尿科行前列腺癌根治性切除术的264例患者的临床资料为基础，根据 术前和术后患者病理得分的变化将其分为升级组和未升级组。运用单因素和多因素logistic 回归分析病理升级的原因，并通过多 因素回归系数建立预测病理升级的诺模图。结果：264 例患者中，共238 例最终纳入统计分析，多因素logistic 回归分析显示前列 腺特异抗原密度(0R=3.854，P=0.001 )和穿刺Gleason(≤ 6)评分是中国人群中前列腺癌根治术后病理升级的独立危险因素。前列腺 特异抗原密度和穿刺得分的ROC 最佳截断取值为0.37 ng/ml 2和8 分。运用上述两个变量建立了一个可用于预测病理升级的诺 模图。结论：前列腺特异抗原密度和穿刺Gleason 评分是预测中国人群中前列腺癌根治术后病理升级的独立危险因素，本研究所 得的诺模图可以很好地预测前列腺癌根治术后的病理升级。     

Predicting toxicity for head and neck cancer patients undergoing radiation therapy: an independent and external validation of MDASI-HN based nomogram

AimWe conducted a study to validate the MDASI-HN based nomogram, which is used to predict the acute toxicities in head and neck cancer patients undergoing radiation therapy with or without chemotherapy.BackgroundTolerance to radiation varies from patient to patient and also depends on various other factors like tumor volume, dose of radiation, chemotherapy. Predicting the toxicities allow us to identify potential candidates who are likely to have a higher toxicity and, in addition, evaluates the nomogram when done on an independent group of patients.Materials and MethodsSixty biopsy confirmed head and neck cancer patients undergoing radiation were the subjects of the study. The patients completed patient reported outcome instrument (PRO) MDASI-HN questionnaire at the beginning and at the fifth week of radiation. The baseline score obtained was used to obtain the predicted score using nomogram. The nomogram was also externally validated as per the TRIPOD guidelines.ResultsThe mean baseline, predicted and score at the fifth week were 27.28 ± 11.04, 73.33 ± 15.51 and 82.62 ± 17.67, respectively, for all sub-sites. A positive, significant correlation (p < 0.01) between the predicted score and the score at the fifth week was seen across all sub sites such as Oral cavity (p = 0.05), Oropharynx (p = 0.02), Hypo pharynx (p = 0.02) and Larynx (p = 0.02).ConclusionThe MDASI-HN questionnaire based nomogram is simple, easily doable and takes into consideration the initial symptoms as well the treatment details; thereby, it is able to predict the toxicities accurately.     

Distributions of oxygen,nutrient, and metabolic waste concentrations in multicellular spheroids and their dependence on spheroid parameters

The distribution of oxygen, nutrients and metabolic wastes in multicellular tumor spheroids and its dependence on the parameters characterizing the spheroid (i.e., spheroid geometry, diffusivity, and consumption/ production rates of biological substances) have been investigated by a theoretical analysis: 1. Parameter dependence is qualitatively demonstrated and visualized. 2. Reduction of the number of variables by specific coordinate transformations made it possible to generate nomograms from which concentration distributions for any choice of parameter values may easily be obtained. In particular, these nomograms may also be used for estimating concentration profiles of metabolic waste products, e.g. of lactate, which are expected to accumulate in the tumor spheroids. 3. An additional set of nomograms is given which is more convenient for determining time courses of these concentrations during spheroid growth. 4. A quantitative sensitivity analysis of parameter dependencies is performed to identify those parameters upon which a concentration of interest depends most critically in a given experimental situation. Offprint requests to: W Mueller-Klieser     

When less is more: A simple predictive model for repeated prostate biopsy outcomes

ObjectivesTo present a new predictive model for repeated prostate biopsy outcomes. Several practical problems are described that arise when searching for a proper model among those that already exist. A new model is developed with only two explanatory variables and a simple graphical output.MethodsThis is a retrospective cohort study based on data collected from December 2006 to June 2011 at the Clinic of Urology of the University Hospital in Olomouc, Czech Republic. The cohort consists of 221 patients who underwent the first repeated biopsy after an initial biopsy with a negative outcome. All patients had prostate-specific antigen (PSA) levels between 1.5 and 16.5 ng/mL and a prostate volume not greater than 100 mL. A logistic regression model was fitted.ResultsOf the 221 patients, 29 (13%) were diagnosed with prostate cancer on the repeated biopsy. The final model includes the PSA level and the transitory zone volume as predictors. Its accuracy is 76.4%. The cut-off point of 0.0687 in the predicted positive repeated biopsy outcome assures 95% sensitivity and prevents 42% of unnecessary biopsies.ConclusionsThe accuracy of the model is comparable to that of more complex models (with more than two predictors) published in the literature. The model includes only two routinely measured variables, and hence it is accessible for a wide range of practitioners. The simple graphical outcome makes the model even more attractive.     

A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.     

经脐单孔腹腔镜胆囊切除术后切口感染的列线图模型预测价值研究及防治策略分析

摘要 目的：探讨经脐单孔腹腔镜胆囊切除术后切口感染的列线图模型预测价值及防治策略。方法：回顾性分析2018年10月-2021年5月合肥市第一人民医院收治的经脐单孔腹腔镜胆囊切除术患者94例的临床资料,根据患者术后是否发生切口感染分为感染组（n=12例）和非感染组（n=82例）。查阅两组病历资料,对患者术后切口感染的可能影响因素进行单因素及多因素Logistic回归分析;引入R软件建立经脐单孔腹腔镜胆囊切除术后切口感染预测模型并绘制列线图模型,获得预测风险值;绘制ROC曲线,分析列线图模型对术后切口感染的预测效能。结果：94例经脐单孔腹腔镜胆囊切除术患者中12例术后切口发生感染,感染率为12.77%。多因素Logistic回归分析结果表明：年龄、手术时间、住院时间、胆囊破裂是经脐单孔腹腔镜胆囊切除术后切口感染发生的影响因素（P<0.05）;列线图模型看出：年龄得分为63分,住院时间得分为37.6分,手术时间得分为71.5分,胆囊破裂得分为50分,预测风险值为2.221,ROC曲线下面积为0.832。结论：经脐单孔腹腔镜胆囊切除术后切口感染率较高,且受到的影响因素较多,基于上述影响因素构建的预测模型能获得较高的预测效能,值得推广应用。     

结合自噬相关基因表达谱和临床因素的肺腺癌预后预测模型

对肺腺癌自噬相关基因进行生物信息学分析,结合多基因预后标志和临床参数构建能够预测肺腺癌患者预后的模型。首先,对TCGA肺腺癌数据中的938个自噬相关基因进行差异分析,获得了82个差异自噬相关基因,使用单因素Cox比例风险回归模型从差异自噬相关基因中筛选出候选基因,通过 lasso回归进一步筛选出预后相关基因,分别是ARNTL2、NAPSA、ATG9B、CAPN12、MAP1LC3C和KRT81。通过多因素Cox回归分析以构建风险评分模型,根据最优cutoff值将患者分为高低风险组,生存曲线显示高低风险组之间生存差异显著,ROC曲线显示风险评分的预测能力良好,并在内、外验证集中得到验证。同时对传统的临床因素进行单因素和多因素Cox回归分析,结果显示Stage、复发和风险评分能够独立预测预后,结合这三个独立的预后参数以构建列线图模型,使用一致性指数、校准曲线评估列线图的预测能力,结果显示预测结果与实际结果之间具有良好的一致性。通过与Stage和风险评分的比较发现,列线图的预测能力表现最佳。基于肺腺癌相关的自噬基因和临床参数构建了一个列线图模型来预测肺腺癌患者的预后生存,这可能为临床医生提供了一种可靠的预后评估工具。     

Development and validation of a nomogram to predict overall survival of T1 esophageal squamous cell carcinoma patients with lymph node metastasis

PurposeTo develop a nomogram for predicting the prognosis of T1 esophageal squamous cell carcinoma (ESCC) patients with positive lymph node.MethodsT1 ESCC patients with lymph node metastasis diagnosed between 2010 and 2015 were selected from the Surveillance, Epidemiology, and Final Results (SEER) database. The entire cohort was randomly divided in the ratio of 7:3 into a training group (n=457) and validation group (n=192), respectively. Prognostic factors were identified by univariate and multivariate Cox regression models. Harrell''s concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to evaluate the discrimination and calibration of the nomogram. The accuracy and clinical net benefit of the nomogram compared with the 7^th AJCC staging system were evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).ResultsThe nomogram consisted of eight factors: insurance, T stage, summary stage, primary site, radiation code, chemotherapy, surgery, and radiation sequence with surgery. In the training and validation cohorts, the AUCs exceeded 0.700, and the C-index scores were 0.749 and 0.751, respectively, indicating that the nomogram had good discrimination. The consistency between the survival probability predicted by the nomogram and the actual observed probability was indicated by the calibration curve in the training and validation cohorts. For NRI>0 and IDI>0, the predictive power of the nomogram was more accurate than that of the 7^th AJCC staging system. Furthermore, the DCA curve indicated that the nomogram achieved better clinical utility than the traditional system.ConclusionsUnlike the 7^th AJCC staging system, the developed and validated nomogram can help clinical staff to more accurately, personally and comprehensively predict the 1-year and 3-year OS probability of T1 ESCC patients with lymph node metastasis.