The comparability of models for predicting the risk of a positive prostate biopsy with prostate-specific antigen alone: a systematic review

CONTEXT: The sensitivity and specificity profile of measuring levels of prostate-specific antigen (PSA) to select men for prostate biopsy is not optimal. This has prompted the construction of nomograms and artificial neural networks (ANNs) to increase the performance of PSA measurements. OBJECTIVE: A systematic review of nomograms and ANNs designed to predict the risk of a positive prostate biopsy for cancer was conducted in order to determine their value versus measuring PSA levels alone. EVIDENCE ACQUISITION: Medical Literature Analysis and Retrieval System Online (U.S. National Library of Medicine's life science database; MEDLINE) was searched using the terms "nomogram" "artificial neural network" and "prostate cancer" for dates up to and including July 2007 and was supplemented by manual searches of reference lists. Included studies used an assessment tool to examine the risk of a positive prostate biopsy in a man without a known cancer diagnosis. Intramodel comparisons with evaluation of PSA levels alone, and intermodel comparisons of area under the curve (AUC) from receiver operating characteristic (ROC) curves were conducted. Individual case examples were also used for comparisons. EVIDENCE SYNTHESIS: Twenty-three studies examining 36 models were included. With the exception of two studies, all the models had AUC values of 0.70 or greater, with eight reporting an AUC of >/=0.80 and four (all ANNs) reporting an AUC >/=0.85, with variable validation status. Fourteen studies compared the AUC with PSA levels alone: all showed a benefit from using AUCs which varied from 0.02 to 0.26. Of the 16 external validation comparisons, in 13 the AUC was lower in the external population than in the model population. CONCLUSIONS: Nomograms and ANNs produce improvements in AUC over measurement of PSA levels alone, but many lack external validation. Where this is available, the benefits are often diminished, although most remain significantly better than with evaluation of PSA levels alone. In men without additional risk factors, PSA cutoff values alone provide a relatively precise risk estimate, but if additional risk factors are known, PSA values alone are less accurate.     

A novel nomogram to predict low anterior resection syndrome (LARS) after ileostomy reversal for rectal cancer patients

Background and aimLow anterior resection syndrome (LARS) in patients undergoing low or ultra-low anterior resection (LAR) is a common problem and significantly impacts the quality of life. Patients with an ileostomy after LAR are more likely to develop LARS. However, there hasn't been a model predicting LARS occurrence in these patients. This study aims to construct a nomogram to predict the probability of LARS occurrence in patients with temporary ileostomy and guide preventive strategies before reversal.Methods168 patients undergoing LAR with ileostomy from one center were enrolled as the training cohort, and 134 patients of the same inclusion criteria from another center were enrolled as the validation cohort. The training cohort was screened for risk factors for major LARS using univariate and multivariate logistic regression. The nomogram was constructed using the filtered variables, the ROC curve was used to describe the model's discrimination, and the calibration was used to describe the accuracy.ResultsThe optimal cut-off value for stoma closure time was 128 days. Three risk factors were identified using logistic regression analysis: preoperative radiotherapy (OR = 3.038, [95%CI 1.75–5.015], P = 0.005), stoma closure time (OR = 2.298, [95%CI 1.088–4.858], P = 0.029) and pN stage (OR = 1.739, [95%CI 1.235–3.980], P = 0.001). A nomogram was constructed based on these three variables and showed good performance predicting major LARS after stoma reversal. The area under the curve (AUC) was 0.827 in the training group and 0.821 in the validation group; The calibration curve suggested good precision in both groups.ConclusionsThis novel nomogram can accurately predict the probability of major LARS occurrence after ileostomy reversal for rectal cancer patients. This model can help screen ileostomy patients with high risks and guide individualized preventive strategies before stoma reversal.     

Preoperative Nomograms and Artificial Neural Networks (ANNs) for Identification of Surgical Candidates

Selection of prostate cancer risk patients for surgical treatment has traditionally been accomplished by the creation of risk groups, like clinical stage, prostate specific antigen and others. Using these data knowledge-based expert systems were created. Among these the most popular model is the logistic regression model. Ideally, this prediction should be as accurate as possible. Many studies have shown that even expert on its field often are incorrect compared to the validated nomograms and artifical neural networks (ANNs) presented herein. Nomograms are instruments that predict outcomes for the individual patient using algorithms that incorporate multiple variables. Nomograms consist of a set of axes. Each variable is represented by a scale, with each value of that variable corresponding to a specific number of points according to its prognostic impact. In a final pair of axes, the total point value from all he variables is converted to the probability of reaching the end point By using scales, nomograms calculate the continuous probability of a certain outcome, resulting in more accurate predictions than models based on risk grouping. ANNs has gained increasing popularity and are the most popular artificial learning tool in biotechnology. This technique can roughly be described as a universal algebraic function that will distinguish dependency between dependent and independent variables, which is either unknown or very complex. The application of ANNs to complex relationships makes them highly attractive for the study of complexed medical decisions like predicting pathological stage or local recurrence after radical prostatectomy (RPE). Accuracy of nomograms and ANNs for pathological staging and PSA recurrence varies between 72–88.3% versus 77–91%, and 75–81% and 67–83%, respectively.     

Adherence to pelvic lymph node dissection recommendations according to the National Comprehensive Cancer Network pelvic lymph node dissection guideline and the D'Amico lymph node invasion risk stratification

Purpose

To assess adherence rates to pelvic lymph node dissection (PLND) according to National Comprehensive Cancer Network (NCCN) PLND guideline (2% or higher risk) and D’Amico lymph node invasion (LNI) risk stratification (intermediate/high risk) in contemporary North American patients with prostate cancer treated with radical prostatectomy (RP).

A Novel Nomogram to Identify Candidates for Extended Pelvic Lymph Node Dissection Among Patients with Clinically Localized Prostate Cancer Diagnosed with Magnetic Resonance Imaging-targeted and Systematic Biopsies

Background

Available models for predicting lymph node invasion (LNI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP) might not be applicable to men diagnosed via magnetic resonance imaging (MRI)-targeted biopsies.

个体化预测剖宫产椎管内麻醉发生低血压风险列线图模型的建立及验证

目的 探讨剖宫产椎管内麻醉发生低血压的危险因素,并建立剖宫产椎管内麻醉发生低血压的列线图模型。 方法 选取马鞍山十七冶医院2019年4月至2022年7月行剖宫产椎管内麻醉的240例产妇作为模型组,选取2022年10月至2023年6月行剖宫产椎管内麻醉的44例产妇作为验证组。采用单因素及多因素logistic回归分析筛选剖宫产椎管内麻醉发生低血压的危险因素,应用R软件建立剖宫产椎管内麻醉发生低血压的列线图模型,并验证剖宫产椎管内麻醉发生低血压的列线图模型。 结果 模型组240例剖宫产椎管内麻醉产妇中有110例产妇发生低血压,低血压的发生率为45.83%。logistic回归分析结果显示,体重指数（BMI）≥28 kg/m ²、先兆子痫、糖尿病、术前心率≥90次/min及宫高>36 cm等是剖宫产椎管内麻醉发生低血压的危险因素（均 P<0.05）。模型组的一致性指数（C-index）为0.719[95%置信区间（CI）0.683~0.756, P<0.05],验证组的C-index为0.731（95%CI 0.699~0.764, P<0.05）;模型组与验证组的校正曲线皆显示预测值与实际值的拟合度较好;模型的受试者操作特征（ROC）曲线下面积0.708（95%CI 0.675~0.741, P<0.05）,敏感度72.93%,特异度80.90%,阴性预测值80.00%,阳性预测值71.43%;验证组的ROC曲线下面积0.720（95%CI 0.687~0.753, P<0.05）,敏感度75.41%,特异度82.36%,阴性预测值83.14%,阳性预测值73.82%;模型组的决策曲线显示阈值概率为24%~84%时,列线图预测剖宫产椎管内麻醉发生低血压的净获益值较高,验证组的决策曲线显示阈值概率为23%~100%时,列线图预测剖宫产椎管内麻醉发生低血压的净获益值较高。 结论 BMI≥28 kg/m ²、先兆子痫、糖尿病、术前心率≥90次/min及宫高>36 cm等是剖宫产椎管内麻醉发生低血压的危险因素,剖宫产椎管内麻醉发生低血压的列线图模型的准确率和临床应用价值一般。