Critical assessment of tools to predict clinically insignificant prostate cancer at radical prostatectomy in contemporary men

BACKGROUND: Overtreatment of prostate cancer (PCa) is a concern, especially in patients who might qualify for the diagnosis of insignificant prostate cancer (IPCa). The ability to identify IPCa prior to definitive therapy was tested. METHODS: In a cohort of 1132 men a nomogram was developed to predict the probability of IPCa. Predictors consisted of prostate-specific antigen (PSA), clinical stage, biopsy Gleason sum, core cancer length and percentage of positive biopsy cores (percent positive cores). IPCa was defined as organ-confined PCa (OC) with tumor volume (TV) <0.5 cc and without Gleason 4 or 5 patterns. Finally, an external validation of the most accurate IPCa nomogram was performed in the same group. RESULTS: IPCa was pathologically confirmed in 65 (5.7%) men. The 200 bootstrap-corrected predictive accuracy of the new nomogram was 90% versus 81% for the older nomogram. However, in cutoff-based analyses of patients who were qualified by our and the older nomograms as high probability for IPCa, respectively 63% and 45% harbored aggressive PCa variants at radical prostatectomy (Gleason score 7-10, ECE, SVI, and/or LNI). CONCLUSIONS: Despite a high accuracy, currently available models for prediction of IPCa are incorrect in 10% to 20% of predictions. The rate of misclassification is even further inflated when specific cutoffs are used. As a consequence, extreme caution is advised when statistical tools are used to assign the diagnosis of IPCa.     

Nomograms for clinically localized disease. Part III: watchful waiting

Patients with newly diagnosed, clinically localized prostate cancer need information concerning long-term outcomes to make informed decisions regarding treatment options. Several nomograms have been developed that can help in this decision process. By using a nomogram originally published in 1998, patients and clinicians can predict the 15-year clinical outcomes in the absence of aggressive treatment based on age and Gleason score at diagnosis. These predictions are based on patients diagnosed and treated before the routine use of PSA that has accelerated the diagnosis of prostate cancer by at least 5 years. Longer follow-up of contemporary patients will determine whether this nomogram remains accurate in the prostate-specific antigen (PSA) era. In view of the lead-time bias resulting from PSA testing, the outcomes of contemporary patients are likely to be better rather than worse than the results shown.     

Predicting clinical end points: treatment nomograms in prostate cancer

Due to the generally indolent nature of prostate cancer, patients must decide among a wide range of treatments, which will significantly affect both quality of life and survival. Thus, there is a need for instruments to aid patients and their physicians in decision analysis. Nomograms are instruments that predict outcomes for the individual patient. Using algorithms that incorporate multiple variables, nomograms calculate the predicted probability that a patient will reach a clinical end point of interest. Nomograms tend to outperform both expert clinicians and predictive instruments based on risk grouping. We outline principles for nomogram construction, including considerations for choice of clinical end points and appropriate predictive variables, and methods for model validation. Currently, nomograms are available to predict progression-free probability after several primary treatments for localized prostate cancer. There is need for additional models that predict other clinical end points, especially survival adjusted for quality of life.     

A nomogram for predicting low-volume/low-grade prostate cancer: a tool in selecting patients for active surveillance

BACKGROUND: The authors reported previously that assessment of the number of positive biopsy cores, maximum tumor length in a core, Gleason score, and prostate volume in an extended biopsy enhanced the accuracy of predicting low-volume/low-grade prostate cancer. On the basis of those findings, they developed a nomogram to predict the probability of low-volume/low-grade prostate cancer specifically for men with a single positive biopsy core. METHODS: The study cohort comprised 258 men who underwent radical prostatectomy without neoadjuvant therapy. Prostate cancer was diagnosed in only 1 core of an extended biopsy scheme. Low-volume/low-grade cancer was defined as pathologic organ-confined disease and a tumor volume<0.5 cc with no Gleason grade 4 or 5 cancer. Patient age, prostate-specific antigen (PSA) level, prostate volume, PSA density (PSAD), and tumor length in a biopsy core were examined as variables. A fitted multiple logistic regression model was used to establish the nomogram. RESULTS: One hundred thirty-three patients (51.6%) had low-volume/low-grade cancer. To establish the nomogram, age, PSAD, and tumor length were adopted as variables. The fitted model suggested that older age, higher PSAD values, and greater tumor length would reduce the probability of low-volume/low-grade cancer. The nomogram predicted low-volume/low-grade cancer with good discrimination (an area under the receiver operating characteristic curve of 0.727). Calibration of this nomogram showed good predicted probability. CONCLUSIONS: The authors established a nomogram with which to predict low-volume/low-grade cancer in men with 1 positive biopsy core in an extended biopsy scheme, and they recommend this nomogram for use in selecting men for active surveillance.     

A population-based study: how to identify high-risk T1 gastric cancer patients?

In T1 gastric cancer (GC), lymph nodes metastasis (LNM) is considered as a significant prognostic predictor and closely associated with following therapeutic approaches as well as distant metastasis (DM). This study aimed to not only seek risk factors of LNM and DM but also unpack the prognosis in T1 GC patients. We performed a retrospective study enrolling 5547 patients in T1 GC from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models were produced to recognize independent risk factors of LNM and DM. Cox regression analyses were performed to identify important prognostic factors of overall survival (OS). Cancer-specific cumulative incidence was plotted by cumulative incidence function. Three nomograms of LNM, DM and OS were established and validated by receiver operating characteristic (ROC) and calibration curves to evaluate discrimination and accuracy. Decision curve analysis (DCA), clinical impact curves (CIC) and subgroups based on risk scores were constructed to measure nomograms clinical utility. The area under the curve (AUC) of LNM nomogram and DM nomogram were 0.735 and 0.896, respectively. OS nomogram was constructed and the corresponding C-index was 0.797. In conclusion, our user-friendly nomograms, which aimed to predict LNM, DM and OS in T1 gastric cancer patients, have shown high efficiency of discrimination and accuracy. These useful and visual tools may have advantageous clinical utility to identify high-risk T1 gastric patients and help clinicians to draw up an individual therapeutic strategy.     

前列腺穿刺对根治性前列腺切除术后标本ISUP升级预测列线图模型的建立及其应用

目的:构建一个从前列腺穿刺组织到根治性前列腺切除术（RP）后标本ISUP分级升高(ISUP grade upgrading,IGU)风险的预测列线图模型并进行内部验证。方法:对2019年05月至2020年05月我院泌尿外科收治的166例前列腺癌患者临床和病理学资料进行回顾性分析。采用单因素及多因素Logistic回归分析得到IGU的独立危险因素,后根据这些因素构建列线图预测模型。通过校准图进行模型校准,C-指数评估模型的预测能力,决策曲线分析用于检验临床效用,采用Bootstrap resampling对模型进行诊断效能内部验证。结果:该研究中ISUP升级组有47例（28.3%）患者,未升级组有119例（71.7%）患者。多因素logistic回归分析发现前列腺穿刺活检组织Gleason评分（P=0.001）、前列腺穿刺活检方法（P=0.03）和穿刺阳性针数（P=0.04）是IGU的独立危险因素。IGU列线图模型是基于上述独立因素而构建,模型的ROC曲线下面积为0.802,C-指数为0.798,校准图显示预测曲线与实际曲线有较好的相符度。列线图模型在内部验证中C-指数达到0.772。决策曲线分析表明,RP-ISUP升级风险的区间阈值为3%~67%。结论:该研究构建了一个准确性相对较高的列线图模型,有助于临床医生评估RP术后标本ISUP分级升高（特别是经直肠穿刺活检诊断的低风险前列腺癌）的风险。     

The addition of interleukin-6 soluble receptor and transforming growth factor beta1 improves a preoperative nomogram for predicting biochemical progression in patients with clinically localized prostate cancer.

PURPOSE: Several preoperative prostate cancer nomograms have been developed that predict risk of progression using pretreatment prostate-specific antigen (PSA) level, clinical stage, and biopsy Gleason grade. We describe the development and performance of a new nomogram. The nomogram adds new markers to the standard clinical predictors that reflect the biologic behavior of prostate cancer: pretreatment plasma levels of interleukin-6 soluble receptor (IL6SR) and transforming growth factor beta1 (TGF-beta1). PATIENTS AND METHODS: Between November 7, 1994 and December 22, 1997, 714 patients with stage cT1c to cT3a prostate cancer and no prior therapy were treated with radical prostatectomy at the Methodist Hospital, Houston TX. Plasma levels of IL6SR and TGF-beta1 were measured in banked preoperative plasma. With these data, a nomogram was developed to predict the probability of PSA progression within 5 years of surgery. The nomogram was validated with bootstrapping to assess its discrimination and calibration performance. RESULTS: In the multivariable Cox model, PSA (P =.004), IL6SR (P <.001), TGF-beta1 (P <.001), primary Gleason grade (P <.002), and secondary Gleason grade (P =.029) were associated with PSA progression, whereas clinical stage (P =.696) was not. The nomogram seemed to be well calibrated and had a bootstrap-corrected area under the receiver operating characteristic curve (ie, concordance index) of 0.83. For comparison, a nomogram that omitted IL6SR and TGF-beta1 achieved a concordance index of only 0.75. CONCLUSION: We found that pretreatment plasma levels of IL6SR and TGF-beta1 improved the ability to predict biochemical progression by a prognostically substantial margin. A nomogram including the pretreatment levels of these molecular markers, along with standard clinical markers, has been developed and internally validated.     

Clinically significant Gleason sum upgrade: external validation and head-to-head comparison of the existing nomograms

BACKGROUND:

Several nomograms have been developed for the purpose of predicting the likelihood of an increase in Gleason sum (GS) from biopsy information compared with the GS determined after examination of the “entire prostate” in patients with prostate cancer. In this study, the authors evaluated and compared the ability of 4 nomograms (published by Capitanio et al, Chun et al, Kulkarni et al, and Moussa et al) to predict GS upgrades for patients with biopsy GS ≤6 prostate cancer who underwent radical prostatectomy (RP) at their center.

METHODS:

The entire study cohort included 942 patients with a biopsy GS ≤6. Predictive performances of the nomograms were compared using area under the receiver operating characteristic curve (AUC‐ROC) analysis, calibration plots, and decision curve analysis (DCA) in the entire cohort, in patients with low‐risk prostate cancer (LRPC), and a subgroup of those patients who underwent extended biopsy with ≥10 cores.

RESULTS:

Patients with a GS ≥7 at prostatectomy included 319 of 942 patients (33.9%) in the entire study cohort, 263 of 814 patients (32.2%) with LRPC, and 84 of 301 patients (27.9%) with LRPC who underwent extended biopsy. With an AUC‐ROC of 0.637 to 0.647 in the different subgroups of patients with low‐risk cancer, the Kulkarni et al nomogram demonstrated significantly higher discriminative ability compared with the other nomograms. The same nomogram provided a small clinical benefit at DCA. All nomograms were poorly calibrated.

CONCLUSIONS:

The available prognostic tools had limited ability to predict clinically significant upgrading in patients with biopsy GS ≤6 and, thus, the authors concluded that these tools are not ready for clinical application. Cancer 2011;. © 2011 American Cancer Society.     

A nomogram to predict Gleason sum upgrading of clinically diagnosed localized prostate cancer among Chinese patients

Although several models have been developed to predict the probability of Gleason sum upgrading between biopsy and radical prostatectomy specimens, most of these models are restricted to prostate- specific antigen screening-detected prostate cancer. This study aimed to build a nomogram for the prediction of Gleason sum upgrading in clinically diagnosed prostate cancer. The study cohort comprised 269 Chinese prostate cancer patients who underwent prostate biopsy with a minimum of 10 cores and were subsequently treated with radical prostatectomy. Of all included patients, 220 （81.8%） were referred with clinical symptoms. The prostate-specific antigen level, primary and secondary biopsy Gleason scores, and clinical T category were used in a multivariate logistic regression model to predict the probability of Gleason sum upgrading. The developed nomogram was validated internally. Gleason sum upgrading was observed in 90 （33.5%） patients. Our nomogram showed a bootstrap-corrected concordance index of 0.789 and good calibration using 4 readily available variables. The nomogram also demonstrated satisfactory statistical performance for predicting significant upgrading. External validation of the nomogram published by Chun et aL in our cohort showed a marked discordance between the observed and predicted probabilities of Gleason sum upgrading. In summary, a new nomogram to predict Gleason sum upgrading in clinically diagnosed prostate cancer was developed, and it demonstrated good statistical performance upon internal validation.     

Validation study of the accuracy of a postoperative nomogram for recurrence after radical prostatectomy for localized prostate cancer.

PURPOSE: A postoperative nomogram for prostate cancer was developed at Baylor College of Medicine. This nomogram uses readily available clinical and pathologic variables to predict 7-year freedom from recurrence after radical prostatectomy. We evaluated the predictive accuracy of the nomogram when applied to patients of four international institutions. PATIENTS AND METHODS: Clinical and pathologic data of 2,908 patients were supplied for validation, and 2,465 complete records were used. Nomogram-predicted probabilities of 7-year freedom from recurrence were compared with actual follow-up in two ways. First, the area under the receiver operating characteristic curve (AUC) was calculated for all patients and stratified by the time period of surgery. Second, calibration of the nomogram was achieved by comparing the predicted freedom from recurrence with that of an ideal nomogram. For patients in whom the pathologic report does not distinguish between focal and established extracapsular extension (an input variable of the nomogram), two separate calculations were performed assuming one or the other. RESULTS: The overall AUC was 0.80 when applied to the validation data set, with individual institution AUCs ranging from 0.77 to 0.82. The predictive accuracy of the nomogram was apparently higher in patients who were operated on between 1997 and 2000 (AUC, 0.83) compared with those treated between 1987 and 1996 (AUC, 0.78). Nomogram predictions of 7-year freedom from recurrence were within 10% of an ideal nomogram. CONCLUSION: The postoperative Baylor nomogram was accurate when applied at international treatment institutions. Our results suggest that accurate predictions may be expected when using this nomogram across different patient populations.     

Pretreatment nomogram that predicts 5-year probability of metastasis following three-dimensional conformal radiation therapy for localized prostate cancer.

PURPOSE: There are several nomograms for the patient considering radiation therapy for clinically localized prostate cancer. Because of the questionable clinical implications of prostate-specific antigen (PSA) recurrence, its use as an end point has been criticized in several of these nomograms. The goal of this study was to create and to externally validate a nomogram for predicting the probability that a patient will develop metastasis within 5 years after three-dimensional conformal radiation therapy (CRT). PATIENTS AND METHODS: We conducted a retrospective, nonrandomized analysis of 1,677 patients treated with three-dimensional CRT at Memorial Sloan-Kettering Cancer Center (MSKCC) from 1988 to 2000. Clinical parameters examined were pretreatment PSA level, clinical stage, and biopsy Gleason sum. Patients were followed until their deaths, and the time at which they developed metastasis was noted. A nomogram for predicting the 5-year probability of developing metastasis was constructed from the MSKCC cohort and validated using the Cleveland Clinic series of 1,626 patients. RESULTS: After three-dimensional CRT, 159 patients developed metastasis. At 5 years, 11% of patients experienced metastasis by cumulative incidence analysis (95% CI, 9% to 13%). A nomogram constructed from the data gathered from these men showed an excellent ability to discriminate among patients in an external validation data set, as shown by a concordance index of 0.81. CONCLUSION: A nomogram with reasonable accuracy and discrimination has been constructed and validated using an external data set to predict the probability that a patient will experience metastasis within 5 years after three-dimensional CRT.     

A study of pretreatment nomograms to predict pathological stage and biochemical recurrence after radical prostatectomy for clinically resectable prostate cancer in Japanese men

BACKGROUND: Accurate pretreatment identification of the risks that prostate cancer has extended beyond the gland and that it will recur would significantly influence practice patterns. Preoperative nomograms to predict such risks have not been developed for the oriental male population. METHODS: Construction of nomograms to predict preoperatively pathological outcome and early biochemical failure following radical prostatectomy in Japanese males was based on logistic regression analysis, with predicted probabilities and 95% confidence intervals for the final model being obtained by repeating the analysis on 1000 bootstrap samples from the original cohort. RESULTS: Prostate-specific antigen level, clinical stage and biopsy Gleason score contributed significantly to the prediction of pathological stage and of biochemical failure in the univariate analysis (p < 0.001). Combined use of these three variables predicted these treatment outcomes better than any single variable (p < 0.001). Nomograms combining these three variables to predict final pathological findings and early biochemical failure were then developed. The medians and 95% confidence intervals of the predicted probabilities are presented in the nomograms. CONCLUSIONS: This information enables clinicians to use their nomograms when counseling Japanese patients, leading to more informed treatment decisions and helping to identify those with a high risk of early biochemical failure. The nomograms may also be used to assure comparability of different treatment modalities in investigational trials.     

预测去势抵抗性前列腺癌患者总生存期Halabi风险列线图的验证研究

目的 在中国上海及周边地区去势抵抗性前列腺癌（CRPC）患者中验证Halabi风险列线图,预测CRPC患者的总生存期。方法 选择2006年8月至2013年12月在复旦大学附属肿瘤医院被确诊为CRPC的228例患者。预测患者生存率通过Halabi风险列线图计算,模型效应用偏倚和校准分析评估。结果 228例患者中死亡150例,中位总生存期为20.8个月（4～48个月）。Halabi风险列线图的准确度为0.61（95％ CI：0.51～0.73）。Halabi列线图预测的中位总生存期为16.4个月（6～31个月）。预测模型校准结果显示,1年和2年肿瘤特异性生存率被低估。结论 Halabi风险列线图未能准确预测中国上海地区CRPC患者的生存率,仍需要更大样本的研究来验证Halabi风险列线图或者建立新的风险列线图来预测CRPC患者的生存情况。     

Postoperative nomogram for disease recurrence after radical prostatectomy for prostate cancer.

PURPOSE: Although models exist that place patients into discrete groups at various risks for disease recurrence after surgery for prostate cancer, we know of no published work that combines pathologic factors to predict an individual's probability of disease recurrence. Because clinical stage and biopsy Gleason grade only approximate pathologic stage and Gleason grade in the prostatectomy specimen, prediction of prognosis should be more accurate when postoperative information is added to preoperative variables. Therefore, we developed a postoperative nomogram that allows more accurate prediction of probability for disease recurrence for patients who have received radical prostatectomy as treatment for prostate cancer, compared with the preoperative nomogram we previously published. PATIENTS AND METHODS: By Cox proportional hazards regression analysis, we modeled the clinical and pathologic data and disease follow-up for 996 men with clinical stage T1a-T3c NXM0 prostate cancer who were treated with radical prostatectomy by a single surgeon at our institution. Prognostic variables included pretreatment serum prostate-specific antigen level, specimen Gleason sum, prostatic capsular invasion, surgical margin status, seminal vesicle invasion, and lymph node status. Treatment failure was recorded when there was either clinical evidence of disease recurrence, a rising serum prostate-specific antigen level (two measurements of 0.4 ng/mL or greater and rising), or initiation of adjuvant therapy. Validation was performed on this set of men and a separate sample of 322 men from five other surgeons' practices from our institution. RESULTS: Cancer recurrence was noted in 189 of the 996 men, and the recurrence-free group had a median follow-up period of 37 months (range, 1 to 168 months). The 7-year recurrence-free probability for the cohort was 73% (95% confidence interval, 68% to 76%). The predictions from the nomogram appeared to be accurate and discriminating, with a validation sample area under the receiver operating characteristic curve (ie, a comparison of the predicted probability with the actual outcome) of 0.89. CONCLUSION: A postoperative nomogram has been developed that can be used to predict the 7-year probability of disease recurrence among men treated with radical prostatectomy.     

Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy

An existing preoperative nomogram predicts the probability of prostate cancer recurrence, defined by prostate-specific antigen (PSA), at 5 years after radical prostatectomy based on clinical stage, serum PSA, and biopsy Gleason grade. In an updated and enhanced nomogram, we have extended the predictions to 10 years, added the prognostic information of systematic biopsy results, and enabled the predictions to be adjusted for the year of surgery. Cox regression analysis was used to model the clinical information for 1978 patients treated by two high-volume surgeons from our institution. The nomogram was externally validated on an independent cohort of 1545 patients with a concordance index of 0.79 and was well calibrated with respect to observed outcome. The inclusion of the number of positive and negative biopsy cores enhanced the predictive accuracy of the model. Thus, a new preoperative nomogram provides robust predictions of prostate cancer recurrence up to 10 years after radical prostatectomy.     

Problems of the preoperative prediction of the pathological stage in prostate cancer

The pathological stage of the tumor is the most influential prognostic factor for progression after radical prostatectomy. However, as many as 50% of men undergoing radical prostatectomy are found to have extraprostatic disease in the pathological specimen. Accurate identification of the risks of disease extension and of disease recurrence prior to radical prostatectomy would thus be useful in counseling men presenting with clinically localized prostate cancer. Nomograms may help patients and physicians make more informed treatment decisions based on the probability of pathological stage. Partin and co-workers popularized the use of a pretreatment nomogram based on PSA (prostate specific antigen), clinical stage (TNM stage) and biopsy Gleason score to predict the pathological stage of localized prostate cancer. However, it may not be directly applicable to Japanese males, and the interpretation and comparison of data sets should be done with caution and careful consideration. Although attempts have been made to establish a nomogram for Japanese patients, been tried, it is still based on the data for a small number of patients. More data from a greater number of patients and validation analysis are essential. Recently, artificial neural networks (ANN) have been shown to be effective in predicting pathologic stage in men with clinically localized prostate cancer. The use of ANNs is a relatively new concept and the data is based on Western people; thus, the data analysis for Japanese patients is necessary. The present paper mainly outlines the usefulness and problems for the preoperative prediction of the pathological stage in prostate cancer by nomograms and artificial neural networks.     

Comparison of nomograms with other methods for predicting outcomes in prostate cancer: a critical analysis of the literature

PURPOSE: Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with prostate cancer. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous patient groups. Because there is more than one model available for prediction of most outcomes, model comparisons are necessary for selection of the best model. We describe the criteria based on which to judge predictive tools, describe the limitations of current predictive tools, and compare the different predictive methodologies that have been used in the prostate cancer literature. EXPERIMENTAL DESIGN: Using MEDLINE, a literature search was done on prostate cancer decision aids from January 1966 to July 2007. RESULTS: The decision aids consist of nomograms, risk groupings, artificial neural networks, probability tables, and classification and regression tree analyses. The following considerations need to be applied when the qualities of predictive models are assessed: predictive accuracy (internal or ideally external validation), calibration (i.e., performance according to risk level or in specific patient subgroups), generalizability (reproducibility and transportability), and level of complexity relative to established models, to assess whether the new model offers advantages relative to available alternatives. Studies comparing decision aids have shown that nomograms outperform the other methodologies. CONCLUSIONS: Nomograms provide superior individualized disease-related risk estimations that facilitate management-related decisions. Of currently available prediction tools, the nomograms have the highest accuracy and the best discriminating characteristics for predicting outcomes in prostate cancer patients.     

Nomograms and instruments for the initial prostate evaluation: the ability to estimate the likelihood of identifying prostate cancer

As a result of prostate cancer screening programs, approximately 10% of otherwise healthy men will be found to have an elevated prostate-specific antigen (PSA) level and therefore be at risk for harboring prostate cancer. Patients with an elevated PSA level have a wide variation in the risk for having prostate cancer diagnosed by transrectal ultrasound (TRUS)-guided prostate biopsy. To adequately counsel these patients, some form of individualized risk assessment must be given. There are several tables, artificial neural network (ANN) models, and nomograms that are available to stratify an individual patients risk for having prostate cancer diagnosed by a TRUS biopsy, either initially or on subsequent biopsies after a previous negative biopsy. Presently, nomograms are also being developed to predict the risk not only for having prostate cancer but also for clinically significant prostate cancer. The difficulty in calculating this risk for an individual patient is that the multiple competing clinical and pathologic factors have varying degrees of effect on the overall risk. This problem of competing risk factors can be overcome by the use of nomograms or ANNs. This article reviews the available instruments that are available to the urologist to enable prediction of the risk for having prostate cancer diagnosed by TRUS-guided prostate biopsy.     

The Karakiewicz nomogram is the most useful clinical predictor for survival outcomes in patients with localized renal cell carcinoma

BACKGROUND:

Outcomes after surgical removal of localized renal cell carcinoma (RCC) are variable. There have been multiple prognostic nomograms and risk groups developed for estimation of survival outcomes, with different models in use for evaluating patient eligibility in ongoing trials of adjuvant therapy. The authors aimed to establish the most useful prognostic model for patients with localized RCC to guide trial design, biomarker research, and clinical counseling.

METHODS:

A total of 390 consecutive patients who underwent nephrectomy for sporadic localized RCC in a tertiary institution (1990‐2006) with 65 months median follow‐up were retrospectively evaluated. The Karakiewicz nomogram, the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model were compared in predicting survival outcomes (overall survival, cancer‐specific survival, and freedom from recurrence) using likelihood analysis, adequacy indices, decision curve analysis, calibration, and concordance indices.

RESULTS:

Overall, the Karakiewicz nomogram outperformed the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model. Highly improved accuracy was seen using the Karakiewicz nomogram in survival prediction, using likelihood ratio analysis in bivariate models including the competing prognostic models. The Karakiewicz nomogram showed higher adequacy and concordance indices and improved clinical benefit relative to all other nomograms. All 4 models were reasonably calibrated. Exploratory comparisons of prespecified discretized Karakiewicz nomograms and the SORCE trial recruitment criteria (a discretized Leibovich model) of high‐risk patients favored the discretized Karakiewicz nomograms.

CONCLUSIONS:

The Karakiewicz nomogram was shown to be superior to the other tested nomograms and risk groups in predicting survival outcomes in localized RCC. Routine integration of this model into trial design and biomarker research should be considered. Cancer 2011;. © 2011 American Cancer Society.     

External validation of three lymph node ratio-based nomograms predicting survival using an international cohort of patients with resected pancreatic head ductal adenocarcinoma

IntroductionLymph node ratio (LNR) is an important prognostic factor of survival in patients with pancreatic ductal adenocarcinoma (PDAC). This study aimed to validate three LNR-based nomograms using an international cohort.Materials and methodsConsecutive PDAC patients who underwent upfront pancreatoduodenectomy from six centers (Europe/USA) were collected (2000–2017). Patients with metastases, R2 resection, missing LNR data, and who died within 90 postoperative days were excluded. The updated Amsterdam nomogram, the nomogram by Pu et al., and the nomogram by Li et al. were selected. For the validation, calibration, discrimination capacity, and clinical utility were assessed.ResultsAfter exclusion of 176 patients, 1′113 patients were included. Median overall survival (OS) of the cohort was 23 months (95% CI: 21–25).For the three nomograms, Kaplan-Meier curves showed significant OS diminution with increasing scores (p < 0.01). All nomograms showed good calibration (non-significant Hosmer-Lemeshow tests). For the Amsterdam nomogram, area under the ROC curve (AUROC) for 3-year OS was 0.64 and 0.67 for 5-year OS. Sensitivity and specificity for 3-year OS prediction were 65% and 59%. Regarding the nomogram by Pu et al., AUROC for 3- and 5-year OS were 0.66 and 0.70. Sensitivity and specificity for 3-year OS prediction were 68% and 53%. For the Li nomogram, AUROC for 3- and 5-year OS were 0.67 and 0.71, while sensitivity and specificity for 3-year OS prediction were 63% and 60%.ConclusionThe three nomograms were validated using an international cohort. Those nomograms can be used in clinical practice to evaluate survival after pancreatoduodenectomy for PDAC.