Decision curve analysis and external validation of the postoperative Karakiewicz nomogram for renal cell carcinoma based on a large single-center study cohort

Zastrow, Stefan; Brookman‐May, Sabine; Cong, Thi Anh Phuong; Jurk, S; Bar, I. Von; Wirth, Manfred P.

doi:10.1007/s00345-014-1321-6

Cited by 17 publications

(10 citation statements)

References 21 publications

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“…DCA provides a way to assess the performance of a model in a specific range of interest. DCA has been extensively used to compare competing methods in several diseases [ 15 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

Using the weighted area under the net benefit curve for decision curve analysis

Talluri

Shete

2016

BMC Med Inform Decis Mak

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BackgroundRisk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients.MethodsWe propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest.ResultsWe compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method.ConclusionsThe proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0336-x) contains supplementary material, which is available to authorized users.

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“…DCA provides a way to assess the performance of a model in a specific range of interest. DCA has been extensively used to compare competing methods in several diseases [ 15 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

Using the weighted area under the net benefit curve for decision curve analysis

Talluri

Shete

2016

BMC Med Inform Decis Mak

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“…1). 1,4,6,7,9,[14][15][16][17][18][19][20][21][22][23][24] In total, the preoperative Karakiewicz nomogram, postoperative Karakiewicz nomogram, Kattan nomogram, and the Cindolo nomogram were validated in 12 065, 12 868, 6036 and 4045 patients, respectively. In all of the included models, we did not observe any publication bias as assessed by the Begg's and Egger's methods (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The internally validated accuracy of the nomogram was 86%, 6 but the externally accuracy reached 90.5%. 1,6,15,24 Karakiewicz and colleagues examined the ability of T and M stages to predict freedom from cancer-specific mortality (CSM) (n = 2474). 7 In addition to T and M stages, other variables, such as age, gender, tumour size, and symptoms, resulted in an integrated staging system that provided predictions of CSM-free survival at 1, 2, 5, and 10 years after nephrectomy.…”

Section: Cancer-specific Survivalmentioning

confidence: 99%

312 Accuracy capabilities comparisons between Karakiewicz, Kattan and Cindolo nomograms in predicting outcomes for renal cancer carcinoma: A systematic review and meta-analysis

Russo¹,

Castelli²,

Rosa³

et al. 2015

European Urology Supplements

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“…A decision curve is used as a simple mathematical model to use the loss of function [30] to examine the effectiveness of a statistical model for inferring the outcome of an event, and it is widely used to evaluate the usefulness and benefit of forecasting models [31][32][33][34]. The results of this study show that applying a threshold probability of 0-92% allows clinicians to achieve superior clinical effects when deciding whether a T3N0M0 or T1N2M0 patient should or should not undergo AC.…”

Section: Discussionmentioning

confidence: 96%

Indications for adjuvant chemotherapy in patients with AJCC stage IIa T3N0M0 and T1N2M0 gastric cancer—an east and west multicenter study

Huang

Desiderio

Chen

et al. 2019

Preprint

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Purpose To determine the indications for adjuvant chemotherapy (AC) in patients with stage IIa gastric cancer (T3N0M0 and T1N2M0) according to the 7 th American Joint Committee on Cancer (AJCC) Methods A total of 1593 patients with T3N0M0 or T1N2M0 stage gastric cancer were identified from the Surveillance, Epidemiology, and End Results (SEER) database for the period 1988.1-2012.12. Cox multiple regression, nomogram and decision curve analyses were performed. External validation was performed using databases of the Fujian Medical University Union Hospital (FJUUH) (n = 241) and Italy IMIGASTRIC center (n = 45). Results Cox multiple regression analysis showed that the risk factors that affected OS in patients receiving AC were age > 65 years old, T1N2M0, LN dissection number ≤ 15, tumor size > 20 mm, and nonadenocarcinoma. A nomogram was constructed to predict 5-year OS, and the patients were divided into those predicted to receive a high benefit (points ≤ 188) or a low benefit from AC (points > 188) according to a recursive partitioning analysis. OS was significantly higher for the high-benefit patients in the SEER database and the FJUUH dataset than in the non-AC patients (Log-rank < 0.05), and there was no significant difference in OS between the low-benefit patients and non-AC patients in any of the three centers (Log-rank = 0.154, 0.470, and 0.434, respectively). The decision curve indicated that the best clinical effect can be obtained when the threshold probability is 0-92%. Conclusion Regarding the controversy over whether T3N0M0 and T1N2M0 gastric cancer patients should be treated with AC, this study presents a predictive model that provides concise and accurate indications. These data show that high-benefit patients should receive AC.

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Decision curve analysis and external validation of the postoperative Karakiewicz nomogram for renal cell carcinoma based on a large single-center study cohort

Cited by 17 publications

References 21 publications

Using the weighted area under the net benefit curve for decision curve analysis

Using the weighted area under the net benefit curve for decision curve analysis

312 Accuracy capabilities comparisons between Karakiewicz, Kattan and Cindolo nomograms in predicting outcomes for renal cancer carcinoma: A systematic review and meta-analysis

Indications for adjuvant chemotherapy in patients with AJCC stage IIa T3N0M0 and T1N2M0 gastric cancer—an east and west multicenter study

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