Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer

Niu, Xiang-Ke; He, Weimer; Zhang, Y.; Das, Sushant Kumar; Li, J.; Xiong, Yan; Wang, Y.-h.

doi:10.1016/j.crad.2016.12.005

Cited by 44 publications

(35 citation statements)

References 23 publications

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“…The closer the AUC value is to 1, the better discrimination capacity the prediction model has. Generally, a prediction model that performs with an AUC of 0.5–0.75 is considered acceptable, and AUC>0.75 indicates the model shows excellent discrimination [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a risk-prediction nomogram for patients with ureteral calculi associated with urosepsis: A retrospective analysis

Zhong

Cui

et al. 2018

PLoS ONE

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ObjectivesTo develop and validate an individualized nomogram to predict probability of patients with ureteral calculi developing into urosepsis.MethodsThe clinical data of 747 patients with ureteral calculi who were admitted from June 2013 to December 2015 in Affiliated Nanhai Hospital of Southern Medical University were selected and included in the development group, while 317 ureteral calculi patients who were admitted from January 2016 to December 2016 were included in the validation group. The independent risk factors of ureteral calculi associated with urosepsis were screened using univariate and multivariate logistic regression analyses. The corresponding nomogram prediction model was drawn according to the regression coefficients. The area under the receiver operating characteristic curves and the GiViTI calibration belts were used to estimate the discrimination and calibration of the prediction model, respectively.ResultsMultivariate logistic regression analysis showed that the five risk factors of gender, mean computed tomography(CT) attenuation value of hydronephrosis, functional solitary kidney, urine white blood cell(WBC) count and urine nitrite were independent risk factors of ureteral calculi associated with urosepsis. The areas under the receiver operating characteristic curve of the development group and validation group were 0.913 and 0.874 respectively, suggesting that the new prediction model had good discrimination capacity. P-values of the GiViTI calibration test of the two groups were 0.247 and 0.176 respectively, and the 95% CIs of GiViTI calibration belt in both groups did not cross the diagonal bisector line. Therefore the predicted probability of the model was consistent with the actual probability which suggested that the calibration of the prediction model in both groups were perfect and prediction model had strong concordance performance.ConclusionThe individualized prediction model for patients with ureteral calculi can facilitate improved screening and early identification of patients having higher risk of urosepsis.

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Section: Methodsmentioning

confidence: 99%

Development and validation of a risk-prediction nomogram for patients with ureteral calculi associated with urosepsis: A retrospective analysis

Zhong

Cui

et al. 2018

PLoS ONE

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“…75 indicated that the model showed an excellent degree of discrimination. 21 However, AUC alone was not enough to show the model's ability in improving decision-making. 22 With regard to clinical practicality, the net income was tested by the decision curve analysis (DCA).…”

Section: Discussionmentioning

confidence: 99%

<p>Development and Validation of a Model for Predicting the Risk of Death in Patients with Acinetobacter baumannii Infection: A Retrospective Study</p>

Zhang¹,

Zhao²,

Zheng³

et al. 2020

IDR

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This study aimed to develop and validate a personalized prediction model of death risk in patients with Acinetobacter baumannii (A. baumannii) infection and thus guide clinical research and support clinical decision-making. Patients and Methods: The development group is comprised of 350 patients with A. baumannii infection admitted between January 2013 and December 2015 in The First Affiliated Hospital of Anhui Medical University. Further, 272 patients in the validation group were admitted between January 2016 and December 2018. The univariate and multivariate logistic regression analyses were used to determine the independent risk factors for death with A. baumannii infection. The nomogram prediction model was established based on the regression coefficients. The discrimination of the proposed prediction model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves and decision curve analysis (DCA). The calibration diagram was used to evaluate the calibration degree of this model. Results: The infectious source, carbapenem-resistant A. baumannii (CRAB), hypoalbuminemia, Charlson comorbidity index (CCI), and mechanical ventilation (MV) were independent risk factors for death. The AUC of the ROC curve of the two groups was 0.768 and 0.792, respectively. The net income was higher when the probability was between 30% and 80%, showing a strong discrimination capacity of the proposed model. The calibration curve swung around the 45° oblique line, indicating a high degree of calibration. Conclusion: The proposed model helped predict the risk of death from A. baumannii infection, improve the early identification of patients with a higher risk of death, and guide clinical treatment.

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“…In order to improve the detection rate of cancer, Dikaios et al proposed the application of mp-MRI to construct Logistic model for the classi cation of csPCa (18). Niu et al believed that the model was effective in reducing the damage caused by unnecessary prostate biopsy while predicting high-risk PCa (19). By incorporating age and PI-RADS v2 score, they adjusted PSAD into the model, based on which a better AUC of 0.86 was achieved.…”

Section: Discussionmentioning

confidence: 99%

The Effect of Different Levels of Prostate-Specific Antigen and Prostate Imaging Report and Data System Scores on Logistic Regression Model to Predict Clinically Significant Prostate Cancer—A Single Center Retrospective Study

Zhang

Zhu

et al. 2020

Preprint

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Background: At present, there are various clinical regression models for predicting prostate cancer. But what about the diagnostic effectiveness of these models in different parameter ranges, and are the models applicable to everyone? This study aimed to study the influence of different levels of prostate-specific antigen (PSA) and Prostate Imaging Report and Data System version 2 (PI-RADS v2) scores on the regression model to predict clinically significant prostate cancer (csPCa).Methods: This retrospective study screened 251 patients from our hospital, who were divided into different groups. The regression model was established for each group to predict csPCa, and the effects of PSA and PI-RADS scores on each model were analyzed through the diagnostic effects of the model.Results: In patients with lower PSA scores, although the model was less sensitive than PSA, the AUC of the model was much greater. With the rise of PSA, the sensitivity of the model surpassed that of PSA, while the specificity became the opposite, and the AUC gap also gradually decreased. In the group with low PI-RADS score, the sensitivity and specificity of PI-RADS were lower than the model, and the gap was larger. Although the gap between the two gradually decreased with the increase of PI-RADS, the diagnostic efficiency of the model was still slightly larger than that of pure PI-RADS.Conclusion: As the PSA and PI-RADS v2 scores increase, the diagnostic advantages of the regression model will gradually decrease. However, for patients with low levels of PSA and PI-RADS scores,the regression model is less affected by PSA and PI-RADS, and can better utilize its clinical diagnostic advantages.

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Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer

Cited by 44 publications

References 23 publications

Development and validation of a risk-prediction nomogram for patients with ureteral calculi associated with urosepsis: A retrospective analysis

Development and validation of a risk-prediction nomogram for patients with ureteral calculi associated with urosepsis: A retrospective analysis

<p>Development and Validation of a Model for Predicting the Risk of Death in Patients with Acinetobacter baumannii Infection: A Retrospective Study</p>

The Effect of Different Levels of Prostate-Specific Antigen and Prostate Imaging Report and Data System Scores on Logistic Regression Model to Predict Clinically Significant Prostate Cancer—A Single Center Retrospective Study

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