A proposal of a new nomogram for predicting upstaging in contemporary D’Amico low-risk prostate cancer patients

Development and validation of a nomogram for evaluating the incident risk of carotid atherosclerosis in patients with type 2 diabetes

INTRODUCTION

The purpose of this study was to evaluate the clinical characteristics of carotid atherosclerotic disease in patients with type 2 diabetes mellitus, investigate its risk factors, and develop and validate an easy-to-use nomogram.

METHODS

1049 patients diagnosed with type 2 diabetes were enrolled and randomly assigned to the training and validation cohorts. Multivariate logistic regression analysis identified independent risk factors. A method combining least absolute shrinkage and selection operator with 10-fold cross-validation was used to screen for characteristic variables associated with carotid atherosclerosis. A nomogram was used to visually display the risk prediction model. Nomogram performance was evaluated using the C index, the area under the receiver operating characteristic curve, and calibration curves. Clinical utility was assessed by decision curve analysis.

RESULTS

Age, nonalcoholic fatty liver disease, and OGTT3H were independent risk factors associated with carotid atherosclerosis in patients with diabetes. Age, nonalcoholic fatty liver disease, smoke, HDL-C, and LDL-C were characteristic variables used to develop the nomogram. The area under the curve for the discriminative power of the nomogram was 0.763 for the training cohort and 0.717 for the validation cohort. The calibration curves showed that the predicted probability matched the actual likelihood. The results of the decision curve analysis indicated that the nomograms were clinically useful.

DISCUSSION

A new nomogram was developed and validated for assessing the incident risk of carotid atherosclerotic in patients with diabetes; this nomogram may act as a clinical tool to assist clinicians in making treatment recommendations.

Nomogram Predicting Downgrading in National Comprehensive Cancer Network High-risk Prostate Cancer Patients Treated with Radical Prostatectomy

BACKGROUND

Some high-risk prostate cancer (PCa) patients may show more favorable Gleason pattern at radical prostatectomy (RP) than at biopsy.

OBJECTIVE

To test whether downgrading could be predicted accurately.

DESIGN, SETTING, AND PARTICIPANTS

Within the Surveillance, Epidemiology and End Results database (2010-2016), 6690 National Comprehensive Cancer Network (NCCN) high-risk PCa patients were identified.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES

We randomly split the overall cohort between development and validation cohorts (both n = 3345, 50%). Multivariable logistic regression models used biopsy Gleason, prostate-specific antigen, number of positive prostate biopsy cores, and cT stage to predict downgrading. Accuracy, calibration, and decision curve analysis (DCA) tested the model in the external validation cohort.

RESULTS AND LIMITATIONS

Of 6690 patients, 50.3% were downgraded at RP, and of 2315 patients with any biopsy pattern 5, 44.1% were downgraded to RP Gleason pattern ≤4 + 4. Downgrading rates were highest in biopsy Gleason pattern 5 + 5 (84.1%) and lowest in 3 + 4 (4.0%). In the validation cohort, the logistic regression model-derived nomogram predicted downgrading with 71.0% accuracy, with marginal departures (±3.3%) from ideal predictions in calibration. In DCA, a net benefit throughout all threshold probabilities was recorded, relative to treat-all or treat-none strategies and an algorithm based on an average downgrading rate of 50.3%. All steps were repeated in the subgroup with any biopsy Gleason pattern 5, to predict RP Gleason pattern ≤4 + 4. Here, a second nomogram (n = 2315) yielded 68.0% accuracy, maximal departures from ideal prediction of ±5.7%, and virtually the same DCA pattern as the main nomogram.

CONCLUSIONS

Downgrading affects half of all high-risk PCa patients. Its presence may be predicted accurately and may help with better treatment planning.

PATIENT SUMMARY

Downgrading occurs in every second high-risk prostate cancer patients. The nomograms developed by us can predict these probabilities accurately.

Predicting medication nonadherence risk in the Chinese type 2 diabetes mellitus population - establishment of a new risk nomogram model: a retrospective study

Objective

To investigate the risk factors of medication nonadherence in patients with type 2 diabetes mellitus (T2DM) and to establish a risk nomogram model.

Methods

This retrospective study enrolled patients with T2DM, which were divided into two groups based on their scores on the Morisky Medication Adherence scale. Univariate and multivariate logistic regression analyses were used to screen for independent risk factors for medication nonadherence. A risk model was then established using a nomogram. The accuracy of the prediction model was evaluated using centrality measurement index and receiver operating characteristic curves. Internal verification was evaluated using bootstrapping validation.

Results

A total of 338 patients with T2DM who included in the analysis. Logistic regression analysis showed that the educational level, monthly per capita income, drug affordability, the number of drugs used, daily doses of drugs and the time spent taking medicine were all independent risk factors for medication nonadherence. Based on these six risk factors, a nomogram model was established to predict the risk of medication nonadherence, which was shown to be very reliable. Bootstrapping validated the nonadherence nomogram model for patients with T2DM.

Conclusions

This nomogram model could be used to evaluate the risks of drug nonadherence in patients with T2DM.

Analysis of risk factors for Gleason score upgrading after radical prostatectomy in a Chinese cohort

Background

To study the risk factors of Gleason score upgrading (GSU) after radical prostatectomy (RP) in a Chinese cohort.

Methods

The data of 637 patients who underwent prostate biopsy and RP in our hospital from January 2014 to January 2021 were retrospectively analyzed. The age, body mass index (BMI), prostate‐specific antigen (PSA) level, testosterone (TT) level, neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), eosinophil‐to‐lymphocyte ratio (ELR), aspartate aminotransferase/alanine transaminase (AST/ALT) ratio, clinical stage, the biopsy method, and pathological characteristics of specimens after biopsy and RP were collected for all patients. Univariate analysis and multivariate logistic regression analysis were used to analyze the risk factors of GSU after RP. The predictive efficacy was verified with the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. We performed the analysis separately in the overall cohort and in the cohort with Gleason score (GS) = 6.

Results

In the overall cohort, 177 patients (27.79%) had GSU, and in the GS = 6 cohort, 68 patients (60.18%) had GSU. Multivariate logistic regression analysis showed that in the overall cohort, clinical stage ≥T2c (OR = 3.201, p < 0.001), the number of positive cores ≥3 (OR = 0.435, p = 0.04), and positive rate of biopsy (OR = 0.990, p = 0.016) can affect whether GS is upgraded, and the AUC of the combination of the three indicators for predicting the occurrence of GSU was 0.627. In the GS = 6 cohort, multivariate logistic regression analysis showed that clinical stage ≥T2c (OR = 4.690, p = 0.001) was a risk factor for GSU, and the AUC predicted to occur GSU is 0.675.

Conclusion

Clinical stage ≥T2c, the number of positive cores <3, and lower positive rate of biopsy are the risk factors of GSU. This study may provide some references for clinicians to judge the accuracy of biopsy pathological grading and formulate treatment strategies, but the specific effect still needs clinical practice certification.

Development of a nomogram predicting the probability of stone free rate in patients with ureteral stones eligible for semi-rigid primary laser uretero-litothripsy

Purpose

Few tools are available to predict uretero-lithotripsy outcomes in patients with ureteral stones. Aim of our study was to develop a nomogram predicting the probability of stone free rate in patients undergoing semi-rigid uretero-lithotripsy (ULT) for ureteral stones.

Methods

From January 2014 onwards, patients undergoing semi-rigid Ho: YAG laser uretero-lithotripsy for ureteral stones were prospectively enrolled in two centers. Patients were preoperatively evaluated with accurate clinical history, urinalysis and renal function. Non-contrast CT was used to define number, location and length of the stones and eventually the presence of hydronephrosis. A nomogram was generated based on the logistic regression model used to predict ULT success.

Results

Overall, 356 patients with mean age of 54 years (IQR 44/65) were enrolled. 285/356 (80%) patients were stone free at 1 month. On multivariate analysis single stone (OR 1.93, 95% CI 1.05–3.53, p = 0.034), stone size (OR 0.92, 95% CI 0.87–0.97, p = 0.005), distal position (OR 2.12, 95% CI 1.29–3.48, p = 0.003) and the absence of hydronephrosis (OR 2.02, 95% CI 1.08–3.78, p = 0.029) were predictors of success and these were used to develop a nomogram. The nomogram based on the model presented good discrimination (area under the curve [AUC]: 0.75), good calibration (Hosmer–Lemeshow test, p > 0.5) and a net benefit in the range of probabilities between 15 and 65%. Internal validation resulted in an AUC of 0.74.

Conclusions

The implementation of our nomogram could better council patients before treatment and could be used to identify patients at risk of failure. External validation is warranted before its clinical implementation.

Prediction of Pathologic Findings with MRI-Based Clinical Staging Using the Bayesian Network Modeling in Prostate Cancer: A Radiation Oncologist Perspective

Purpose

This study aimed to develop a model for predicting pathologic extracapsular extension (ECE) and seminal vesicle invasion (SVI) while integrating magnetic resonance imaging-based T-staging (cTMRI, cT1c-cT3b).

Materials and Methods

A total of 1,915 who underwent radical prostatectomy between 2006-2016 met the inclusion/exclusion criteria. We performed a multivariate logistic regression analysis as well as Bayesian network (BN) modeling based on possible confounding factors. The BN model was internally validated using 5-fold validation.

Results

According to the multivariate logistic regression analysis, initial prostate-specific antigen (iPSA) (β=0.050, p<0.001), percentage of positive biopsy cores (PPC) (β=0.033, p<0.001), both lobe involvement on biopsy (β=0.359, p=0.009), Gleason score (β=0.358, p<0.001), and cTMRI (β=0.259, p<0.001) were significant factors for ECE. For SVI, iPSA (β=0.037, p<0.001), PPC (β=0.024, p<0.001), GS (β=0.753, p<0.001), and cTMRI (β=0.507, p<0.001) showed statistical significance. BN models to predict ECE and SVI were also successfully established. The overall AUC/accuracy of the BN models were 0.76/73.0% and 0.88/89.6% for ECE and SVI, respectively. According to internal comparison between the BN model and Roach formula, BN model had improved AUC values for predicting ECE (0.76 vs. 0.74; p=0.060) and SVI (0.88 vs. 0.84, p<0.001).

Conclusion

wo models to predict pathologic ECE and SVI integrating cTMRI were established and installed on a separate website for public access to guide radiation oncologists.

Prostate Cancer, Version 2.2019, NCCN Clinical Practice Guidelines in Oncology

The NCCN Guidelines for Prostate Cancer include recommendations regarding diagnosis, risk stratification and workup, treatment options for localized disease, and management of recurrent and advanced disease for clinicians who treat patients with prostate cancer. The portions of the guidelines included herein focus on the roles of germline and somatic genetic testing, risk stratification with nomograms and tumor multigene molecular testing, androgen deprivation therapy, secondary hormonal therapy, chemotherapy, and immunotherapy in patients with prostate cancer.

Urotensin II receptor expression in prostate cancer patients: A new possible marker

BACKGROUND

Urotensin II receptor has been poorly studied in prostate cancer. To evaluate the expression of urotensin II receptor (UII-R) in patients undergoing radical prostatectomy.

METHODS

Overall, we identified 140 patients treated with retropubic radical prostatectomy (RP) in one center. UII-R was evaluated in prostate biopsies with immunohistochemical staining, resulting in a granular cytoplasmic positivity, through automated system using the kit Urotensin II Receptor Detection System provided by Pharmabullet srl. Immunostained slides were independently and blindly evaluated by ten uro-pathologists. To evaluate UTII-R expression three different parameters were considered: localization, granules dimensions and intensity of expression. A score from 0 to 3 was applied to each parameter to obtain a score from 0 to 9. Each parameter and the total score were evaluated as predictors of high grade disease on surgical pathology and of advanced stage disease. Accuracy of total score for the prediction of upgrading and upstaging was analyzed using receiver operator characteristics curve and decision curve analysis (DCA).

RESULTS

On radical prostatectomy 92/140 (66%) presented high grade disease on surgical pathology. Patients with high grade disease presented an apical distribution of the receptor, larger granules and a more intense expression when compared to patients with low grade disease. A well they presented a higher total score. Subscores and total scores were found to be predictors of upgrading and upstaging. On ROC analysis total score presented an AUC of 0.72 and 0.70, respectively, for the prediction of upgrading and upstaging. On DCA total score showed a clinical benefit in the prediction of adverse pathological outcomes.

CONCLUSION

Urotensin II receptor is a potential marker of adverse pathological outcomes. Further studies should confirm our data and evaluate its role as a prognostic marker.

External validation of Imamura nomogram as a tool to predict preoperatively laser semi-rigid ureterolithotripsy outcomes

BACKGROUND

We aimed to validate Imamura nomogram for prediction of stone free rate in patients undergoing ureterolithotripsy (ULT).

METHODS

From January 2013 to June 2016, patients undergoing laser semi-rigid ULT were prospectively enrolled at our center. All patients were preoperatively assessed with clinical history, blood samples, uranalysis and non-contrast enhanced computed tomography (CT). Treatment efficacy was assessed 1 month later by non-contrast enhanced CT. ROC curve was used to evaluate the performance characteristics of Imamura nomogram.

RESULTS

Overall, we enrolled 275 patients. Median age was 55 years (IQR: 46/64), median length of stone was 9.8 mm (IQR: 7.5/12). Pyuria was detected in 6/275 (2.1%) patients. Stones were located at ureteropelvic junction in 55/275 (19%) patients, proximal ureter in 74/275 (26%) patients, middle and distal ureter in 66/275 (24%) patients and 82/275 (30%) patients, respectively. At 1-month follow-up, 209/275 (76%) patients were stone free. Imamura nomogram presented an AUC of 0.67 (95% CI: 0.580-0.761) for the prediction of stone free rate. At the best cut-off value of 75%, sensitivity was 76%, specificity was 55%, positive predictive value (PPV) was 83% and negative predictive value was 45%.

CONCLUSIONS

We firstly validated Imamura nomogram in a European cohort study. It proved a reasonable accuracy (area under curve: 0.67) and a good PPV (83%). Further studies should confirm our results to support the routine clinical use of Imamura nomogram as a tool to predict ULT outcomes.

Metabolic syndrome increases the risk of upgrading and upstaging in patients with prostate cancer on biopsy: a radical prostatectomy multicenter cohort study

BACKROUND

Recently metabolic syndrome has been associated to an increased risk of advanced disease. Aim of our study is to investigate the association of metabolic syndrome (MetS) with the risk of prostate cancer (PCa) upgrading and upstaging after radical prostatectomy (RP).

METHODS

From 2012 and 2016, 400 consecutive men underwent RP at three referral centers in Italy and were enrolled into a prospective database. Blood pressure, body mass index and waist circumference were measured before RP. Blood samples were also collected and tested for total PSA, fasting glucose, triglycerides and HDLs. Logistic regression analyses were used to assess the association between MetS, defined according to Adult Treatment Panel III, and the risk of upgrading and upstaging), using the new Prognostic Grade Group (PGG) classification system.

RESULTS

Overall 148/400 (37%) men were diagnosed with MetS and most of these reported up-grading (54.5%) and up-staging (56.8%). These events were significantly more common in this population and MetS was a risk factor for up-staging and up-grading on multivariable analysis. Patients with MetS presented worst accuracy (72 vs. 84%; p = 0.001) and worst kappa coefficient of agreement (k = 0.439 ± 0.071 vs. k = 0.553 ± 0.071) between needle biopsy and radical prostatectomy specimens when compared to patients without MetS.

CONCLUSIONS

MetS represents a significant risk factor for upgrading and upstaging. Accuracy of PGG system on biopsy is poor in patients with MetS, therefore results should be evaluated carefully in this population.

Prediction models for prostate cancer outcomes: what is the state of the art in 2017?

PURPOSE OF REVIEW

Prostate cancer (PCa) remains a significant public health burden, with multiple points for decision-making at all stages of the disease. Given the amount and variety of data that may influence disease management, prediction models have been published to assist clinicians and patients in making decisions about the best next course of action at many disease states. We sought to review the most important studies related to PCa prediction models since 2016 and evaluate their impact upon the evolving field of risk modeling in PCa.

RECENT FINDINGS

There has been a significant amount of work published in the past year concerning risk modeling in PCa at all stages of disease, ranging from screening to predicting survival with metastatic disease. The majority of recent publications focus upon the addition of a new biomarker to prediction models or upon validating previously published prediction models. In particular, MRI has been the topic of a number of more recent studies.

SUMMARY

Prediction modeling in PCa currently compares the area under the receiver operating curve between models with and without the biomarker of interest to predict the outcome of interest in multiple disease states, ranging from diagnosis to prediction of survival with metastatic disease. Future work should provide additional information regarding clinical impact and measures of prediction confidence.

Development of novel nomograms to predict renal functional outcomes after laparoscopic adrenalectomy in patients with primary aldosteronism

PURPOSE

Most patients with primary aldosteronism (PA) show a significant decrease in kidney function after surgery. Glomerular hyperfiltration peculiar to PA can mask mild renal failure before surgery. The aim of this retrospective study was to investigate postoperative renal functional outcomes in PA patients from different viewpoints and to develop novel nomograms that can predict renal functional outcomes in PA patients after surgery.

METHODS

130 Japanese PA patients treated by unilateral laparoscopic adrenalectomy were retrospectively surveyed. Pre- and postoperative changes of estimated glomerular filtration rates (eGFRs) and the distribution of eGFR classification were compared. Furthermore, predictors of the following renal functional outcomes were investigated: (I) the percentage decrease >25% in eGFR and (II) the presence of new-onset eGFR <45 ml/min/1.73 m². Finally, two nomograms that predicted postoperative renal functional outcomes were developed and internally validated.

RESULTS

At 6 months, the average decrease in eGFR was 16.7 mL/min/1.73 m² (corresponding percent decrease: 19.7%). Upstaging of eGFR classification was observed in 54.6% of patients. Age, potassium, plasma aldosterone concentration, and initial eGFR were incorporated into a nomogram predicting a >25% postoperative decrease in eGFR. Duration of hypertension and initial eGFR were incorporated into a nomogram predicting new-onset eGFR <45 ml/min/1.73 m². The value of the area under the receiver operating characteristics curve for each nomogram was 0.82 and 0.74, respectively.

CONCLUSION

The first nomograms that can predict postoperative renal outcomes in PA patients were developed. They will help clinicians calculate the probability of renal dysfunction in PA patients after laparoscopic adrenalectomy.