scholarly journals External Validation of the Extraprostatic Extension Grade on MRI and Its Incremental Value to Clinical Models for Assessing Extraprostatic Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Lili Xu ◽  
Gumuyang Zhang ◽  
Xiaoxiao Zhang ◽  
Xin Bai ◽  
Weigang Yan ◽  
...  

ObjectivesTo externally validate the extraprostatic extension (EPE) grade criteria on MRI and analyze the incremental value of EPE grade to clinical models of prostate cancer.MethodsA consecutive 130 patients who underwent preoperative prostate MRI followed by radical prostatectomy between January 2015 to January 2020 in our institution were retrospectively enrolled. The EPE grade, Cancer of the Prostate Risk Assessment (CAPRA), and Memorial Sloan Kettering Cancer Center nomogram (MSKCCn) score for each patient were assigned. Significant clinicopathological factors in univariate and multivariate analyses were combined with EPE grade to build the Clinical + EPE grade model, and the CAPRA and MSKCCn score were also combined with EPE grade to build the CAPRA + EPE grade and MSKCCn + EPE grade model, respectively. The area under the curve (AUC), sensitivity and specificity of these models were calculated to evaluate their diagnostic performance. Calibration and decision curve analyses were used to analyze their calibration performance and clinical utility.ResultsThe AUC for predicting EPE was 0.767–0.778 for EPE grade, 0.704 for CAPRA, and 0.723 for MSKCCn. After combination with EPE grade, the AUCs of these clinical models increased significantly than using clinical models along (P < 0.05), but was comparable with using EPE grade alone (P > 0.05). The calibration curves of EPE grade, clinical models and combined models showed that these models are well-calibrated for EPE. In the decision curve analysis, EPE grade showed slightly higher net benefit than MSKCCn and CAPRA.ConclusionThe EPE grade showed good performance for evaluating EPE in our cohort and possessed well clinical utility. Further combinations with the EPE grade could improve the diagnostic performance of clinical models.

2021 ◽  
Vol 10 (5) ◽  
pp. 999
Author(s):  
Zilvinas Venclovas ◽  
Tim Muilwijk ◽  
Aivaras J. Matjosaitis ◽  
Mindaugas Jievaltas ◽  
Steven Joniau ◽  
...  

Introduction: The aim of the study was to compare the performance of the 2012 Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms as a predictor for pelvic lymph node invasion (LNI) in men who underwent radical prostatectomy (RP) with pelvic lymph node dissection (PLND), to examine their performance and to analyse the therapeutic impact of using 7% nomogram cut-off. Materials and Methods: The study cohort consisted of 807 men with clinically localised prostate cancer (PCa) who underwent open RP with PLND between 2001 and 2019. The area under the curve (AUC) of the receiver operator characteristic analysis was used to quantify the accuracy of the 2012 Briganti and MSKCC nomograms to predict LNI. Calibration plots were used to visualise over or underestimation by the models and a decision curve analysis (DCA) was performed to evaluate the net benefit associated with the used nomograms. Results: A total of 97 of 807 patients had LNI (12%). The AUC of 2012 Briganti and MSKCC nomogram was 80.6 and 79.2, respectively. For the Briganti nomogram using the cut-off value of 7% would lead to reduce PLND in 47% (379/807), while missing 3.96% (15/379) cases with LNI. For the MSKCC nomogram using the cut-off value of 7% a PLND would be omitted in 44.5% (359/807), while missing 3.62% (13/359) of cases with LNI. Conclusions: Both analysed nomograms demonstrated high accuracy for prediction of LNI. Using a 7% nomogram cut-off would allow the avoidance up to 47% of PLNDs, while missing less than 4% of patients with LNI.


2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 353.1-353
Author(s):  
E. Van Delft ◽  
D. Lopes Barreto ◽  
A. Van der Helm - van Mil ◽  
C. Alves ◽  
J. Hazes ◽  
...  

Background:The Rotterdam Early Arthritis Cohort (REACH) rule [1] and Clinical Arthritis RulE (CARE) [2] are both evidence-based and easy-to-use methods developed to identify the presence of inflammatory arthritis (IA) in patients suspected by their general practitioner (GP). However, the clinical utility of both models in daily clinical practice in an independent primary care setting has not yet been established. While developed for recognizing IA, we believe that it is also important that the broader spectrum of inflammatory rheumatic diseases (IRDs) is correctly classified from primary care, to facilitate appropriate referral towards outpatient rheumatology clinics.Objectives:The primary objective was to determine the diagnostic performance and clinical utility of the REACH and CARE referral rules in identifying IA in an independent population of unselected suspected patients from primary care. Secondly we will assess the diagnostic performance and clinical utility of both models in identifying IRDs.Methods:This prospective observational diagnostic study consisted of adults newly suspected by their GP for the need of referral to the rheumatology outpatient clinic of the Maasstad Hospital in Rotterdam. Primary outcome was IA, consisting of rheumatoid arthritis, axial spondylitis and psoriatic arthritis. Secondary outcome was IRD, defined as IA plus arthritis in systemic disorders such as systemic lupus erythematosus, systemic sclerosis and morbus sjögren. Rheumatologist diagnosis was used as gold standard. To evaluate the clinical performance of the REACH and CARE referral rules in this population, diagnostic accuracy measures were investigated using the Youden index (J) [3]. Moreover, a net benefit approach [4] was used to determine clinical utility of both rules when compared to usual care.Results:This study consisted of 250 patients (22.8% male) with a mean age of 50.8 years (SD 13.9 years). In total 42 (17%) patients were diagnosed with IA and 55 (22%) with an IRD. Figure 1 presents the diagnostic performance in IA (Figure 1A) and in IRD (Figure 1B). For the primary outcome, the REACH model shows an AUC of 0.72 (95% CI 0.64-0.80) and the optimal cut-off point is indicated (J). The CARE model shows an AUC of 0.82 (95% CI 0.75-0.88) and at J there is a somewhat higher sensitivity and specificity. When taking the broader spectrum of IRDs as outcome, the AUC was 0.66 (95% CI 0.58-0.74) for the REACH and 0.76 (95% CI 0.69-0.83) for the CARE model. The net benefit analysis with either IA or IRD as outcome showed that the CARE was of the highest clinical value when compared to usual care.Conclusion:Both the REACH and CARE model showed a good diagnostic performance for detecting IA in an independent population of unselected suspected patients from primary care. Although not specifically developed to recognize the entire spectrum of IRDs, the CARE shows a good performance in doing so. When evaluating clinical utility, we see that both rules have a net benefit in recognizing IA as well as IRDs compared to usual care, however the CARE shows superiority over the REACH. By using the CARE, over half of all suspected patients can be withheld from expensive outpatient rheumatology care, implied by the high specificity of 70%. These results support the idea that incorporating these easy-to-use methods into primary care could lead to providing patients the right care at the right place and improving value based health care.References:[1]ten Brinck RM, van Dijk BT, van Steenbergen HW, le Cessie S, Numans ME. Development and validation of a clinical rule for recognition of early inflammatory arthritis. BMJ Open; 2018: 8[2]Alves, C. Improving early referral of inflammatory arthritis. In Early detection of patients at risk for rheumatoid arthritis – a challenge for primary and secondary care; 2015: 27-38 Ridderkerk, the Netherlands.[3]Fluss R, Faraggi D, Reiser B. Estimation of the Youden Index and its associated cutoff point. Biom J; 2005: 47(4): 458-472[4]Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making; 2006: 26(6): 565-574Disclosure of Interests:None declared


2020 ◽  
Vol 56 (6) ◽  
pp. 2003498 ◽  
Author(s):  
Rishi K. Gupta ◽  
Michael Marks ◽  
Thomas H.A. Samuels ◽  
Akish Luintel ◽  
Tommy Rampling ◽  
...  

The number of proposed prognostic models for coronavirus disease 2019 (COVID-19) is growing rapidly, but it is unknown whether any are suitable for widespread clinical implementation.We independently externally validated the performance of candidate prognostic models, identified through a living systematic review, among consecutive adults admitted to hospital with a final diagnosis of COVID-19. We reconstructed candidate models as per original descriptions and evaluated performance for their original intended outcomes using predictors measured at the time of admission. We assessed discrimination, calibration and net benefit, compared to the default strategies of treating all and no patients, and against the most discriminating predictors in univariable analyses.We tested 22 candidate prognostic models among 411 participants with COVID-19, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. Highest areas under receiver operating characteristic (AUROC) curves were achieved by the NEWS2 score for prediction of deterioration over 24 h (0.78, 95% CI 0.73–0.83), and a novel model for prediction of deterioration <14 days from admission (0.78, 95% CI 0.74–0.82). The most discriminating univariable predictors were admission oxygen saturation on room air for in-hospital deterioration (AUROC 0.76, 95% CI 0.71–0.81), and age for in-hospital mortality (AUROC 0.76, 95% CI 0.71–0.81). No prognostic model demonstrated consistently higher net benefit than these univariable predictors, across a range of threshold probabilities.Admission oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated here offered incremental value for patient stratification to these univariable predictors.


2020 ◽  
Vol 61 (11) ◽  
pp. 1570-1579 ◽  
Author(s):  
Are Losnegård ◽  
Lars A. R. Reisæter ◽  
Ole J. Halvorsen ◽  
Jakub Jurek ◽  
Jörg Assmus ◽  
...  

Background To investigate whether magnetic resonance (MR) radiomic features combined with machine learning may aid in predicting extraprostatic extension (EPE) in high- and non-favorable intermediate-risk patients with prostate cancer. Purpose To investigate the diagnostic performance of radiomics to detect EPE. Material and Methods MR radiomic features were extracted from 228 patients, of whom 86 were diagnosed with EPE, using prostate and lesion segmentations. Prediction models were built using Random Forest. Further, EPE was also predicted using a clinical nomogram and routine radiological interpretation and diagnostic performance was assessed for individual and combined models. Results The MR radiomic model with features extracted from the manually delineated lesions performed best among the radiomic models with an area under the curve (AUC) of 0.74. Radiology interpretation yielded an AUC of 0.75 and the clinical nomogram (MSKCC) an AUC of 0.67. A combination of the three prediction models gave the highest AUC of 0.79. Conclusion Radiomic analysis combined with radiology interpretation aid the MSKCC nomogram in predicting EPE in high- and non-favorable intermediate-risk patients.


2021 ◽  
Vol 11 (4) ◽  
pp. 763-775
Author(s):  
Natalia Majchrzak ◽  
Piotr Cieśliński ◽  
Maciej Głyda ◽  
Katarzyna Karmelita-Katulska

Introduction: Proper planning of laparoscopic radical prostatectomy (RP) in patients with prostate cancer (PCa) is crucial to achieving good oncological results with the possibility of preserving potency and continence. Aim: The aim of this study was to identify the radiological and clinical parameters that can predict the risk of extraprostatic extension (EPE) for a specific site of the prostate. Predictive models and multiparametric magnetic resonance imaging (mpMRI) data from patients qualified for RP were compared. Material and methods: The study included 61 patients who underwent laparoscopic RP. mpMRI preceded transrectal systematic and cognitive fusion biopsy. Martini, Memorial Sloan-Kettering Cancer Center (MSKCC), and Partin Tables nomograms were used to assess the risk of EPE. The area under the curve (AUC) was calculated for the models and compared. Univariate and multivariate logistic regression analyses were used to determine the combination of variables that best predicted EPE risk based on final histopathology. Results: The combination of mpMRI indicating or suspecting EPE (odds ratio (OR) = 7.49 (2.31–24.27), p < 0.001) and PSA ≥ 20 ng/mL (OR = 12.06 (1.1–132.15), p = 0.04) best predicted the risk of EPE for a specific side of the prostate. For the prediction of ipsilateral EPE risk, the AUC for Martini’s nomogram vs. mpMRI was 0.73 (p < 0.001) vs. 0.63 (p = 0.005), respectively (p = 0.131). The assessment of a non-specific site of EPE by MSKCC vs. Partin Tables showed AUC values of 0.71 (p = 0.007) vs. 0.63 (p = 0.074), respectively (p = 0.211). Conclusions: The combined use of mpMRI, the results of the systematic and targeted biopsy, and prostate-specific antigen baseline can effectively predict ipsilateral EPE (pT3 stage).


2019 ◽  
Vol 99 (1) ◽  
pp. 44-50
Author(s):  
J.A. Shariff ◽  
B. Cheng ◽  
P.N. Papapanou

A practical method to identify people who are most affected by periodontitis in their age group is currently unavailable. We focused on individuals with mean clinical attachment loss (CAL) above the 80th percentile within each of 10 age groups (5-y intervals between 30 and 74 y as well as ≥75 y). We developed predictive models using combined data from 2 cohorts (2009 to 2010 and 2011 to 2012) from the NHANES (National Health and Nutrition Examination Survey; development cohort [DC], n = 6,757), and we carried out external validation using data from a third NHANES cohort (2013 to 2014; validation cohort [VC], n = 3,447). We used 1) age-specific logistic regression models with stepwise selection to identify significant demographic variables, habits, medical conditions, and selected clinical periodontal parameters (proportion of teeth with probing depth ≥4 mm at incisors and molars and with visible [≥2 mm] recession) and to calculate propensity scores (PSs); 2) Youden’s J statistic to select optimum PS cutoffs to maximize diagnostic performance using receiver operating characteristic curves; and 3) bootstrap resampling with 1,000 replicates to validate the age-specific models and adjust the PS and optimal PS cutoffs for overfitting. The bootstrap-adjusted PSs were used as single predictors of mean CAL over the 80th percentile in the VC. The age-specific upper quintiles of mean CAL ranged between 1.63 and 3.24 mm in the DC and between 1.87 and 3.20 mm in the VC. The area under the curve of the models exceeded 0.85 in all age groups in the DC and 0.84 in the VC, indicating well-validated diagnostic performance. In the DC, sensitivity values ranged between 0.75 and 0.97 and exceeded 0.83 in 8 of 10 age groups. Corresponding values in the VC ranged between 0.56 and 0.89 and exceeded 0.68 in 8 of 10 age groups. We conclude that modeling that incorporates readily obtainable variables through a brief patient interview and an abbreviated periodontal examination accurately identifies individuals who are most affected by periodontitis in different ages.


Author(s):  
Cosimo De Nunzio ◽  
Jamil Ghahhari ◽  
Riccardo Lombardo ◽  
Giorgio Ivan Russo ◽  
Ana Albano ◽  
...  

Abstract 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.


2021 ◽  
Author(s):  
Satoshi Katayama ◽  
Victor M. Schuettfort ◽  
Benjamin Pradere ◽  
Keiichiro Mori ◽  
Hadi Mostafaei ◽  
...  

Abstract PurposeThe HGF/MET pathway is involved in cell motility, angiogenesis, proliferation, and cancer invasion. We assessed the clinical utility of plasma HGF level as a prognostic biomarker in patients with MIBC.MethodsWe retrospectively analyzed 565 patients with MIBC who underwent radical cystectomy. Logistic regression and Cox regression models were used, and predictive accuracies were estimated using the area under the curve and concordance index. To estimate the clinical utility of HGF, DCA and MCID were applied.ResultsPlasma HGF level was significantly higher in patients with advanced pathologic stage and LN metastasis (p=0.01 and p<0.001, respectively). Higher HGF levels were associated with an increased risk of harboring LN metastasis and non-organ-confined disease (OR1.21, 95%CI 1.12-1.32, p<0.001, and OR1.35, 95%CI 1.23-1.48, p<0.001, respectively) on multivariable analyses; the addition of HGF improved the predictive accuracies of a standard preoperative model (+7%, p<0.001 and +8%, p<0.001, respectively). According to the DCA and MCID, half of the patients had a net benefit by including HGF, but the absolute magnitude remained limited. In pre- and postoperative predictive models, a higher HGF level was significant prognosticator of worse RFS, OS, and CSS; in the preoperative model, the addition of HGF improved accuracies by 6% and 5% for RFS and CSS, respectively.ConclusionPreoperative HGF identified MIBC patients who harbored features of clinically and biologically aggressive disease. Plasma HGF could serve, as part of a panel, as a biomarker to aid in preoperative treatment planning regarding intensity of treatment in patients with clinically MIBC.


Author(s):  
Satoshi Katayama ◽  
Victor M. Schuettfort ◽  
Benjamin Pradere ◽  
Keiichiro Mori ◽  
Hadi Mostafaei ◽  
...  

Abstract Purpose The HGF/MET pathway is involved in cell motility, angiogenesis, proliferation, and cancer invasion. We assessed the clinical utility of plasma HGF level as a prognostic biomarker in patients with MIBC. Methods We retrospectively analyzed 565 patients with MIBC who underwent radical cystectomy. Logistic regression and Cox regression models were used, and predictive accuracies were estimated using the area under the curve and concordance index. To estimate the clinical utility of HGF, DCA and MCID were applied. Results Plasma HGF level was significantly higher in patients with advanced pathologic stage and LN metastasis (p = 0.01 and p < 0.001, respectively). Higher HGF levels were associated with an increased risk of harboring LN metastasis and non-organ-confined disease (OR1.21, 95%CI 1.12–1.32, p < 0.001, and OR1.35, 95%CI 1.23–1.48, p < 0.001, respectively) on multivariable analyses; the addition of HGF improved the predictive accuracies of a standard preoperative model (+ 7%, p < 0.001 and + 8%, p < 0.001, respectively). According to the DCA and MCID, half of the patients had a net benefit by including HGF, but the absolute magnitude remained limited. In pre- and postoperative predictive models, a higher HGF level was significant prognosticator of worse RFS, OS, and CSS; in the preoperative model, the addition of HGF improved accuracies by 6% and 5% for RFS and CSS, respectively. Conclusion Preoperative HGF identified MIBC patients who harbored features of clinically and biologically aggressive disease. Plasma HGF could serve, as part of a panel, as a biomarker to aid in preoperative treatment planning regarding intensity of treatment in patients with clinical MIBC.


BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuxin Ding ◽  
Runyi Jiang ◽  
Yuhong Chen ◽  
Jing Jing ◽  
Xiaoshuang Yang ◽  
...  

Abstract Background Previous studies reported cutaneous melanoma in head and neck (HNM) differed from those in other regions (body melanoma, BM). Individualized tools to predict the survival of patients with HNM or BM remain insufficient. We aimed at comparing the characteristics of HNM and BM, developing and validating nomograms for predicting the survival of patients with HNM or BM. Methods The information of patients with HNM or BM from 2004 to 2015 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The HNM group and BM group were randomly divided into training and validation cohorts. We used the Kaplan-Meier method and multivariate Cox models to identify independent prognostic factors. Nomograms were developed via the rms and dynnom packages, and were measured by the concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration plots. Results Of 70,605 patients acquired, 21% had HNM and 79% had BM. The HNM group contained more older patients, male sex and lentigo maligna melanoma, and more frequently had thicker tumors and metastases than the BM group. The 5-year cancer-specific survival (CSS) and overall survival (OS) rates were 88.1 ± 0.3% and 74.4 ± 0.4% in the HNM group and 92.5 ± 0.1% and 85.8 ± 0.2% in the BM group, respectively. Eight variables (age, sex, histology, thickness, ulceration, stage, metastases, and surgery) were identified to construct nomograms of CSS and OS for patients with HNM or BM. Additionally, four dynamic nomograms were available on web. The internal and external validation of each nomogram showed high C-index values (0.785–0.896) and AUC values (0.81–0.925), and the calibration plots showed great consistency. Conclusions The characteristics of HNM and BM are heterogeneous. We constructed and validated four nomograms for predicting the 3-, 5- and 10-year CSS and OS probabilities of patients with HNM or BM. These nomograms can serve as practical clinical tools for survival prediction and individual health management.


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