How to implement magnetic resonance imaging before prostate biopsy in clinical practice: nomograms for saving biopsies

Borque‐Fernando, Ángel; Esteban, Luis; Celma, A.; Roche, S.; Planas, Jacques; Regis, Lucas; Torres, Inés de; Semidey, María Eugenia; Trilla, Enrique; Moróte, Juan

doi:10.1007/s00345-019-02946-w

Cited by 7 publications

(32 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, validated independent risk factors were used to construct a nomogram to assess the odds of BMs in patients with RCC. The predictive performance of this nomogram was explored by receiver operating characteristic (ROC) curves, calibration plots, probability density functions (PDF), and clinical utility curve (CUC) ( 17 ). The OS of mRCC patients with BMs was demonstrated by Kaplan-Meier curves.…”

Section: Methodsmentioning

confidence: 99%

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

et al. 2021

View full text Add to dashboard Cite

BackgroundBone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.MethodsPatients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.ResultsA total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.ConclusionsThe comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.

show abstract

Section: Methodsmentioning

confidence: 99%

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Current guidelines recommend the use of risk calculators (RCs), multiparametric magnetic resonance imaging (mpMRI), %free PSA (%fPSA), or other biomarkers to support biopsy decision in patients with PSA in the 2-10 ng/ml range [5]. While prostate volume assessment by ultrasound is not done routinely in men suspicious for PCa, it is available in men undergoing mpMRI, and PSA density can be used as an indicator for the presence of cancer [6]. However, to our knowledge, none of these have been evaluated specifically in the most challenging population, that is, men with elevated PSA, digitorectal (digital rectal examination [DRE]) finding consistent with an enlarged prostate, and no suspicion of cancer.…”

Section: Introductionmentioning

confidence: 99%

PROPOSe: A Real-life Prospective Study of Proclarix, a Novel Blood-based Test to Support Challenging Biopsy Decision-making in Prostate Cancer

Steuber

Heidegger

Kafka

et al. 2022

European Urology Oncology

View full text Add to dashboard Cite

“…The main reason for the current resurgence of PSAD may be the spread of pre-biopsy MRI [73], which provides the most accurate prostate volume assessment without additional cost; this helps avoid TRUS, which is time-consuming and cumbersome [74]. mPSAD has been directly incorporated as ng/mL 2 into some MRI-PM [23,26,32,33,75] or indirectly incorporated from serum PSA and prostate volume into other MRI-PM [28,38,[76][77][78][79]. Logistical regressions performed to develop MRI-PM have shown PSAD to be the most powerful predictor of csPCa after the PI-RADS score.…”

Section: Discussionmentioning

confidence: 99%

“…However, few MRI-PMs have been developed from the latest versions of PI-RADS; furthermore, external validation should be performed before their use. Easily accessible risk calculators (RCs) are essential for avoiding nomograms, which are cumbersome and time-consuming [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Recently, the Barcelona MRI-PM was developed from the following independent predictors: PI-RADS score v.2.0, age, serum PSA, DRE, PCa family history, type of biopsy (initial vs. repeat), and MRI-derived prostate volume.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of PSA Density and an MRI-Based Predictive Model to Improve the Selection of Candidates for Prostate Biopsy

Morote

Borque‐Fernando

Triquell

et al. 2022

Cancers

Self Cite

View full text Add to dashboard Cite

This study is a head-to-head comparison between mPSAD and MRI-PMbdex. The MRI-PMbdex was created from 2432 men with suspected PCa; this cohort comprised the development and external validation cohorts of the Barcelona MRI predictive model. Pre-biopsy 3-Tesla multiparametric MRI (mpMRI) and 2 to 4-core transrectal ultrasound (TRUS)-guided biopsies for suspicious lesions and/or 12-core TRUS systematic biopsies were scheduled. Clinically significant PCa (csPCa), defined as Gleason-based Grade Group 2 or higher, was detected in 934 men (38.4%). The area under the curve was 0.893 (95% confidence interval [CI]: 0.880–0.906) for MRI-PMbdex and 0.764 (95% CI: 0.774–0.783) for mPSAD, with p < 0.001. MRI-PMbdex showed net benefit over biopsy in all men when the probability of csPCa was greater than 2%, while mPSAD did the same when the probability of csPCa was greater than 18%. Thresholds of 13.5% for MRI-PMbdex and 0.628 ng/mL2 for mPSAD had 95% sensitivity for csPCa and presented 51.1% specificity for MRI-PMbdex and 19.6% specificity for mPSAD, with p < 0.001. MRI-PMbdex exhibited net benefit over mPSAD in men with prostate imaging report and data system (PI-RADS) <4, while neither exhibited any benefit in men with PI-RADS 5. Hence, we can conclude that MRI-PMbdex is more accurate than mPSAD for the proper selection of candidates for prostate biopsy among men with suspected PCa, with the exception of men with a PI-RAD S 5 score, for whom neither tool exhibited clinical guidance to determine the need for biopsy.

show abstract

How to implement magnetic resonance imaging before prostate biopsy in clinical practice: nomograms for saving biopsies

Cited by 7 publications

References 25 publications

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

PROPOSe: A Real-life Prospective Study of Proclarix, a Novel Blood-based Test to Support Challenging Biopsy Decision-making in Prostate Cancer

Comparative Analysis of PSA Density and an MRI-Based Predictive Model to Improve the Selection of Candidates for Prostate Biopsy

Contact Info

Product

Resources

About