OBJECTIVES: To develop and validate strategies, using new ultrasound-based mathematical models, for the prediction of high-risk endometrial cancer and compare them with strategies using previously developed models or the use of preoperative grading only. METHODS: Women with endometrial cancer were prospectively examined using two-dimensional (2D) and three-dimensional (3D) gray-scale and color Doppler ultrasound imaging. More than 25 ultrasound, demographic and histological variables were analyzed. Two logistic regression models were developed: one 'objective' model using mainly objective variables; and one 'subjective' model including subjective variables (i.e. subjective impression of myometrial and cervical invasion, preoperative grade and demographic variables). The following strategies were validated: a one-step strategy using only preoperative grading and two-step strategies using preoperative grading as the first step and one of the new models, subjective assessment or previously developed models as a second step. RESULTS: One-hundred and twenty-five patients were included in the development set and 211 were included in the validation set. The 'objective' model retained preoperative grade and minimal tumor-free myometrium as variables. The 'subjective' model retained preoperative grade and subjective assessment of myometrial invasion. On external validation, the performance of the new models was similar to that on the development set. Sensitivity for the two-step strategy with the 'objective' model was 78% (95% CI, 69-84%) at a cut-off of 0.50, 82% (95% CI, 74-88%) for the strategy with the 'subjective' model and 83% (95% CI, 75-88%) for that with subjective assessment. Specificity was 68% (95% CI, 58-77%), 72% (95% CI, 62-80%) and 71% (95% CI, 61-79%) respectively. The two-step strategies detected up to twice as many high-risk cases as preoperative grading only. The new models had a significantly higher sensitivity than did previously developed models, at the same specificity. CONCLUSION: Two-step strategies with 'new' ultrasound-based models predict high-risk endometrial cancers with good accuracy and do this better than do previously developed models.Copyright © 2013 ISUOG. Published by John Wiley & Sons Ltd.

• MeSH
• časná detekce nádoru MeSH
• cervix uteri * diagnostické zobrazování   patologie   MeSH
• dospělí MeSH
• hodnocení rizik MeSH
• invazivní růst nádoru MeSH
• lidé středního věku MeSH
• lidé MeSH
• myometrium * diagnostické zobrazování   patologie   MeSH
• nádory endometria * diagnostické zobrazování   patologie   MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• staging nádorů MeSH
• teoretické modely MeSH
• ultrasonografie dopplerovská barevná * MeSH
• ultrazvuk * trendy   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH

BACKGROUND: Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection. METHODS: The IARC-ARCAGE European case-control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics. RESULTS: 1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74-0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61-0.64). CONCLUSION: We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction.

• MeSH
• dospělí MeSH
• hodnocení rizik MeSH
• lidé středního věku MeSH
• lidé MeSH
• logistické modely MeSH
• nádory hlavy a krku * epidemiologie   MeSH
• rizikové faktory MeSH
• senioři MeSH
• studie případů a kontrol MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• validační studie MeSH
• Geografické názvy
• Evropa MeSH
• Spojené království MeSH

BACKGROUND: Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis. METHODS: We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16-35 years) with psychosis from commonly recorded information at baseline. We developed two PsyMetRiC versions using the forced entry method: a full model (including age, sex, ethnicity, body-mass index, smoking status, prescription of a metabolically active antipsychotic medication, HDL concentration, and triglyceride concentration) and a partial model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early intervention services (Jan 1, 2013, to Nov 4, 2020) and externally validated in another UK early intervention service (Jan 1, 2012, to June 3, 2020). A sensitivity analysis was done in UK birth cohort participants (aged 18 years) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (C statistic) and calibration (calibration plots). We did a decision curve analysis and produced an online data-visualisation app. FINDINGS: 651 patients were included in the development samples, 510 in the validation sample, and 505 in the sensitivity analysis sample. PsyMetRiC performed well at internal (full model: C 0·80, 95% CI 0·74-0·86; partial model: 0·79, 0·73-0·84) and external validation (full model: 0·75, 0·69-0·80; and partial model: 0·74, 0·67-0·79). Calibration of the full model was good, but there was evidence of slight miscalibration of the partial model. At a cutoff score of 0·18, in the full model PsyMetRiC improved net benefit by 7·95% (sensitivity 75%, 95% CI 66-82; specificity 74%, 71-78), equivalent to detecting an additional 47% of metabolic syndrome cases. INTERPRETATION: We have developed an age-appropriate algorithm to predict the risk of incident metabolic syndrome, a precursor of cardiometabolic morbidity and mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for early intervention service clinicians and could enable personalised, informed health-care decisions regarding choice of antipsychotic medication and lifestyle interventions. FUNDING: National Institute for Health Research and Wellcome Trust.

• MeSH
• algoritmy * MeSH
• dospělí MeSH
• kardiometabolické riziko * MeSH
• lidé MeSH
• metabolický syndrom diagnóza   MeSH
• mladiství MeSH
• mladý dospělý MeSH
• psychotické poruchy * diagnóza   MeSH
• reprodukovatelnost výsledků MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• validační studie MeSH

INTRODUCTION: Historical nomograms for the prediction of cancer on prostate biopsy, developed in the sextant biopsy era are no more accurate today. The aim of this study was an independent external validation of a 10-core biopsy nomogram by Chun et al. (2007). MATERIAL AND METHODS: A total of 322 patients who presented for their initial biopsy in a tertiary care center and had all the necessary data available were included in the retrospective analysis. To validate the nomogram, receiver operator characteristic (ROC) curves and calibration plots were constructed. RESULTS: Area under the ROC curve calculated for our data using the nomogram was 0.773, similar to that reported originally. However, the nomogram systematically overestimated prostate cancer risk, which, for our data, could be resolved by subtracting 24 points from the total number of points of the nomogram. CONCLUSIONS: The nomogram yielded overall good predictive accuracy as measured by the area under the ROC curve, but it systematically overestimated PC probability in individual patients. However, we showed how the nomogram could easily be adapted to our patient sample, resolving the bias issue.

• Publikační typ
• časopisecké články MeSH

BACKGROUND: Particularly in the pediatric clinical pharmacology field, data-sharing offers the possibility of making the most of all available data. In this study, we utilize previously collected therapeutic drug monitoring (TDM) data of term and preterm newborns to develop a population pharmacokinetic model for phenobarbital. We externally validate the model using prospective phenobarbital data from an ongoing pharmacokinetic study in preterm neonates. METHODS: TDM data from 53 neonates (gestational age (GA): 37 (24-42) weeks, bodyweight: 2.7 (0.45-4.5) kg; postnatal age (PNA): 4.5 (0-22) days) contained information on dosage histories, concentration and covariate data (including birth weight, actual weight, post-natal age (PNA), postmenstrual age, GA, sex, liver and kidney function, APGAR-score). Model development was carried out using NONMEM® 7.3. After assessment of model fit, the model was validated using data of 17 neonates included in the DINO (Drug dosage Improvement in NeOnates)-study. RESULTS: Modelling of 229 plasma concentrations, ranging from 3.2 to 75.2mg/L, resulted in a one compartment model for phenobarbital. Clearance (CL) and volume (Vd) for a child with a birthweight of 2.6kg at PNA day 4.5 was 0.0091L/h (9%) and 2.38L (5%), respectively. Birthweight and PNA were the best predictors for CL maturation, increasing CL by 36.7% per kg birthweight and 5.3% per postnatal day of living, respectively. The best predictor for the increase in Vd was actual bodyweight (0.31L/kg). External validation showed that the model can adequately predict the pharmacokinetics in a prospective study. CONCLUSION: Data-sharing can help to successfully develop and validate population pharmacokinetic models in neonates. From the results it seems that both PNA and bodyweight are required to guide dosing of phenobarbital in term and preterm neonates.

• MeSH
• fenobarbital aplikace a dávkování   MeSH
• kojenec MeSH
• lidé MeSH
• monitorování léčiv metody   MeSH
• novorozenec nedonošený MeSH
• novorozenec MeSH
• prospektivní studie MeSH
• šíření informací metody   MeSH
• vztah mezi dávkou a účinkem léčiva MeSH
• Check Tag
• kojenec MeSH
• lidé MeSH
• mužské pohlaví MeSH
• novorozenec MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

OBJECTIVE: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. DESIGN: Two stage individual participant data meta-analysis. SETTING: Secondary and tertiary care. PARTICIPANTS: 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. DATA SOURCES: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge. MODEL SELECTION AND ELIGIBILITY CRITERIA: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. METHODS: Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. MAIN OUTCOME MEASURES: 30 day mortality or in-hospital mortality. RESULTS: Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28). CONCLUSION: The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

• MeSH
• analýza dat MeSH
• COVID-19 * MeSH
• lidé MeSH
• mortalita v nemocnicích MeSH
• prognóza MeSH
• statistické modely * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH

Burkitt lymphoma (BL) is a rare subtype of non-Hodgkin's lymphoma with an aggressive course. To refine the individual patient's prognosis, the International Prognostic Index for BL (BL-IPI) was recently developed and 4 risk factors (RF) were determined as optimal prognostic cut-off by multivariate analysis: age ≥40 years, lactate dehydrogenase >3× upper limit of normal, ECOG performance status ≥2, and central nervous system involvement. The BL-IPI distinguishes 3 prognostic groups, low (without RF), intermediate (1 RF), and high risk (2-4 RF), with significant differences in survival. The aim of the current project was to perform an external validation of the BL-IPI in 101 patients from the Registry of Czech Lymphoma Study Group diagnosed between 1999 and 2016 (median age, 45 years). The median follow-up was 50.4 months. The induction treatment included rituximab plus chemotherapy in 82% and chemotherapy alone in 18%. The overall response rate was 78% and the complete remission rate was 73%. According to BL-IPI, low/intermediate/high risk was present in 21/35/45% of patients, showing high similarity to the training BL-IPI US (United States) dataset (18/36/46%). There were significant differences in progression-free survival (PFS) and overall survival (OS) between patients with high vs. intermediate risk (PFS: hazard ratio 0.16, 95% confidence interval 0.08-0.31, p<0.0001; OS: hazard ratio 0.17, 95% confidence interval 0.09-0.35, p<0.0001) but not between patients with low vs. intermediate risk. The 3-year OS probability according to BL-IPI with low/intermediate/high risk was 96/76/59% in the BL-IPI training dataset vs. 95/85/45% in our external validation cohort; the 3-year PFS probability with low/intermediate/high risk was 92/72/53% in the BL-IPI training dataset vs. 95/85/42% in our cohort. In summary, our external validation of the BL-IPI confirmed a good separation of high-risk patients, who have a poor prognosis and for whom the new therapeutic approaches are needed; patients with low and intermediate risk had favorable clinical outcomes, and differences between these groups were not significant, likely due to a small number of patients.

• MeSH
• Burkittův lymfom * farmakoterapie   MeSH
• difúzní velkobuněčný B-lymfom * farmakoterapie   MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• prognóza MeSH
• protokoly protinádorové kombinované chemoterapie terapeutické užití   MeSH
• registrace MeSH
• retrospektivní studie MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH

AIM: A diabetes-related foot ulcer (DFU) is a major risk factor for lower-extremity amputation (LEA). To help clinicians predict the risk of LEA in people with DFU, the Diabetic Foot Risk Assessment (DIAFORA) system was developed but has never been externally validated. METHODS: In this study, 317 people presenting with a new DFU were included. At baseline, participants were grouped into three groups based on their DIAFORA score: low-risk (<15), medium-risk (15-25), and high-risk (>25). Participants were followed until healing, LEA, death, or at least 3 months. Discriminative accuracy was evaluated using sensitivity, specificity, likelihood ratios (LRs) and the area under the curve (AUC). RESULTS: All 317 participants completed at least 3 months of follow-up for a median duration of 146 days, during which 12.6% underwent minor amputation and 2.5% major amputation. People in the low- and medium-risk categories had major amputation rates of 0.9% and 2.1%, respectively, and negative LR of major LEA of 0.10 and 0.38, respectively, while the people in the high-risk category had an amputation rate of 25.0% and a positive LR of 12.9. The DIAFORA risk groups had a sensitivity of 75.0% and a specificity of 65.7%, with a corresponding AUC of 0.78 (95% CI 0.68-0.87) for the prediction of major LEA. CONCLUSION: The DIAFORA score is a useful tool for risk stratification of people presenting with a newly occurred DFU, with the external validation presenting results similar to those presented in the original study. The DIAFORA score may guide clinicians towards more individualized DFU treatment regimens.

• MeSH
• amputace * statistika a číselné údaje   MeSH
• diabetická noha * chirurgie   epidemiologie   MeSH
• dolní končetina chirurgie   MeSH
• hodnocení rizik metody   MeSH
• kohortové studie MeSH
• lidé středního věku MeSH
• lidé MeSH
• prediktivní hodnota testů MeSH
• prospektivní studie MeSH
• rizikové faktory MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• validační studie MeSH
• Geografické názvy
• Dánsko MeSH

OBJECTIVES: To externally and prospectively validate the International Ovarian Tumor Analysis (IOTA) Simple Rules (SRs), Logistic Regression model 2 (LR2) and Assessment of Different NEoplasias in the adneXa (ADNEX) model in a Portuguese population, comparing these approaches with subjective assessment and the risk-of-malignancy index (RMI), as well as with each other. This study also aimed to retrospectively validate the IOTA two-step strategy, using modified benign simple descriptors (MBDs) followed by the ADNEX model in cases in which MBDs were not applicable. METHODS: This was a prospective multicenter diagnostic accuracy study conducted between January 2016 and December 2021 of consecutive patients with an ultrasound diagnosis of at least one adnexal tumor, who underwent surgery at one of three tertiary referral centers in Lisbon, Portugal. All ultrasound assessments were performed by Level-II or -III sonologists with IOTA certification. Patient clinical data and serum CA 125 levels were collected from hospital databases. Each adnexal mass was classified as benign or malignant using subjective assessment, RMI, IOTA SRs, LR2 and the ADNEX model (with and without CA 125). The reference standard was histopathological diagnosis. In the second phase, all adnexal tumors were classified retrospectively using the two-step strategy (MBDs + ADNEX). Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios and overall accuracy were determined for all methods. Receiver-operating-characteristics curves were constructed and corresponding areas under the curve (AUC) were determined for RMI, LR2, the ADNEX model and the two-step strategy. The ADNEX model calibration plots were constructed using locally estimated scatterplot smoothing (LOESS). RESULTS: Of the 571 patients included in the study, 428 had benign disease and 143 had malignant disease (prevalence of malignancy, 25.0%), of which 42 had borderline ovarian tumor, 93 had primary invasive adnexal cancer and eight had metastatic tumors in the adnexa. Subjective assessment had an overall sensitivity of 97.9% and a specificity of 83.6% for distinguishing between benign and malignant lesions. RMI showed high specificity (95.6%) but very low sensitivity (58.7%), with an AUC of 0.913. The IOTA SRs were applicable in 80.0% of patients, with a sensitivity of 94.8% and specificity of 98.6%. The IOTA LR2 had a sensitivity of 84.6%, specificity of 86.9% and an AUC of 0.939, at a malignancy risk cut-off of 10%. At the same cut-off, the sensitivity, specificity and AUC for the ADNEX model with vs without CA 125 were 95.8% vs 98.6%, 82.5% vs 79.7% and 0.962 vs 0.960, respectively. The ADNEX model gave heterogeneous results for distinguishing between benign masses and different subtypes of malignancy, with the highest AUC (0.991) for discriminating benign masses from primary invasive adnexal cancer Stages II-IV, and the lowest AUC (0.696) for discriminating primary invasive adnexal cancer Stage I from metastatic lesion in the adnexa. The calibration plot suggested underestimation of the risk by the ADNEX model compared with the observed proportion of malignancy. The MBDs were applicable in 26.3% (150/571) of cases, of which none was malignant. The two-step strategy using the ADNEX model in the second step only, with and without CA 125, had AUCs of 0.964 and 0.961, respectively, which was similar to applying the ADNEX model in all patients. CONCLUSIONS: The IOTA methods showed good-to-excellent performance in the Portuguese population, outperforming RMI. The ADNEX model was superior to other methods in terms of accuracy, but interpretation of its ability to distinguish between malignant subtypes was limited by sample size and large differences in the prevalence of tumor subtypes. The IOTA MBDs are reliable in identifying benign disease. The two-step strategy comprising application of MBDs followed by the ADNEX model if MBDs are not applicable, is suitable for daily clinical practice, circumventing the need to calculate the risk of malignancy in all patients. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

• MeSH
• antigen CA-125 krev   MeSH
• diferenciální diagnóza MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• logistické modely MeSH
• nádory vaječníků * diagnostické zobrazování   patologie   klasifikace   krev   MeSH
• nemoci děložních adnex * diagnostické zobrazování   MeSH
• prediktivní hodnota testů MeSH
• prospektivní studie MeSH
• reprodukovatelnost výsledků MeSH
• retrospektivní studie MeSH
• ROC křivka MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• ultrasonografie * metody   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• validační studie MeSH
• Geografické názvy
• Portugalsko MeSH

PURPOSE: Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). METHODS: A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. RESULTS: Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. CONCLUSIONS: We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy.

• MeSH
• hodnocení rizik metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• nádory prostaty * patologie   diagnostické zobrazování   MeSH
• prediktivní hodnota testů MeSH
• prostata * patologie   diagnostické zobrazování   MeSH
• senioři MeSH
• ultrazvukem navigovaná biopsie metody   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• srovnávací studie MeSH
• validační studie MeSH