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Objective: Many patients with localized prostate cancer (PCa) do not immediately undergo radical prostatectomy (RP) after biopsy confirmation. The aim of this study was to investigate the influence of “time-from-biopsy-to- prostatectomy” on adverse pathological outcomes.
Materials and Methods: Between January 2014 and December 2019, 437 patients with intermediate- and high risk PCa who underwent RP were retrospectively identified within our prospective institutional database. For the aim of our study, we focused on patients with intermediate- (n = 285) and high-risk (n = 151) PCa using D'Amico risk stratification. Endpoints were adverse pathological outcomes and proportion of nerve-sparing procedures after RP stratified by “time-from-biopsy-to-prostatectomy”: ≤3 months vs. >3 and < 6 months. Medians and interquartile ranges (IQR) were reported for continuously coded variables. The chi-square test examined the statistical significance of the differences in proportions while the Kruskal-Wallis test was used to examine differences in medians. Multivariable (ordered) logistic regressions, analyzing the impact of time between diagnosis and prostatectomy, were separately run for all relevant outcome variables (ISUP specimen, margin status, pathological stage, pathological nodal status, LVI, perineural invasion, nerve-sparing).
Results: We observed no difference between patients undergoing RP ≤3 months vs. >3 and <6 months after diagnosis for the following oncological endpoints: pT-stage, ISUP grading, probability of a positive surgical margin, probability of lymph node invasion (LNI), lymphovascular invasion (LVI), and perineural invasion (pn) in patients with intermediate- and high-risk PCa. Likewise, the rates of nerve sparing procedures were 84.3 vs. 87.4% (p = 0.778) and 61.0% vs. 78.8% (p = 0.211), for intermediate- and high-risk PCa patients undergoing surgery after ≤3 months vs. >3 and <6 months, respectively. In multivariable adjusted analyses, a time to surgery >3 months did not significantly worsen any of the outcome variables in patients with intermediate- or high-risk PCa (all p > 0.05).
Conclusion: A “time-from-biopsy-to-prostatectomy” of >3 and <6 months is neither associated with adverse pathological outcomes nor poorer chances of nerve sparing RP in intermediate- and high-risk PCa patients.
hintergrund: Männer in Deutschland sterben früher als Frauen und nehmen weniger häufig Krebsvorsorgeuntersuchungen wahr.
Fragestellung: Ziel war die prospektive Evaluation einer „Movember-Gesundheitsinitiative“ am Universitätsklinikum Frankfurt (UKF) im November 2019.
Methoden: Im Rahmen der „Movember-Gesundheitsinitiative“ wurde allen männlichen Mitarbeitern des UKF ab dem 45. Lebensjahr und bei erstgradiger familiärer Vorbelastung eines Prostatakarzinoms ab dem 40. Lebensjahr im November 2019 gemäß S3-Leitlinien der Deutschen Gesellschaft für Urologie (DGU) eine Prostatakarzinom-Vorsorgeuntersuchung angeboten.
Ergebnisse: Insgesamt nahmen 14,4 % der Mitarbeiter teil. Eine familiäre Vorbelastung gaben insgesamt 14,0 % Teilnehmer an. Das mediane Alter betrug 54 Jahre. Der mediane PSA(prostataspezifisches Antigen)-Wert lag bei 0,9 ng/ml, der mediane PSA-Quotient bei 30 %. Bei 5 % (n = 6) zeigte sich ein suspekter Tastbefund in der DRU (digital-rektale Untersuchung). Nach Altersstratifizierung (≤ 50 vs. > 50 Lebensjahre) zeigten sich signifikante Unterschiede im medianen PSA-Wert (0,7 ng/ml vs. 1,0 ng/ml, p < 0,01) und der bereits zuvor durchgeführten urologischen Vorsorge (12,1 vs. 42,0 %, p < 0,01). Vier Teilnehmer (3,3 %) zeigten erhöhte Gesamt-PSA-Werte. Bei 32,2 % der Teilnehmer zeigte sich mindestens ein kontrollbedürftiger Befund. Insgesamt wurden 6 Prostatabiopsien durchgeführt. Hierbei zeigte sich in einem Fall ein intermediate-risk Prostatakarzinom (Gleason 3 + 4, pT3a, pPn1, pNx, R0).
Schlussfolgerungung: Im Rahmen der UKF-Movember-Gesundheitsinitiative 2019 konnten durch ein Vorsorgeangebot 121 Männer für eine Prostatakrebs-Vorsorge inklusive PSA-Testung gewonnen werden. Auffällige/kontrollbedürftige Befunde zeigten sich bei 32,2 %. Bei einem Mitarbeiter wurde ein therapiebedürftiges Prostatakarzinom entdeckt und therapiert.
Wenn in Deutschland im Jahre 2003 die Siedlungs- und Verkehrsfläche täglich um 93 Hektar zunahm, sind die Auswirkungen dieser fortschreitenden Flächeninanspruchnahme gewaltig. Eine Schlüsselrolle beim Flächenverbrauch kommt den Gewerbegebieten zu. Der erste Teil der Studie analysiert die Ausweisung von insgesamt 51 Gewerbegebieten in Bayern, Baden-Württemberg und Thüringen. Nach definierten Auswahlkriterien werden die Gebiete in „positive“ und „negative“ Fallbeispiele gegliedert. In übersichtlicher Weise sind die wichtigsten Aspekte und Kriterien der verschiedenen Fallbeispiele nachvollziehbar aufgelistet und zusammengefasst. Im zweiten Teil der Studie werden mit Hilfe von Fotos, Luftbildern und Karten die flächenhaften Auswirkungen der Gewerbegebiete anschaulich dargestellt.
The aim of this study is to investigate the incidental prostate cancer (iPCa) detection rates of different embedding methods in a large, contemporary cohort of patients with bladder outlet obstruction (BOO) treated with transurethral surgery. We relied on an institutional tertiary-care database to identify BOO patients who underwent either transurethral loop resection or laser (Holmium:yttrium–aluminium garnet) enucleation of the prostate (HoLEP) between 01/2012 and 12/2019. Embedding methods differed with regard to the extent of the additional prostate tissue submitted following the first ten cassettes of primary embedding (cohort A: one [additional] cassette/10 g residual tissue vs. cohort B: complete embedding of the residual tissue). Detection rates of iPCa among the different embedding methods were compared. Subsequently, subgroup analyses by embedding protocol were repeated in HoLEP-treated patients only. In the overall cohort, the iPCa detection rate was 11% (46/420). In cohort A (n = 299), tissue embedding resulted in a median of 8 cassettes/patient (range 1–38) vs. a median of 15 (range 2–74) in cohort B (n = 121) (p < .001). The iPCa detection rate was 8% (23/299) and 19% (23/121) in cohort A vs. cohort B, respectively (p < .001). Virtual reduction of the number of tissue cassettes to ten cassettes resulted in a iPCa detection rate of 96% in both cohorts, missing one stage T1a/ISUP grade 1 carcinoma. Increasing the number of cassettes by two and eight cassettes, respectively, resulted in a detection rate of 100% in both cohorts without revealing high-grade carcinomas. Subgroup analyses in HoLEP patients confirmed these findings, demonstrated by a 100 vs. 96% iPCa detection rate following examination of the first ten cassettes, missing one case of T1a/ISUP 1. Examination of 8 additional cassettes resulted in a 100% detection rate. The extent of embedding of material obtained from transurethral prostate resection correlates with the iPCa detection rate. However, the submission of 10 cassettes appears to be a reasonable threshold to reduce resource utilization while maintaining secure cancer detection.
Background: To test the value of immunohistochemistry (IHC) staining in prostate biopsies for changes in biopsy results and its impact on treatment decision-making. Methods: Between January 2017–June 2020, all patients undergoing prostate biopsies were identified and evaluated regarding additional IHC staining for diagnostic purpose. Final pathologic results after radical prostatectomy (RP) were analyzed regarding the effect of IHC at biopsy. Results: Of 606 biopsies, 350 (58.7%) received additional IHC staining. Of those, prostate cancer (PCa) was found in 208 patients (59.4%); while in 142 patients (40.6%), PCa could be ruled out through IHC. IHC patients harbored significantly more often Gleason 6 in biopsy (p < 0.01) and less suspicious baseline characteristics than patients without IHC. Of 185 patients with positive IHC and PCa detection, IHC led to a change in biopsy results in 81 (43.8%) patients. Of these patients with changes in biopsy results due to IHC, 42 (51.9%) underwent RP with 59.5% harboring ≥pT3 and/or Gleason 7–10. Conclusions: Patients with IHC stains had less suspicious characteristics than patients without IHC. Moreover, in patients with positive IHC and PCa detection, a change in biopsy results was observed in >40%. Patients with changes in biopsy results partly underwent RP, in which 60% harbored significant PCa.
Background: The impact of MRI-lesion targeted (TB) and systematic biopsy (SB) Gleason score (GS) as a predictor for final pathological GS still remains unclear. Methods: All patients with TB + SB, and subsequent radical prostatectomy (RP) between 01/2014-12/2020 were analyzed. Rank correlation coefficient predicted concordance with pathological GS for patients’ TB and SB GS, as well as for the combined effect of SB + TB. Results: Of 159 eligible patients, 77% were biopsy naïve. For SB taken in addition to TB, a Spearman’s correlation of +0.33 was observed regarding final GS. Rates of concordance, upgrading, and downgrading were 37.1, 37.1 and 25.8%, respectively. For TB, a +0.52 correlation was computed regarding final GS. Rates of concordance, upgrading and downgrading for TB biopsy GS were 45.9, 33.3, and 20.8%, respectively. For the combination of SB + TB, a correlation of +0.59 was observed. Rates of concordance, upgrading and downgrading were 49.7, 15.1 and 35.2%, respectively. The combined effect of SB + TB resulted in a lower upgrading rate, relative to TB and SB (both p < 0.001), but a higher downgrading rate, relative to TB (p < 0.01). Conclusions: GS obtained from TB provided higher concordance and lower upgrading and downgrading rates, relative to SB GS with regard to final pathology. The combined effect of SB + TB led to the highest concordance rate and the lowest upgrading rate.
This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, |GE | and |GM|, using the ¯pp → μ+μ− reaction at PANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at PANDA, using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is ¯pp → π+π−,due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distribuations of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.