Public-Private Partnership: Allheilmittel für die Finanzkrisen der öffentlichen Haushalte oder Risikofaktor?

Public Private Partnership (PPP) findet in Deutschland in den letzten Jahren immer mehr Verbreitung. Die Bandbreite für PPP-Projekte reicht von Bundesfernstraßen bis zu Schulen, Verwaltungsgebäuden, Krankenhäusern, Schwimmbädern etc. Führt dieses Modell zu einer Optimierung von Kosten und Erträgen oder stellt es einen Risikofaktor für den öffentlichen Sektor dar? Gerold Krause-Junk, Universität Hamburg, sieht darin vor allem einen Weg, den Konflikt zwischen "Effizienz- und Verteilungszielen" zu entschärfen: "Die allokative Aufgabe wird dann dem privaten bzw. einem weitgehend nach privatwirtschaftlichem Kalkül handelnden Anbieter überlassen; die Verteilungsaufgabe bleibt beim Staat ..." Für Frank Littwin, Finanzministerium des Landes Nordrhein-Westfalen, sind die PPP-Projekte kein Allheilmittel und auch kein wesentlicher Beitrag zur Haushaltskonsolidierung, sie führen aber zu deutlich mehr Kostentransparenz und befördern wirtschaftliches Handeln. Und nicht zuletzt sind sie ein wichtiges Instrument zur Verwaltungsmodernisierung. Dietrich Budäus und Birgit Grüb, Universität Hamburg, betonen, dass die Wirtschaftlichkeitsbeurteilung bei PPP-Projekten eine Reihe von Problemen aufwirft. Und für Lars P. Feld und Jan Schnellenbach, Universität Heidelberg, hängt die finanzpolitische Sinnhaftigkeit von PPP-Arrangements von den Details der Kooperation ab. Dabei sollte der öffentliche Sektor vor allem die langfristigen Folgekosten seiner Investitionen im Blick haben, wenn er die Zusammenarbeit mit den Privaten sucht.Public Private Partnership, Öffentlicher Sektor, Öffentlicher Haushalt, Finanzmarktkrise, Deutschland

Increasing the attractiveness of surgical disciplines for students: Implications of a robot-assisted hands-on training course for medical education

BackgroundStructured implementation of robot-assisted surgery in the field of medical education is lacking. We assessed students' interest in robot-assisted surgery and tested if the implementation of a hands-on robotic course into the curriculum could increase the interest to join a surgical discipline in general and especially in female students, since women are clearly underrepresented in surgical disciplines.MethodsAfter a prostate cancer focused seminar, 100 students were 1:1 randomized into two groups. Group B: Baseline characteristics and professional interest were assessed prior and after a hands-on robotic course, using a da Vinci® console with simulator (da Vinci® Surgical training, Intuitive Surgical Inc., USA). Group A served as post-interventional consistency control group, received the questionnaire only once after the hands-on training.ResultsThe male to female ratio of students was 54% and 46%. The interest to turn into urology/surgery, categorized as yes”, “no”, “maybe” changed from 18 to 16%, 36 to 30% and 46 to 54% respectively after the hands-on robotic course (p < 0.001). Also, the positive attitude towards the surgical field significantly increased (20 vs. 48%; p < 0.001). Comparing male and female students, virtually identical proportions (23 vs. 23%) opted for joining urology or surgery as a discipline, whereas rejection (45 vs. 25%) and perchance (32 vs. 50%) of that notion differed between genders (p = 0.12).ConclusionOur results demonstrate great demand for implementing robotic training into medical education for an up-to-date curriculum. Although the decision process on career choice is widely multifactorial, stereotypes associated with surgical disciplines should be eliminated. This could have a particularly positive effect on the recruitment of female medical students since women are clearly underrepresented in surgical disciplines although currently and with increasing proportions, more female students are enrolled in medical schools then male

MRI-targeted or standard biopsy for prostate-cancer diagnosis

Background Multiparametric magnetic resonance imaging (MRI), with or without targeted biopsy, is an alternative to standard transrectal ultrasonography-guided biopsy for prostate-cancer detection in men with a raised prostate-specific antigen level who have not undergone biopsy. However, comparative evidence is limited. Methods In a multicenter, randomized, noninferiority trial, we assigned men with a clinical suspicion of prostate cancer who had not undergone biopsy previously to undergo MRI, with or without targeted biopsy, or standard transrectal ultrasonography-guided biopsy. Men in the MRI-targeted biopsy group underwent a targeted biopsy (without standard biopsy cores) if the MRI was suggestive of prostate cancer; men whose MRI results were not suggestive of prostate cancer were not offered biopsy. Standard biopsy was a 10-to-12-core, transrectal ultrasonography-guided biopsy. The primary outcome was the proportion of men who received a diagnosis of clinically significant cancer. Secondary outcomes included the proportion of men who received a diagnosis of clinically insignificant cancer. Results A total of 500 men underwent randomization. In the MRI-targeted biopsy group, 71 of 252 men (28%) had MRI results that were not suggestive of prostate cancer, so they did not undergo biopsy. Clinically significant cancer was detected in 95 men (38%) in the MRI-targeted biopsy group, as compared with 64 of 248 (26%) in the standard-biopsy group (adjusted difference, 12 percentage points; 95% confidence interval [CI], 4 to 20; P=0.005). MRI, with or without targeted biopsy, was noninferior to standard biopsy, and the 95% confidence interval indicated the superiority of this strategy over standard biopsy. Fewer men in the MRI-targeted biopsy group than in the standard-biopsy group received a diagnosis of clinically insignificant cancer (adjusted difference, -13 percentage points; 95% CI, -19 to -7; P<0.001). Conclusions The use of risk assessment with MRI before biopsy and MRI-targeted biopsy was superior to standard transrectal ultrasonography-guided biopsy in men at clinical risk for prostate cancer who had not undergone biopsy previously. (Funded by the National Institute for Health Research and the European Association of Urology Research Foundation; PRECISION ClinicalTrials.gov number, NCT02380027 .)

Multiparametric ultrasound: evaluation of greyscale, shear wave elastography and contrast-enhanced ultrasound for prostate cancer detection and localization in correlation to radical prostatectomy specimens

Abstract Background The diagnostic pathway for prostate cancer (PCa) is advancing towards an imaging-driven approach. Multiparametric magnetic resonance imaging, although increasingly used, has not shown sufficient accuracy to replace biopsy for now. The introduction of new ultrasound (US) modalities, such as quantitative contrast-enhanced US (CEUS) and shear wave elastography (SWE), shows promise but is not evidenced by sufficient high quality studies, especially for the combination of different US modalities. The primary objective of this study is to determine the individual and complementary diagnostic performance of greyscale US (GS), SWE, CEUS and their combination, multiparametric ultrasound (mpUS), for the detection and localization of PCa by comparison with corresponding histopathology. Methods/design In this prospective clinical trial, US imaging consisting of GS, SWE and CEUS with quantitative mapping on 3 prostate imaging planes (base, mid and apex) will be performed in 50 patients with biopsy-proven PCa before planned radical prostatectomy using a clinical ultrasound scanner. All US imaging will be evaluated by US readers, scoring the four quadrants of each imaging plane for the likelihood of significant PCa based on a 1 to 5 Likert Scale. Following resection, PCa tumour foci will be identified, graded and attributed to the imaging-derived quadrants in each prostate plane for all prostatectomy specimens. Primary outcome measure will be the sensitivity, specificity, negative predictive value and positive predictive value of each US modality and mpUS to detect and localize significant PCa evaluated for different Likert Scale thresholds using receiver operating characteristics curve analyses. Discussion In the evaluation of new PCa imaging modalities, a structured comparison with gold standard radical prostatectomy specimens is essential as first step. This trial is the first to combine the most promising ultrasound modalities into mpUS. It complies with the IDEAL stage 2b recommendations and will be an important step towards the evaluation of mpUS as a possible option for accurate detection and localization of PCa. Trial registration The study protocol for multiparametric ultrasound was prospectively registered on Clinicaltrials.gov on 14 March 2017 with the registry name ‘Multiparametric Ultrasound-Study for the Detection of Prostate Cancer’ and trial registration number NCT0309123

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

Objectives: The aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound. Methods: This study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation. Results: The multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored. Conclusions: In this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa. Key Points: • Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

\u3cp\u3eObjectives: The aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound. Methods: This study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation. Results: The multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored. Conclusions: In this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa. Key Points: • Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.\u3c/p\u3

Bone Scan Index as an Imaging Biomarker in Metastatic Castration-resistant Prostate Cancer : A Multicentre Study Based on Patients Treated with Abiraterone Acetate (Zytiga) in Clinical Practice

Background Abiraterone acetate (AA) prolongs survival in metastatic castration-resistant prostate cancer (mCRPC) patients. To measure treatment response accurately in bone, quantitative methods are needed. The Bone Scan Index (BSI), a prognostic imaging biomarker, reflects the tumour burden in bone as a percentage of the total skeletal mass calculated from bone scintigraphy. Objective To evaluate the value of BSI as a biomarker for outcome evaluation in mCRPC patients on treatment with AA according to clinical routine. Design, setting, and participants We retrospectively studied 104 mCRPC patients who received AA following disease progression after chemotherapy. All patients underwent whole-body bone scintigraphy before and during AA treatment. Baseline and follow-up BSI data were obtained using EXINI BoneBSI software (EXINI Diagnostics AB, Lund, Sweden). Outcome measurements and statistical analysis Associations between change in BSI, clinical parameters at follow-up, and overall survival (OS) were evaluated using the Cox proportional hazards regression models and Kaplan-Meier estimates. Discrimination between variables was assessed using the concordance index (C-index). Results and limitations Patients with an increase in BSI at follow-up of at most 0.30 (n = 54) had a significantly longer median survival time than those with an increase of BSI >0.30 (n = 50) (median: 16 vs 10 mo; p = 0.001). BSI change was also associated with OS in a multivariate Cox analysis including commonly used clinical parameters for prognosis (C-index = 0.7; hazard ratio: 1.1; p = 0.03). The retrospective design was a limitation. Conclusions Change in BSI was significantly associated with OS in mCRPC patients undergoing AA treatment following disease progression in a postchemotherapy setting. BSI may be a useful imaging biomarker for outcome evaluation in this group of patients, and it could be a valuable complementary tool in monitoring patients with mCRPC on second-line therapies. Patient summary Bone Scan Index (BSI) change is related to survival time in metastatic castration-resistant prostate cancer (mCRPC) patients on abiraterone acetate. BSI may be a valuable complementary decision-making tool supporting physicians monitoring patients with mCRPC on second-line therapies