Honors & Awards
Early Investigator Award for Prostate Cancer, Stanford (09/2018)
School of Medicine Dean's Postdoctoral Fellowship, Stanford Medical School (01/2018)
Best poster in the session "Infertility: Basic to clinical", European Urological Association Meeting 2017, London (03/2017)
1st Best Presentation, 20th Surgical Research Day (09/2016)
Research Scholarship, Dr. Werner jackstädt foundation (02/2016)
First and third best poster prizes, Kongress der Nordrhein-Westfälischen Gesellschaft für Urologie eV (2015)
Research Project Grant, Köln Fortune (04/2014-03/2015)
Best poster in the session "How to staging Prostate Cancer", European Urological Association Metting 2013, Milan (03/2013)
German Board for Urology, the North Rhine Medical Association, Urology (2015)
PhD in medicine, Westfälische Wilhelms-Universität Münster, Urology/Medical Informatics/Pathology (2010)
Staatsexamen, Westfälische Wilhelms-Universität Münster, Medicine (2009)
Specific spatial distribution patterns of tumor foci are associated with a low risk of biochemical recurrence in pT2pN0R0 prostate cancer.
World journal of urology
BACKGROUND: The previous attempts for pT2 substaging of prostate cancer (PCa) were insufficient in providing prognostic subgroups and the search for new prognostic parameters to subcategorize pT2 PCa is, therefore, needed. Therefore, the current study investigated the association between tumor distribution patterns and the biochemical recurrence (BCR)-free survival rate in pT2pN0R0 PCa.METHODS: Following radical prostatectomy, the anatomical distribution of PCa in 743 men with pT1-pT3pN0 disease was analyzed to determine 20 types of PCa distribution patterns. Then, 245 men with pT2pN0R0 PCa was considered for prognostic evaluation with a mean follow-up period of 60months. The spatial distribution patterns of PCa were evaluated using a cMDX©-based map model of the prostate. An analysis including 552,049 comparison operations was performed to assist in the evaluation of the similarity levels of the distribution patterns. A k-mean cluster analysis was applied to determine groups with similar distribution patterns. A decision-tree analysis was performed to divide these groups according to frequency of BCR. The BCR-free survival rate was analyzed using Kaplan-Meier curves. Predictors of progression were investigated using a Cox proportional hazards model.RESULTS: BCR occurred in 8.2% of the 245 men with pT2pN0R0 PCa. The median time of recurrence was 60months (interquartile range [IQR]: 42-77). In univariate and multivariate analyses, the prostate volume and the distribution patterns were independent predictors for BCR, whereas the sub-staging of pT2 tumors, Gleason grading, prostate-specific antigen (PSA) level, and relative tumor volume were not. In the patients with pT2pN0R0 disease, PCa distribution patterns with the apical involvement were significantly associated with the risk of BCR (P=0.001).CONCLUSION: The spread tumor patterns with the apical involvement are associated with a high-risk of BCR in the pT2 tumor stage. The vertical tumor spread could be considered in developing improved prognostic pT2 sub-categories.
View details for DOI 10.1007/s00345-020-03323-8
View details for PubMedID 32591903
The upregulation of hypoxia-related miRNA 210 in primary tumor of lymphogenic metastatic prostate cancer
2018; 10 (10): 1347–59
To show the association between the expression level of hsa-miR-210 (miR-210) and tumor progression in prostate cancer (PCa).Quantitative PCR was performed to measure miR-210 on 55 subjects with different tumor stages; our results were then validated using three external datasets. ANOVA and Tukey's post hoc analysis were performed for comparative analyses between different tumor stages. Using the transcriptome data from The Cancer Genome Atlas for CaP, the gene expression analyses were performed on experimentally validated target genes of miR-210 identified in Tarbase and miRWalk datasets.miR-210 was significantly higher in N1 PCa compared with nonmetastatic PCa, whereas the metastatic tumor revealed a lower expression level of miR-210 than the primary tumor.
View details for PubMedID 30109809
Novel lincRNA SLINKY is a prognostic biomarker in kidney cancer
2017; 8 (12): 18657-18669
Clear cell renal cell carcinomas (ccRCC) show a broad range of clinical behavior, and prognostic biomarkers are needed to stratify patients for appropriate management. We sought to determine whether long intergenic non-coding RNAs (lincRNAs) might predict patient survival. Candidate prognostic lincRNAs were identified by mining The Cancer Genome Atlas (TCGA) transcriptome (RNA-seq) data on 466 ccRCC cases (randomized into discovery and validation sets) annotated for ~21,000 lncRNAs. A previously uncharacterized lincRNA, SLINKY (Survival-predictive LINcRNA in KidneY cancer), was the top-ranked prognostic lincRNA, and validated in an independent University of Tokyo cohort (P=0.004). In multivariable analysis, SLINKY expression predicted overall survival independent of tumor stage and grade [TCGA HR=3.5 (CI, 2.2-5.7), P < 0.001; Tokyo HR=8.4 (CI, 1.8-40.2), P = 0.007], and by decision tree, ROC and decision curve analysis, added independent prognostic value. In ccRCC cell lines, SLINKY knockdown reduced cancer cell proliferation (with cell-cycle G1 arrest) and induced transcriptome changes enriched for cell proliferation and survival processes. Notably, the genes affected by SLINKY knockdown in cell lines were themselves prognostic and correlated with SLINKY expression in the ccRCC patient samples. From a screen for binding partners, we identified direct binding of SLINKY to Heterogeneous Nuclear Ribonucleoprotein K (HNRNPK), whose knockdown recapitulated SLINKY knockdown phenotypes. Thus, SLINKY is a robust prognostic biomarker in ccRCC, where it functions possibly together with HNRNPK in cancer cell proliferation.
View details for PubMedID 28423633
Postoperative Nomogram for Relapse-Free Survival in Patients with High Grade Upper Tract Urothelial Carcinoma
JOURNAL OF UROLOGY
2017; 197 (3): 580-588
We developed a prognostic nomogram for patients with high grade urothelial carcinoma of the upper urinary tract after extirpative surgery.Clinical data were available for 2,926 patients diagnosed with high grade urothelial carcinoma of the upper urinary tract who underwent extirpative surgery. Cox proportional hazard regression models identified independent prognosticators of relapse in the development cohort (838). A backward step-down selection process was applied to achieve the most informative nomogram with the least number of variables. The L2-regularized logistic regression was applied to generate the novel nomogram. Harrell's concordance indices were calculated to estimate the discriminative accuracy of the model. Internal validation processes were performed using bootstrapping, random sampling, tenfold cross-validation, LOOCV, Brier score, information score and F1 score. External validation was performed on an external cohort (2,088). Decision tree analysis was used to develop a risk classification model. Kaplan-Meier curves were applied to estimate the relapse rate for each category.Overall 35.3% and 30.7% of patients experienced relapse in the development and external validation cohort. The final nomogram included age, pT stage, pN stage and architecture. It achieved a discriminative accuracy of 0.71 and 0.76, and the AUC was 0.78 and 0.77 in the development and external validation cohort, respectively. Rigorous testing showed constant results. The 5-year relapse-free survival rates were 88.6%, 68.1%, 40.2% and 12.5% for the patients with low risk, intermediate risk, high risk and very high risk disease, respectively.The current nomogram, consisting of only 4 variables, shows high prognostic accuracy and risk stratification for patients with high grade urothelial carcinoma of the upper urinary tract following extirpative surgery, thereby adding meaningful information for clinical decision making.
View details for DOI 10.1016/j.juro.2016.09.078
View details for Web of Science ID 000395869600015
View details for PubMedID 27670916
- Blind Biobanking of the Prostatectomy Specimen: Critical Evaluation of the Existing Techniques and Development of the New 4-Level Tissue Extraction Model With High Sampling Efficacy. PROSTATE 2017
MUC1 Expression by Immunohistochemistry Is Associated with Adverse Pathologic Features in Prostate Cancer: A Multi-Institutional Study
2016; 11 (11)
The uncertainties inherent in clinical measures of prostate cancer (CaP) aggressiveness endorse the investigation of clinically validated tissue biomarkers. MUC1 expression has been previously reported to independently predict aggressive localized prostate cancer. We used a large cohort to validate whether MUC1 protein levels measured by immunohistochemistry (IHC) predict aggressive cancer, recurrence and survival outcomes after radical prostatectomy independent of clinical and pathological parameters.MUC1 IHC was performed on a multi-institutional tissue microarray (TMA) resource including 1,326 men with a median follow-up of 5 years. Associations with clinical and pathological parameters were tested by the Chi-square test and the Wilcoxon rank sum test. Relationships with outcome were assessed with univariable and multivariable Cox proportional hazard models and the Log-rank test.The presence of MUC1 expression was significantly associated with extracapsular extension and higher Gleason score, but not with seminal vesicle invasion, age, positive surgical margins or pre-operative serum PSA levels. In univariable analyses, positive MUC1 staining was significantly associated with a worse recurrence free survival (RFS) (HR: 1.24, CI 1.03-1.49, P = 0.02), although not with disease specific survival (DSS, P>0.5). On multivariable analyses, the presence of positive surgical margins, extracapsular extension, seminal vesicle invasion, as well as higher pre-operative PSA and increasing Gleason score were independently associated with RFS, while MUC1 expression was not. Positive MUC1 expression was not independently associated with disease specific survival (DSS), but was weakly associated with overall survival (OS).In our large, rigorously designed validation cohort, MUC1 protein expression was associated with adverse pathological features, although it was not an independent predictor of outcome after radical prostatectomy.
View details for DOI 10.1371/journal.pone.0165236
View details for PubMedID 27846218
Analysis of topographical distribution of prostate cancer and related pathological findings in prostatectomy specimens using cMDX document architecture.
JOURNAL OF BIOMEDICAL INFORMATICS
View details for DOI 10.1016/j.jbi.2015.12.009
Does postoperative radiation therapy impact survival in non-metastatic sarcomatoid renal cell carcinoma? A SEER-based study.
International Urology and Nephrology
View details for DOI 10.1007/s11255-015-1093-y
Clinical map document based on XML (cMDX): document architecture with mapping feature for reporting and analysing prostate cancer in radical prostatectomy specimens.
BMC Med Inform Decis Mak.
View details for DOI 10.1186/1472-6947-10-71
Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.
2022; 14 (13)
BACKGROUND: Prognostication is essential to determine the risk profile of patients with urologic cancers.METHODS: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability.RESULTS: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795-0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable.CONCLUSIONS: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.
View details for DOI 10.3390/cancers14133135
View details for PubMedID 35804904
Current Trends in Artificial Intelligence Application for Endourology and Robotic Surgery.
The Urologic clinics of North America
2021; 48 (1): 151–60
With the advent of electronic medical records and digitalization of health care over the past 2decades, artificial intelligence (AI) has emerged as an enabling tool to manage complex datasets and deliver streamlined data-driven patient care. AI algorithms have the ability to extract meaningful signal from complex datasets through an iterative process akin to human learning. Through advancements over the past decade in deep learning, AI-driven innovations have accelerated applications in health care. Herein, the authors explore the development of these emerging AI technologies, focusing on the application of AI to endourology and robotic surgery.
View details for DOI 10.1016/j.ucl.2020.09.004
View details for PubMedID 33218590
A prognostic score for overall survival in patients treated with abiraterone in the pre- and post-chemotherapy setting.
2019; 10 (49): 5082–91
Background: Therapy resistance remains a serious dilemma in metastatic castration-resistant prostate cancer (mCRPC) with primary or secondary resistance frequently occurring against any given therapy. Available prognostic models for Abiraterone Acetate (AA) are specifically designed for either pre- or post-chemotherapy settings and mostly based on trial datasets not necessarily reflecting real-life. Results: A score of 0-2 (low-risk) is associated with an OS-probability of 80.0% (95%CI: 71.3-90.6) and 50.5% (95%CI: 38.7-66.0) after 1 and 2 years while a score of 3-4 (high risk) is associated with an OS-probability of 35.3% (95%CI: 22.3-55.8) and 5.7% (95%CI: 1.5-21.8), respectively. The bootstrapping survival analysis of the scoring-system revealed a median c-index of 0.80 (IQR: 0.79-0.82). Material and Methods: We developed a scoring-system using four real-life parameters 117 mCRPC patients treated with AA either pre- or post-chemotherapy. These parameters were evaluated using COX regression analysis. The scoring-system consists of binary-categorized parameters; when any of these exceeds the given cut-off, one point is added up to a final score ranging between 0-4 points. The final score was stratified by a median threshold of 2 into low- and high-risk groups. We evaluated the discriminative ability of our scoring-system using concordance probability (C-index) and Kaplan-Meier-analysis and applied a 100-times bootstrap for survival analysis. Conclusions: Our study introduces a novel prognostic scoring-system for OS of real-life mCRPC patients receiving AA treatment irrespective of the line of therapy. The scoring-system is simple and can be easily utilized based on PSA and LDH values, neutrophil to lymphocyte ratio, and ECOG performance status.
View details for DOI 10.18632/oncotarget.27133
View details for PubMedID 31489117
Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer.
Health informatics journal
This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient's subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.
View details for DOI 10.1177/1460458219855884
View details for PubMedID 31238766
Genomic analysis of benign prostatic hyperplasia implicates cellular re-landscaping in disease pathogenesis.
Benign prostatic hyperplasia (BPH) is the most common cause of lower urinary tract symptoms in men. Current treatments target prostate physiology rather than BPH pathophysiology and are only partially effective. Here, we applied next-generation sequencing to gain new insight into BPH. By RNAseq, we uncovered transcriptional heterogeneity among BPH cases, where a 65-gene BPH stromal signature correlated with symptom severity. Stromal signaling molecules BMP5 and CXCL13 were enriched in BPH while estrogen regulated pathways were depleted. Notably, BMP5 addition to cultured prostatic myofibroblasts altered their expression profile towards a BPH profile that included the BPH stromal signature. RNAseq also suggested an altered cellular milieu in BPH, which we verified by immunohistochemistry and single-cell RNAseq. In particular, BPH tissues exhibited enrichment of myofibroblast subsets, whilst depletion of neuroendocrine cells and an estrogen receptor (ESR1)-positive fibroblast cell type residing near epithelium. By whole-exome sequencing, we uncovered somatic single-nucleotide variants (SNVs) in BPH, of uncertain pathogenic significance but indicative of clonal cell expansions. Thus, genomic characterization of BPH has identified a clinically-relevant stromal signature and new candidate disease pathways (including a likely role for BMP5 signaling), and reveals BPH to be not merely a hyperplasia, but rather a fundamental re-landscaping of cell types.
View details for DOI 10.1172/jci.insight.129749
View details for PubMedID 31094703
Diagnostic Classification of Cystoscopic Images Using Deep Convolutional Neural Networks
Diagnostic Classification of Cystoscopic Images Using Deep Convolutional Neural Networks
View details for DOI 10.1200/CCI.17.00126
Preoperative Serum Prostate-Specific Antigen Levels Vary According to the Topographical Distribution of Prostate Cancer in Prostatectomy Specimens
2015; 86 (4): 798-804
To evaluate whether the spatial distribution of prostate cancer (PCa) influences the concentration of prostate-specific antigen (PSA).An observational prospective study was performed in 775 consecutive men with preoperative PSA levels ≤20 ng/mL who underwent radical prostatectomy for organ-confined PCa. We evaluated prostate specimens using a cMDX-based map model of the prostate and determined the prostate volume, number of cancer foci, relative tumor volume, Gleason score, zone of origin, localization, and pathologic stage after stratification according to PSA levels categorized into 3 groups: <4 ng/mL, 4-10 ng/mL, and 10.1-20 ng/mL. The distribution of 5254 PCa foci was analyzed after stratification according to PSA levels and visualized on heat maps. A logistic regression analysis was performed to assess the odds ratios of PSA levels for the presence of PCa in 16 regions.PCa with PSA <4 ng/mL was predominantly localized to the apical part and the peripheral zone of the prostate. PCa with a PSA level 10.1-20 ng/mL (16.4% of cases) was observed more frequently in the anterior part and the base of the prostate than PCa with a PSA level <4 or 4-10 ng/mL (6% and 10%, respectively).Preoperative PSA levels vary according to the spatial distribution of PCa in radical prostatectomy specimens. The probability of anterior PCa is increased with higher PSA serum levels. Regions of interest harboring the PCa can be defined according to preoperative PSA and prostate volume. These findings are useful to optimize the focal therapy or to adjust the radiation fields.
View details for DOI 10.1016/j.urology.2015.07.029
View details for Web of Science ID 000366464300037
View details for PubMedID 26255036
Prostate cancers detected on repeat prostate biopsies show spatial distributions that differ from those detected on the initial biopsies
2015; 116 (1): 57-64
To evaluate the spatial distribution of prostate cancer detected at a single positive biopsy (PBx) and a repeat PBx (rPBx).We evaluated 533 consecutive men diagnosed with prostate cancer who underwent radical prostatectomy using a clinical map document based on XML (cMDX©)-based map model of the prostate. We determined the number of cancer foci, relative tumour volume, Gleason score, zone of origin, localisation, and pathological stage after stratification according to the number of PBx sessions (PBx vs rPBx). The distribution of 3966 prostate cancer foci was analysed and visualised on heat maps. The colour gradient of the heat map was reduced to six colours representing the frequency classification of prostate cancer using an image posterisation effect. Additionally, the spatial distribution of organ-confined prostate cancer between PBx and rPBx was evaluated.Prostate cancer diagnosed on PBx was mostly localised to the apical portion and the peripheral zone of the prostate. Prostate cancer diagnosed on rPBx was more frequently found in the anterior portion and the base of the prostate. Organ-confined prostate cancer foci were mostly localised in the dorsolateral zone of the prostate in men at PBx, whereas men at rPBx had more prostate cancer foci in the anterior portion.The spatial distribution of prostate cancer with rPBx differs significantly from the spatial distribution of prostate cancer with PBx. The whole anterior portion of the prostate should be considered by rPBx.
View details for DOI 10.1111/bju.12691
View details for Web of Science ID 000357048700012
View details for PubMedID 24552505
An electronic specimen collection protocol schema (eSCPS). Document architecture for specimen management and the exchange of specimen collection protocols between biobanking information systems.
Methods of information in medicine
2014; 53 (1): 29-38
The integrity of collection protocols in biobanking is essential for a high-quality sample preparation process. However, there is not currently a well-defined universal method for integrating collection protocols in the biobanking information system (BIMS). Therefore, an electronic schema of the collection protocol that is based on Extensible Markup Language (XML) is required to maintain the integrity and enable the exchange of collection protocols.The development and implementation of an electronic specimen collection protocol schema (eSCPS) was performed at two institutions (Muenster and Cologne) in three stages. First, we analyzed the infrastructure that was already established at both the biorepository and the hospital information systems of these institutions and determined the requirements for the sufficient preparation of specimens and documentation. Second, we designed an eSCPS according to these requirements. Finally, a prospective study was conducted to implement and evaluate the novel schema in the current BIMS.We designed an eSCPS that provides all of the relevant information about collection protocols. Ten electronic collection protocols were generated using the supplementary Protocol Editor tool, and these protocols were successfully implemented in the existing BIMS. Moreover, an electronic list of collection protocols for the current studies being performed at each institution was included, new collection protocols were added, and the existing protocols were redesigned to be modifiable. The documentation time was significantly reduced after implementing the eSCPS (5 ± 2 min vs. 7 ± 3 min; p = 0.0002).The eSCPS improves the integrity and facilitates the exchange of specimen collection protocols in the existing open-source BIMS.
View details for DOI 10.3414/ME13-01-0035
View details for PubMedID 24317441
The presence of positive surgical margins in patients with organ-confined prostate cancer results in biochemical recurrence at a similar rate to that in patients with extracapsular extension and PSA <= 10 ng/ml
UROLOGIC ONCOLOGY-SEMINARS AND ORIGINAL INVESTIGATIONS
2014; 32 (1)
We investigated whether patients with organ-confined prostate cancer (PCa) and positive surgical margins (SMs) had a similar biochemical recurrence (BCR) risk compared with patients with pT3a and preoperative prostate-specific antigen (PSA) levels ≤ 10ng/ml. Furthermore, we examined the effects of incorporating SM status, Gleason score (Gls), and preoperative PSA level into the discrimination accuracy of the current tumor node metastasis-staging system.We analyzed 863 PCa patients treated with radical prostatectomy from 1999 to 2008. Only individuals with pT2N0 or pT3N0, without neoadjuvant or adjuvant therapy, were included. We performed chi-square automatic interaction detection analysis to generate a classification model for predicting BCR by analyzing interactions between age at surgery, SM status, Gls, PSA, and tumor stage, tumor volume and relative tumor volume. Cox regression analyses tested the relationship between SM status and BCR rate after stratification according to T-stage and the novel classification. The predictive and discrimination accuracy of the current T-stage and of the classification model was quantified with time-dependent receiver operating characteristics and integrated discrimination improvement. The topographical association between extracapsular extension of PCa and positive SM was analyzed in patients with pT3aR1 using a computational reconstruction diagram of the prostate.The chi-square automatic interaction detection analysis found interactions among pT Stage, SM status, PSA and Gls and generated a classification model for BCR prediction: pT2R0, pT2R1, pT3a PSA ≤ 10 ng/ml, pT3a PSA>10 ng/ml and pT3b. Men with pT2R1 had a shorter time to BCR compared with men with pT3a-PSA ≤ 10 ng/ml (P<0.0001). Gls≥7a was correlated with a poorer BCR rate than Gls≤7a in men with pT2R1 or pT3a PSA ≤ 10 ng/ml (P = 0.012). The rank order (highest to lowest) for the risk of developing BCR was pT3b>pT2R1/pT3a-PSA>10 ng/ml>pT2R1/pT3a PSA ≤ 10 ng/ml>pT2R0 (P<0.0001). Discrimination accuracy gains were observed when PCa was stratified according to the novel classification (P<0.0001). A topographical association between extracapsular extension and positive SM was found in patients with pT3aR1 (P = 0.01).Patients with pT2R1 develop a similar BCR risk to that of patients with pT3a PSA ≤ 10 ng/ml. Gls≥7b is associated with a high BCR risk in these patient groups. Including SM status, PSA, and Gls in pT stage appears to improve prognostic stratification in patients with PCa.
View details for DOI 10.1016/j.urolonc.2012.11.021
View details for Web of Science ID 000347243300036
View details for PubMedID 23434425
Linkage of Data from Diverse Data Sources (LDS): A Data Combination Model Provides Clinical Data of Corresponding Specimens in Biobanking Information System
JOURNAL OF MEDICAL SYSTEMS
2013; 37 (5)
To provide sufficient clinical data for corresponding specimens from diverse databases established before the implementation of biobanks for research purposes with respect to data privacy regulations. For this purpose, we developed a data model called "linkage of data from diverse data sources (LDS)". The data model was developed to merge clinical data from an existing local database with biospecimen repository data in our serum bank for uro-oncology. This concept combines two data models based on XML: the first stores information required to connect multiple data sources and retrieve clinical data, and the second provides a data architecture to acquire clinical and repository data. All data were anonymized and encrypted using the Advanced Encryption Standard. X.509 certificates were applied to secure data access. Furthermore, we tested the feasibility of implementing these models in the information management system for biobanking. The data concept can provide clinical and repository data of biospecimens. Only authorized receivers can access these data. Sensitive and personal data are not accessible by the data receivers. The data receiver cannot backtrack to the individual donor using the data model. The acquired data can be converted into a text file format supported by familiar statistical software. Supplementary tools were implemented to generate and view XML documents based on these data models. This data model provides an effective approach to distribute clinical and repository data from different data sources to enable data analysis compliant with data privacy regulations.
View details for DOI 10.1007/s10916-013-9975-y
View details for Web of Science ID 000325022000003
View details for PubMedID 24022214
High-Grade Prostatic Intraepithelial Neoplasia (HGPIN) and topographical distribution in 1,374 prostatectomy specimens: Existence of HGPIN near prostate cancer
2013; 73 (10): 1115-1122
High-grade prostatic intraepithelial neoplasia (HGPIN) is believed to be a precursor of prostate cancer (PCa). This study evaluated whether HGPIN was located close to PCa in whole radical prostatectomy specimens (RPSs).We evaluated 1,374 prostate specimens from 1999 to 2010 using a cMDX-based map model of the prostate. The distribution of 10,439 PCa foci was analyzed and visualized on a heat map. The color gradient of the heat map was reduced to six colors representing the frequency classification of the relative frequency of PCa using an image posterization effect. We defined 22 regions in the prostate according to the frequency of PCa occurrence. Seven hundred ninety RPSs containing 6,374 PCa foci and 4,502 HGPIN foci were evaluated. The topographical association between PCa and HGPIN in the RPSs was analyzed by estimating the frequencies of PCa and HGPIN in 22 regions. A logistic regression analysis was performed to assess the odds ratios of HGPIN for the presence of PCa in 22 regions.Fifty-eight percent of PCa specimens included HGPIN and had significantly more favorable Gleason scores, lower PSA levels and smaller relative tumor volumes than isolated PCa specimens. HGPIN (68%) and PCa (69%) were predominantly localized to the apical half of the prostate. HGPIN was mainly concentrated in the peripheral zone medial to regions with high PCa frequencies. Upon logistic regression analysis, HGPIN was a significant predictor of PCa co-existence in 11 regions.HGPIN was located adjacent to PCa in whole RPSs. PCa concomitant with HGPIN had more favorable pathologic features than isolated PCa.
View details for DOI 10.1002/pros.22660
View details for Web of Science ID 000319680000010
View details for PubMedID 23532797
CMDX©-based single source information system for simplified quality management and clinical research in prostate cancer.
BMC medical informatics and decision making
2012; 12: 141-?
Histopathological evaluation of prostatectomy specimens is crucial to decision-making and prediction of patient outcomes in prostate cancer (PCa). Topographical information regarding PCa extension and positive surgical margins (PSM) is essential for clinical routines, quality assessment, and research. However, local hospital information systems (HIS) often do not support the documentation of such information. Therefore, we investigated the feasibility of integrating a cMDX-based pathology report including topographical information into the clinical routine with the aims of obtaining data, performing analysis and generating heat maps in a timely manner, while avoiding data redundancy.We analyzed the workflow of the histopathological evaluation documentation process. We then developed a concept for a pathology report based on a cMDX data model facilitating the topographical documentation of PCa and PSM; the cMDX SSIS is implemented within the HIS of University Hospital Muenster. We then generated a heat map of PCa extension and PSM using the data. Data quality was assessed by measuring the data completeness of reports for all cases, as well as the source-to-database error. We also conducted a prospective study to compare our proposed method with recent retrospective and paper-based studies according to the time required for data analysis.We identified 30 input fields that were applied to the cMDX-based data model and the electronic report was integrated into the clinical workflow. Between 2010 and 2011, a total of 259 reports were generated with 100% data completeness and a source-to-database error of 10.3 per 10,000 fields. These reports were directly reused for data analysis, and a heat map based on the data was generated. PCa was mostly localized in the peripheral zone of the prostate. The mean relative tumor volume was 16.6%. The most PSM were localized in the apical region of the prostate. In the retrospective study, 1623 paper-based reports were transferred to cMDX reports; this process took 15 ± 2 minutes per report. In a paper-based study, the analysis data preparation required 45 ± 5 minutes per report.cMDX SSIS can be integrated into the local HIS and provides clinical routine data and timely heat maps for quality assessment and research purposes.
View details for DOI 10.1186/1472-6947-12-141
View details for PubMedID 23206574
View details for PubMedCentralID PMC3519791
871 - Diagnostic classification of cystoscopic images using deep convolutional neural networks
View details for DOI 10.1016/S1569-9056(18)31703-2