Electronic Documentation of Intraoperative Observation of Cystoscopic Procedures Using the cMDX Information System.

PURPOSE: Accurate documentation of lesions during transurethral resection of bladder tumors (TURBT) is essential for precise diagnosis, treatment planning, and follow-up care. However, optimizing schematic documentation techniques for bladder lesions has received limited attention. MATERIALS AND METHODS: This prospective observational study used a cMDX-based documentation system that facilitates graphical representation, a lesion-specific questionnaire, and heatmap analysis with a posterization effect. We designed a graphical scheme for bladder covering bladder landmarks to visualize anatomic features and to document the lesion location. The lesion-specific questionnaire was integrated for comprehensive lesion characterization. Finally, spatial analyses were applied to investigate the anatomic distribution patterns of bladder lesions. RESULTS: A total of 97 TURBT cases conducted between 2021 and 2023 were included, identifying 176 lesions. The lesions were distributed in different bladder areas with varying frequencies. The distribution pattern, sorted by frequency, was observed in the following areas: posterior, trigone, lateral right and anterior, and lateral left and dome. Suspicious levels were assigned to the lesions, mostly categorized either as indeterminate or moderate. Lesion size analysis revealed that most lesions fell between 5 and 29 mm. CONCLUSION: The study highlights the potential of schematic documentation techniques for informed decision making, quality assessment, primary research, and secondary data utilization of intraoperative data in the context of TURBT. Integrating cMDX and heatmap analysis provides valuable insights into lesion distribution and characteristics.

Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.

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.

Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer.

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.

Diagnostic Classification of Cystoscopic Images Using Deep Convolutional Neural Networks.

PURPOSE: The recognition of cystoscopic findings remains challenging for young colleagues and depends on the examiner's skills. Computer-aided diagnosis tools using feature extraction and deep learning show promise as instruments to perform diagnostic classification. MATERIALS AND METHODS: Our study considered 479 patient cases that represented 44 urologic findings. Image color was linearly normalized and was equalized by applying contrast-limited adaptive histogram equalization. Because these findings can be viewed via cystoscopy from every possible angle and side, we ultimately generated images rotated in 10-degree grades and flipped them vertically or horizontally, which resulted in 18,681 images. After image preprocessing, we developed deep convolutional neural network (CNN) models (ResNet50, VGG-19, VGG-16, InceptionV3, and Xception) and evaluated these models using F1 scores. Furthermore, we proposed two CNN concepts: 90%-previous-layer filter size and harmonic-series filter size. A training set (60%), a validation set (10%), and a test set (30%) were randomly generated from the study data set. All models were trained on the training set, validated on the validation set, and evaluated on the test set. RESULTS: The Xception-based model achieved the highest F1 score (99.52%), followed by models that were based on ResNet50 (99.48%) and the harmonic-series concept (99.45%). All images with cancer lesions were correctly determined by these models. When the focus was on the images misclassified by the model with the best performance, 7.86% of images that showed bladder stones with indwelling catheter and 1.43% of images that showed bladder diverticulum were falsely classified. CONCLUSION: The results of this study show the potential of deep learning for the diagnostic classification of cystoscopic images. Future work will focus on integration of artificial intelligence-aided cystoscopy into clinical routines and possibly expansion to other clinical endoscopy applications.

From a Content Delivery Portal to a Knowledge Management System for Standardized Cancer Documentation.

Heterogeneous tumor documentation and its challenges of interpretation of medical terms lead to problems in analyses of data from clinical and epidemiological cancer registries. The objective of this project was to design, implement and improve a national content delivery portal for oncological terms. Data elements of existing handbooks and documentation sources were analyzed, combined and summarized by medical experts of different comprehensive cancer centers. Informatics experts created a generic data model based on an existing metadata repository. In order to establish a national knowledge management system for standardized cancer documentation, a prototypical tumor wiki was designed and implemented. Requirements engineering techniques were applied to optimize this platform. It is targeted to user groups such as documentation officers, physicians and patients. The linkage to other information sources like PubMed and MeSH was realized.

Preoperative prostate-specific antigen isoform p2PSA ≤ 22.5 pg/ml predicts advanced prostate cancer in patients undergoing radical prostatectomy.

BACKGROUND: The prediction value of prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA) for detecting advanced prostate cancer (PCa) remains unclear. Our objective was to evaluate the additional clinical utility of p2PSA compared with total PSA (tPSA), free PSA (fPSA), and preoperative Gleason score (Gls) in predicting locally advanced PCa (pT3/T4) with high-accuracy discrimination. The aim was to develop a novel classification based on p2PSA and preoperative Gls for predicting advanced PCa. MATERIALS AND METHODS: In 208 consecutive men diagnosed with clinically localized PCa who underwent radical prostatectomy, we determined the predictive and discriminatory accuracy of serum tPSA, fPSA, percentage of fPSA to tPSA, p2PSA, p2PSA density, percentage of p2PSA to fPSA, and the Prostate Health Index. The cutoff level of p2PSA with best accuracy was estimated. The novel classification was developed by analyzing the interaction between p2PSA and Gls in predicting pathologic outcomes using a chi-square automatic interaction detection analysis. Decision curve analysis was applied to test the clinical consequences of using the novel classification. RESULTS: On univariate analyses, p2PSA, p2PSA density, percentage of p2PSA to fPSA, and Prostate Health Index were accurate but were not independent predictors by multivariate analysis. The p2PSA cutoff level of 22.5 pg/ml showed the best accuracy level for predicting and discriminating advanced diseases (area under the curve [AUC] = 0.725, sensitivity = 51.4%, specificity = 81.8%). By chi-square automatic interaction detection, univariate and multivariate analysis, a p2PSA level > 22.5 pg/ml was significantly associated with an increased frequency and risk of advanced disease. In patients with a p2PSA level ≤ 22.5 pg/ml, 91.8% of Gleason sum 6 PCa was organ confined. The combination of p2PSA and Gls enhanced slightly but significantly the predictive and discriminatory accuracy for advanced disease (0.6%-3.6%). CONCLUSIONS: The p2PSA cutoff level of 22.5 pg/ml can accurately discriminate between organ-confined and advanced PCa. The additional use of p2PSA enhanced slightly the predictive accuracy for advanced PCa (pT3/pT4) and has limited additional predictive value in identifying aggressive PCa (Gls > 7a).

The need for harmonized structured documentation and chances of secondary use - results of a systematic analysis with automated form comparison for prostate and breast cancer.

INTRODUCTION: Medical documentation is a time-consuming task and there is a growing number of documentation requirements. In order to improve documentation, harmonization and standardization based on existing forms and medical concepts are needed. Systematic analysis of forms can contribute to standardization building upon new methods for automated comparison of forms. Objectives of this research are quantification and comparison of data elements for breast and prostate cancer to discover similarities, differences and reuse potential between documentation sets. In addition, common data elements for each entity should be identified by automated comparison of forms. MATERIALS AND METHODS: A collection of 57 forms regarding prostate and breast cancer from quality management, registries, clinical documentation of two university hospitals (Erlangen, Münster), research datasets, certification requirements and trial documentation were transformed into the Operational Data Model (ODM). These ODM-files were semantically enriched with concept codes and analyzed with the compareODM algorithm. Comparison results were aggregated and lists of common concepts were generated. Grid images, dendrograms and spider charts were used for illustration. RESULTS: Overall, 1008 data elements for prostate cancer and 1232 data elements for breast cancer were analyzed. Average routine documentation consists of 390 data elements per disease entity and site. Comparisons of forms identified up to 20 comparable data elements in cancer conference forms from both hospitals. Urology forms contain up to 53 comparable data elements with quality management and up to 21 with registry forms. Urology documentation of both hospitals contains up to 34 comparable items with international common data elements. Clinical documentation sets share up to 24 comparable data elements with trial documentation. Within clinical documentation administrative items are most common comparable items. Selected common medical concepts are contained in up to 16 forms. DISCUSSION: The amount of documentation for cancer patients is enormous. There is an urgent need for standardized structured single source documentation. Semantic annotation is time-consuming, but enables automated comparison between different form types, hospital sites and even languages. This approach can help to identify common data elements in medical documentation. Standardization of forms and building up forms on the basis of coding systems is desirable. Several comparable data elements within the analyzed forms demonstrate the harmonization potential, which would enable better data reuse. CONCLUSION: Identifying common data elements in medical forms from different settings with systematic and automated form comparison is feasible.

Interoperability in clinical research: from metadata registries to semantically annotated CDISC ODM.

Planning case report forms for data capture in clinical trials is a labor-insensitive and not formalized process. These CRFs are often neither standardized nor using defined data elements. Metadata registries as the NCI caDSR provide the capability to create forms based on common data elements. However, an exchange of these forms into clinical trial management systems through a standardized format like CDISC ODM is currently not offered. Thus, our objectives were to develop a mapping model between NCI forms and ODM. We analyzed 3012 NCI forms and included common data elements regarding their frequency and uniqueness. In this paper, we have created a mapping model between both formats and identified limitations in the conversion process: Semantic codes requested from the caDSR registry did not allow a proper mapping to ODM items and information like the number of module repetitions got lost. Summarized, it can be stated that our mapping model is feasible. However, mapping of semantic concepts in ODM needs to be specified more precisely.

Populating the i2b2 database with heterogeneous EMR data: a semantic network approach.

In an ongoing effort to share heterogeneous electronic medical record (EMR) data in an i2b2 instance between the University Hospitals Münster and Erlangen for joint cancer research projects, an ontology based system for the mapping of EMR data to a set of common data elements has been developed. The system translates the mappings into local SQL scripts, which are then used to extract, transform and load the facts data from each EMR into the i2b2 database. By using Semantic Web standards, it is the authors' goal to reuse the laboriously compiled "mapping knowledge" in future projects, such as a comprehensive cancer ontology or even a hospital-wide clinical ontology.

Mapping turnaround times (TAT) to a generic timeline: a systematic review of TAT definitions in clinical domains.

BACKGROUND: Assessing turnaround times can help to analyse workflows in hospital information systems. This paper presents a systematic review of literature concerning different turnaround time definitions. Our objectives were to collect relevant literature with respect to this kind of process times in hospitals and their respective domains. We then analysed the existing definitions and summarised them in an appropriate format. METHODS: Our search strategy was based on Pubmed queries and manual reviews of the bibliographies of retrieved articles. Studies were included if precise definitions of turnaround times were available. A generic timeline was designed through a consensus process to provide an overview of these definitions. RESULTS: More than 1000 articles were analysed and resulted in 122 papers. Of those, 162 turnaround time definitions in different clinical domains were identified. Starting and end points vary between these domains. To illustrate those turnaround time definitions, a generic timeline was constructed using preferred terms derived from the identified definitions. The consensus process resulted in the following 15 terms: admission, order, biopsy/examination, receipt of specimen in laboratory, procedure completion, interpretation, dictation, transcription, verification, report available, delivery, physician views report, treatment, discharge and discharge letter sent. Based on this analysis, several standard terms for turnaround time definitions are proposed. CONCLUSION: Using turnaround times to benchmark clinical workflows is still difficult, because even within the same clinical domain many different definitions exist. Mapping of turnaround time definitions to a generic timeline is feasible.

HIS-based Kaplan-Meier plots--a single source approach for documenting and reusing routine survival information.

BACKGROUND: Survival or outcome information is important for clinical routine as well as for clinical research and should be collected completely, timely and precisely. This information is relevant for multiple usages including quality control, clinical trials, observational studies and epidemiological registries. However, the local hospital information system (HIS) does not support this documentation and therefore this data has to generated by paper based or spreadsheet methods which can result in redundantly documented data. Therefore we investigated, whether integrating the follow-up documentation of different departments in the HIS and reusing it for survival analysis can enable the physician to obtain survival curves in a timely manner and to avoid redundant documentation. METHODS: We analysed the current follow-up process of oncological patients in two departments (urology, haematology) with respect to different documentation forms. We developed a concept for comprehensive survival documentation based on a generic data model and implemented a follow-up form within the HIS of the University Hospital Muenster which is suitable for a secondary use of these data. We designed a query to extract the relevant data from the HIS and implemented Kaplan-Meier plots based on these data. To re-use this data sufficient data quality is needed. We measured completeness of forms with respect to all tumour cases in the clinic and completeness of documented items per form as incomplete information can bias results of the survival analysis. RESULTS: Based on the form analysis we discovered differences and concordances between both departments. We identified 52 attributes from which 13 were common (e.g. procedures and diagnosis dates) and were used for the generic data model. The electronic follow-up form was integrated in the clinical workflow. Survival data was also retrospectively entered in order to perform survival and quality analyses on a comprehensive data set. Physicians are now able to generate timely Kaplan-Meier plots on current data. We analysed 1029 follow-up forms of 965 patients with survival information between 1992 and 2010. Completeness of forms was 60.2%, completeness of items ranges between 94.3% and 98.5%. Median overall survival time was 16.4 years; median event-free survival time was 7.7 years. CONCLUSION: It is feasible to integrate survival information into routine HIS documentation such that Kaplan-Meier plots can be generated directly and in a timely manner.

Transferring HIS data to population-based cancer registries - concept and first implementations.

Cancer is the second leading cause of death worldwide and in focus of epidemiological research. In Germany the cancer registration law stipulates an electronic report to the population-based cancer registry (PBCR). In this context the Comprehensive Cancer Centre Münster (CCCM) required a new concept to support the obligation to register cancer diseases. We analysed Hospital Information System (HIS) data structures related to cancer documentation and PBCR documents. Our main idea was to export available data items from the HIS and to convert them into the import format of the PBCR. We analysed HIS data and developed an XML-based converter to support an electronic reporting procedure. Using available HIS data can avoid redundant data entry and supports information workflow within the CCCM. HIS data can provide a secondary use beyond clinical routine in form of reporting, quality assurance and clinical research.

Estimation of patient accrual rates in clinical trials based on routine data from hospital information systems.

BACKGROUND: Delayed patient recruitment is a common problem in clinical trials. According to the literature, only about a third of medical research studies recruit their planned number of patients within the time originally specified. OBJECTIVES: To provide a method to estimate patient accrual rates in clinical trials based on routine data from hospital information systems (HIS). METHODS: Based on inclusion and exclusion criteria for each trial, a specific HIS report is generated to list potential trial subjects. Because not all information relevant for assessment of patient eligibility is available as coded HIS items, a sample of this patient list is reviewed manually by study physicians. Proportions of matching and non-matching patients are analyzed with a Chi-squared test. An estimation formula for patient accrual rate is derived from this data. RESULTS: The method is demonstrated with two datasets from cardiology and oncology. HIS reports should account for previous disease episodes and eliminate duplicate persons. CONCLUSION: HIS data in combination with manual chart review can be applied to estimate patient recruitment for clinical trials.

HIS-based electronic documentation can significantly reduce the time from biopsy to final report for prostate tumours and supports quality management as well as clinical research.

BACKGROUND: Timely and accurate information is important to guide the medical treatment process. We developed, implemented and assessed an order-entry system to support documentation of prostate histologies involving urologists, pathologists and physicians in private practice. METHODS: We designed electronic forms for histological prostate biopsy reports in our hospital information system (HIS). These forms are created by urologists and sent electronically to pathologists. Pathological findings are entered into the system and sent back to the urologists. We assessed time from biopsy to final report (TBF) and compared pre-implementation phase (paper-based forms) and post-implementation phase. In addition we analysed completeness of the electronic data. RESULTS: We compared 87 paper-based with 86 electronic cases. Using electronic forms within the HIS decreases time span from biopsy to final report by more than one day per patient (p < 0.0001). Beyond the optimized workflow we observed a good acceptance because physicians were already familiar with the HIS. The possibility to use these routine data for quality management and research purposes is an additional important advantage of the electronic system. CONCLUSION: Electronic documentation can significantly reduce the time from biopsy to final report of prostate biopsy results and generates a reliable basis for quality management and research purposes.