CMDX©-based single source information system for simplified quality management and clinical research in prostate cancer

Background: 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. Methods: 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. Results: 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. Conclusions: cMDX SSIS can be integrated into the local HIS and provides clinical routine data and timely heat maps for quality assessment and research purposes.

Clinical map document based on XML (cMDX): document architecture with mapping feature for reporting and analysing prostate cancer in radical prostatectomy specimens

<p>Abstract</p> <p>Background</p> <p>The pathology report of radical prostatectomy specimens plays an important role in clinical decisions and the prognostic evaluation in Prostate Cancer (PCa). The anatomical schema is a helpful tool to document PCa extension for clinical and research purposes. To achieve electronic documentation and analysis, an appropriate documentation model for anatomical schemas is needed. For this purpose we developed cMDX.</p> <p>Methods</p> <p>The document architecture of cMDX was designed according to Open Packaging Conventions by separating the whole data into template data and patient data. Analogue custom XML elements were considered to harmonize the graphical representation (e.g. tumour extension) with the textual data (e.g. histological patterns). The graphical documentation was based on the four-layer visualization model that forms the interaction between different custom XML elements. Sensible personal data were encrypted with a 256-bit cryptographic algorithm to avoid misuse. In order to assess the clinical value, we retrospectively analysed the tumour extension in 255 patients after radical prostatectomy.</p> <p>Results</p> <p>The pathology report with cMDX can represent pathological findings of the prostate in schematic styles. Such reports can be integrated into the hospital information system. "cMDX" documents can be converted into different data formats like text, graphics and PDF. Supplementary tools like cMDX Editor and an analyser tool were implemented. The graphical analysis of 255 prostatectomy specimens showed that PCa were mostly localized in the peripheral zone (Mean: 73% ± 25). 54% of PCa showed a multifocal growth pattern.</p> <p>Conclusions</p> <p>cMDX can be used for routine histopathological reporting of radical prostatectomy specimens and provide data for scientific analysis.</p

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&ndash;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&ndash;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

Advances in deep learning-based cancer outcome prediction using multi-omics data

Cancer prognosis reflects a complex biological process measured by multiple types of omics data. Deep learning frameworks have been proposed to integrate multi-omics data and predict patient outcomes in different cancer types, potentially revolutionizing cancer prognosis with superior performance. This minireview summarizes the advances in the strategies for multi-omics data integration and the performance of different deep learning models in prognosis prediction of diverse cancer types using multi-omics data published in the past 18 months. The challenges and limitations of deep learning models for predicting cancer outcomes based on multi-omics data are discussed

Treatment of Incidental Prostate Cancer by Active Surveillance: Results of the HAROW Study

Objective: To report on a cohort of patients with incidental prostate cancer (IPC) that was treated by an active surveillance (AS) protocol in the HAROW study. Materials and Methods: The HAROW study is an observational study on the management of localized prostate cancer in Germany. Treating urologists were reporting clinical parameters, information on therapy and clinical course of disease at 6-month intervals. Results: In total, 3,169 patients were enrolled. In 224 patients were found an IPC and 104 (46%) of them were put on an AS protocol. The mean follow-up was 26.5 months. Tumor progression was noted in 16 patients. In 11 patients, AS was replaced by a definite intervention. In univariate and multivariate analyses, only PSA density correlated with progression. Conclusion: This is the first prospective description of an IPC patient cohort on AS as part of an outcomes research study. AS was selected as a therapeutic strategy in nearly half of the patients (46%). Only a minor proportion (16%) displayed progression. Of the clinical parameters, only PSA density correlated with progression. (C) 2015 S. Karger AG, Base

Does postoperative radiation therapy impact survival in non-metastatic sarcomatoid renal cell carcinoma? A SEER-based study

The effect of adjuvant radiation therapy on survival in sarcomatoid renal cell carcinoma (sRCC) with no evidence of distant metastasis remains unclear. Subjects diagnosed with non-metastatic sRCC were identified using the Surveillance Epidemiology and End Results (SEER) (2004-2012) database and divided into groups based on their surgical treatment (ST): no surgery or radiation therapy (NSR); partial nephrectomy (PNE); radical nephrectomy with ureterectomy and bladder cuff resection (RNE + UE + BLAD); and radical nephrectomy (RNE). Certain radical nephrectomy cases also received adjuvant external-beam radiation therapy (RNE + RAD). The Kaplan-Meier method was used to estimate overall survival (OS). A multivariable competing risks regression analysis was used to calculate disease-specific survival (DSS) probability and to determine factors associated with cause-specific mortality (CSM). A total of 408 patients were included in this study. The 5-year OS and predicted DSS were significantly higher in the patients who underwent STs (i.e., PNE, RNE + UE + BLAD, RNE, and RNE + RAD) (20.1-54.0 and 20.1-59.9 %, respectively) than in the NSR group (9.0 and 11.6 %, respectively) (P < 0.001). ST was independently associated with a decreased CSM (P < 0.0001). No significant differences in OS or the 1-, 3-, or 5-year DSS probabilities between the RNE and RNE + RAD groups were observed. RNE + RAD was not significantly associated with a decrease in 1-year CSM [subhazard ratio (SHR) 0.95; 95 % CI 0.23-3.96; P = 0.947]. Adjuvant external-beam radiation therapy did not increase OS in non-metastatic sRCC patients