Tools for large-scale data analytics of an international multi-center study in radiation oncology for cervical cancer. (May 2023) - Ecker, Stefan; Kirisits, Christian; Schmid, Maximilian; De Leeuw, Astrid; Seppenwoolde, Yvette; Knoth, Johannes; Trnkova, Petra; Heilemann, Gerd; Sturdza, Alina; Kirchheiner, Kathrin; Spampinato, Sofia; Serban, Monica; et al.
Highlights: Development of a comprehensive data dashboard for radiation oncology. Tested within an international multi-center study. Centers can compare their treatment approach with reference population. Automatically detect implausibilities in patient, treatment, and outcome data. Overview of the most common pitfalls and uncertainties in current radiotherapy for cervical cancer. Abstract: Purpose: To develop and implement a software that enables centers, treating patients with state-of-the-art radiation oncology, to compare their patient, treatment, and outcome data to a reference cohort, and to assess the quality of their treatment approach. Materials and Methods: A comprehensive data dashboard was designed, which al- lowed holistic assessment of institutional treatment approaches. The software was tested in the ongoing EMBRACE-II study for locally advanced cervical cancer. The tool created individualized dashboards and automatic analysis scripts, verified pro- tocol compliance and checked data for inconsistencies. Identified quality assurance (QA) events were analysed. A survey among users was conducted to assess usability. Results: The survey indicated favourable feedback to the prototype and highlighted its value for internal monitoring. Overall, 2302 QA events were identified (0.4% of all collected data). 54% were due to missing or incomplete data, and 46% originated from other causes. At least one QA event was found in 519/1001 (52%) of patients. QA events related to primary study endpoints were found in 16% of patients. Sta- tistical methods demonstrated good performance in detecting anomalies, with precisions ranging from 71% to 100%. Most frequent QA event categories were Treatment Technique (27%), Patient Characteristics (22%), Dose Reporting (17%), Outcome 156 (15%), Outliers (12%), and RT Structures (8%). Conclusion: A software tool was developed and tested within a clinical trial in radia- tion oncology. It enabled the quantitative and qualitative comparison of institutional patient and treatment parameters with a large multi-center reference cohort. We demonstrated the value of using statistical methods to automatically detect implau- sible data points and highlighted common pitfalls and uncertainties in radiotherapy for cervical cancer.