TY - JOUR
T1 - Tools for large-scale data analytics of an international multi-center study in radiation oncology for cervical cancer
AU - Ecker, Stefan
AU - Kirisits, Christian
AU - Schmid, Maximilian
AU - De Leeuw, Astrid
AU - Seppenwoolde, Yvette
AU - Knoth, Johannes
AU - Trnkova, Petra
AU - Heilemann, Gerd
AU - Sturdza, Alina
AU - Kirchheiner, Kathrin
AU - Spampinato, Sofia
AU - Serban, Monica
AU - Jürgenliemk-Schulz, Ina
AU - Chopra, Supriya
AU - Nout, Remi
AU - Tanderup, Kari
AU - Pötter, Richard
AU - Eder-Nesvacil, Nicole
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
KW - Data Science
KW - Female
KW - Humans
KW - Quality Assurance, Health Care/methods
KW - Radiation Oncology
KW - Radiotherapy Planning, Computer-Assisted
KW - Surveys and Questionnaires
KW - Uterine Cervical Neoplasms/radiotherapy
KW - IGABT
KW - Clinical trial monitoring
KW - Data analytics
KW - Cervical cancer
UR - http://www.scopus.com/inward/record.url?scp=85149230187&partnerID=8YFLogxK
U2 - 10.1016/j.radonc.2023.109524
DO - 10.1016/j.radonc.2023.109524
M3 - Article
C2 - 36764459
SN - 0167-8140
VL - 182
JO - Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
JF - Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
M1 - 109524
ER -