Implementation of a knowledge-based decision support system for treatment plan auditing through automation Journal Article
| Authors: | Liu, S.; Chapman, K. L.; Berry, S. L.; Bertini, J.; Ma, R.; Fu, Y.; Yang, D.; Moran, J. M.; Della-Biancia, C. |
| Article Title: | Implementation of a knowledge-based decision support system for treatment plan auditing through automation |
| Abstract: | Background: Independent auditing is a necessary component of a comprehensive quality assurance (QA) program and can also be utilized for continuous quality improvement (QI) in various radiotherapy processes. Two senior physicists at our institution have been performing a time intensive manual audit of cross-campus treatment plans annually, with the aim of further standardizing our planning procedures, updating policies and guidelines, and providing training opportunities of all staff members. Purpose: A knowledge-based automated anomaly-detection algorithm to provide decision support and strengthen our manual retrospective plan auditing process was developed. This standardized and improved the efficiency of the assessment of our external beam radiotherapy (EBRT) treatment planning across all eight campuses of our institution. Methods: A total of 843 external beam radiotherapy plans for 721 lung patients from January 2020 to March 2021 were automatically acquired from our clinical treatment planning and management systems. From each plan, 44 parameters were automatically extracted and pre-processed. A knowledge-based anomaly detection algorithm, namely, “isolation forest” (iForest), was then applied to the plan dataset. An anomaly score was determined for each plan using recursive partitioning mechanism. Top 20 plans ranked with the highest anomaly scores for each treatment technique (2D/3D/IMRT/VMAT/SBRT) including auto-populated parameters were used to guide the manual auditing process and validated by two plan auditors. Results: The two auditors verified that 75.6% plans with the highest iForest anomaly scores have similar concerning qualities that may lead to actionable recommendations for our planning procedures and staff training materials. The time to audit a chart was approximately 20.8 min on average when done manually and 14.0 min when done with the iForest guidance. Approximately 6.8 min were saved per chart with the iForest method. For our typical internal audit review of 250 charts annually, the total time savings are approximately 30 hr per year. Conclusion: iForest effectively detects anomalous plans and strengthens our cross-campus manual plan auditing procedure by adding decision support and further improve standardization. Due to the use of automation, this method was efficient and will be used to establish a standard plan auditing procedure, which could occur more frequently. © 2023 American Association of Physicists in Medicine. |
| Keywords: | adult; retrospective studies; major clinical study; intensity modulated radiation therapy; treatment planning; radiotherapy dosage; radiotherapy; practice guideline; retrospective study; automation; standardization; radiation oncology; radiotherapy, intensity-modulated; quality assurance; total quality management; lung; staff training; radiotherapy planning, computer-assisted; external beam radiotherapy; forest; signal detection; quality improvement; treatment plans; decision support system; knowledge based systems; forestry; procedures; recursive partitioning; personnel training; humans; human; male; female; article; decision support systems; anomaly detection; physicist; outlier detection; decision supports; knowledge-based decision support; quality improvement (qi); treatment plan auditing; anomaly-detection algorithms; auditing process; knowledge based; planning procedure |
| Journal Title: | Medical Physics |
| Volume: | 50 |
| Issue: | 11 |
| ISSN: | 0094-2405 |
| Publisher: | American Association of Physicists in Medicine |
| Date Published: | 2023-11-01 |
| Start Page: | 6978 |
| End Page: | 6989 |
| Language: | English |
| DOI: | 10.1002/mp.16472 |
| PUBMED: | 37211898 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding MSK author is Shi Liu -- Source: Scopus |
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