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Guided undersampling classification for automated radiation therapy quality assurance of prostate cancer treatment
- Source :
- Medical physics. 45(4)
- Publication Year :
- 2017
-
Abstract
- PURPOSE To test the use of well-studied and widely used classification methods alongside newly developed data-filtering techniques specifically designed for imbalanced-data classification in order to demonstrate proof of principle for an automated radiation therapy (RT) quality assurance process on prostate cancer treatment. METHODS A series of acceptable (majority class, n = 61) and erroneous (minority class, n = 12) RT plans as well as a disjoint set of acceptable plans used to develop features (n = 273) were used to develop a dataset for testing. A series of five widely used imbalanced-data classification algorithms were tested with a modularized guided undersampling procedure that includes ensemble-outlier filtering and normalized-cut sampling. RESULTS Hybrid methods including either ensemble-outlier filtering or both filtering and normalized-cut sampling yielded the strongest performance in identifying unacceptable treatment plans. Specifically, five methods demonstrated superior performance in both area under the receiver operating characteristics curve and false positive rate when the true positive rate is equal to one. Furthermore, ensemble-outlier filtering significantly improved results in all but one hybrid method (p < 0.01). Finally, ensemble-outlier filtering methods identified four minority instances that were considered outliers in over 96% of cross-validation iterations. Such instances may be considered distinct planning errors and merit additional inspection, providing potential areas of improvement for the planning process. CONCLUSIONS Traditional imbalanced-data classification methods combined with ensemble-outlier filtering and normalized-cut sampling provide a powerful framework for identifying erroneous RT treatment plans. The proposed methodology yielded strong classification performance and identified problematic instances with high accuracy.
- Subjects :
- Male
Quality Assurance, Health Care
Computer science
medicine.medical_treatment
Statistics as Topic
Machine learning
computer.software_genre
030218 nuclear medicine & medical imaging
03 medical and health sciences
Prostate cancer
Automation
0302 clinical medicine
medicine
Humans
Receiver operating characteristic
business.industry
Radiotherapy Planning, Computer-Assisted
Sampling (statistics)
Prostatic Neoplasms
General Medicine
medicine.disease
Support vector machine
Radiation therapy
Statistical classification
ComputingMethodologies_PATTERNRECOGNITION
Undersampling
030220 oncology & carcinogenesis
Outlier
False positive rate
Artificial intelligence
business
Quality assurance
computer
Subjects
Details
- ISSN :
- 24734209
- Volume :
- 45
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Medical physics
- Accession number :
- edsair.doi.dedup.....993f9736de611b9d176961810d4414ff