Detection of unwarranted CT radiation exposure from patient and imaging protocol meta-data using regularized regression
| العنوان: | Detection of unwarranted CT radiation exposure from patient and imaging protocol meta-data using regularized regression |
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| المؤلفون: | Hiroto Hatabu, Vladimir I. Valtchinov, Ruidi Chen, Jenifer Siegelman, Ioannis Ch. Paschalidis |
| المصدر: | European Journal of Radiology Open European Journal of Radiology Open, Vol 6, Iss, Pp 206-211 (2019) |
| بيانات النشر: | Elsevier, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | lcsh:Medical physics. Medical radiology. Nuclear medicine, medicine.medical_specialty, business.industry, lcsh:R895-920, Regression model, Feature selection, Retrospective cohort study, Regression analysis, Regression, Article, 3. Good health, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Lasso (statistics), 030220 oncology & carcinogenesis, Outlier, medicine, Outlier detection, Radiology, Nuclear Medicine and imaging, Anomaly detection, Medical physics, CT radiation dose safety, F1 score, business |
| الوصف: | Background: Variability in radiation exposure from CT scans can be appropriate and driven by patient features such as body habitus. Quantitative analysis may be performed to discover instances of unwarranted radiation exposure and to reduce the probability of such occurrences in future patient visits. No universal process to perform identification of outliers is widely available, and access to expertise and resources is variable. Objective: The goal of this study is to develop an automated outlier detection procedure to identify all scans with an unanticipated high radiation exposure, given the characteristics of the patient and the type of the exam. Materials and methods: This Institutional Review Board-approved retrospective cohort study was conducted from June 30, 2012 - December 31, 2013 in a quaternary academic medical center. The de-identified dataset contained 28 fields for 189,959 CT exams. We applied the variable selection method Least Absolute Shrinkage and Selection Operator (LASSO) to select important variables for predicting CT radiation dose. We then employed a regression approach that is robust to outliers, to learn from data a predictive model of CT radiation doses given important variables identified by LASSO. Patient visits whose predicted radiation dose was statistically different from the radiation dose actually received were identified as outliers. Results: Our methodology identified 1% of CT exams as outliers. The top-5 predictors discovered by LASSO and strongly correlated with radiation dose were Tube Current, kVp, Weight, Width of collimator, and Reference milliampere-seconds. A human expert validation of the outlier detection algorithm has yielded specificity of 0.85 [95% CI 0.78-0.92] and sensitivity of 0.91 [95% CI 0.85-0.97] (PPV = 0.84, NPV = 0.92). These values substantially outperform alternative methods we tested (F1 score 0.88 for our method against 0.51 for the alternatives). Conclusion: The study developed and tested a novel, automated method for processing CT scanner meta-data to identify CT exams where patients received an unwarranted amount of radiation. Radiation safety and protocol review committees may use this technique to uncover systemic issues and reduce future incidents. Keywords: Outlier detection, CT radiation dose safety, Regression model |
| اللغة: | English |
| تدمد: | 2352-0477 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e6008fb85c564594542459f7779483f http://europepmc.org/articles/PMC6551377 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....4e6008fb85c564594542459f7779483f |
| قاعدة البيانات: | OpenAIRE |
Full Text via ClinicalKey | تدمد: | 23520477 |
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