دورية أكاديمية
أعرض في EDS

Detection of unwarranted CT radiation exposure from patient and imaging protocol meta-data using regularized regression.

التفاصيل البيبلوغرافية
العنوان: Detection of unwarranted CT radiation exposure from patient and imaging protocol meta-data using regularized regression.
المؤلفون: Chen R; Department of Biomedical Engineering, Boston University, United States.; Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's Street, Boston, MA 02215, USA., Paschalidis IC; Department of Biomedical Engineering, Boston University, United States.; Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's Street, Boston, MA 02215, USA., Hatabu H; Center for Evidence-Based Imaging (CEBI), Brigham and Women's Hospital, United States.; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, United States., Valtchinov VI; Center for Evidence-Based Imaging (CEBI), Brigham and Women's Hospital, United States.; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, United States.; Department of Biomedical Informatics, Harvard Medical School, United States., Siegelman J; Takeda Pharmaceuticals, United States.
المصدر: European journal of radiology open [Eur J Radiol Open] 2019 Jun 05; Vol. 6, pp. 206-211. Date of Electronic Publication: 2019 Jun 05 (Print Publication: 2019).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101650225 Publication Model: eCollection Cited Medium: Print ISSN: 2352-0477 (Print) Linking ISSN: 23520477 NLM ISO Abbreviation: Eur J Radiol Open Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [Oxford] : Elsevier Ltd., [2014]-
مستخلص: 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.
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معلومات مُعتمدة: R01 GM135930 United States GM NIGMS NIH HHS; UL1 TR001430 United States TR NCATS NIH HHS
فهرسة مساهمة: Keywords: CT radiation dose safety; Outlier detection; Regression model
تواريخ الأحداث: Date Created: 20190614 Latest Revision: 20240723
رمز التحديث: 20240723
مُعرف محوري في PubMed: PMC6551377
DOI: 10.1016/j.ejro.2019.04.007
PMID: 31194104
قاعدة البيانات: MEDLINE
الوصف
تدمد:2352-0477
DOI:10.1016/j.ejro.2019.04.007