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

Abstract

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: CT radiation dose safety; Outlier detection; Regression model.

Fig. 1

Comparison between OLS and Regularized Regression.

Fig. 2

Schematic representation of the data pre-processing and analysis steps in the proposed LASSO + RR-based pipeline. N denotes the number of CT exams used and p the number of features (variables) retained for each exam.

Fig. 3

The coefficients’ paths for numerical predictors in LASSO. Lambda refers to the coefficient of the sparsity-inducing penalty in LASSO. A collimator is a metallic barrier with an aperture of variable width used to control the diameter of the X-ray beam.

Fig. 4

The coefficients’ paths for the top 5 predictors in LASSO. Range Name is a parameter specified by the protocol as to which region is being scanned in a multiple body part evaluation.

Fig. 5

Outliers identified in the entire dataset (after pre-processing, N = 88,566) by the three methods, Cutoff, OLS, and our proposed RR-based method.