Factors Associated With Image-Guided Radiation Therapy Image Rejection in a Multisite Institution.

PURPOSE: Nonclinical factors and cognitive biases have been shown to significantly affect clinical decision making. In this study, we aimed to identify clinical and environmental factors that might influence the decision to approve or reject image-guided radiation therapy (IGRT) images in a large multisite institution.

METHODS: We identified all IGRT image approval and rejection decisions recorded within an electronic imaging system from July 1, 2016, to June 30, 2018. For each decision, we tabulated the following parameters: the attending physician of the patient, the physician reviewing the image, total images reviewed by the physician that day, time of day, day of week, treatment site, and imaging modality (kilovoltage or cone beam computed tomography [CBCT]). We created a binary multivariable logistic regression model to identify factors associated with IGRT image rejection.

RESULTS: Overall, of 51,797 total image records evaluated, 881 (1.70%) were rejected and 50,916 (98.30%) were approved. Univariable analysis revealed that images reviewed by physicians with high rejection rates (odds ratio [OR], 3.16; P < .001) and by physicians reviewing fewer IGRT images (OR, 0.99; P = .024), images from various anatomic sites (particularly skin, breast, and head and neck), and CBCT imaging compared with kilovoltage imaging (OR, 1.49; P < .001) were associated with the increased rate of rejection. On multivariable analysis, images reviewed by physicians with high rejection rates (OR, 3.28; P < .001), images from specific anatomic sites including breast ( P < .001), and CBCT imaging ( P < .001) persisted as independent predictors of image rejection.

CONCLUSION: These data provide important insight into the clinical, cognitive, and environmental factors that might influence the routine clinical decision of IGRT image approval. Recognition of these factors may not only improve the quality of individual decisions but also identify opportunities for systems-based quality improvement in IGRT.