RESUMO
Purpose: Because of the automation of radiation therapy, competencies of radiation technologists (RTTs) change, and training methods are challenged. This study aims to develop, and pilot test an innovative training method based on lean management principles. Methods and Materials: A new training method was developed for lung cancer treatment planning (TP). The novelty is summarized by including a stable environment and an increased focus on the how and why of key decision making. Trainees have to motivate their decisions during TP process, and to argue their choices with peers. Six students and 6 RTTs completed this training for lung cancer TP. Effects of the training were measured by (1) quality of TP, using doses in organs at risk and target volumes, (2) perceived experiences (survey), measured at baseline (T0); after peer session (T1); and 6 months later (T2). Finally, training throughput time was measured. Results: At T0, RTTs showed a larger intragroup interquartile range (IIR) (2.63Gy vs 1.51Gy), but lower mean doses to heart and esophagus than students (6.79Gy vs 8.49Gy; 20.87Gy vs 24.62Gy). At T1, quality of TPs was similar between RTTs and students (IIR: 1.39Gy vs 1.33Gy) and no significant differences in mean dose to heart and esophagus (4.48Gy vs 4.69Gy; 17.75Gy vs 18.47Gy). At T2, students still performed equal to RTTs (IIR: 1.07Gy vs 1.45Gy) and achieved lower maximum dose to esophagus (44.75Gy vs 46.45Gy). The training method and peer sessions were experienced positive: at baseline (T0): 8 score on a scale 1-10, directly after the peer sessions; (T1): 8 by the students and 7 by the RTTs, after 9 months; (T2): 9 by the students and 7 by the RTTs. Training throughput time decreased from 12 to 3 months. Conclusions: This training method based on lean management principles was successfully applied to training of RTTs for lung cancer TP. Training throughput time was reduced dramatically and TP quality sustained after 6 months. This method can potentially improve training efficiency in diverse situations with complex decision-making.
RESUMO
PURPOSE: Automated techniques for estimating the contours of organs and structures in medical images have become more widespread and a variety of measures are available for assessing their quality. Quantitative measures of geometric agreement, for example, overlap with a gold-standard delineation, are popular but may not predict the level of clinical acceptance for the contouring method. Therefore, surrogate measures that relate more directly to the clinical judgment of contours, and to the way they are used in routine workflows, need to be developed. The purpose of this study is to propose a method (inspired by the Turing Test) for providing contour quality measures that directly draw upon practitioners' assessments of manual and automatic contours. This approach assumes that an inability to distinguish automatically produced contours from those of clinical experts would indicate that the contours are of sufficient quality for clinical use. In turn, it is anticipated that such contours would receive less manual editing prior to being accepted for clinical use. In this study, an initial assessment of this approach is performed with radiation oncologists and therapists. METHODS: Eight clinical observers were presented with thoracic organ-at-risk contours through a web interface and were asked to determine if they were automatically generated or manually delineated. The accuracy of the visual determination was assessed, and the proportion of contours for which the source was misclassified recorded. Contours of six different organs in a clinical workflow were for 20 patient cases. The time required to edit autocontours to a clinically acceptable standard was also measured, as a gold standard of clinical utility. Established quantitative measures of autocontouring performance, such as Dice similarity coefficient with respect to the original clinical contour and the misclassification rate accessed with the proposed framework, were evaluated as surrogates of the editing time measured. RESULTS: The misclassification rates for each organ were: esophagus 30.0%, heart 22.9%, left lung 51.2%, right lung 58.5%, mediastinum envelope 43.9%, and spinal cord 46.8%. The time savings resulting from editing the autocontours compared to the standard clinical workflow were 12%, 25%, 43%, 77%, 46%, and 50%, respectively, for these organs. The median Dice similarity coefficients between the clinical contours and the autocontours were 0.46, 0.90, 0.98, 0.98, 0.94, and 0.86, respectively, for these organs. CONCLUSIONS: A better correspondence with time saving was observed for the misclassification rate than the quantitative contour measures explored. From this, we conclude that the inability to accurately judge the source of a contour indicates a reduced need for editing and therefore a greater time saving overall. Hence, task-based assessments of contouring performance may be considered as an additional way of evaluating the clinical utility of autosegmentation methods.