Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images. Issue 3 (22nd January 2022) - Casati, Marta; Piffer, Stefano; Calusi, Silvia; Marrazzo, Livia; Simontacchi, Gabriele; Di Cataldo, Vanessa; Greto, Daniela; Desideri, Isacco; Vernaleone, Marco; Francolini, Giulio; Livi, Lorenzo; Pallotta, Stefania
Abstract: Purpose: This retrospective work aims to evaluate the possible impact on intra‐ and inter‐observer variability, contouring time, and contour accuracy of introducing a pelvis computed tomography (CT) auto‐segmentation tool in radiotherapy planning workflow. Methods: Tests were carried out on five structures (bladder, rectum, pelvic lymph‐nodes, and femoral heads) of six previously treated subjects, enrolling five radiation oncologists (ROs) to manually re‐contour and edit auto‐contours generated with a male pelvis CT atlas created with the commercial software MIM MAESTRO. The ROs first delineated manual contours (M). Then they modified the auto‐contours, producing automatic‐modified (AM) contours. The procedure was repeated to evaluate intra‐observer variability, producing M1, M2, AM1, and AM2 contour sets (each comprising 5 structures × 6 test patients × 5 ROs = 150 contours), for a total of 600 contours. Potential time savings was evaluated by comparing contouring and editing times. Structure contours were compared to a reference standard by means of Dice similarity coefficient (DSC) and mean distance to agreement (MDA), to assess intra‐ and inter‐observer variability. To exclude any automation bias, ROs evaluated both M and AM sets as "clinically acceptable" or "to be corrected" in a blind test. Results: Comparing AM to M sets, a significant reduction of both inter‐observer variability ( p < 0.001) and contouring time (‐45% whole pelvis, p < 0.001) was obtained. Intra‐observer variability reduction was significant only for bladder and femoral heads ( p < 0.001). The statistical test showed no significant bias. Conclusion: Our atlas‐based workflow proved to be effective for clinical practice as it can improve contour reproducibility and generate time savings. Based on these findings, institutions are encouraged to implement their auto‐segmentation method.