{Reference Type}: Journal Article
{Title}: Clinical validation of an automatic atlas-based segmentation tool for male pelvis CT images.
{Author}: Casati M;Piffer S;Calusi S;Marrazzo L;Simontacchi G;Di Cataldo V;Greto D;Desideri I;Vernaleone M;Francolini G;Livi L;Pallotta S;
{Journal}: J Appl Clin Med Phys
{Volume}: 23
{Issue}: 3
{Year}: Mar 2022
{Factor}: 2.243
{DOI}: 10.1002/acm2.13507
{Abstract}: <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.