Open AccessA mathematical and dosimetric approach to validate auto‐contouring by Varian Smart segmentation for prostate cancer patients
Author(s) -
Mandal Sudipta,
Kale Shrikant N.,
Kinhikar Rajesh A.
Publication year - 2022
Publication title -
precision radiation oncology
Language(s) - English
Resource type - Journals
ISSN - 2398-7324
DOI - 10.1002/pro6.1147
Subject(s) - contouring , segmentation , medicine , nuclear medicine , standard deviation , computer science , dose volume histogram , radiation treatment planning , mathematics , artificial intelligence , radiation therapy , radiology , statistics , computer graphics (images)
Abstract Purpose The aim of this study was to quantify the discrepancies in geometrical and dosimetric impacts (in volumetric modulated arc therapy) between manually segmented (MS) contours and smart segmentation (SS) auto‐contours (by Varian Eclipse Treatment Planning System SS v13.5) for prostate cancer patients. Methods The automated segmentation was carried out by Eclipse Treatment Planning System (Varian, version 13.5) Smart Segmentation (SS) workspace of 10 prostate cancer patients for four regions of interest; such as, bladder, rectum, femoral head left, and femoral head right. The geometric and dosimetric deviation between SS and MS contours have been quantified in the form of different parameters. The organ‐wise correlation between different validation parameters was addressed. Results The organ‐wise correlation analysis showed the good and consistent correlation between different geometric validation parameters for the bladder. The hypothesis test for checking compliance of different parameters with AAPM 132 tolerance was addressed and validated between MS and SS bladder with p‐value = 0.01 and 0.05. There was no significant dosimetric difference between the dose–volume histogram (DVH) estimated for the SS bladder and standard DVH constraints protocol (as per the TMH PRIME trial) with p‐value = 0.01 and 0.05. The difference between DVH estimated for MS and SS bladder was also not significant, with p‐value = 0.05. Conclusion This study shows that “well correlated validation parameters infer correctly about the matching or coincidence between auto and manually segmented contours,” and the bladder contouring by Smart Segmentation and plan optimization can achieve acceptable DVH constraints.