A mathematical and dosimetric approach to validate auto‐contouring by Varian Smart segmentation for prostate cancer patients

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.

The geometric and dosimetric effect of algorithm choice on propagated contours from CT to cone beam CTs

“…To the best of the authors knowledge, this work is the first study directly reviewing the clinical dose implications of using different nonrigid registration algorithms for head and neck cancer OARs compared to the geometric accuracy, and the first reviewing and comparing five widely available commercial packages [18,33,34] with most previous studies focussing on only assessing the geometric accuracy of one such packages [35][36][37][38][39][40]. There has been some limited work in the related field of automatically, deep learning, generated contours for prostate cancer [41][42][43]. These have shown similar conclusions to here; with the generated contours producing acceptable dose distributions although these studies have not assessed the reliability of propagated contours.…”

Clinical assessment of a novel machine‐learning automated contouring tool for radiotherapy planning

“…In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body, but also to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. The purpose of this study was to evaluate the quality of contours generated by AI‐Rad in different anatomical areas and, where possible, compare it to the quality of contours generated by Smart Segmentation™ (SS) (Version 16.0) (Varian, Palo Alto, CA, USA), a commercial automated segmentation solution that has been verified by various studies 25 , 26 and implemented clinically at the authors’ department. A timing analysis was also performed to explore potential time savings achieved by AI‐Rad compared to manual delineation.…”