In:
Physics in Medicine & Biology, IOP Publishing, Vol. 68, No. 5 ( 2023-03-07), p. 055012-
Abstract:
Objective . The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures derived from Deep learning (DL) based auto-segmentation compared with standard manual delineation for postoperative cervical cancer. Approach . We introduced a convolutional neural networks (CNN) which was developed and presented for auto-segmentation in cervical cancer radiotherapy. The dataset of 60 patients received BT of postoperative cervical cancer was used to train and test this model for delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs). Dice similarity coefficient (DSC), 95% Hausdorff distance (95%HD), Jaccard coefficient (JC) and dose-volume index (DVI) were used to evaluate the accuracy. The correlation between geometric metrics and dosimetric difference was performed by Spearman’s correlation analysis. The radiation oncologists scored the auto-segmented contours by rating the lever of satisfaction (no edits, minor edits, major edits). Main results . The mean DSC values of DL based model were 0.87, 0.94, 0.86, 0.79 and 0.92 for HRCTV, bladder, rectum, sigmoid and small intestine, respectively. The Bland-Altman test obtained dose agreement for HRCTV_D 90% , HRCTV_D mean , bladder_D 2cc , sigmoid_D 2cc and small intestine_D 2cc . Wilcoxon’s signed-rank test indicated significant dosimetric differences in bladder_D 0.1cc , rectum_D 0.1cc and rectum_D 2cc ( P 〈 0.05). A strong correlation between HRCTV_D 90% with its DSC ( R = −0.842, P = 0.002) and JC ( R = −0.818, P = 0.004) were found in Spearman’s correlation analysis. From the physician review, 80% of HRCTVs and 72.5% of OARs in the test dataset were shown satisfaction (no edits). Significance . The proposed DL based model achieved a satisfied agreement between the auto-segmented and manually defined contours of HRCTV and OARs, although the clinical acceptance of small volume dose of OARs around the target was a concern. DL based auto-segmentation was an essential component in cervical cancer workflow which would generate the accurate contouring.
Type of Medium:
Online Resource
ISSN:
0031-9155
,
1361-6560
DOI:
10.1088/1361-6560/acba76
Language:
Unknown
Publisher:
IOP Publishing
Publication Date:
2023
detail.hit.zdb_id:
1473501-5
SSG:
12
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