eISSN: 2081-2841
ISSN: 1689-832X
Journal of Contemporary Brachytherapy
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2/2023
vol. 15
 
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abstract:
Original paper

Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning

Hongling Xie
1
,
Jiahao Wang
1
,
Yuanyuan Chen
1
,
Yeqiang Tu
1
,
Yukai Chen
1
,
Yadong Zhao
1
,
Pengfei Zhou
1
,
Shichun Wang
2
,
Zhixin Bai
2
,
Qiu Tang
1

  1. Department of Radiation Oncology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
  2. Hangzhou Ruicare MedTech Co., Ltd., Hangzhou, Zhejiang, China
J Contemp Brachytherapy 2023; 15, 2: 134–140
DOI: https://doi.org/10.5114/jcb.2023.126514
Online publish date: 2023/04/06
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Purpose:
The purpose of this study was to investigate the precision of deep learning (DL)-based auto-reconstruction in localizing interstitial needles in post-operative cervical cancer brachytherapy (BT) using three-dimensional (3D) computed tomography (CT) images.

Material and methods:
A convolutional neural network (CNN) was developed and presented for automatic reconstruction of interstitial needles. Data of 70 post-operative cervical cancer patients who received CT-based BT were used to train and test this DL model. All patients were treated with three metallic needles. Dice similarity coefficient (DSC), 95% Hausdorff distance (95% HD), and Jaccard coefficient (JC) were applied to evaluate the geometric accuracy of auto-reconstruction for each needle. Dose-volume indexes (DVI) between manual and automatic methods were used to analyze the dosimetric difference. Correlation between geometric metrics and dosimetric difference was evaluated using Spearman correlation analysis.

Results:
The mean DSC values of DL-based model were 0.88, 0.89, and 0.90 for three metallic needles. Wilcoxon signed-rank test indicated no significant dosimetric differences in all BT planning structures between manual and automatic reconstruction methods (p > 0.05). Spearman correlation analysis demonstrated weak link between geometric metrics and dosimetry differences.

Conclusions:
DL-based reconstruction method can be used to precisely localize the interstitial needles in 3D-CT images. The proposed automatic approach could improve the consistency of treatment planning for post-operative cervical cancer brachytherapy.

keywords:

deep learning, interstitial needles, brachytherapy, cervical cancer

  
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