Dataset Information

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy.

ABSTRACT:

Background

Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model.

Methods

To aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance.

Results

The average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P < 0.05). The average DSC of the deep-learning model with prior information was higher than that of the traditional method (0.808 vs. 0.622, P < 0.05).

Conclusions

The introduction of prior information in deep-learning model can improve segmentation accuracy of CTV-TB. The proposed method provided an effective way to automatically delineate CTV-TB in postoperative breast cancer radiotherapy.

SUBMITTER: Xie X

PROVIDER: S-EPMC10577969 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy.

Xie Xin X Song Yuchun Y Ye Feng F Wang Shulian S Yan Hui H Zhao Xinming X Dai Jianrong J

Radiation oncology (London, England) 20231015 1

<h4>Background</h4>Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model.<h4>Methods</h4>To aid the delineation of CTV-TB, the tum ...[more]

PMID: 37840132

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Dataset Information

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy.

Background

Methods

Results

Conclusions

Publications

Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy.

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