Extensive clinical testing of Deep Learning Segmentation models for thorax and breast cancer radiotherapy planning

Abstract

Background

The performance of deep learning segmentation (DLS) models for automatic organ extraction from CT images in the thorax and breast regions was investigated. Furthermore, the readiness and feasibility of integrating DLS into clinical practice were addressed by measuring the potential time savings and dosimetric impact.

Material and Methods

Thirty patients referred to radiotherapy for breast cancer were prospectively included. A total of 23 clinically relevant left- and right-sided organs were contoured manually on CT images according to ESTRO guidelines. Next, auto-segmentation was executed, and the geometric agreement between the auto-segmented and manually contoured organs was qualitatively assessed applying a scale in the range [0-not acceptable, 3-no corrections]. A quantitative validation was carried out by calculating Dice coefficients (DSC) and the 95% percentile of Hausdorff distances (HD95). The dosimetric impact of optimizing the treatment plans on the uncorrected DLS contours, was investigated from a dose coverage analysis using DVH values of the manually delineated contours as references.

Results

The qualitative analysis showed that 93% of the DLS generated OAR contours did not need corrections, except for the heart where 67% of the contours needed corrections. The majority of DLS generated CTVs needed corrections, whereas a minority were deemed not acceptable. Still, using the DLS-model for CTV and heart delineation is on average 14 minutes faster. An average DSC=0.91 and H95=9.8 mm were found for the left and right breasts, respectively. Likewise, and average DSC in the range [0.66, 0.76]mm and HD95 in the range [7.04, 12.05]mm were found for the lymph nodes.

Conclusion

The validation showed that the DLS generated OAR contours can be used clinically. Corrections were required to most of the DLS generated CTVs, and therefore warrants more attention before possibly implementing the DLS models clinically.

Keywords:

• Breast cancer
• automatic target segmentation
• deep learning segmentation
• automatic organs at risk segmentation
• artificial intelligence

Acknowledgements

We would like to thank Fredrik Lövman, Jonas Söderberg, and Elin Samuelsson at RaySearch Laboratories AB for helpful discussions and guidance to the use and understanding of the DLS models. Jonas Söderberg and Elin Samuelsson also contributed with calculating the Dice and Hausdorff metrics.

Disclosure statement

The study was performed as part of a collaboration and data transfer agreement with RaySearch Laboratories AB.

Data availability statement

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.