Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance

Clinical Snapshot

Key Findings

• P Value: P = 0.005 for time savings; P < 0.001 for clinical factors

• Effect Size: Median DSC: 0.941 (left), 0.937 (right); HD95: 1.75mm (left), 2.47mm (right)

• Primary Outcome: Geometric accuracy measured by Dice Similarity Coefficient and Hausdorff Distance

• Nnt Or Sensitivity: Auto-contouring outperformed 1/5 physicians by DSC and 3/5 by HD95

• Confidence Interval: Not consistently reported

Clinical Application

Requires deep learning infrastructure and validation in local practice settings Relevant to Australian radiation oncology practice but requires validation in local population and integration with existing TGA-approved planning systems Breast cancer patients undergoing breast-conserving surgery requiring radiation therapy planning

Abstract

OBJECTIVES: To develop and validate a deep learning model for whole breast clinical target volume (CTV) contouring and evaluate clinical features affecting its performance. METHODS: Five datasets with 857 patients from a single center were used. Dataset 1 (n = 300) trained and tested the model. Dataset 2 (n = 10) evaluated contouring time and dosimetric parameters. Datasets 3 (n = 20) and 4 (n = 10) were for clinical evaluation. Dataset 5 (n = 517) identified clinical factors influencing auto-contouring accuracy. Model performance was assessed using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95). RESULTS: The median DSC and HD95 for left- and right-sided models in Dataset 1 were 0.941, 1.75 mm and 0.937, 2.47 mm, respectively. In Dataset 2, both auto-contouring and auto-contouring with manual corrections were significantly faster than manual contouring (P = .005 for both), while still achieving clinically acceptable dosimetric results. In Dataset 3, two physicians rated automatic and manual contours as equivalent (P = .214, P = .075), while the other rated auto-contouring higher (P < .001). In Dataset 4, the auto-contouring model outperformed 1/5 physicians by DSC (P = .009) and 3/5 by HD95 (P = .015, P = .007, P = .017). In Dataset 5, peripheral tumor-bed and low-density breast tissue were associated with lower DSC (P < .001 for both) and higher HD95 (P < .001 for both). Cases without unfavorable factors performed better than those with (P < .001 for both). CONCLUSIONS: The proposed model demonstrated acceptable accuracy, consistency, and efficiency in breast CTV contouring. Peripheral tumor-bed and low-density breast tissue reduced auto-contouring performance. ADVANCES IN KNOWLEDGE: The characteristics of challenging cases in whole breast CTV auto-contouring should be identified.

References

1. 1.Zhang, M., Zhang, Y., Cao, L., Cai, R., Xu, H., Xu, C., Xiong, W., Zhang, W., Wu, X., Chen, J., & Cai, G. (2024). Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance. British Journal of Radiology. https://doi.org/10.1093/bjr/tqag036