Original ArticleClinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer☆
Section snippets
Data
Sixty-two breast cancer patients, with a mix of left- and right-sided breast cancer, who received RT after breast-conserving surgery between May 1st, 2016 and May 1st, 2019 and underwent contrast-enhanced planning computed tomography (CT) were selected for this study after Institutional Review Board approval. The CT scans were acquired on a Siemens Sensation Open (Siemens, Forchheim, Germany) and a Toshiba Aquilion (Toshiba Medical Systems, Japan), using the following common CT image
Results
A typical example of an axial CT scan segmentation produced from DLBAS and ABAS can be seen in Supplementary Video 1. Fig. 2A shows an example of CTV auto-segmentation from ABAS and DLBAS. Among 14 CTV structures, DLBAS produced the highest average DSCs in 11 of them. The statistical test reflects that these differences were significant for left and right AXL3 and IMN (Supplementary Table 1). However, the HD95 comparisons of CTVs reveal that DLBAS produced smaller surface discrepancies compared
Discussion
To the best of our knowledge, this is the first study that compares the performance of ABAS and DLBAS methods for breast cancer RT planning that includes node regions and heart structures. In this study, we demonstrated the efficacy of our DLBAS by measuring the performance across a range of structures (CTVs, OARs, and heart substructures) in a head-to-head comparison study with commercial ABAS solutions. Our results indicate that, while ABAS offered an acceptable performance level, DLBAS
Conflict of Interest
J.S.K is cofounder of Oncosoft and serves as an advisor to Rayence. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A4A1016619).
References (45)
Mapping patterns of locoregional recurrence following contemporary treatment with radiation therapy for breast cancer: a multi-institutional validation study of the ESTRO consensus guideline on clinical target volume
Radiother Oncol
(2018)Three-dimensional analysis of patterns of locoregional recurrence after treatment in breast cancer patients: validation of the ESTRO consensus guideline on target volume
Radiother Oncol
(2017)- et al.
The risk of cardiac disease in asian breast cancer patients: impact of patient-specific factors and heart dose based on individual heart dose calculation from three-dimensional RT planning
Int J Radiat Oncol Biol Phys
(2019) ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer
Radiother Oncol
(2015)Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring
Radiother Oncol
(2020)Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer
Radiother Oncol
(2018)- et al.
Quality assurance for prospective EORTC radiation oncology trials: the challenges of advanced technology in a multicenter international setting
Radiother Oncol
(2011) - et al.
Impact of deviations in target volume delineation – Time for a new RTQA approach?
Radiother Oncol
(2019) - et al.
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
CA Cancer J Clin
(2018) Internal mammary and medial supraclavicular irradiation in breast cancer
N Engl J Med
(2015)
Regional nodal irradiation in early-stage breast cancer
N Engl J Med
DBCG-IMN: A population-based cohort study on the effect of internal mammary node irradiation in early node-positive breast cancer
J Clin Oncol
Radiation therapy field design and lymphedema risk after regional nodal irradiation for breast cancer
Int J Radiat Oncol Biol Phys
Risk of lymphedema following contemporary treatment for breast cancer: an analysis of 7,426 consecutive patients from a multidisciplinary perspective
Int J Radiat Oncol
Hypofractionated radiotherapy dose scheme and application of new techniques are associated to a lower incidence of radiation pneumonitis in breast cancer patients
Front Oncol
Risk of radiation pneumonitis following individualized modern radiation therapy with IMRT, a breath-holding technique and prone positioning for breast cancer
Int J Radiat Oncol
The effect of regional node irradiation on the thyroid gland in the breast cancer patients: the clinical significance of optimization of radiation target volume
Int J Radiat Oncol
Development and evaluation of an auto-segmentation tool for the left anterior descending coronary artery of breast cancer patients based on anatomical landmarks
Radiother Oncol
Variability of target and normal structure delineation for breast cancer radiotherapy: an RTOG multi-institutional and multiobserver study
Int J Radiat Oncol Biol Phys
Clinical evaluation of commercial atlas-based auto-segmentation in the head and neck region
Front Oncol
Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer
Radiat Oncol
Cited by (59)
Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions
2024, Advances in Radiation OncologyArtificial Intelligence–Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics
2024, Advances in Radiation OncologyAutomated contouring and statistical process control for plan quality in a breast clinical trial
2023, Physics and Imaging in Radiation OncologyInvestigation and benchmarking of U-Nets on prostate segmentation tasks
2023, Computerized Medical Imaging and GraphicsClinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer
2023, Technical Innovations and Patient Support in Radiation Oncology
- ☆
We would like to express our gratitude to MIM Software and Mirada Medical for granting us access to their software and for their assistance in this study.