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. 2023 Oct 26;9(3):101394.
doi: 10.1016/j.adro.2023.101394. eCollection 2024 Mar.

Artificial Intelligence-Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics

Gustavo R Sarria  1 , Fabian Kugel  1 , Fred Roehner  1 , Julian Layer  1   2 , Cas Dejonckheere  1 , Davide Scafa  1 , Muemtaz Koeksal  1 , Christina Leitzen  1 , Leonard Christopher Schmeel  1
Affiliations

Artificial Intelligence-Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics

Gustavo R Sarria et al. Adv Radiat Oncol. .

Abstract

Purpose: The study's purpose was to compare the performance of artificial intelligence (AI) in auto-contouring compared with a human practitioner in terms of precision, differences in dose distribution, and time consumption.

Methods and materials: Datasets of previously irradiated patients in 3 different segments (head and neck, breast, and prostate cancer) were retrospectively collected. An experienced radiation oncologist (MD) performed organs-at-risk (OARs) and standard clinical target volume delineations as baseline structures for comparison. AI-based autocontours were generated in 2 additional CT copies; therefore, 3 groups were assessed: MD alone, AI alone, and AI plus MD corrections (AI+C). Differences in Dice similarity coefficient (DSC) and person-hour burden were assessed. Furthermore, changes in clinically relevant dose-volume parameters were evaluated and compared.

Results: Seventy-five previously treated cases were collected (25 per segment) for the analysis. Compared with MD contours, the mean DSC scores were higher than 0.7 for 74% and 80% of AI and AI+C, respectively. After corrections, 17.1% structures presented DSC score deviations higher than 0.1 and 10.4% dose-volume parameters significantly changed in AI-contoured structures. The time consumption assessment yielded mean person-hour reductions of 68%, 51%, and 71% for breast, prostate, and head and neck cancer, respectively.

Conclusions: In great extent, AI yielded clinically acceptable OARs and certain clinical target volumes in the explored anatomic segments. Sparse correction and assessment requirements place AI+C as a standard workflow. Minimal clinically relevant differences in OAR exposure were identified. A substantial amount of person-hours could be repurposed with this technology.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Dice similarity coefficient scores in breast cancer. MD baseline contours were compared with AI alone and AI plus MD corrections. Abbreviations: AI = artificial intelligence; MD = experienced radiation oncologist.
Figure 2
Figure 2
Dice similarity coefficient scores in prostate cancer. MD baseline contours were compared with AI alone and AI plus MD corrections. Abbreviations: AI = artificial intelligence; MD = experienced radiation oncologist.
Figure 3
Figure 3
Dice similarity coefficient scores in head and neck cancer. MD baseline contours were compared with AI alone and AI plus MD corrections. Abbreviations: AI = artificial intelligence; MD = experienced radiation oncologist.
Figure 4
Figure 4
Mean (± standard deviation) segmentation times in seconds for MD, AI, and AI+C contours in all 3 investigated anatomic segments. Abbreviations: AI = artificial intelligence; C = corrections; MD = experienced radiation oncologist.
See this image and copyright information in PMC

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