Comparison of Ethos template-based planning and AI-based dose prediction: General performance, patient optimality, and limitations.
Phys Med
; 116: 103178, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-38000099
ABSTRACT
PURPOSE:
Ethos proposes a template-based automatic dose planning (Etb) for online adaptive radiotherapy. This study evaluates the general performance of Etb for prostate cancer, as well as the ability to generate patient-optimal plans, by comparing it with another state-of-the-art automatic planning method, i.e., deep learning dose prediction followed by dose mimicking (DP + DM). MATERIALS General performances and capability to produce patient-optimal plan were investigated through two studies Study-S1 generated plans for 45 patients using our initial Ethos clinical goals template (EG_init), and compared them to manually generated plans (MG). For study-S2, 10 patients which showed poor performances at study-S1 were selected. S2 compared the quality of plans generated with four differentmethods:
1) Ethos initial template (EG_init_selected), 2) Ethos updated template-based on S1 results (EG_upd_selected), 3) DP + DM, and 4) MG plans.RESULTS:
EG_init plans showed satisfactory performance for dose level above 50 Gy reported mean metrics differences (EG_init minus MG) never exceeded 0.6 %. However, lower dose levels showed loosely optimized metrics, mean differences for V30Gy to rectum and V20Gy to anal canal were of 6.6 % and 13.0 %. EG_init_selected showed amplified differences in V30Gy to rectum and V20Gy to anal canal 8.5 % and 16.9 %, respectively. These dropped to 5.7 % and 11.5 % for EG_upd_selected plans but strongly increased V60Gy to rectum for 2 patients. DP + DM plans achieved differences of 3.4 % and 4.6 % without compromising any V60Gy.CONCLUSION:
General performances of Etb were satisfactory. However, optimizing with template of goals might be limiting for some complex cases. Over our test patients, DP + DM outperformed the Etb approach.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Planejamento da Radioterapia Assistida por Computador
/
Radioterapia de Intensidade Modulada
Limite:
Humans
/
Male
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article