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Validation of an automated contouring and treatment planning tool for pediatric craniospinal radiation therapy.
Hernandez, Soleil; Burger, Hester; Nguyen, Callistus; Paulino, Arnold C; Lucas, John T; Faught, Austin M; Duryea, Jack; Netherton, Tucker; Rhee, Dong Joo; Cardenas, Carlos; Howell, Rebecca; Fuentes, David; Pollard-Larkin, Julianne; Court, Laurence; Parkes, Jeannette.
Afiliación
  • Hernandez S; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States.
  • Burger H; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
  • Nguyen C; Department Medical Physics, Groote Schuur Hospital and University of Cape Town, Cape Town, South Africa.
  • Paulino AC; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
  • Lucas JT; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
  • Faught AM; Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States.
  • Duryea J; Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States.
  • Netherton T; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
  • Rhee DJ; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
  • Cardenas C; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
  • Howell R; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States.
  • Fuentes D; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States.
  • Pollard-Larkin J; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
  • Court L; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
  • Parkes J; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, United States.
Front Oncol ; 13: 1221792, 2023.
Article en En | MEDLINE | ID: mdl-37810961
Purpose: Treatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St. Jude Children's Research Hospital. Methods: Sixty-three CSI patient CT scans were involved in the study. Pre-planning scripts were used to automatically verify anatomical compatibility with the autoplanning tool. The autoplanning pipeline generated 15 contours and a composite CSI treatment plan for each of the compatible test patients (n=51). Plan quality was evaluated quantitatively with target coverage and dose to normal tissue metrics and qualitatively with physician review, using a 5-point Likert scale. Three pediatric radiation oncologists from 3 institutions reviewed and scored 15 contours and a corresponding composite CSI plan for the final 51 test patients. One patient was scored by 3 physicians, resulting in 53 plans scored total. Results: The algorithm automatically detected 12 incompatible patients due to insufficient junction spacing or head tilt and removed them from the study. Of the 795 autosegmented contours reviewed, 97% were scored as clinically acceptable, with 92% requiring no edits. Of the 53 plans scored, all 51 brain dose distributions were scored as clinically acceptable. For the spine dose distributions, 92%, 100%, and 68% of single, extended, and multiple-field cases, respectively, were scored as clinically acceptable. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan than create a new plan. Conclusions: We successfully validated an autoplanning pipeline on 51 patients from another institution, indicating that our algorithm is robust in its adjustment to differing patient populations. We automatically generated 15 contours and a comprehensive CSI treatment plan for each patient without physician intervention, indicating the potential for increased treatment planning efficiency and global access to high-quality radiation therapy.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Front Oncol Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Front Oncol Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos