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Prospective Evaluation of Automated Contouring for CT-Based Brachytherapy for Gynecologic Malignancies.
Kraus, Abigayle C; Iqbal, Zohaib; Cardan, Rex A; Popple, Richard A; Stanley, Dennis N; Shen, Sui; Pogue, Joel A; Wu, Xingen; Lee, Kevin; Marcrom, Samuel; Cardenas, Carlos E.
Afiliação
  • Kraus AC; Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.
  • Iqbal Z; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Cardan RA; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Popple RA; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Stanley DN; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Shen S; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Pogue JA; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Wu X; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Lee K; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Marcrom S; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
  • Cardenas CE; Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Adv Radiat Oncol ; 9(4): 101417, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38435965
ABSTRACT

Purpose:

The use of deep learning to auto-contour organs at risk (OARs) in gynecologic radiation treatment is well established. Yet, there is limited data investigating the prospective use of auto-contouring in clinical practice. In this study, we assess the accuracy and efficiency of auto-contouring OARs for computed tomography-based brachytherapy treatment planning of gynecologic malignancies. Methods and Materials An inhouse contouring tool automatically delineated 5 OARs in gynecologic radiation treatment planning the bladder, small bowel, sigmoid, rectum, and urethra. Accuracy of each auto-contour was evaluated using a 5-point Likert scale a score of 5 indicated the contour could be used without edits, while a score of 1 indicated the contour was unusable. During scoring, automated contours were edited and subsequently used for treatment planning. Dice similarity coefficient, mean surface distance, 95% Hausdorff distance, Hausdorff distance, and dosimetric changes between original and edited contours were calculated. Contour approval time and total planning time of a prospective auto-contoured (AC) cohort were compared with times from a retrospective manually contoured (MC) cohort.

Results:

Thirty AC cases from January 2022 to July 2022 and 31 MC cases from July 2021 to January 2022 were included. The mean (±SD) Likert score for each OAR was the following bladder 4.77 (±0.58), small bowel 3.96 (±0.91), sigmoid colon 3.92 (±0.81), rectum 4.6 (±0.71), and urethra 4.27 (±0.78). No ACs required major edits. All OARs had a mean Dice similarity coefficient > 0.86, mean surface distance < 0.48 mm, 95% Hausdorff distance < 3.2 mm, and Hausdorff distance < 10.32 mm between original and edited contours. There was no significant difference in dose-volume histogram metrics (D2.0 cc/D0.1 cc) between original and edited contours (P values > .05). The average time to plan approval in the AC cohort was 19% less than the MC cohort. (AC vs MC, 117.0 + 18.0 minutes vs 144.9 ± 64.5 minutes, P = .045).

Conclusions:

Automated contouring is useful and accurate in clinical practice. Auto-contouring OARs streamlines radiation treatment workflows and decreases time required to design and approve gynecologic brachytherapy plans.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Adv Radiat Oncol Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Adv Radiat Oncol Ano de publicação: 2024 Tipo de documento: Article