Customizable landmark-based field aperture design for automated whole-brain radiotherapy treatment planning

Abstract

Purpose: To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based auto-contouring and customizable landmark-based field aperture design.

Methods: The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original clinical plans. The clinical acceptability of the plans was assessed by five radiation oncologists from four hospitals.

Results: The performance of the generated field apertures was evaluated by the Hausdorff distance (HD) and mean surface distance (MSD) from 182 patients' field apertures used in the clinic. The average HD and MSD for the generated field apertures were 16 ± 7 and 7 ± 3 mm for the first approach, respectively, and 17 ± 7 and 7 ± 3 mm, respectively, for the second approach. The differences regarding HD and MSD between the first and the second approaches were 1 ± 2 and 1 ± 3 mm, respectively. A clinical review of the field aperture design, conducted using 30 patients, achieved a 100% acceptance rate for both the first and second approaches, and the plan review achieved a 100% acceptance rate for the first approach and a 93% acceptance rate for the second approach. The average acceptance rate for meeting lens dosimetric recommendations was 80% (left lens) and 77% (right lens) for the first approach, and 70% (both left and right lenses) for the second approach, compared with 50% (left lens) and 53% (right lens) for the clinical plans.

Conclusion: This study provided an automated pipeline with two field aperture generation approaches to automatically generate WBRT treatment plans. Both quantitative and qualitative evaluations demonstrated that our novel pipeline was comparable with the original clinical plans.

Keywords: automation; customizable field aperture; deep learning; whole-brain radiotherapy.

FIGURE 1

Automated whole‐brain radiotherapy (WBRT) treatment planning workflow

FIGURE 2

Field aperture design using different landmarks

FIGURE 3

Generated field apertures based on different options. Options 1–6 are configured to address different clinical preferences, and options 7 and 8 are configured to address different clinical purposes: (1) the initial setting; (2) the line shapes for AB (horizontal or slash/diagonal‐like); (3) the option of adjusting the line BC at different distances between the eyes and the cribriform plates (close to the lenses or eyes at the cranial–posterior boundary); (4) the line shapes for CE (along the brain expand or a straight line directly connected CE); (5) different sizes for the extent of brain expansion (10, 15, or 20 mm); (6) different sizes for the extent of skin flash (10, 15, or 20 mm); (7) the selection of the vertebral bodies C1 and C2; (8) the option of treatment includes the orbitals.

FIGURE 4

Box plots of the quantitative metrics of the field aperture design indicating the first and second approaches performed similarly

FIGURE 5

Radar plots of the clinical review results based on a five‐point Likert scale

FIGURE 6

Comparisons of the resulting field apertures from the first (orange) and second (white) approaches and the clinical plans (green). Field apertures in orange and white are almost overlapped.