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Clinical target volumes for glioma - Automated delineation to improve neuroanatomic consistency
Gregory Buti1, Marcela Giovenco2, Tugba Yilmaz1,3
1Massachusetts General Hospital/Mass General Brigham and Harvard Medical School, Department of Radiat. Oncol., 100 Blossom St, Boston, MA 02114, USA.
Background And Purpose:
Delineating clinical target volumes (CTVs) for glioma is challenging as consistency with the neuroanatomy needs to be carefully verified. We developed an automated approach that incorporates tumor infiltration pathways and anatomic barriers to improve the neuroanatomical consistency and efficiency of CTV delineation.
Materials And Methods:
A deep learning model for brain structure segmentation was developed based on manual delineations of hemispheres, brainstem, cerebellum, optic chiasm, optic nerves, ventricles, and midline on CT images of ninety-nine glioma patients. Brain structures predictions are integrated into a constrained distance transform that defines the CTV as a 15-mm expansion of the gross tumor volume. Connecting structures with white matter tracts allow for expansions across different structure boundaries, e.g., cerebellum and brainstem connecting at the cerebellar peduncles.
Results:
Mean (±std) Dice Similarity Coefficient (DSC) for the hemispheres, brainstem, cerebel-lum, chiasm, optic nerves, midline and ventricles were (98.5 ± 0.8)%, (92.5 ± 2.8)%, (96.7 ± 2.2)% (63.9 ± 12.2)%, (83.8 ± 9.0)%, (81.2 ± 7.0) and (91.5 ± 3.9)%. Mean (±std) 95 % Hausdorff distance (HD95) were, in mm, 1.9 ± 2.5, 7.0 ± 5.4, 1.8 ± 1.2, 7.2 ± 3.2, 2.3 ± 1.0, 9.5 ± 10.5, and 3.8 ± 3.1, respectively. Auto-generated CTVs are compared against reference CTVs (15-mm expansion constrained by manually-contoured brain structures). The automatic CTVs showed excellent similarity to the reference CTVs with mean (±std) Surface DSC with 2 mm tolerance and HD95 scores of (95.6 ± 3.4)% and (1.4 ± 1.2) mm, respectively. A physician's quality assessment reported that the automated method would result in a substantial amount of time saved in 85 % of CTV delineations.
Conclusion:
We have successfully incorporated expert knowledge to improve the neuroanatom-ical consistency of automatically-generated CTVs for glioma.
Insights
This study introduces an automated method for delineating clinical target volumes (CTVs) in glioma patients, improving neuroanatomical consistency and efficiency. The AI-driven approach accurately generates CTVs, saving significant physician time.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Delineating clinical target volumes (CTVs) for glioma is complex due to the need for neuroanatomical verification.
- Existing methods can be time-consuming and lack consistent adherence to anatomical boundaries.
Purpose of the Study:
- To develop an automated approach for CTV delineation in glioma that enhances neuroanatomical consistency and efficiency.
- To integrate tumor infiltration pathways and anatomical barriers into the CTV delineation process.
Main Methods:
- A deep learning model was trained on CT images from 99 glioma patients for brain structure segmentation.
- Predictions were used in a constrained distance transform to define CTVs as 15-mm expansions of the gross tumor volume.
- White matter tracts were utilized to enable expansions across different anatomical structure boundaries.
Main Results:
- The automated CTVs demonstrated excellent similarity to reference CTVs, with a mean Surface DSC of (95.6 ± 3.4)% and HD95 of (1.4 ± 1.2) mm.
- The deep learning model achieved high Dice Similarity Coefficients for segmented brain structures (e.g., 98.5% for hemispheres, 92.5% for brainstem).
- Physician assessment indicated that the automated method could save substantial time in 85% of CTV delineations.
Conclusions:
- An automated method successfully incorporated expert knowledge to improve neuroanatomical consistency of CTVs for glioma.
- The developed approach offers a more efficient and consistent alternative for CTV delineation in clinical practice.
