Automated population‐based planning for whole brain radiation therapy. (8th September 2015)

• Record Type: Journal Article
• Authors: Schreibmann, Eduard; Fox, Tim; Curran, Walter; Shu, Hui‐Kuo; Crocker, Ian
• Is Part Of: Journal of applied clinical medical physics. Volume 16:Number 5(2015)
• Page Start: 76
• Page End: 86
• Publication Date: 2015-09-08
• Subjects: knowledge‐based planning -- automation -- whole brain planning; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1120/jacmp.v16i5.5258
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract : Treatment planning for whole‐brain radiation treatment is technically a simple process, but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues, and creates a treatment plan in only a few minutes after patient simulation. Segmentation of target and critical structures is performed automatically through morphological operations on the soft tissue and was validated by comparing with manual clinical segmentation using the Dice coefficient and Hausdorff distance. The treatment plan is generated by searching a database of previous cases for patients with similar anatomy. In this search, each database case is ranked in terms of similarity using a customized metric designed for sensitivity by including only geometrical changes that affect the dose distribution. The database case with the best match is automatically modified to replace relevant patient info and isocenter position while maintaining original beam and MLC settings. Fifteen patients with marginally acceptable treatment plans were used to validate the method. In each of these cases the anatomy was accurately segmented, but the beams and MLC settings led to a suboptimal treatment plan by either underdosing the brain or excessively irradiating critical normal tissues. For each case, the anatomy was automatically segmented with the proposed method, and the automated and manual segmentations were then compared. The mean Dice coefficient was 0.97, with a standard deviation of 0.008 for the brain, 0.85 ± 0.009 for the eyes, and 0.67 ± 0.11 for the lens. The mean Euclidian distance was 0.13 ± 0.13 mm for the brain, 0.27 ± 0.31 for the eye, and 2.34 ± 7.23 for the lens. Each case was then subsequently matched against a database of 70 validated treatment plans and the best matching plan (termed autoplanned), was compared retrospectively with the clinical plans in terms of brain coverage and maximum doses to critical structures. Maximum doses were reduced by a maximum of 8.37 Gy for the left eye (mean 2.08), 11.67 for the right eye (1.90) and, respectively, 25.44 (5.59) for the left lens and 24.40 (4.85) for the right lens. Time to generate the autoplan, including the segmentation, was 3 − 4 min . Automated database‐ based matching is an alternative to classical treatment planning that improves quality while providing a cost‐effective solution to planning through modifying previous validated plans to match a current patient's anatomy. PACS number: 87.55.D, 87.55.tg, 87.57.nm