A feature alignment score for online cone-beam CT-based image-guided radiotherapy for prostate cancer

被引:6
|
作者
Hargrave, Catriona [1 ,2 ,3 ]
Deegan, Timothy [1 ]
Poulsen, Michael [1 ,4 ]
Bednarz, Tomasz [2 ,5 ,6 ]
Harden, Fiona [2 ]
Mengersen, Kerrie [2 ]
机构
[1] Queensland Hlth, Princess Alexandra Hosp, Radiat Oncol, Raymond Terrace, Brisbane, Qld 4101, Australia
[2] Queensland Univ Technol, Sch Math Sci, Fac Sci & Engn, Brisbane, Qld 4000, Australia
[3] Queensland Univ Technol, Sch Clin Sci, Fac Hlth, Brisbane, Qld 4000, Australia
[4] Univ Queensland, Fac Med, Brisbane, Qld 4072, Australia
[5] CSIRO, Data 61, Brisbane, Qld 4102, Australia
[6] Univ New South Wales, Expanded Percept & Interact Ctr, Paddington, NSW 2021, Australia
关键词
cone-beam computed tomography; decision support; image-guided radiotherapy; prostate cancer; SETUP CORRECTION STRATEGIES; RADIATION-THERAPY; HAUSDORFF DISTANCE; REGRESSION TREES; MOTION;
D O I
10.1002/mp.12980
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PurposeTo develop a method for scoring online cone-beam CT (CBCT)-to-planning CT image feature alignment to inform prostate image-guided radiotherapy (IGRT) decision-making. The feasibility of incorporating volume variation metric thresholds predictive of delivering planned dose into weighted functions, was investigated. MethodsRadiation therapists and radiation oncologists participated in workshops where they reviewed prostate CBCT-IGRT case examples and completed a paper-based survey of image feature matching practices. For 36 prostate cancer patients, one daily CBCT was retrospectively contoured then registered with their plan to simulate delivered dose if (a) no online setup corrections and (b) online image alignment and setup corrections, were performed. Survey results were used to select variables for inclusion in classification and regression tree (CART) and boosted regression trees (BRT) modeling of volume variation metric thresholds predictive of delivering planned dose to the prostate, proximal seminal vesicles (PSV), bladder, and rectum. Weighted functions incorporating the CART and BRT results were used to calculate a score of individual tumor and organ at risk image feature alignment (FAS(TV_OAR)). Scaled and weighted FAS(TV_OAR) were then used to calculate a score of overall treatment compliance (FAS(global)) for a given CBCT-planning CT registration. The FAS(TV_OAR) were assessed for sensitivity, specificity, and predictive power. FAS(global) thresholds indicative of high, medium, or low overall treatment plan compliance were determined using coefficients from multiple linear regression analysis. ResultsThirty-two participants completed the prostate CBCT-IGRT survey. While responses demonstrated consensus of practice for preferential ranking of planning CT and CBCT match features in the presence of deformation and rotation, variation existed in the specified thresholds for observed volume differences requiring patient repositioning or repeat bladder and bowel preparation. The CART and BRT modeling indicated that for a given registration, a Dice similarity coefficient >0.80 and >0.60 for the prostate and PSV, respectively, and a maximum Hausdorff distance <8.0 mm for both structures were predictive of delivered dose 5% of planned dose. A normalized volume difference <1.0 and a CBCT anterior rectum wall >1.0 mm anterior to the planning CT anterior rectum wall were predictive of delivered dose >5% of planned rectum dose. A normalized volume difference <0.88, and a CBCT bladder wall >13.5 mm inferior and >5.0 mm posterior to the planning CT bladder were predictive of delivered dose >5% of planned bladder dose. A FAS(TV_OAR) >0 is indicative of delivery of planned dose. For calculated FAS(TV_OAR) for the prostate, PSV, bladder, and rectum using test data, sensitivity was 0.56, 0.75, 0.89, and 1.00, respectively; specificity 0.90, 0.94, 0.59, and 1.00, respectively; positive predictive power 0.90, 0.86, 0.53, and 1.00, respectively; and negative predictive power 0.56, 0.89, 0.91, and 1.00, respectively. Thresholds for the calculated FAS(global) of were low <60, medium 60-80, and high >80, with a 27% misclassification rate for the test data. ConclusionsA FAS(global) incorporating nested FAS(TV_OAR) and volume variation metric thresholds predictive of treatment plan compliance was developed, offering an alternative to pretreatment dose calculations to assess treatment delivery accuracy.
引用
收藏
页码:2898 / 2911
页数:14
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