Atlas construction and spatial normalisation to facilitate radiation-induced late effects research in childhood cancer

Abstract

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e. spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95 ± 0.05, 0.85 ± 0.04, 0.96 ± 0.01, 0.91 ± 0.03, 0.83 ± 0.06 and 0.65 ± 0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy.

Image-based data mining applies to data collected from children

“…We found similar DIR uncertainty values for our selected brain structures, with slightly less deviation, most likely because of the rigidity of structures contained within the skull. Veiga et al found and values of 0.7 ± 1.0 mm and 2.9 ± 5.7 mm, respectively, for DIR of the central nervous system in children [27] . Although our was consistent with that of Veiga et al, we found a slightly higher ( Table 1 ).…”

“…Although our study is the first to evaluate the feasibility of the complete IBDM workflow in pediatric CT data, the spatial normalization step can be compared to previous CT-based studies implementing IBDM in adult cohorts of lung cancer patients [16] , [30] , [31] , head and neck cancer patients [19] and a recent study evaluating spatial normalization in data from children [27] . Beasley et al found a DIR uncertainty (SD of center of mass) of 4.0, 0.34 and 0.30 mm in the left–right, anteroposterior and superior-inferior directions for the muscles of mastication [19] .…”

“…Additionally, we evaluated the performance of the DIR via surface-distance and center-of-mass analyses for all delineated structures. For surface-distance analysis, we calculated two distance metrics: the 95% Hausdorff ( ) [37] and average ( ) distances as, respectively, where t represents the reference-anatomy contour and represents the individual subject’s contour propagated to the reference anatomy [27] . For center-of-mass analysis, we calculated the Euclidian norm of the center-of-mass displacement ( ), or.…”

“…Although Palma et al [26] recently demonstrated the utility of IBDM for adult multi-center studies, the relatively larger and age-related anatomic variability characteristic of childhood, which may result in larger uncertainties associated with the spatial normalization needed for IBDM, remained an outstanding question. A recent study investigated this spatial normalization in CT images from a small cohort of children and suggested that satisfactory normalization is feasible [27] .…”

Pre‐treatment analysis of non‐rigid variations can assist robust intensity‐modulated proton therapy plan selection for head and neck patients

“…To apply the deformations between groups of subjects, we need to project the SVFs into the atlas space, in which all the SVFs have the same position and resolution. The atlas was obtained from a group‐wise registration which spatially normalized a cohort of patients [1] 16,31 . In the procedure of the projection, the planning CT (pCT) of each patient was the reference geometry, and the CT acquired during the first treatment week (CT t ) was registered to the pCT to produce $$\pmb {v}_{p\rightarrow t}$$, where p stands for pCT and t stands for the week (in this case $$t=1$$) when the weekly CT acquired.…”