Accuracy of deformable image registration for contour propagation in adaptive lung radiotherapy

Dose response data has shown that escalating the tumour dose can improve the probability of local tumour control [1‐4] in radiotherapy for non-small cell lung cancer (NSCLC). Escalation of tumour dose without exceeding tolerance doses requires the use of highly conformal treatment techniques. Anatomical changes are often observed during radiotherapy treatment of lung cancer; both normal tissues and tumor volumes can deform over time as a response to the radiation therapy. This can impact the delivered dose when using highly conformal treatment techniques. Obtaining volumetric images during treatment fractions allows tracking of any anatomical changes for adaptive protocols, thus allowing for maintenance of treatment objectives. It has been shown that adaptive radiotherapy (ART) for shrinking lung tumours allows dose escalation and can reduce dose to normal tissues [5, 6].

ART requires re-contouring of target and organ at risk (OAR) regions of interest (ROIs) for evaluation and re-planning. Currently, re-contouring of images during a radiotherapy course can be very time-intensive. Automatic propagation of target and OAR contours between two image sets is thus an attractive approach to reducing adaptive radiotherapy resource requirements [7]. DIR has been investigated extensively for automatic propagation of ROIs in 4DCT lung image sets [8‐14] where the anatomy is highly correlated but subject to respiratory motion. DIR has also been investigated for automatic propagation of ROIs in adaptive radiotherapy for head and neck cancer [15‐17]. However, there has been limited investigation of DIR for automatic propagation of ROIs in adaptive lung radiotherapy where significant anatomical changes due to disease progression or response to radiotherapy may be observed between images; Lu et. al. investigated automated ROI propagation between kVCT and MVCT scans in the Tomotherapy adaptive lung radiotherapy paradigm [18]. It is also acknowledged that some DIR algorithms may be more suitable for specific anatomies due to different image features and the mathematical basis of DIR algorithms. Therefore the aim of this study was to evaluate the clinical utility of three theoretically different DIR algorithms for the purpose of propagating contours from pre- to mid-treatment lung kVCT scans in the adaptive radiotherapy setting.

Table 1 shows the change in GTV volume between the pre- and mid-treatment images: GTV volumes reduced in 14 out of 17 patients. For 3/17 patients the GTV volume increased, in all three cases by more than 10%. The average, standard error and range of the Dice for each of the six structures are shown in Figure 1. For the target ROIs, the DIR-algorithms were more accurate for the GTV than the nodal-GTV. This is expected due to the GTV being more easily defined on a CT due to higher contrast between the GTV and the surrounding lung. For the spinal cord and the lungs, the three algorithms were able to accurately track the clearly defined boundaries of the organs. For the esophagus, where the organ boundaries are not as clear on a CT scan, some discrepancies were observed.

No statistically significant differences between the three algorithms were observed with the exception of the left lung, for which SFBR-propagated ROIs had a lower average Dice score than those from Demons and Morphons (p = 0.0030 & p = 0.0001 respectively). Figure 2 shows the MSHDs for the six structures. For the lungs, the SFBR-propagated ROIs had statistically significantly higher MSHDs than those from the Demons and Morphons algorithms (p = 0.009 and p = 0.005 respectively for left lung, p = 0.025 and p = 0.027 respectively for right lung). For all other ROIs, there was no statistically significant difference between the algorithms. The average COM differences (Figure 3) for the GTV-tumor and nodal-GTVs ranged from 0.27 – 0.29 cm and 0.31 – 0.37 cm. There was no statistically significant difference between the algorithms for COM location.

The results of the qualitative evaluation of the ROIs by an expert physician are presented in Figure 4. The majority of the target ROIs were scored either 1 (41%) or 2 (44%), with 15% of targets scored 3. The majority of the OARs were scored 1 (82%) or 2 (14%), with 4% of OARs scored 3, of which 3% were esophagus. All scores of 3 for the esophagus were for the same two patients for all three algorithms. For the cord, all ROIs were scored 1. There was no statistical difference between three algorithms based on the physician scores. The physician scores were compared with the Dice scores and MSHDs to determine any correlation between the two methods of evaluation. Figure 5 shows the histograms of the Dice scores and MSHDs split up between the ROIs scored 1, 2 or 3. The point-biserial test was performed to compare the ROIs scored 1 (no edits required) with the ROIs scored 2 or 3 (minor or major edits required) for the targets and the OARs. A weak negative correlation was observed between the Dice scores and MSHD and physician scores for the targets. For the OARs, moderate positive correlation between the Dice scores and physician scores was observed but weak negative correlation between the MSHDs and physician scores was observed.

This study investigates the ability of three DIR algorithms to automatically propagate ROIs in adaptive lung radiotherapy. The three algorithms used in this study all derive deformations between two images using different properties of the image – image intensity, landmarks and quadrature phase difference with the Demons, SFBR and Morphons algorithms respectively. Despite the differences between the algorithms in terms of how they derive deformation fields, there was very little difference in the quality of automatically propagated ROIs between the three algorithms. As expected, all three algorithms had limited success with tracking anatomical changes in the esophagus and nodal-GTVs, most likely due to limited soft tissue contrast in the kVCT image around these structures. For clearly defined anatomical structures such as the cord, lungs and GTV, the performance of the algorithms was superior to that for low contrast organs. The high Dice scores for the lung need to be taken with some context – the Dice score relies on the volume of the ROIs therefore for very large ROIs such as the lung, a large deviation between two volumes has to occur before the Dice score starts to decrease. Therefore the MSHD and physician scores are likely to be more relevant metrics for the lungs.

Comparing the physician scores for the GTV and the nodal-GTV, the nodal-GTVs received a higher rate of scores of 1 and lower rate of scores of 3 than the GTV. This difference between the GTVs and the nodal-GTVs did not correlate well with the Dice scores; the GTVs had greater average Dice scores than the nodal-GTVs. The range of the MSHDs however was smaller with the nodal-GTVs. These discrepancies could partly be due to the differences in the ROI sizes – small changes in the physically smaller nodal-GTV ROIs would have a larger impact on Dice scores than the same change in the larger GTV ROI. There may also be clinical reasons for the discrepancy. Clinically, the mid-treatment nodal-GTV volumes were delineated on the mid-ventilation phase of a 4DCT scan. Thus, the DIR algorithms had access to the same image information as that used clinically to determine nodal-GTV borders. This suggests that the anatomy surrounding the nodal-GTV could provide adequate information for the algorithms to derive the local nodal-GTV deformation or that there was minimal deformation of the nodal-GTV between the two scans. This finding could also be explained by the fact that visualizing nodes using a non-contrast-enhanced CT is difficult, therefore a propagated contour might be considered adequate, whereas a re-delineation of the node introduces again some intra- or inter-observer variability. It should be noted however that any disagreement between the automated or manually delineated nodal-GTV ROI and the actual nodal-GTV would possibly be different, since the automated method could lead to systematic differences whereas the manual delineation could lead to random differences. A definitive answer to this question is out of the scope of this study but would warrant further investigation. The inconsistent results with the propagated and physician delineated GTVs represented by the Dice scores are in our opinion also partly caused by the observer variations. Another factor contributing to this is the fact that often the target volume definition is modified between the two images, based on information not available in the CT image. An example of this is shown in Figure 6, which shows for two patients the differences in GTV definition between the two images, based on information not contained in the CT image hence not accessible to the DIR algorithms. Thus it is highly recommended that automatically propagated target volumes be thoroughly assessed by the treating physician, since the shape of the target structures in the pre-treatment images influence the shape of the propagated structures independent of the employed algorithm, if there are only weak contrast or gradient differences in the mid-treatment image.

The automatic propagation of the ROIs using the three algorithms took on average 3 min 39 s (Demons), 4 min 16 s (SFBR) and approximately 60 min (Morphons). Although at the time of use, none of the algorithms were optimised for speed, Demons and SFBR both have elements that are multi-threaded and were performed on a 16-core Sun Fire x4450 system. DIR with the Morphons algorithm in contrast was performed using 8 dual-core Intel Xeon X5550 2.66 GHz processors but the implementation was not (yet) multi-threaded. Use of 16 available cores and assuming a fully multi-threaded implementation of the algorithm with no serial component in the code remaining could theoretically could reduce calculation times 16-fold and would result in a calculation time of the same order of magnitude as that for Demons and SFBR.

Although the physician scores represent the opinion of one expert physician, this is still a useful test of the clinical utility of automatically propagated ROIs in adaptive radiotherapy. The weak correlation between the physician scores and the Dice scores and MSHDs, suggest that the use of Dice scores and MSHDs alone are not sufficient for complete evaluation of automatically propagated ROIs in the lung/thorax region. The fact that 30% of structures required editing implies that the treating physician must assess all automatically propagated ROIs to ensure all propagated ROIs are sufficiently accurate for adaptive assessment and re-planning. The comparison of the automatically propagated with the physician drawn ROIs must be done in the context of inter-observer variation. In this study all ROIs were delineated by an expert physician using PET/CT image data, which significantly reduces inter-observer variation, thus it is expected that the physician drawn ROIs used for comparison in this study would not vary significantly from those obtained from multiple observers [27, 28].

Three DIR algorithms were used to automatically propagate both normal tissue and target volumes in repeat lung kVCT scans. Reasonably good agreement with physician drawn contours was observed for normal tissues. DIR-propagated nodal-GTV and esophagus structures were not as accurate, most probably due to less soft tissue contrast for these structures. This system is nevertheless a major step forwards for fast and accurate delineation for adaptive radiotherapy. Further work should include a robust analysis of efficiency gains when using automatic propagation of contours in adaptive radiotherapy.