International Journal of Radiation Oncology*Biology*Physics
Clinical InvestigationWhat Is the Best Way to Contour Lung Tumors on PET Scans? Multiobserver Validation of a Gradient-Based Method Using a NSCLC Digital PET Phantom
Introduction
Fluorodeoxyglucose positron emission tomography (FDG-PET) scans are used in lung cancer management for the initial staging (1), radiation therapy (RT) planning (2), and the evaluation of tumor response to therapy 3, 4. Advances in radiation therapy technology have improved the ability to deliver highly conformal therapy for smaller tumors and have increased the need for accurate and consistent definition of tumor boundaries. It has been demonstrated that applying PET to RT planning changes gross target volume (GTV) in more than 50% of non–small cell lung cancer (NSCLC) cases (2) and is particularly valuable in patients whose tumors blend with atelectasis in computed tomography (CT) image volumes. Great interobserver variability has been reported in CT definition of GTV in lung cancer (5), indicating the limitations of CT for tumor definition. Therefore, there is great interest in lowering this variability, possibly with application of PET images for tumor delineation. Before PET can be widely applied for this purpose, standards must be established for the contouring technique.
There is currently no consensus as to the optimal technique for delineating (segmenting) PET target volumes. Various approaches are used, including the following:
- 1.
Manual contouring (MANUAL), in which the physician determines the tumor outline on the basis of visual perception of the tumor border.
- 2.
Threshold methods, which define the tumor border within a region-of-interest placed over the tumor by including all tissue with activity greater then a defined level. Absolute thresholds define the tumor border on the basis of a minimum SUV level. Suggested standardized uptake value levels have included 2.0 (5), 2.5 (6), or 3.0 ± 1.6 from a recent study looking for the absolute threshold level that produced volumes most similar to pathology measurements for nine NSCLC patients (7). Percent constant threshold methods (THRESHOLD) define the tumor border on the basis of a percentage of the maximum activity within the tumor. All tissue with activity greater than that percentage is included within the tumor volume. The impact of lesion size and source-to-background ratio on volumes obtained with constant threshold methods has been reported previously 1, 8, 9, 10. A recent study demonstrated that to obtain image-derived volumes equal to pathology volumes in nine NSCLC patients, constant thresholds levels between 20% and 42% of maximum were required (7). Adaptive threshold methods use parameters such as tumor size and the ratio of tumor to background levels to define the threshold level (8). Currently, there is no consensus as to the appropriate threshold method or best threshold levels for tumor segmentation. This variability is one factor limiting use of PET for tumor definition in radiation oncology. Most clinicians continue to rely on the CT-derived volume as the gold standard for GTV contouring and use PET as an ancillary tool, mostly to prevent omitting hypermetabolic areas from patient’s GTV or to identify the interface between tumor and atelectasis.
- 3.
Gradient edge detection identifies tumor on the basis of a change in count levels at the tumor border. One proposed method requires, in the following order, a denoising tool, a deblurring tool, a gradient estimator, and a watershed transform (11). This method is sensitive to voxel size, varying image resolution, and noise, which requires adjusting one or more of these tools and making it less realistic for routine clinical use. The gradient method evaluated in this article, GRADIENT (MIM Software, Cleveland, OH), calculates spatial derivatives along tumor radii then defines the tumor edge on the basis of derivative levels and continuity of the tumor edge.
Our goal was to evaluate the accuracy, bias, and consistency of GRADIENT compared with traditional manual and percent threshold contouring methods. In this article, we first used the experimental sphere phantoms to evaluate the impact of various PET cameras, sphere sizes, reconstruction methods, and source-to-background ratios on border detection with both THRESHOLD and GRADIENT. Subsequently, to emulate clinical reality more closely, we evaluated and compared three methods of PET tumor contouring: MANUAL, THRESHOLD, and GRADIENT. We used Monte Carlo PET thorax phantoms (12), which have been designed to simulate both lung tumors and mediastinal lymph node metastases. Because the true volumes of these simulated tumors and lymph nodes are known, they serve as the gold standard for the volumes contoured by the physicians.
Section snippets
Contouring methods
MANUAL: Each observer used a manual contouring tool of their choice (pen, 2D, or 3D paintbrush) provided in MIM (MIM Software) to delineate the structure of interest by visually outlining the boundaries. Five observers used both 3D and 2D brushes, one used 3D only, and one used pen only. The structure could be contoured in any cross-section and viewed in either a single slice or a splash page of contiguous slices. Each observer was able to adjust image contrast levels according to his or her
Sphere phantom
The spherical diameter that would result in the volume obtained by each segmentation method, THRESHOLD and GRADIENT, was calculated. This calculated diameter was compared with the known diameter of the sphere to quantify the accuracy of each segmentation algorithm. The mean and absolute percent error in diameter was combined for all cameras with source to background ratios between 5:1 to 10:1 using thresholds at 20, 25, 30, 35, 40, 45, and 50% (Fig. 2). Results were separated into spheres
Discussion
We have compared a gradient-based segmentation technique to percent constant threshold and manual contouring for delineating lesions on PET scans. We have obtained results in sphere phantoms (to assess the influence of technical factors related to PET scanning) and then in a realistic thorax phantom, imitating human lung tumors. In particular, GRADIENT was compared with the commonly used THRESHOLD method.
The GRADIENT method was more accurate in measuring sphere diameter than the 45% THRESHOLD
Conclusion
GRADIENT was the most accurate and consistent method for contouring tumor volumes on PET compared with manual and constant threshold methods for both sphere phantoms and clinically realistic Monte Carlo PET phantoms with simulated lung and nodal lesions in the thorax. Additionally, GRADIENT was the most robust technique for different PET cameras and varying imaging conditions. These encouraging results will have to be validated in PET scans from actual patients with lung cancer. Improved
Acknowledgments
We thank Micahlis Aristophanous, Bill Penney, and Charles Pelizzari for supplying the Monte Carlo thorax phantoms used in this study (see reference 12).
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Conflicts of interest: none.