Defining a radiotherapy target with positron emission tomography

Tumor delineation is a critical aspect in radiotherapy treatment planning and is usually performed with the anatomical images of a computed tomography (CT) scan. For non‐small cell lung cancer, it has been recommended to use functional positron emission tomography (PET) images to take into account the biological target characteristics. However, today, there is no satisfactory segmentation technique for PET images in clinical applications. In the present study, a solution to this problem is proposed. The development of the segmentation technique is based on the threshold's adjustment directly from patients, rather than from phantoms. To this end, two references were chosen: measurements performed on CT images of the selected lesions, and histological measurements of surgically removed tumors. The inclusion and exclusion criteria were chosen to produce references that are assumed to have measured tumor sizes equal to the true in vivo tumor sizes. In total, for the two references, 65 lung lesions of 54 patients referred for FDG‐PET/CT exams were selected. For validation, measurements of segmented lesions on PET images using this technique were also compared to CT and histological measurements. For lesions greater than 20 mm, our segmentation technique showed a good estimation of histological measurements (mean difference between measured and calculated data equal to −0.8±9.0%) and an acceptable estimation of CT measurements. For lesions smaller than or equal to 20 mm, the method showed disagreement with the measurements derived from histological or CT data. This novel segmentation technique shows high accuracy for the lesions with largest axes between 2 and 4.5 cm. However, it does not correctly evaluate smaller lesions, likely due to the partial volume effect and/or respiratory motions.PACS numbers: 87.53.Bn, 87.53.Kn, 87.55.D, 87.57.nm, 87.57.U

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New strategy for automatic tumor segmentation by adaptive thresholding on PET/CT images

Moussallem

Valette

Traverse-Gléhen

et al. 2012

“…To date, the delineation of PET images has relied upon either the judgment of a nuclear medicine physician or threshold segmentation based on standard uptake values ( 10 – 15 ) or local contrast. ( 16 , 17 ) As evidenced by the literature, no universal agreement currently exists concerning the appropriate threshold level (or range of levels) required to accurately outline PET target volumes.…”

Section: Introductionmentioning

confidence: 99%

“…The threshold technique presented here differs from those previously used ( 16 , 18 ) in that it focuses on the delineation of individual cross‐sections rather than just on total volume.…”

Section: Introductionmentioning

confidence: 99%

Comparison of three image segmentation techniques for target volume delineation in positron emission tomography

Drever

Roa²,

McEwan³

et al. 2007

Incorporation of positron emission tomography (PET) data into radiotherapy planning is currently under investigation for numerous sites including lung, brain, head and neck, breast, and prostate. Accurate tumor‐volume quantification is essential to the proper utilization of the unique information provided by PET. Unfortunately, target delineation within PET currently remains a largely unaddressed problem. We therefore examined the ability of three segmentation methods—thresholding, Sobel edge detection, and the watershed approach—to yield accurate delineation of PET target cross‐sections. A phantom study employing well‐defined cylindrical and spherical volumes and activity distributions provided an opportunity to assess the relative efficacy with which the three approaches could yield accurate target delineation in PET. Results revealed that threshold segmentation can accurately delineate target cross‐sections, but that the Sobel and watershed techniques both consistently fail to correctly identify the size of experimental volumes. The usefulness of threshold‐based segmentation is limited, however, by the dependence of the correct threshold (that which returns the correct area at each image slice) on target size.PACS numbers: 87.58.Fg, 87.57.Nk

“…Methods used for segmenting tumor from normal tissue, such as threshold boundaries using a fraction of the standardized uptake value (SUV), are subject to uncertainties. Although phantom studies have been performed to quantify segmentation threshold as a function of target size, ( 10 , 11 ) such phantom studies are unlikely to accurately represent true clinical conditions where organ motion becomes a confounding factor (12) . PET is a “slow” scan, which is acquired over all phases of breathing; the PET positive volume therefore includes motion of the tumor.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo‐based lung cancer treatment planning incorporating PET‐defined target volumes

Chetty

Fernando

Kessler

et al. 2005

Despite the well‐known benefits of positron emission tomography (PET) imaging in lung cancer diagnosis and staging, the poor spatial resolution of PET has limited its use in radiotherapy planning. Methods used for segmenting tumor from normal tissue, such as threshold boundaries using a fraction of the standardized uptake value (SUV), are subject to uncertainties. The issue of respiratory motion in the thorax confounds the problem of accurate target definition. In this work, we evaluate how changing the PET‐defined target volume by varying the threshold value in the segmentation process impacts target and normal lung tissue doses. For each of eight lung cancer patients we retrospectively generated multiple PET‐target volumes; each target volume corresponds to those voxels with intensities above a given threshold level, defined by a percentage of the maximum voxel intensity. PET‐defined targets were compared to those from CT; CT targets comprise a composite volume generated from breath‐hold inhale and exhale datasets; the CT dataset therefore also includes the extents of tumor motion. Treatment plans using Monte Carlo dose calculation were generated for all targets; the dose uniformity was approximately 100±5% within the internal target volume (ITV) (formed by a uniform 8‐mm expansion of the composite gross target volume (GTV)). In all cases differences were observed in the generalized equivalent uniform doses (gEUDs) to the targets and in the mean lung doses (MLDs) and normal tissue complication probabilities (NTCPs) to the normal lung tissues. The magnitudes of the dose differences were found to depend on the target volume, location, and amount of irradiated normal lung tissue, and in many instances were clinically meaningful (greater than a single 2 Gy fraction). For those patients studied, results indicate that accurate dosimetry using PET volumes is highly dependent on accurate target segmentation. Further study with correlation to clinical outcome will be helpful in determining how to apply these various PET and CT volumes in treatment planning, to potentially improve local tumor control and reduce normal tissue toxicities.PACS number: 87.52.Tf