Selecting the optimal image segmentation strategy in the era of multitracer multimodality imaging: a critical step for image-guided radiation therapy

Abstract

The study by Vees et al. [1] illustrates the potential application of the image segmentation approach in a promising “non-FDG” PET tracer that is being investigated for its potential in patients with high-grade glioma. This study is a comparative evaluation of the various image segmentation techniques for the delineation of gross tumour volume (GTV) in this group of patients. The study illustrates the complexity of current image segmentation algorithms and vividly demonstrates the difference in the results obtained and the possible implications for brain tumour GTV delineation, and in so doing sounds a cautionary note. The authors deserve recognition for employing this novel quantitative approach with novel PET tracers in this promising arena of image-guided radiotherapy. A number of innovative PET tracers are being tested around the world for probing biochemical and pharmacological functions, and combined with a further understanding of human biology at the molecular level, the area of imageguided radiation therapy continues to evolve. The availability of PET-CT as a practical tool has further enhanced the role of metabolic imaging in radiotherapy planning in several malignancies. F-Fluoroethyl-L-tyrosine (FET) is a promising F-labelled amino acid analogue that has been shown to reliably differentiate tumour recurrence from reactive changes following various therapies, particularly external radiation therapy [2, 3]. This tumour specificity has been the major reason for the interest generated in this tracer, particularly in the setting of cerebral glioma. Another potential area of interest is its usefulness in assessing tumour grading where standard evaluations have been considered of little value because of marked overlap reported between histological grades [4, 5]. A recent report on FET uptake kinetics in untreated glioma patients has demonstrated a significant difference in the uptake values in the early phase (0–10 min after injection) but not in the later period (30– 40 min after injection) between lowand high-grade gliomas [4]. On the basis of their results, the authors hypothesized that differentiation between lowand high-grade gliomas is possible by taking into account the different kinetic behaviour of FET in these tumours. The preliminary results demonstrate that highand low-grade brain tumours exhibit different uptake kinetics of FET, and a kinetic analysis of FET PET, therefore, may provide important information on the differentiation of suspected brain lesions [4]. The factors that have been suggested to contribute to the different kinetic behaviour in lowand high-grade tumours include increased angiogenesis and intratumoral microvessel density and increased amino acid transporter expression in tumour vessels [4, 5]. In recent years, multimodality imaging information is being increasingly examined for its potential utility in radiotherapy treatment planning for cancer patients. It is imperative to utilize and integrate all the imaging data available for target image coregistration and segmentation to generate combined information to define the overall biophysical characteristics of the tumour. Methods for accurate tumour volume segmentation of PET images have been under investigation in recent years partly as a result of the increased use of PET in radiation treatment planning. At this point, however, image segmentation for PET target volume delineation in radiation treatment planning is “a work in progress”. Also, concurrent multimodality segmentation methods have been proposed as feasible and accurate for integrating multimodality imaging information (consisting of Eur J Nucl Med Mol Imaging (2009) 36:180–181 DOI 10.1007/s00259-008-1033-5