International Journal of Radiation Oncology*Biology*Physics
Clinical investigation: lungCT and 18F-deoxyglucose (FDG) image fusion for optimization of conformal radiotherapy of lung cancers
Introduction
The central objective of three-dimensional conformal radiotherapy (3D-CRT) is to ensure a high dose distribution tailored to the limits of the target volume while reducing exposure of healthy tissues to a minimum. This high-precision technique requires a very rigorous approach throughout the treatment process, from acquisition of anatomic data until the dose delivery. One of the most important steps of 3D-CRT is precise delimitation of the tumor volume, defined by drawing the necessary margins around what is considered to be the macroscopic and microscopic tumor volume. Recent data have shown that the error in target volume delineation may be the biggest error in the entire radiotherapy chain (1).
The best examination for the lung appears to be computed tomography (CT), both in terms of the primary tumor and mediastinal lymph nodes analysis 2, 3, 4, 5. It also provides information on the electron densities of the various tissues, which is essential to obtain accurate dose distribution from computerized treatment planning systems. However, this advantage of CT over the other currently available techniques is only relative, as its sensitivity and specificity are limited in terms of tissue characterization 3, 6. 18F-deoxyglucose (FDG) imaging, a new metabolic imaging technique, has already demonstrated its value in oncology, especially in lung cancer 7, 8, 9, 10, 11, 12, 13, 14, 15, 16. Positron emission tomography (PET) allows a 3D whole body study. FDG-PET provides a functional image of tissue or organs. The ability to differentiate between malignant and benign pulmonary nodules by PET was shown by Patz et al., who found a sensitivity ranging from 93% to 100%, a specificity ranging from 52% to 88%, and an accuracy ranging from 92% to 94% (14). For mediastinal involvement, there are now abundant published data indicating that PET has superior sensitivity and specificity relative to CT. The sensitivity, specificity, and accuracy of PET for evaluation of mediastinal extension range from 76% to 92%, 81% to 100%, and 80% to 100%, respectively, while CT provides much lower results: 56–65%, 73–87%, and 77–82%, respectively 14, 17. PET can detect tumor in some normal-sized nodes seen in CT and can exclude tumor in enlarged nodes, modifying the classical management of mediastinal spread (17). In addition, FDG-PET is also accurate in assessing extrathoracic metastases in bone and soft tissues (18).
Two types of detectors can be used to perform images of FDG biodistribution: dedicated PET cameras with multiple detectors placed in a ring surrounding the patient, which are expensive but present a high sensitivity; and dual-head coincidence (CDET) gamma cameras, which are “conventional” gamma cameras modified for coincidence detection. The latter are slightly less efficient than PET cameras, but are much less expensive and more easily accessible, especially in France, where the number of PET cameras is very limited compared to our European neighbors 14, 19, 20, 21, 22. Several studies have compared FDG-PET and FDG-CDET. It appears that the sensitivity of CDET for pulmonary lesions, with a diameter of at least 1–2 cm, was comparable to that of PET on visual analysis 11, 19, 23, 24, 25. The image contrast in CDET relative to the PET deteriorated with decreasing lesion size. For lesions with a diameter of 1 cm, the image contrast was approximately 60% lower than the PET; however, the specificity for the evaluation of lung lesion appears to be comparable to that of PET (19). Regarding these advantages, expense, and availability, CDET systems seem to be of interest. However, according to their few anatomic landmarks, FDG images, both PET and CDET, do not provide sufficient anatomic definition to allow precise spatial landmarking and they also do not provide any information about tissue electron densities for 3D-CRT 12, 19, 23, 26.
We have therefore attempted to develop “hybrid” 3D images comprising both anatomic data, derived from CT, and FDG functional information. Recently, some studies have shown the possible impact of FDG-PET on the planning of the radiotherapy with substantial effect on the design of target volume. However, no study has been performed on a 3D fusion completely integrated in the treatment planning chain and no data have been published concerning the potential efficacy of CDET.
The objective of this study was to validate and assess a fusion modality between CT and FDG-CDET procedure as used for planning 3D-CRT of lung cancer.
Section snippets
Methods and materials
Our study consists of 2 parts: (a) a preclinical phantom study designed to validate the image fusion technique and the computer link between the various imaging systems; and (b) a clinical feasibility study with 12 patients intended to assess the value of FDG-CDET and CT images fusion compared to CT based planning.
Preclinical study
The objective of this preclinical study was to analyze and validate CT and FDG-CDET image fusion by using a phantom with known characteristics. Images derived from the two imaging modalities were easily matched with a reproducible and constant difference < 2 mm due to scatter of the FDG external markers, making it difficult to ensure precise correspondence with CT markers. This small difference was acceptable considering the intrinsic resolution of CDET. The entire computer processing chain was
Discussion
The performance of FDG imaging is clearly superior to those of conventional imaging techniques, especially CT (Table 2). The indications for FDG imaging in the primary evaluation of lung tumors are multiple, and include characterization of an isolated nodule and initial staging of lung cancer, especially mediastinal staging. It also allows evaluation of the response to chemotherapy or radiotherapy, and can even represent a predictive factor of local control and survival (30). Several studies
Conclusion
We performed a simple procedure, which can be used routinely, to superimpose CT and FDG-CDET images. This technique, validated on a phantom, combined complementary anatomic and metabolic information on a composite image and improved local staging of lung cancer. It therefore appeared to be particularly useful for the detection of metastatic lymph nodes and the differentiation between atelectatic lung tissue and tumor tissue. Improvement of the CT tissue characterization resulted in adaptation
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