Impact of FDG-labelled positron emission tomography imaging on the management of non-small-cell lung cancer

Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer

BACKGROUND

Maximum, mean and peak SUV of primary tumor at baseline FDG-PET scans, have often been found predictive for overall survival in non-small cell lung cancer (NSCLC) patients. In this study we further investigated the prognostic power of advanced metabolic metrics derived from intensity volume histograms (IVH) extracted from PET imaging.

METHODS

A cohort of 220 NSCLC patients (mean age, 66.6 years; 149 men, 71 women), stages I-IIIB, treated with radiotherapy with curative intent were included (NCT00522639). Each patient underwent standardized pre-treatment CT-PET imaging. Primary GTV was delineated by an experienced radiation oncologist on CT-PET images. Common PET descriptors such as maximum, mean and peak SUV, and metabolic tumor volume (MTV) were quantified. Advanced descriptors of metabolic activity were quantified by IVH. These comprised five groups of features: absolute and relative volume above relative intensity threshold (AVRI and RVRI), absolute and relative volume above absolute intensity threshold (AVAI and RVAI), and absolute intensity above relative volume threshold (AIRV). MTV was derived from the IVH curves for volumes with SUV above 2.5, 3 and 4, and of 40% and 50% maximum SUV. Univariable analysis using Cox Proportional Hazard Regression was performed for overall survival assessment.

RESULTS

Relative volume above higher SUV (80%) was an independent predictor of OS (p = 0.05). None of the possible surrogates for MTV based on volumes above SUV of 3, 40% and 50% of maximum SUV showed significant associations with OS [p (AVAI3) = 0.10, p (AVAI4) = 0.22, p (AVRI40%) = 0.15, p (AVRI50%) = 0.17]. Maximum and peak SUV (r = 0.99) revealed no prognostic value for OS [p (maximum SUV) = 0.20, p (peak SUV) = 0.22].

CONCLUSIONS

New methods using more advanced imaging features extracted from PET were analyzed. Best prognostic value for OS of NSCLC patients was found for relative portions of the tumor above higher uptakes (80% SUV).

An iterative technique to segment PET lesions using a Monte Carlo based mathematical model

PURPOSE

The need for an accurate lesion segmentation tool in 18FDG PET is a prerequisite for the estimation of lesion response to therapy, for radionuclide dosimetry, and for the application of 18FDG PET to radiotherapy planning. In this work, the authors have developed an iterative method based on a mathematical fit deduced from Monte Carlo simulations to estimate tumor segmentation thresholds.

METHODS

The GATE software, a GEANT4 based Monte Carlo tool, was used to model the GE Advance PET scanner geometry. Spheres ranging between 1 and 6 cm in diameters were simulated in a 10 cm high and 11 cm in diameter cylinder. The spheres were filled with water-equivalent density and simulated in both water and lung equivalent background. The simulations were performed with an infinite, 8/1, and 4/1 target-to-background ratio (T/B). A mathematical fit describing the correlation between the lesion volume and the corresponding optimum threshold value was then deduced through analysis of the reconstructed images. An iterative method, based on this mathematical fit, was developed to determine the optimum threshold value. The effects of the lesion volume and T/B on the threshold value were investigated. This method was evaluated experimentally using the NEMA NU2-2001 IEC phantom, the ACNP cardiac phantom, a randomly deformed aluminum can, and a spheroidal shape phantom implemented artificially in the lung, liver, and brain of patient PET images. Clinically, the algorithm was evaluated in six lesions from five patients. Clinical results were compared to CT volumes.

RESULTS

This mathematical fit predicts an existing relationship between the PET lesion size and the percent of maximum activity concentration within the target volume (or threshold). It also showed a dependence of the threshold value on the T/B, which could be eliminated by background subtraction. In the phantom studies, the volumes of the segmented PET targets in the PET images were within 10% of the nominal ones. Clinically, the PET target volumes were also within 10% of those measured from CT images.

CONCLUSIONS

This iterative algorithm enabled accurately segment PET lesions, independently of their contrast value.

Clinical implications of defining the gross tumor volume with combination of CT and 18FDG-positron emission tomography in non-small-cell lung cancer

PURPOSE

To compare the planning target volume (PTV) definitions for computed tomography (CT) vs. positron emission tomography (PET) in non-small-cell lung cancer (NSCLC).

METHODS AND MATERIALS

A total of 21 patients with NSCLC underwent three-dimensional conformal radiotherapy planning. All underwent a staging F-18 fluorodeoxyglucose-position emission tomography (18FDG-PET) scan and underwent treatment simulation using CT plus a separate planning 18FDG-PET scan. Three sets of target volumes were defined: Set 1, CT volumes (CT tumor + staging PET nodal disease); Set 2, PET volumes (planning PET tumor {gross tumor volume (GTV) = [(0.3069 x mean standardized uptake value) + 0.5853])}; Set 3, composite CT-PET volumes (fused CT-PET tumor). Sets 1 and 2 were compared using a matching index. Three-dimensional conformal radiotherapy plans were created using the Set 1 (CT) volumes; and coverage of the Set 3 (composite) volumes was evaluated. Separate three-dimensional conformal radiotherapy plans were designed for the Set 3 volumes.

RESULTS

For the primary tumor GTV, the Set 1 (CT) volume was larger than the Set 2 (PET) volume in 48%, smaller in 33%, and equal in 19%. The mean matching index was 0.65 (35% CT-PET mismatch). Although quantitatively similar, the volumes differed qualitatively. The Set 3 (composite) volume was larger than either CT or PET alone in 62%, smaller in 24%, and equal in 14%. The dose-volume histogram parameters did not differ among the plans for Set 1 (CT) vs. Set 3 (composite) volumes. Small portions of the Set 3 PTV were significantly underdosed in 40% of cases using the CT-only plan.

CONCLUSION

Computed tomography and PET are complementary and should be obtained in the treatment position and fused to define the GTV for NSCLC. Although the quantitative absolute target volume is sometimes similar, the qualitative target locations can be substantially different, leading to underdosage of the target when planning is done using CT alone without PET fusion.

Defining a radiotherapy target with positron emission tomography

PURPOSE

F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) imaging is now considered the most accurate clinical staging study for non-small-cell lung cancer (NSCLC) and is also important in the staging of multiple other malignancies. Gross tumor volume (GTV) definition for radiotherapy, however, is typically based entirely on computed tomographic data. We performed a series of phantom studies to determine an accurate and uniformly applicable method for defining a GTV with FDG-PET.

METHODS AND MATERIALS

A model-based method was tested by a phantom study to determine a threshold, or unique cutoff of standardized uptake value based on body weight (standardized uptake value [SUV]) for FDG-PET based GTV definition. The degree to which mean target SUV, background FDG concentration, and target volume influenced that GTV definition were evaluated. A phantom was constructed consisting of a 9.0-L cylindrical tank. Glass spheres with volumes ranging from 12.2 to 291.0 cc were suspended within the tank, with a minimum separation of 4 cm between the edges of the spheres. The sphere volumes were selected based on the range of NSCLC patient tumor volumes seen in our clinic. The tank and spheres were filled with a variety of known concentrations of FDG in several experiments and then scanned using a General Electric Advance PET scanner. In the initial experiment, six spheres with identical volumes were filled with varying concentrations of FDG (mean SUV = 1.85 approximately 9.68) and suspended within a background bath of FDG at a similar concentration to that used in clinical practice (0.144 muCi/mL). The second experiment was identical to the first, but was performed at 0.144 and 0.036 muCi/mL background concentrations to determine the effect of background FDG concentration on sphere definition. In the third experiment, six spheres with volumes of 12.2 to 291.0 cc were filled with equal concentrations of FDG and suspended in a standard background FDG concentration of 0.144 muCi/mL. Sphere images in each experiment were auto-contoured (simulating a GTV) using the threshold SUV that yielded a volume matching that of the known sphere volume. A regressive function was constructed to represent the relationship between the threshold SUV and the mean target SUV. This function was then applied to define the GTV of 15 NSCLC patients. The GTV volumes were compared to those determined by a fixed image intensity threshold proposed by other investigators.

RESULTS

There was a strong linear relationship between the threshold SUV and the mean target SUV. The linear regressive function derived was: threshold SUV = 0.307 x (mean target SUV) + 0.588. The background concentration and target volume indirectly affect the threshold SUV by way of their influence on the mean target SUV. We applied the linear regressive function, as well as a fixed image intensity threshold (42% of maximum intensity) to the sphere phantoms and 15 patients with NSCLC. The results indicated that a much smaller deviation occurred when the threshold SUV regressive function was utilized to estimate the phantom volume as compared to the fixed image intensity threshold. The average absolute difference between the two methods was 21% with respect to the true phantom volume. The deviation became even more pronounced when applied to true patient GTV volumes, with a mean difference between the two methods of 67%. This was largely due to a greater degree of heterogeneity in the SUV of tumors over phantoms.

CONCLUSIONS

An FDG-PET-based GTV can be systematically defined using a threshold SUV according to the regressive function described above. The threshold SUV for defining the target is strongly dependent on the mean target SUV of the target, and can be uniquely determined through the proposed iteration process.

FDG-PET imaging in the management of non-small-cell lung cancer

Lung cancer is currently the leading cause of cancer-related death in both men and women in most Western countries. Tumour stage is the strongest prognostic factor and the most important parameter guiding treatment decision making. Metabolic positron emission tomography imaging with fluorodeoxyglucose (FDG-PET) has consistently proved superior to conventional imaging for staging of non-small-cell lung cancer and provides information of greater prognostic significance than can be obtained using conventional approaches. FDG-PET has been approved in the USA, Germany and the UK as a basic and invaluable tool in the management of lung cancer.

Special techniques for imaging blood flow to tumors

In this review, the current imaging methods of assessing tumor perfusion are discussed and compared. Because most of the techniques depend on the administration of contrast agents, we first consider some general principles underlying the fate of contrast agents and their behavior inside the unique vascular environment of tumors.