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Open AcceResearchRelation between nodule size and 18F-FDG-PET SUV for malignant and benign pulmonary nodules. Majid Khalaf*1,4, Hani Abdel-Nabi1, John Baker1, Yiping Shao1, Dominick Lamonica2 and Jayakumari Gona3

Address: 1Department of Nuclear Medicine, University at Buffalo (SUNY), Buffalo, New York, USA, 2Department of Nuclear Medicine, Roswell Park Cancer Institute, Buffalo, New York, USA, 3Department of Nuclear Medicine, Veteran Affairs Western New York Healthcare System, Buffalo, New York, USA and 4PET Center, Children's Hospital of Michigan, 3901 Beaubien Blvd, Detroit, MI 48201, USA

Email: Majid Khalaf* - majid@pet.wayne.edu; Hani Abdel-Nabi - hha@buffalo.edu; John Baker - jgbaker@buffalo.edu; Yiping Shao - Yiping.Shao@di.mdacc.tmc.edu; Dominick Lamonica - dominick.lamonica@roswellpark.org; Jayakumari Gona - jayakumari.gona@med.va.gov

* Corresponding author

Abstract: The most common semiquantitative method of evaluation of pulmonary lesions using 18F-FDGPET is FDG standardized uptake value (SUV). An SUV cutoff of 2.5 or greater has been used todifferentiate between benign and malignant nodules. The goal of our study was to investigate thecorrelation between the size of pulmonary nodules and the SUV for benign as well as for malignantnodules.

Methods: Retrospectively, 173 patients were selected from 420 referrals for evaluation ofpulmonary lesions. All patients selected had a positive CT and PET scans and histopathology biopsy.A linear regression equation was fitted to a scatter plot of size and SUVmax for malignant and benignnodules together. A dot diagram was created to calculate the sensitivity, specificity, and accuracyusing an SUVmax cutoff of 2.5.

Results: The linear regression equations and (R2)s as well as the trendlines for malignant andbenign nodules demonstrated that the slope of the regression line is greater for malignant than forbenign nodules. Twenty-eight nodules of group one (≤ 1.0 cm) are plotted in a dot diagram usingan SUVmax cutoff of 2.5. The sensitivity, specificity, and accuracy were calculated to be 85%, 36%and 54% respectively. Similarly, sensitivity, specificity, and accuracy were calculated for an SUVmaxcutoff of 2.5 and found to be 91%, 47%, and 79% respectively for group 2 (1.1–2.0 cm); 94%, 23%,and 76%, respectively for group 3 (2.1–3.0 cm); and 100%, 17%, and 82%,, respectively for group 4(> 3.0 cm). The previous results of the dot diagram indicating that the sensitivity and the accuracyof the test using an SUVmax cutoff of 2.5 are increased with an increase in the diameter of pulmonarynodules.

Conclusion: The slope of the regression line is greater for malignant than for benign nodules.Although, the SUVmax cutoff of 2.5 is a useful tool in the evaluation of large pulmonary nodules (>1.0 cm), it has no or minimal value in the evaluation of small pulmonary nodules (≤ 1.0 cm).

Published: 22 September 2008

Journal of Hematology & Oncology 2008, 1:13 doi:10.1186/1756-8722-1-13

Received: 1 July 2008Accepted: 22 September 2008

This article is available from: http://www.jhoonline.org/content/1/1/13

© 2008 Khalaf et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IntroductionMetabolic imaging with 18F-FDG PET is a well-establishedindication for the evaluation of pulmonary nodules. Incurrent practice, standardized uptake value (SUV) is oneof the most common methods to evaluate pulmonarynodules. Semiquantitative determination of FDG activityis obtained by calculating SUV in a given region of interest(ROI). An SUV cutoff of 2.5 or greater has been tradition-ally associated with malignant pulmonary nodules [1].However, Thie (2) has previously reported many factorsthat influence the calculation of SUV. These mightinclude: 1) the shape of ROI; 2) partial-volume and spill-over effects; 3) attenuation correction; 4) reconstructionmethod and parameters for scanner type; 5) counts' noisebias effect; 6) time of SUV evaluation; 7) competing trans-port effects; and 8) body size. Factors obtained in smallphantom data allow observed ROI activity to be correctedto that truly present. There is dependency on the recon-structed resolution, the size and geometry, and the ratio ofactivities in the ROI region and the surrounding region.Motion blurring (e.g., from the diaphragm) also undesir-ably averages pixel intensities [2]. In addition to theequipment and physical factors, the biological factors ofthe nodules have an influence on SUV. The slowly grow-ing and well-differentiated tumors generally have lowerSUVs than rapidly growing and undifferentiated ones.Bronchoalveolar and carcinoid tumors have beenreported to have lower SUVs than non-small cell lung can-cers [3-5]. On other hand, some acute infectious andinflammatory processes such as TB, Cryptococcus infec-tion, and rheumatoid nodules might have high SUVs thatoften overlap with the SUVs of rapidly growing and undif-ferentiated tumors [6-8]. Moreover, different papers [9-13] reported that the semiquantitative method of SUV isnot superior to the visual assessment in the characteriza-tion of pulmonary nodules, particularly for smallnodules.

Despite the major role of metabolic imaging with 18F-FDGPET in management of pulmonary lesions, in the currentclinical practice, the characterization of small pulmonarynodules remains a challenge for clinicians. The goal of ourstudy was to investigate the correlation between the size ofpulmonary nodules and the SUV for benign as well as formalignant nodules. We examined the sensitivity, specifi-city and accuracy of the 18F- FDG PET SUVmax cutoff of 2.5in differentiating between malignant and benign pulmo-nary nodules. In addition, we examined an SUVmax cutoffof less than 2.5 for characterizing pulmonary nodules of1.0 cm or less.

Materials and methodsPatientsPatients were selected retrospectively from PET centerdatabases of Veteran Affairs Western New York HealthcareSystem, referred to as medical center A (MC-A) and

Roswell Park Cancer Institute, referred to as medicalcenter B (MC-B) in Buffalo, New York. Samples of 173patients were selected from 420 referrals for 18F-FDG PETevaluation of pulmonary lesion(s) in the two medicalcenters between February 2004 and November 2005. Thereminder was ineligible for the study due to unavailabilityof pathological diagnosis or CT-thorax; or PET scan wasnegative. There were 147 males and 26 females; aged 67years ± 11.6, with a range between 25–89 years. A phan-tom study was performed to measure the difference inSUV between the two scanners. All patients who wereselected for the study had positive CT scans of the chest forpulmonary nodule(s), a histopathology biopsy, and apositive PET scan for nodule(s) to measure the SUV.Patients who had negative PET scan, negative CT or nohistopathology of the nodule(s) were excluded from thestudy. The last two were excluded because the SUV or thesize of the nodule cannot be measured. The measure-ments of nodules were obtained from CT reports. All PETscans were adjusted for body weight for SUV calculation.The study was approved by Institutional review Boards(IRB) of (MC-A) and (MC-B), and given exempt statusfrom the informed consent requirement.

Imaging protocol of 18F-FDG PET scansAll patients fasted at least 4 hours before receiving a 10–15 mCi (370 MBq-555 MBq) dose of intravenous 18F-FDG. PET scans were performed approximately 60 min-utes after the injection of the 18F-FDG dose. Emission andtransmission acquisition times were 5 and 3 minutes,respectively, per bed position. All SUV measurementswere adjusted for body weight and blood glucose wasmeasured for all diabetic patients to ensure that it waswithin acceptable limits. The PET Model of MC-A Scannerwas Siemens ECAT EXACT HR+ with detector type ofBGO, 288 detectors (16 Crystals: 1 PNT), 18, 432 crystals(4,04 + 4.39 × 30 mm). The Axial Coverage was 15.5 cmwith Spatial Resolution of TA: 5.5, A: 4.7 mm FWHM. ThePET Model of MC-B Scanner was GE Advance S9110JFwith detector type of BGO, 366 detectors (18) Rings,12,096 (4 × 8 × 30 mm). The Axial Coverage is 15.2 cmwith Spatial Resolution of TA: 5.5, A: 5.3 mm FWHM.Attenuation was corrected by standard transmission scan-ning with 68 Ge sources. Acquisition mode was 2-dimen-sional from skull vertex to mid thigh. Images werereconstructed in coronal, sagittal and axial tomographicplanes, using a Gaussian filter with a cutoff frequency of0.6 cycles per pixel, ordered-subset expectation maximiza-tion (OSEM) with 2 iterations and 8 subsets, and a matrixsize of 128 × 128. The images were interpreted on work-stations in coronal, sagittal and axial tomographic planes.

Data and statistical analysisUsing 75% isocontour, regions of interest (ROIs) weredrawn around the lesions after these were visuallyassessed, and identified as corresponding to the lesions on

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the CT scan and histopathology reports. The scanners'analysis software tools calculated both maximum andmean SUV values. After all nodules from both centerswere pooled together, they were divided into 4 groupsaccording to their longest axial dimensions. Group 1 nod-ules were equal or less than 1 cm in diameter; group 2nodules ranged from 1.1-to-2.0 cm; group 3 nodulesranged from 2.1-to-3.0 cm; and group 4 nodules/masswere more than 3 cm. Nodules were separated into malig-nant and benign categories according to the histopathol-ogy. We thus obtained 12 groups of nodules: all nodulespooled together irrespective of pathology (n = 4), malig-nant nodules (n = 4) and benign nodules (n = 4). TheSUVmax with standard deviation and range, and SUVmeanwith standard deviation and range of each group were cal-culated using Microsoft Excel. T-tests were used to com-pare differences in SUVmax values between malignant andbenign nodules for the four size groups.

A linear regression equation was fitted to a scatter plot ofsize and SUVmax for malignant and benign nodulestogether, using Microsoft Excel. A dot diagram was createdusing MedCalc software version 9.2 for SUVmax cutoff of2.5 to calculate the true positive (TP), false positive (FP),true negative (TN) and false negative (FN) rates for allnodules together and for each mixed (benign and malig-nant) nodule group. Accordingly, the sensitivity, specifi-city, and accuracy of an SUVmax cut-off of 2.5 indifferentiating between benign and malignant noduleswere calculated for all nodules together and for each sizegroup. In addition, the accuracy was calculated for allnodules of MC-A and MC-B separately. The accuracy wascalculated according the following formula: Accuracy =TP+TN/TP+TN+FP+FN.

Phantom studyA cylindrical phantom (8.5 inches diameter and 7.5inches long) 2 sets of 5 hot spheres (from 6 to 25 mmdiameters) was imaged with the scanners of MC-A andMC-B with their normal clinical protocols. One set of thespheres was concentrically located around the phantomaxial line, and the other set was not, so that the locationdependency of spheres would simulate the clinical caseswhere the nodules might be central or peripheral in thechest. Images were acquired with two target-to-back-ground (T/B) activity ratios of FDG: 5:1 initially, and 2.5:1with increased background activity. In order to get highquality image data, the activity concentration of thespheres at the beginning of the imaging was around 1.0micro Ci/cc. Emission and transmission acquisition timeswere 5 and 3 minutes respectively. Images were recon-structed using the same software, the same methods, andthe same criteria as clinical studies. ROI's were drawn tosurround sphere boundaries by the investigators, and the

Scanners' analysis software tools calculated both maxi-mum and mean SUV.

ResultsPatients characteristicsTable 1 summarizes the characteristics of patients. Thepopulations of the two medical centers were similar inage, however, they differ in the percentage of femalepatients and the proportion of small nodules (≤ 1 cm).The female percentage of MC-A is very low due to the factthat the veteran patients are predominantly male. Theproportion of small nodules for MC-A was 9% and forMC-B was 23%. The difference in the proportion of smallnodules between the two centers may be related to differ-ences in the protocols of the two medical centers to eval-uate and follow up small pulmonary nodules.

Characteristics of nodulesTable 2 summarizes the characteristics of nodules. One ofthe main findings in table 2 is that the percentage ofmalignancy increases as the nodule size increases. Itincreased from 47% for group 1 to 80% for group 4.Another significant finding is the average SUVmax ofbenign nodules increased from 3.34 for small nodule (≤ 1cm) to 5.78 for nodules/mass (> 3 cm), while averageSUVmax of malignant nodules increased from 3.28 forsmall malignant nodules to 10.67 for large malignantnodules (Figure 1). The increase in the average SUVmax wasmore prominent for malignant nodules than benign nod-ules indicating that there is a stronger relation between theSUVmax and the size of the malignant nodule groups thanfor benign nodules. The histopathology of malignant andbenign nodules is listed in table 3.

Result of the phantom studySpheres with diameters 10 to 25 mm were confidentlyidentified in all images for 5:1 T/B ratio, and 16 to 25 mmfor 2.5:1 ratio. The data has shown that SUV values fromtwo different scanners follow a very similar function withrespect to the sphere sizes, and the values from the scan-

Table 1: Characteristics of patients

Variable MC A MC B Total

No. of patients 110 63 173Mean age (Range) 68 (46–89) 66 (25–89) 67 (25–89)Male (5%) 108 (98) 42 (67) 150 (87)Female (%) 2 (2) 21 (33) 23 (13)All Nodule 127 75 202

Malignant (%) 92 (72) 55 (73) 147 (72)Benign (%) 35 (28) 20 (27) 55 (28)

Nodules ≤ 1 cm 11 17 28Malignant (%) 4 (37) 9 (53) 13 (47)Benign (%) 7 (63) 8 (47) 15 (53)

MC = medical center

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ner of MC-A were consistently ~1.3× higher than the onesfrom the scanner of MC-B.

Data analysis-linear regression equationA linear regression equation fitted to all malignant andbenign nodules was generated using Microsoft Excelspreadsheet. For malignant nodules, the linear regressionequation parameters and percentage of varianceaccounted for (R2) were (y = 1.2523x + 4.2949) and (R2 =0.2492). The linear regression equation parameters and(R2) for benign nodules were (y = 0.4555x + 3.5469) and(R2 = 0.0766). The equations and trendlines demonstratethat the slope of the regression line is greater for malig-nant than for benign nodules. The larger the diameter ofthe malignant nodule is, the higher the possibility of ahigher SUV. As the pathology of malignant nodules dis-tributed randomly, the smaller nodules tended to have

lower SUV than larger nodules of the same pathology(Figure 2).

Statistical analysis using t-tests revealed that there were nosignificant differences in SUVmax values between malig-nant and benign nodules for Group 1 (t (26) = 0.3, ns)and for Group 2 (t (56) = -0.2, ns). The differences in SUV-

max values between malignant and benign nodules didreach statistical significance for Group 3 (t (44) = -3.1, p <.004) and for Group 4 (t (65) = -3.3, P < .002).

Accordingly, SUVmax becomes useful as a tool to differen-tiate between malignant and benign lesions for largernodules. However, when we examine the standard devia-tion (SD) of the average of the SUVmax for larger malignantand benign nodules, there is obvious overlap. There wasno predetermined fixed SUV cutoff that able to differenti-ate pulmonary nodules as definitely benign or definitelymalignant, regardless of the nodule size (Table 2).

Data Analysis-dot diagramA total of two hundred-and-two nodules of all groupswere plotted in a dot diagram, using an SUVmax cutoff of2.5. The number of TP, FP, TN and FN nodules was 138,40, 15 and 9, respectively. The sensitivity, specificity, andaccuracy were calculated to be 93%, 27% and 76%,respectively. Since all negative PET scan were excluded

Table 2: Characteristics of nodules

Number of nodules SUVmax

Groups MS in cm Total M (%) B (%) M (SD) B (SD)

≤ 1.0 cm 0.78 28 13 (47) 15 (53) 3.28 (1.28) 3.34 (1.09)1.1–2.0 cm 1.58 58 42 (72) 16 (28) 5.52 (2.64) 4.90 (3.98)2.1–3.3 cm 2.61 47 36 (76) 11 (24) 9.27 (5.33) 4.67 (2.72)> 3.0 cm 5.08 69 55 (80) 14 (20) 10.67 (4.84) 5.78 (3.12)

MS = mean size, cm = centimeter, M = malignant, B = benign, SD = standard deviation

Table 3: Histopathology of malignant and benign nodules

HP of malignant nodules (n = 147) Number of nodules (%)

Adenocarcinoma 59 (40)Squamous cell carcinoma 40 (27)Large cell cancer 11 (7.5)Carcinoid tumor 11 (7.5)Non-specified NSCLC 9 (6.1)Small cell lung cancer 8 (5.4)Other 9 (6.1)HP of benign nodules (n = 55)

Non-specified benign 10 (18)Fibrosis-elastosis 9 (16)Chronic inflammation 7 (13)Lymphoid tissue hyperplasia 4 (7.2)Squamous metaplasia 4 4 (7.2)Granuloma 3 (5.5)Atypical cytology 3 (5.5)Tuberculosis 3 (5.5)Rheumatoid nodules 2 (3.6)Silicoanthracotic nodules 2 (3.6)Cryptococcus infection 2 (3.6)Other 6 (11)

HP = Histopathology

Histogram of malignant versus benign nodules for groups one to fourFigure 1Histogram of malignant versus benign nodules for groups one to four.

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from the study, the sensitivity, specificity, and accuracymentioned in this study do not apply for PET as a test butfor SUVmax cutoff of 2.5 as a test. Twenty-eight nodules ofgroup 1 were plotted in the same manner. The sensitivity,specificity, and accuracy was 85%, 36% and 54% respec-tively (Figure 3), compared to 91%, 47%, and 79% fornodules in Group 2 (1.1 – 2.0 cm). These values tended toimprove with increasing size of nodules. Using a SUVmaxcutoff of 1.8 or less for the smaller nodules increased thesensitivity to 100% from 85%; however, there weredecline in the specificity and the accuracy of the test to dif-ferentiate between the malignant and benign nodules.

DiscussionThe data of this study is collected from two PET centers, aphantom study is used to examine the SUV measurementon both scanners. The experiment indicates that SUV fromdifferent scanners under the same image protocols andsame scintillation detector type (BGO for both scanners)can be quite different in value. However, they follow verysimilar trends as size increases, the SUV value increaseddespite all spheres having the same T/B activity ratios,which is consistent with our clinical result. Accordingly,we recommend that the follow up scans to evaluate treat-ment response or re-stage the disease be performed on the

Linear regression equation fitted to all malignant and benign nodulesFigure 2Linear regression equation fitted to all malignant and benign nodules.

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same scanner to be comparable. The difference in SUV ondifferent scanners despite the same T/B activity ratiosmight be attributed to the difference in calibration andmachine-identity-features. Although, there was a differ-ence in the SUVmax value between our two scanners of afactor of ~1.3× in the phantom study, we chose not toapply an adjustment of SUVmax for our clinical resultbecause the average SUVmax of each nodule group fromboth centers were close to each other, particularly forgroup 1 and group 2. The averages of the SUVmax of group1 and group were 3.03 and 5.28 for MC-1, respectively,and 3.3 and 5.43 for MC-2, respectively. In addition, over-all accuracy using an SUVmax cutoff of 2.5 were similar.The accuracies were 77% and 75% for MC-1 and MC-2,respectively. The trendline, linear regression equation andR2 of malignant and benign nodules for MC-1 and forMC-2 demonstrate the same relation between nodule sizeand SUVmax. The relation is stronger for malignant than

benign lesions. Consequently, we selected to keep theclinical data as it is without adjustment of SUVmaxbetween the two scanners.

The results of the present study indicate that there is a rela-tion between the size of pulmonary nodules and the SUVvalue. The linear regression equation and R2 for malignantnodules and for benign nodules, as well as the trendlinesfor malignant and benign nodules demonstrated that theslope of the regression line was greater for malignant thanfor benign nodules. In Figure 2, it can be seen that on theleft side of the graph, where the small nodules (≤ 1 cm)are plotted, the nodules mixed randomly with no pre-dominant areas for benign or malignant nodules. NoSUVmax cutoff can separate them. However on the middleand right side, where larger size nodules (> 2.0 cm) areplotted, the nodules become more polarized, and themalignant nodules predominate in the upper portion of

Dot diagram for groups one and two using SUVmax cut-off of 2.5Figure 3Dot diagram for groups one and two using SUVmax cut-off of 2.5.

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the plot area where the SUV is high, while the benign nod-ules predominate in the lower portion of the plot areawhere SUV is lower. Determination of an SUV cutoff forlarger nodules is more feasible but not definite in the diag-nosis of pulmonary nodules.

When the SUVmax cutoff of 2.5 was used to differentiatebetween malignant and benign pulmonary nodules. Thesensitivity, specificity and accuracy of nodules for group 2was 91%, 47%, and 79%, respectively. For group 3 it was94%, 23%, and 76%, respectively. For group 4 it was100%, 17%, and 82%, respectively. Although, the sensi-tivity and accuracy of the test increased with the increasein the size, reaching 100% and 82% respectively for nod-ules greater than 3.0 cm, the specificity declined from47% for group 2 to 17% for group 4. The accuracy of dif-ferentiating large pulmonary nodules (> 1.0 cm) usingSUVmax cutoff of 2.5 seems reasonable. However, no pre-determined fixed SUVmax cutoff is able to differentiate pul-monary nodules as definitely benign or definitelymalignant, regardless of the nodule's size.

One of the main findings of the present study was thatthe small nodules (≤ 1 cm) tend to have lower SUVs thanlarger nodules. The small benign pulmonary noduleshave average SUV as equal as to malignant nodules.Thus, maximum or mean SUV is not accurate tool in theevaluation of small pulmonary nodules. Only 54% ofthe time was the test able to differentiate between malig-nant and benign nodules. Attempting to lower SUVmax toless that 2.5, such as 1.8 might increase the sensitivity ofthe test, however, the specificity is decreased resulting inno clinically significant improvement in the accuracy ofthe test to differentiate between the malignant andbenign nodules. The sensitivity, specificity, and accuracyof a cutoff of 1.8 were 100%, 0.0%, and 46%, respec-tively. This result reflects the fact that FDG is not a spe-cific tracer for malignancy. In our study, a variety ofsmall benign nodules (≤ 1 cm) presented with mean andmaximum SUV more than 2.5 and resulted in a false pos-itive PET scan. (e.g., the SUVmax was 5.3 for squamousmetaplasia, 4.6 for rheumatoid nodules, 4.2 for lym-phoid tissue and 3.9 for TB). Other benign nodules suchas granuloma, chronic inflammation, cryptococcusinfection, reactive nodules and atypical hyperplasia alsopresented with high SUVmax leading to reading a falsepositive PET scan. On the other hand, some of well-dif-ferentiated and slow growing malignant nodules pre-sented with SUVmax less than 2.5 (1.34 for squamous cellcarcinoma, 1.77 for adenocarcinoma and 2.15 for smallcell lung cancer).

The data above support that although, the SUVmax cutoffof 2.5 is a useful tool in the evaluation of large pulmonarynodules (> 1.0 cm), it has no or minimal value in the eval-uation of small pulmonary nodules (≤ 1.0 cm). However,

the combination of flexible value of SUVmax cutoff accord-ing to the size of the nodule, visual assessment, and CTcharacteristics of the nodules, in addition to pretest prob-ability of malignancy, is the most appropriate approach tocharacterize small pulmonary nodules. To increase thesensitivity of the test of SUVmax cutoff for characterizingsmall nodules (≤ 1 cm), we recommend reducing the cut-off of less than 2.5

The limitation of this study is the exclusion of the negativePET scans. We exclude negative PET scan because the SUVof a non-FDG-avid nodule cannot be measured. Thus, thespecificity of PET scan using an SUVmax cutoff of 2.5 calcu-lated on this study is not reflecting the actual specificity ofPET in the characterizing of pulmonary nodules

The introduction of dedicated PET/CT scanners to theclinical arena in early 2001 [14], has resulted in improvedaccuracy in the characterization of pulmonary nodules[13], by maintaining the synergism between the anatomicsensitivity of CT, and metabolic specificity of PET.

Although, FDG-PET/CT is a valuable diagnostic tool, ithas multiple pitfalls that limit its accuracy in the evalua-tion of pulmonary nodules, particularly small nodules.There are three potential directions for future research toimprove PET/CT accuracy in the evaluation of pulmonarynodules. One direction involves improvement of PET/CTscanner to provide better sensitivity, resolution and co-registration which potentially enhance its sensitivity todetect small pulmonary nodules, in addition to providebetter quantitative and qualitative evaluation of pulmo-nary nodules. The second direction of future researchinvolves imaging processing and display formats thatmight enhance the reader delectability. A PET/CT with vir-tual bronchoscopy provides virtual 3-dimensional imageswhich enhances the intraluminal lesions [15]. The thirddirection involves development and investigation of newPET radiotracers that might have better sensitivity andspecificity to differentiate pulmonary nodules. Both 18F-fluorothymidine (18F-FLT) and 18F-fluorocholine (18F-FCH) have been developed and investigated for use inlung cancer [16-18], however neither tracer has shownclear improvement over 18F-FDG. Eventually, these threedirections of future research will improve the delectabilityand categorization of the pulmonary nodules.

ConclusionThe slope of the regression line is greater for malignantthan for benign nodules. Although, the SUVmax cutoff of2.5 is a useful tool in the evaluation of large pulmonarynodules (> 1.0 cm), it has no or minimal value in the eval-uation of small pulmonary nodules (≤ 1.0 cm).

Competing interestsThe authors declare that they have no competing interests.

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Authors' contributionsMK curried out the collection of the data, design of thestudy, data analysis and drafting of the manuscript. HNconceived of the study; participated in design of the studyand the draft of the manuscript. JB curried out the statisti-cal analysis; participated in design of the study and thedrafting of the manuscript. YS curried out the phantomstudy. DL participated in the data analysis and study coor-dination. JK participated in the data analysis and studycoordination.

Acknowledgements1. Authors should acknowledge the contribution of Paul Galantowiczand1

John Warne2 in imaging and processing of the phantom study.

1Department of Nuclear Medicine, Veteran Affairs Western New York Healthcare System, Buffalo, New York.

2Department of Nuclear Medicine, Roswell Park Cancer Institute, Buffalo, New York.

2. Part of this study has been presented as an abstract for oral presentation at 53rd SNM annual meeting in June 2006.

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