(18)F-FDG-PET/CT in the assessment of pulmonary solitary nodules: comparison of different analysis methods and risk variables in the prediction of malignancy

An 18F-FDG-PET/CT-based radiomics signature for estimating malignance probability of solitary pulmonary nodule

BACKGROUND

Some solitary pulmonary nodules (SPNs) as early manifestations of lung cancer, it is difficult to determine its nature, which brings great trouble to clinical diagnosis and treatment. Radiomics can deeply explore the essence of images and provide clinical decision support for clinicians. The purpose of our study was to explore the effect of positron emission tomography (PET) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose integrated with computed tomography (CT; 18F-FDG-PET/CT) combined with radiomics for predicting probability of malignancy of SPNs.

METHODS

We retrospectively enrolled 190 patients with SPNs confirmed by pathology from January 2013 to December 2019 in our hospital. SPNs were benign in 69 patients and malignant in 121 patients. Patients were randomly divided into a training or testing group at a ratio of 7:3. Three-dimensional regions of interest (ROIs) were manually outlined on PET and CT images, and radiomics features were extracted. Synthetic minority oversampling technique (SMOTE) method was used to balance benign and malignant samples to a ratio of 1:1. In the training group, least absolute shrinkage and selection operator (LASSO) regression analyses and Spearman correlation analyses were used to select the strongest radiomics features. Three models including PET model, CT model, and joint model were constructed using multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were plotted to evaluate diagnostic efficiency, calibration degree, and clinical usefulness of all models in training and testing groups.

RESULTS

The estimative effectiveness of the joint model was superior to the CT or PET model alone in the training and testing groups. For the joint model, CT model, and PET model, area under the ROC curve was 0.929, 0.819, 0.833 in the training group, and 0.844, 0.759, 0.748 in the testing group, respectively. Calibration and decision curves showed good fit and clinical usefulness for the joint model in both training and testing groups.

CONCLUSION

Radiomics models constructed by combining PET and CT radiomics features are valuable for distinguishing benign and malignant SPNs. The combined effect is superior to qualitative diagnoses with CT or PET radiomics models alone.

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

BACKGROUND

Several studies to date have reported on the development of positron emission tomography (PET)/computed tomography (CT)-based models intended to effectively distinguish between benign and malignant pulmonary nodules (PNs). This meta-analysis was designed with the goal of clarifying the utility of these PET/CT-based conventional parameter models as diagnostic tools in the context of the differential diagnosis of PNs.

METHODS

Relevant studies published through September 2023 were identified by searching the Web of Science, PubMed, and Wanfang databases, after which Stata v 12.0 was used to conduct pooled analyses of the resultant data.

RESULTS

This meta-analysis included a total of 13 retrospective studies that analyzed 1,731 and 693 malignant and benign PNs, respectively. The respective pooled sensitivity, specificity, PLR, and NLR values for the PET/CT-based studies developed in these models were 88% (95%CI: 0.86-0.91), 78% (95%CI: 0.71-0.85), 4.10 (95%CI: 2.98-5.64), and 0.15 (95%CI: 0.12-0.19). Of these endpoints, the pooled analyses of model sensitivity (I2 = 69.25%), specificity (I2 = 78.44%), PLR (I2 = 71.42%), and NLR (I2 = 67.18%) were all subject to significant heterogeneity. The overall area under the curve value (AUC) value for these models was 0.91 (95%CI: 0.88-0.93). When differential diagnosis was instead performed based on PET results only, the corresponding pooled sensitivity, specificity, PLR, and NLR values were 92% (95%CI: 0.85-0.96), 51% (95%CI: 0.37-0.66), 1.89 (95%CI: 1.36-2.62), and 0.16 (95%CI: 0.07-0.35), with all four being subject to significant heterogeneity (I2 = 88.08%, 82.63%, 80.19%, and 86.38%). The AUC for these pooled analyses was 0.82 (95%CI: 0.79-0.85).

CONCLUSIONS

These results suggest that PET/CT-based models may offer diagnostic performance superior to that of PET results alone when distinguishing between benign and malignant PNs.

18F-FDG distribution pattern improves the diagnostic accuracy of single pulmonary solid nodule

Background

The main purpose is to explore the use of visual assessment of the heterogeneous distribution of ¹⁸F-FDG in single pulmonary solid lesions to differentiate the benign from the malignant.

Methods

The 200 cases of pulmonary nodules or masses examined by ¹⁸F-FDG PET/CT were retrospectively analyzed. The heterogeneity of ¹⁸F-FDG distribution of the lesion was visually and quantitatively evaluated and the higher part of metabolism was observed and measured at the proximal or distal part to determine the lesion nature. The sensitivity, specificity, PPV, NPV, and accuracy of this method were calculated.

Results

Total 171 pulmonary lesions showed heterogeneity of ¹⁸F-FDG uptake, including the 111 malignant and 60 benign. 54/60 (90.00%) benign lesions showed higher ¹⁸F-FDG uptake visually at distal part, while 104/111 (93.69%) malignant lesions showed higher ¹⁸F-FDG uptake visually at the proximal part. This visual method has good repeatability with a high kappa value (0.821, p<0.001). 52/60 (86.67%) benign lesions showed higher ¹⁸F-FDG uptake quantitatively at distal part, while 107/111 (96.40%) malignant lesions showed higher ¹⁸F-FDG uptake quantitatively at the proximal part. The sensitivity, specificity, PPV, NPV and accuracy of visual and quantitative methods were 93.69%; 96.40%, 90.0%; 86.67%, 94.55%; 93.04%, 88.52%; 92.86%, 92.40%; 92.98%, respectively (p<0.001). When combining the metabolic value and morphological characteristics of PET/CT with visual¹⁸F-FDG heterogeneous features, the accuracy reached to 98.25%. The other 29 lesions (14.5%) with no heterogeneity were smaller (2.17 ± 1.06 vs 3.58 ± 1.48, P<0.001).

Conclusions

Benign and malignant lung lesions showed different heterogeneity of ¹⁸F-FDG uptake. Lung cancer can be effectively distinguished from infectious or inflammatory lesions by this simple and convenient method.

Prediction of VEGF and EGFR Expression in Peripheral Lung Cancer Based on the Radiomics Model of Spectral CT Enhanced Images

Background

Energy spectrum CT is an effective method to evaluate the biological behavior of lung cancer. Radiomics is a non-invasive technology to obtain histological information related to lung cancer.

Purpose

To investigate the value of the radiomics models on the bases of enhanced spectral CT images of peripheral lung cancer to predict the expression of the vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR).

Material and Methods

This study retrospectively analyzed 73 patients with peripheral lung cancer confirmed by postoperative pathology. All patients underwent dual-phase enhanced spectral CT scans before surgery. Regions of interest (ROI) were delineated in the arterial phase and venous phase. Key radiomics features were extracted and models were established to predict the expression of VEGF and EGFR, respectively. All models were established based on the expression levels of VEGF and EGFR in tissues detected by immunohistochemical staining as reference standards. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of each model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the models.

Results

In predicting the expression level of VEGF, the combined (COMB) model composed of one spectral feature and two radiomics features achieved the best performance with area under ROC (AUC) 0.867 (95% CI: 0.767–0.966), accuracy of 0.812, sensitivity of 0.879, and specificity of 0.667. According to the expression level of EGFR, three importance radiomics features were retained in the arterial and venous phases to establish the multiphase phase model which has the best performance with AUC of 0.950 (95% confidence interval: 0.89–1.00), accuracy of 0.896, sensitivity of 0.868, and specificity of 1.

Conclusion

The radiomics model of enhanced spectral CT images of peripheral lung cancer can predict the expression of EGFR and VEGF.

Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

BACKGROUND

Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.

METHODS

We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles.

RESULTS

A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice.

CONCLUSIONS

The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020161559.

Characterization of 18F-fluorodeoxyglucose metabolic spatial distribution improves the differential diagnosis of indeterminate pulmonary nodules and masses with high fluorodeoxyglucose uptake

Background

The aim of this study was to investigate the value of visual assessment of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) metabolic spatial distribution (V-FMSD) in the diagnosis of indeterminate pulmonary nodules and masses with high ¹⁸F-FDG uptake.

Methods

A total of 301 patients with indeterminate pulmonary nodules or masses who underwent ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) imaging were retrospectively studied. The characteristics of ¹⁸F-FDG metabolic spatial distribution (FMSD) in the proximal and distal regions of the lesions were visually analyzed using a 5-point scoring system. The sensitivity, specificity, accuracy, and area under receiver operating characteristic curve (AUC) were compared between V-FMSD and conventional PET/CT methods for the diagnosis of hypermetabolic indeterminate pulmonary nodules and masses.

Results

The V-FMSD results showed that 180 (92.8%) malignant lesions had a score of ≥3 and 78 (72.9%) benign lesions had a score of ≤2. This indicated that the FMSD in the proximal region of malignant lesions was significantly higher than that of the distal region, and the FMSD in the proximal region of benign lesions was significantly lower than that of the distal region. V-FMSD had a specificity of 72.9%, which was markedly higher than those of the maximum standard uptake value (SUV_max; 0%, P<0.001) and the retention index (RI; 26.2%, P<0.001). The AUC of V-FMSD was 0.886, which was significantly larger than those of the SUV_max (0.626, P<0.001), RI (0.670, P<0.001), and PET/CT (0.788, P<0.05).

Conclusions

Our study found that pulmonary benign and malignant lesions have distinct FMSD characteristics. V-FMSD can therefore be used as a novel auxiliary marker to improve the diagnostic accuracy of hypermetabolic indeterminate pulmonary nodules and masses.

Quantitative volumetric metabolic measurement of solitary pulmonary nodules by F-18 fluorodeoxyglucose positron emission tomography-computed tomography

Background

This study aims to evaluate the effect of quantitative volumetric metabolic measurements in F-18 fluorodeoxyglucose positron emission tomographycomputed tomography to distinguish benign and malignant solitary pulmonary nodules.

Methods

We retrospectively reviewed 78 patients (56 males; 22 females; mean age 61±11.9 years; range, 32 to 82 years) with solitary pulmonary nodules who underwent F-18 fluorodeoxyglucose positron emission tomography-computed tomography. Patients were classified as benign, malignant and metastatic lesions according to pathology results. Metabolic volume, maximum standardized uptake value, mean standardized uptake value, maximum metabolic index and mean metabolic index were measured. Mean, median and standard error values were calculated for each group. Nonparametric tests were used for the comparison of each group. Partial correlation analysis was used for the relationship between parameters. For all parameters, cut-off values were obtained with receiver operating characteristic analysis.

Results

Of 78 lesions, 10 were benign (12.8%), 38 were primary lung carcinoma (48.7%) and 30 were metastatic lung nodules (38.5%). There was a significant difference between benign lesions and primary lung cancer and between primary lung cancer and metastatic groups in all parameters (p<0.05). We determined highly significant positive correlation between maximum standardized uptake value and maximum metabolic index (r=0.73; p<0.05), and moderate positive correlation between mean standardized uptake value and mean metabolic index (r=0.56; p<0.05). In receiver operating characteristic analysis, maximum standardized uptake value and mean standardized uptake value were found to be the most sensitive and specific methods for benign/malignant discrimination. In the cut-off value=2.59, the sensitivity and specificity for maximum standardized uptake value were 98.0% and 91.7%, respectively. In the cut-off value=1.65, the sensitivity and specificity for mean standardized uptake value were 94.0% and 91.7%, respectively.

Conclusion

Maximum metabolic index value is highly correlated with maximum standardized uptake value in benign/malignant solitary pulmonary nodules discrimination by F-18 fluorodeoxyglucose positron emission tomographycomputed tomography. Maximum metabolic index can also be used for discrimination of primary/metastatic malignant lesions.

18F-fluorodeoxyglucose positron emission tomography/computed tomography in the diagnosis of benign pulmonary lesions in sarcoidosis

Background

Many benign pulmonary lesions, especially sarcoidosis, are metabolically active and are indistinguishable from lung cancer using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging. This study sought to analyze the 18F-FDG PET/CT imaging features of benign pulmonary lesions and to improve the differential diagnosis of benign pulmonary lesions by 18F-FDG PET/CT imaging.

Methods

One hundred and thirteen patients with benign pulmonary lesions were studied retrospectively. Each patient underwent an 18F-FDG PET/CT scan. All cases were identified by pathology, diagnostic therapy or follow-up. The maximum standardized uptake value (SUVmax) was calculated for each pulmonary lesion.

Results

According to the final results, the benign pulmonary lesions were classified as inflammatory lesions (n=77) and granulomas (n=36) by histopathological diagnoses. The SUVmax of inflammatory lesions and granulomas were both high (4.55±2.77 and 6.81±3.96, respectively; P<0.05). When the benign pulmonary lesions were classified by clinical diagnoses, the SUVmax of sarcoidosis was significantly different from other diseases (15.12±5.67; P<0.01).

Conclusions

Inflammatory lesions and granulomas show moderate or high FDG uptake on 18F-FDG PET/CT, but granulomas have higher values. 18F-FDG PET/CT appeared to have a higher SUVmax for the differential diagnosis of sarcoidosis and benign pulmonary lesions.

Comparing the diagnostic value of 18F-FDG-PET/CT versus CT for differentiating benign and malignant solitary pulmonary nodules: a meta-analysis

Background

This quantitative meta-analysis was conducted to provide an indirect comparison of the diagnostic value of computed tomography (CT) with positron emission tomography (PET)/CT for differentiating benign and malignant solitary pulmonary nodules (SPNs).

Methods

PubMed, Embase, and the Cochrane Library were searched to identify eligible studies throughout November 2018, which differentiated benign and malignant SPNs using CT or PET/CT. The summary sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR), diagnostic odds ratio (DOR), and area under the receiver operating characteristic curve (AUC) were calculated using bivariate generalized linear mixed model and random-effects model. The diagnostic value of CT with PET/CT was indirectly evaluated using the ratio for diagnostic parameters.

Results

The sensitivity, specificity, PLR, NLR, DOR, and AUC for CT were 0.94 [95% confidence interval (CI): 0.87-0.97], 0.73 (95% CI: 0.64-0.80), 3.45 (95% CI: 2.60-4.58), 0.09 (95% CI: 0.04-0.17), 32.01 (95% CI: 15.10-67.86), and 0.89 (95% CI: 0.86-0.91), respectively. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC for PET/CT were 0.89 (95% CI: 0.85-0.92), 0.78 (95% CI: 0.66-0.86), 3.97 (95% CI: 2.57-6.13), 0.15 (95% CI: 0.10-0.20), 24.04 (95% CI: 12.71-45.48), and 0.91 (95% CI: 0.89-0.94), respectively. No significant differences were observed between CT and PET/CT for sensitivity, specificity, PLR, NLR, DOR, and AUC.

Conclusions

This study used both CT and PET/CT with a moderate-to-high diagnostic value for differentiating benign and malignant SPNs and showed no significant differences in diagnostic parameters between CT and PET/CT.

Performance of FDG-PET/CT in solitary pulmonary nodule based on pre-test likelihood of malignancy: results from the ITALIAN retrospective multicenter trial

PURPOSE

The aim of this study was to determine the performance of ¹⁸F-FDG-PET/CT in patients with solitary pulmonary nodule (SPN), stratifying the risk according to the likelihood of pulmonary malignancy.

METHODS

FDG-PET/CT of 502 patients, stratified for pre-test cancer risk, were retrospectively analyzed. FDG uptake in SPN was assessed by a 4-point scoring system and semiquantitative analysis using the ratio between SUVmax in SPN and SUVmean in mediastinal blood pool (BP) and between SUVmax in SPN and SUVmean in liver (L). Histopathology and/or follow-up data were used as standard of reference.

RESULTS

SPN was malignant in 180 (36%) patients, benign in 175 (35%), and indeterminate in 147 (29%). The 355 patients with a definitive SPN nature (malignant or benign) were considered for the analysis. Considering FDG uptake ≥ 2, sensitivity, specificity, positive (PPV) and negative (NPV) predictive values, and accuracy were 85.6%, 85.7%, 86%, 85.2%, and 85.6% respectively. Sensitivity and PPV were higher (P < 0.05) in intermediate and high-risk patients, while specificity and NPV were higher (P < 0.05) in low-risk patients. On receiver operating characteristic curve analysis, the cut-offs for better discrimination between benign and malignant SPN were 1.56 (sensitivity 81% and specificity 87%) and 1.12 (sensitivity 81% and specificity 86%) for SUVmax/SUVmeanBP and SUVmax/SUVmeanL respectively. In intermediate and high-risk patients, including the SUVmax/SUVmeanBP, the specificity shifted from 85% and 50% to 100%.

CONCLUSION

Visual FDG-PET/CT has an acceptable performance in patients with SPN, but accuracy improves when SUVratios are considered, particularly in patients with intermediate and high risk of malignancy.

Diagnostic performance of fluorine-18 fluorodeoxyglucose positron emission tomography in the management of solitary pulmonary nodule: a meta-analysis

Background

In the setting of solitary pulmonary nodules (SPNs), fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT) is considered a useful non-invasive diagnostic tool though false positive (FP) and false negative (FN) results affects accuracy due to different conditions, such as inflammatory diseases or low-uptake neoplasms. Aim of this study is to evaluate overall diagnostic performance of ¹⁸F-FDG-PET/CT for malignant pulmonary nodules.

Methods

A computerized research, including published articles from 2012 and 2017, was carried out. ¹⁸F-FDG-PET/CT overall sensitivity (Se), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR), positive predictive value (PPV), negative predictive value (NPV), diagnostic index and odds ratio were pooled. No selection-bias were found according to asymmetry test.

Results

A total of twelve studies were included in the meta-analysis. The pooled Se, Spe, PLR, NLR, PPV, NPV and accuracy index (AI) with relative 95% confidence intervals (CI) were 0.819 (95% CI: 0.794-0.843), 0.624 (95% CI: 0.582-0.665), 2.190 (95% CI: 1.950-2.440), 0.290 (95% CI: 0.250-0.330), 0.802 (95% CI: 0.783-0.819), 0.652 (95% CI: 0.618-0.684) and 0.649 (95% CI: 0.625-0.673), respectively. The diagnostic odds ratio (DOR) was 7.049 with a relative 95% CI between 5.550 and 8.944.

Conclusions

The results suggest ¹⁸F-FDG-PET/CT has good diagnostic accuracy in SPNs evaluation; but, it should not be considered as a discriminatory test rather than a method to be included in a clinical and diagnostic pathway.

The value of 18F-FDG-PET/CT in the diagnosis of solitary pulmonary nodules: A meta-analysis

BACKGROUND

Solitary pulmonary nodules (SPNs) are common imaging findings. Many studies have indicated that F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG-PET/CT) is an accurate test for distinguishing benign and malignant SPNs. The aim of this study was to investigate the value of F-FDG-PET/CT in the diagnosis of malignant SPNs.

METHODS

We systematically searched the PubMed and Embase databases up to March 2017, and published data on sensitivity, specificity, and other measures of diagnostic accuracy of F-FDG-PET/CT in the diagnosis of malignant SPNs were meta-analyzed. Statistical analyses were undertaken using Meta-DiSc 1.4 software and Stata version 12.0. The measures of accuracy of F-FDG-PET/CT in the diagnosis of malignant SPNs were pooled using random-effects models.

RESULTS

A total of 20 publications reporting 21 studies were identified. Pooled results indicated that F-FDG-PET/CT showed a diagnostic sensitivity of 0.89 (95% confidence interval [CI], 0.87-0.91) and a specificity of 0.70 (95% CI, 0.66-0.73). The positive likelihood ratio was 3.33 (95% CI, 2.35-4.71) and the negative likelihood ratio was 0.18 (95% CI, 0.13-0.25). The diagnostic odds ratio was 22.43 (95% CI, 12.55-40.07).

CONCLUSIONS

F-FDG-PET/CT showed insufficient sensitivity and specificity for diagnosing malignant SPNs; it cannot replace the "gold standard" pathology by resection or percutaneous biopsy. Larger studies are required for further evaluation.

[Combined use of thin-section CT and 18F-FDG PET/CT for characterization of solitary pulmonary nodules]

OBJECTIVE

To investigate whether fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) combined with thin-section CT improves the diagnostic performance for solitary pulmonary nodules (SPNs).

METHODS

A total of 267 patients underwent examinations with 18F-FDG PET/CT and thin-section CT for evaluating the SPNs with undetermined nature, which was further confirmed by pathological examination or clinical follow-up. The performance of two diagnostic criteria based on findings in PET/CT alone (Criterion 1) and in PET/CT combined with thin-section CT (Criterion 2) were compared.

RESULTS

Thin-section CT provided greater diagnostic information for SPNs in 84.2% of the patients. Compared with Criterion 1, the diagnosis based on Criterion 2 significantly increased the diagnostic sensitivity (80.4% vs 91%, P&lt;0.01) and accuracy (76.4% vs 87.2%, P<0.01) for lung cancer. The lesion size and the CT features including lobulation, air bronchogram, and feeding vessel, but not SUV_max, were all helpful for characterizing non-solid SPNs. Thin-section CT rectified diagnostic errors in 50% (20/40) of the cancerous lesions, which had been diagnosed as benign by PET due to their low metabolism. For non-solid SPNs, Criterion 2 showed a significantly higher diagnostic sensitivity than Criterion 1 (90.0% vs 40.0%, P=0.000) but their diagnostic specificity were comparable (75.2% vs 58.3%, P=0.667). For solid nodules, the use of thin-section CT resulted in no significant improvement in the diagnostic performance (P>0.05).

CONCLUSION

The combination of PET/CT and thin-section CT creates a synergistic effect for the characterization of SPNs, especially non-solid nodules.

Contribution of nonattenuation-corrected images on FDG-PET/CT in the assessment of solitary pulmonary nodules

In this study, we aim to determine the diagnostic performance of nonattenuation-corrected (NAC) and attenuation-corrected (AC) FDG-PET/CT images in the assessment of solitary pulmonary nodule (SPN). We reviewed the images of 41 patients who underwent FDG-PET/CT to diagnose SPNs. The visual analysis of FDG uptake intensity in SPN on AC and NAC PET images was made using a four-point score from 1 to 4 on both AC and NAC PET images. The cutoff value of SUVmax and visual uptake scores for malignancy were defined as ≥2.5 and ≥3, respectively. The significant visual uptake (≥2 visual point score) on AC and NAC PET images was considered to be positive 18F-FDG PET findings for lesion detectability. The sensitivity, specificity and diagnostic accuracy were calculated for AC and NAC PET images. Based on the histopathology and imaging data, 22 of the SPNs (54 %) were malignant and 19 of them (46 %) were benign. The sensitivity and NPV were found to be 100 % in the detection of SPNs for AC and NAC PET images. For all SPNs and SPNs ≤2 cm, NAC PET image had a higher diagnostic performance for the SPN characterization as malignant or benign, when compared with AC PET image. The success rates of AC and NAC PET images were found to be similar for the detection of SPNs. NAC PET image had a higher diagnostic performance for the SPN characterization. It is thought that NAC PET image may provide additional contributions for characterization of SPNs.