Malignant Pulmonary Nodules Research Articles

Comparing diffusion-weighted imaging and positron emission tomography for pulmonary nodules measuring from 1 to 3 cm in size

To examine whether diffusion-weighted magnetic resonance imaging (DWI) is as useful as fluorodeoxyglucose positron emission tomography (FDG-PET) for discriminating between malignant and benign pulmonary nodules measuring less than 3 cm in size, as well as for predicting tumor aggressiveness. PET and DWI were carried out on 87 pulmonary nodules measuring from 1 to 3 cm in size (66 NSCLCs and 21 benign nodules). The signal intensity (SI) of DWI was measured by the contrast ratio (CR) between the lesions and spinal cord, i.e., SI-CR. The maximum standard uptake value (SUV) of PET was measured by CR between the lesions and contralateral lung, i.e., SUV-CR. DWI and PET showed sensitivities of 0.86 and 0.71, and specificities of 0.90 and 0.81, respectively. While there was no significant difference in the specificity between the two, DWI showed a significantly higher sensitivity than PET (p = 0.013). While the difference in the sensitivity was significant in lung adenocarcinoma (p = 0.012), there was no difference in the other histological types. Both the SI-CR and SUV-CR were significantly higher in the tumors with either histological invasiveness or lymphatic metastasis than in those without. DWI is thus considered to be useful, not only to diagnose NSCLCs, especially in lung adenocarcinoma, but also for predicting tumor aggressiveness as well as FDG-PET.

Characterization of solitary pulmonary nodules with 18F-FDG PET/CT relative activity distribution analysis.

To compare the capability of relative activity distribution (RAD), a new index of fluorodeoxyglucose F18 ((18)F-FDG) uptake, with those of the typical markers for differentiating benign and malignant solitary pulmonary nodules (SPNs) by integrated positron emission tomography (PET)/computed tomography (CT). RAD, maximal standardised uptake value (SUV(max)), partial volume corrected SUV(max) (corrSUV(max)), and retention index (RI) were calculated prospectively for 115 malignant and 60 benign SPNs. Area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were compared (P < 0.05). Malignant lesions (0.98 ± 0.03) had significantly lower RAD than benign lesions (1.01 ± 0.02). AUC (0.935) was significantly larger and specificity (96.67%) was significantly higher for RAD than for SUV(max) (P ≤ 0.0001), corrSUV(max) (P < 0.0001), RI (P < 0.0001), and visual assessment (P = 0.01 and 0.002, respectively). Further, RAD had significantly higher sensitivity (92.17%) than SUV(max) (P = 0.0007) and higher accuracy (93.71%) than SUV(max) (P < 0.0001), corrSUV(max) (P < 0.0001), and RI (P = 0.002). RAD seems to be more specific and accurate than the typical markers for differentiating malignant and benign SPNs by (18)F-FDG PET/CT. • Relative activity distribution index is assessable by (18)F-FDG PET/CT • The index effectively characterises solitary pulmonary nodules • RAD is more specific and accurate than the typical markers in (18)F-FDG PET/CT • RAD provides additional options for small solitary pulmonary nodules.

Classification between malignant pulmonary nodules and benign ones in 3D X-ray CT images

Classification between malignant pulmonary nodules and benign ones in 3D X-ray CT images

Establishment and validation of mathematics model for differentiating benign and malignant solitary pulmonary nodules

Establishment and validation of mathematics model for differentiating benign and malignant solitary pulmonary nodules

Analysis of the Discriminative Methods for Diagnosis of Benign and Malignant Solitary Pulmonary Nodules Based on Serum Markers

Background: Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Methods: Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. Results: The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). Conclusion: This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN.

Vegetable Aspiration: Mimicking as a Malignant Pulmonary Nodule in a Non-small Cell Lung Cancer (NSCLC) Survivor

Vegetable Aspiration: Mimicking as a Malignant Pulmonary Nodule in a Non-small Cell Lung Cancer (NSCLC) Survivor

Value of circulating insulin-like growth factor–associated proteins for the detection of stage I non–small cell lung cancer

Circulating biomarkers related to insulin-like growth factor (IGF) signaling are associated with disease progression in multiple carcinomas, but their potential diagnostic value for lung cancer screening has been inadequately examined. We evaluated 9 circulating IGF-related factors for their ability to assign clinical significance to indeterminate pulmonary nodules identified via computed tomography-based radiologic studies. Patients (n = 224 stage I non-small cell lung cancer; n = 123 benign) were enrolled by Rush University and the Mayo Clinic and had pretreatment serum evaluated for levels of IGF-1, IGF-2, and insulin-like growth factor binding proteins (IGFBPs) 1-7. The Mann-Whitney rank-sum test and receiver-operator characteristics curves were used to assess differences in biomarker concentrations relevant to malignant versus benign pathology. These targets were used to help refine our companion blood test for assigning clinical significance to computed tomography-detected solitary nodules (discovery cohort, n = 94) and were validated against an independent cohort from the Mayo Clinic (n = 81). Patients with benign pulmonary nodules were found to have serum concentrations of IGFBP-3, IGFBP-5, IGF-1, and IGF-2 that were higher (P = .001, P < .001, P = .002, and P = .011, respectively) than those with non-small cell lung cancer, with distinct associations with histologic subtypes observed. Refinement of our multianalyte classification algorithm using IGF-related factors provided a new panel consisting of interleukin-6, interleukin-1 receptor antagonist, interleukin-10, stromal cell-derived factor-1(α + β), IGFBP-4, IGFBP-5, and IGF-2 with improved assay performance-achieving a (validated) negative predictive value of 100%. Our findings suggest a divergent role for IGF signaling in the biology of benign and malignant pulmonary nodules. Upon further validation, these observations may help identify cases of false positives resulting from computed tomography-based screening studies.

Indeterminate pulmonary nodules in colorectal-cancer: do radiologists agree?

The clinical significance of indeterminate pulmonary nodules (IPN) at staging computed tomography (CT) for colorectal cancer (CRC), and the optimal diagnostic approach, are debated. This study aimed to analyse variability in radiologists' detection of IPN at staging CT for CRC. All patients with CRC referred to our center between 2006 and 2011 were included. Primary staging CT scans were re-evaluated by an experienced thoracic radiologist whose findings were entered into a dedicated database and merged with data from the Danish Colorectal Cancer Group database, the National Patient Registry, the Danish Pathology Registry, and the primary CT evaluation. Inter-reader agreement was calculated by Kappa statistics, and associations between variables and malignancy of pulmonary nodules were analyzed with χ (2) and Mann-Whitney-Wilcoxon tests. Multivariable logistic regression analyses were used to adjust for potential confounding variables. In total, 841 patients were included. The primary CT assessment reported IPN in 9.8% of patients and pulmonary metastases in 5.1% of patients compared with 5.6 and 7.0%, respectively, reported by the experienced thoracic radiologist. Kappa for agreement between the primary assessor and the thoracic radiologist on IPN was 0.31 and 0.65 for pulmonary metastases. Synchronous liver metastases were predictive of malignancy of IPN (adjusted odds ratio 20.1; 95% confidence interval 2.64-437.66; p=0.012), whereas no other investigated radiological characteristics or clinicopathological factors were significantly associated with malignancy of IPN. The characterization of pulmonary findings on staging CT for CRC varied greatly between the radiologists, and double-reading of scans with IPN is recommended prior to further diagnostic work-up.

Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes.

To explore the role of the texture features of images in the diagnosis of solitary pulmonary nodules (SPNs) in different sizes. A total of 379 patients with pathologically confirmed SPNs were enrolled in this study. They were divided into three groups based on the SPN sizes: ≤10, 11-20, and >20 mm. Their texture features were segmented and extracted. The differences in the image features between benign and malignant SPNs were compared. The SPNs in these three groups were determined and analyzed with the texture features of images. These 379 SPNs were successfully segmented using the 2D Otsu threshold method and the self-adaptive threshold segmentation method. The texture features of these SPNs were obtained using the method of grey level co-occurrence matrix (GLCM). Of these 379 patients, 120 had benign SPNs and 259 had malignant SPNs. The entropy, contrast, energy, homogeneity, and correlation were 3.5597±0.6470, 0.5384±0.2561, 0.1921±0.1256, 0.8281±0.0604, and 0.8748±0.0740 in the benign SPNs and 3.8007±0.6235, 0.6088±0.2961, 0.1673±0.1070, 0.7980±0.0555, and 0.8550±0.0869 in the malignant SPNs (all P<0.05). The sensitivity, specificity, and accuracy of the texture features of images were 83.3%, 90.0%, and 86.8%, respectively, for SPNs sized ≤10 mm, and were 86.6%, 88.2%, and 87.1%, respectively, for SPNs sized 11-20 mm and 94.7%, 91.8%, and 93.9%, respectively, for SPNs sized >20 mm. The entropy and contrast of malignant pulmonary nodules have been demonstrated to be higher in comparison to those of benign pulmonary nodules, while the energy, homogeneity correlation of malignant pulmonary nodules are lower than those of benign pulmonary nodules. The texture features of images can reflect the tissue features and have high sensitivity, specificity, and accuracy in differentiating SPNs. The sensitivity and accuracy increase for larger SPNs.

Texture feature analysis for computer-aided diagnosis on pulmonary nodules.

Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance. Texture features of pulmonary nodules in CT images reflect a powerful character of the malignancy in addition to the geometry-related measures. This study first compared three well-known types of two-dimensional (2D) texture features (Haralick, Gabor, and local binary patterns or local binary pattern features) on CADx of lung nodules using the largest public database founded by Lung Image Database Consortium and Image Database Resource Initiative and then investigated extension from 2D to three-dimensional (3D) space. Quantitative comparison measures were made by the well-established support vector machine (SVM) classifier, the area under the receiver operating characteristic curves (AUC) and the p values from hypothesis t tests. While the three feature types showed about 90% differentiation rate, the Haralick features achieved the highest AUC value of 92.70% at an adequate image slice thickness, where a thinner or thicker thickness will deteriorate the performance due to excessive image noise or loss of axial details. Gain was observed when calculating 2D features on all image slices as compared to the single largest slice. The 3D extension revealed potential gain when an optimal number of directions can be found. All the observations from this systematic investigation study on the three feature types can lead to the conclusions that the Haralick feature type is a better choice, the use of the full 3D data is beneficial, and an adequate tradeoff between image thickness and noise is desired for an optimal CADx performance. These conclusions provide a guideline for further research on lung nodule differentiation using CT imaging.

Resource Use and Guideline Concordance in Evaluation of Pulmonary Nodules for Cancer

Pulmonary nodules are common, and more will be found with implementation of lung cancer screening. How potentially malignant pulmonary nodules are evaluated may affect patient outcomes, health care costs, and effectiveness of lung cancer screening programs. Guidelines for evaluating pulmonary nodules for cancer exist, but little is known about how nodules are evaluated in the usual care setting. To characterize nodule evaluation and concordance with guidelines. A retrospective cohort study was conducted including detailed review of medical records from pulmonary nodule detection through evaluation completion, cancer diagnosis, or study end (December 31, 2012). The participants included 300 adults with pulmonary nodules from 15 Veterans Affairs hospitals. Resources used for evaluation at any Veterans Affairs facility and guideline-concordant evaluation served as the main outcomes. Twenty-seven of 300 patients (9.0%) with pulmonary nodules ultimately received a diagnosis of lung cancer: 1 of 57 (1.8%) with a nodule of 4 mm or less, 4 of 134 (3.0%) with a nodule of 5 to 8 mm, and 22 of 109 (20.2%) with a nodule larger than 8 mm. Nodule evaluation entailed 1044 imaging studies, 147 consultations, 76 biopsies, 13 resections, and 21 hospitalizations. Radiographic surveillance (n = 277) lasted a median of 13 months but ranged from less than 0.5 months to 8.5 years. Forty-six patients underwent invasive procedures (range per patient, 1-4): 41.3% (19 patients) did not have cancer and 17.4% (8) experienced complications, including 1 death. Notably, 15 of the 300 (5.0%) received no purposeful evaluation and had no obvious reason for deferral, seemingly "falling through the cracks." Among 197 patients with a nodule detected after release of the Fleischner Society guidelines, 44.7% received care inconsistent with guidelines (17.8% overevaluation, 26.9% underevaluation). In multivariable analyses, the strongest predictor of guideline-inconsistent care was inappropriate radiologist recommendations (overevaluation relative risk, 4.6 [95% CI, 2.3-9.2]; underevaluation, 4.3 [2.7-6.8]). Other systems factors associated with underevaluation included receiving care at more than 1 facility (2.0 [1.5-2.7]) and nodule detection during an inpatient or preoperative visit (1.6 [1.1-2.5]). Pulmonary nodule evaluation is often inconsistent with guidelines, including cases with no workup and others with prolonged surveillance or unneeded procedures that may cause harm. Systems to improve quality (eg, aligning radiologist recommendations with guidelines and facilitating communication across providers) are needed before lung cancer screening is widely implemented.

Role of MRI for detection and characterization of pulmonary nodules

Due to physical and technical limitations, magnetic resonance imaging (MRI) has hitherto played only a minor role in image-based diagnostics of the lungs. However, as a consequence of important methodological developments during recent years, MRI has developed into a technically mature and clinically well-proven method for specific pulmonary questions. The purpose of this article is to provide an overview on the currently available sequences and techniques for assessment of pulmonary nodules and analyzes the clinical significance according to the current literature. The main focus is on the detection of lung metastases, the detection of primary pulmonary malignancies in high-risk individuals and the differentiation between pulmonary nodules of benign and malignant character. The MRI technique has a sensitivity of approximately 80 % for detection of malignant pulmonary nodules compared to the reference standard low-dose computed tomography (CT) and is thus somewhat inferior to CT. Advantages of MRI on the other hand are a higher specificity in differentiating malignant and benign pulmonary nodules and the absence of ionizing radiation exposure. A systematic use of MRI as a primary tool for detection and characterization of pulmonary nodules is currently not recommended due to insufficient data. The diagnostic potential of MRI for early detection and staging of malignant pulmonary diseases, however, seems promising. Therefore, further evaluation of MRI as a secondary imaging modality in clinical trials is highly warranted.

Development and validation of clinical diagnostic models for the probability of malignancy in solitary pulmonary nodules.

It is critical to develop a non-invasive and accurate method for differentiating between malignant and benign solitary pulmonary nodules. In large sample studies, the effectiveness of the diagnostic prediction model as a tool of assessment of the probability of malignancy is still unclear. The establishment of a diagnostic model based on large samples is needed. In this study, 3358 patients diagnosed with a solitary pulmonary nodule between January 2005 and March 2013, were enrolled. All patients received surgery for pulmonary nodule resection. Clinical characters, preoperative biomarker results, and computed tomography scan findings were collected. All patients were randomly separated into a training set (n = 1679) and a test set (n = 1679); we used training sets to build a diagnostic model for the malignancy probability of pulmonary nodules, and applied the test set to validate our model, as well as other published diagnostic models. Logistic regression analysis identified 11 clinical characteristics as independent predictors of malignancy in patients with a solitary pulmonary nodule. The goodness-of-fit statistic for the model indicated that the observed proportion of malignancies did not differ from the predicted proportion (P = 0.571). The area under the curves of the receiver operator characteristic curve for our model in the training set was 0.935. As the accuracy of the model was high, we suggest that the diagnostic model can be used as a tool to help guiding clinical decisions, when the clinician cannot make a definitive diagnosis of a solitary pulmonary nodule.

Biomarkers to the Rescue in a Lung Nodule Epidemic

Biomarkers to the Rescue in a Lung Nodule Epidemic

A Quantitative Method for Differentiating Malignant and Benign Pulmonary Nodules

It is common sense that CAD has great significance in the lung nodule detection. But it is still controversial whether the CAD can also automatically differentiates between malignant and benign pulmonary nodules. The primary cause of this controversy is due to the subjective definition of 9 characteristics of nodules which are important basis of nodule identification. In other word, these characteristics are too dependent on the doctor scoring, and no objective standard of them has built which make these characteristics can be obtained by calculation.The main aim of this paper is to establish a quantitative method of the characteristics and refine these nine characteristics. This new method is used to find the objective replacement (a series features which can be measured through algorithms) of these subjective characteristics of the pulmonary nodule detection with Bayesian analysis.The experiment of our method proves that it is feasible to substitute the features of Pulmonary Nodule obtained by calculating for the characteristics of the nodule which only used to be gotten by the subjective judgment of doctors.

Computerized Determination of the Likelihood of Malignancy of Pulmonary Nodules Using High-resolution CT and Positron Emission Tomography Scans

Computerized Determination of the Likelihood of Malignancy of Pulmonary Nodules Using High-resolution CT and Positron Emission Tomography Scans

FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules

PurposeThe present study aimed to determine whether fractal analysis of morphological complexity and intratumoral heterogeneity of FDG uptake can help to differentiate malignant from benign pulmonary nodules. Materials and methodsWe retrospectively analyzed data from 54 patients with suspected non-small cell lung cancer (NSCLC) who were examined by FDG PET/CT. Pathological assessments of biopsy specimens confirmed 35 and 19 nodules as NSCLC and inflammatory lesions, respectively. The morphological fractal dimension (m-FD), maximum standardized uptake value (SUVmax) and density fractal dimension (d-FD) of target nodules were calculated from CT and PET images. Fractal dimension is a quantitative index of morphological complexity and tracer uptake heterogeneity; higher values indicate increased complexity and heterogeneity. ResultsThe m-FD, SUVmax and d-FD significantly differed between malignant and benign pulmonary nodules (p<0.05). Although the diagnostic ability was better for d-FD than m-FD and SUVmax, the difference did not reach statistical significance. Tumor size correlated significantly with SUVmax (r=0.51, p<0.05), but not with either m-FD or d-FD. Furthermore, m-FD combined with either SUVmax or d-FD improved diagnostic accuracy to 92.6% and 94.4%, respectively. ConclusionThe d-FD of intratumoral heterogeneity of FDG uptake can help to differentially diagnose malignant and benign pulmonary nodules. The SUVmax and d-FD obtained from FDG-PET images provide different types of information that are equally useful for differential diagnoses. Furthermore, the morphological complexity determined by CT combined with heterogeneous FDG uptake determined by PET improved diagnostic accuracy.

A Systematic Review and Meta-analysis of the Accuracy of Diffusion-weighted MRI in the Detection of Malignant Pulmonary Nodules and Masses

To perform a meta-analysis to assess the diagnostic performance of the diffusion-weighted magnetic resonance imaging (DWI) technique in discrimination of benign and malignant pulmonary nodules or masses. Data sources were studies published in PubMed, MEDLINE, EMBASE, Cochrane Library, and China National Knowledge Infrastructure databases from January 2001 to May 2013. Studies evaluating the diagnostic accuracy of DWI for benign/malignant discrimination of pulmonary nodules in English or Chinese language were considered for inclusion. Methodological quality was assessed by the quality assessment of diagnostic studies instrument. Sensitivities, specificities, predictive values, diagnostic oddsratios (DORs), and areas under the receiver operating characteristic curve (AUCs) were calculated. Potential threshold effect, heterogeneity, and publication bias were investigated. We also evaluated the clinical utility of DWI in diagnosis of lung lesions. Seventeen studies comprising 855 malignant and 322 benign lesions were included in this meta-analysis. There was no significant threshold effect. Summary receiver operating characteristic curve showed that AUC was 0.909 (95% confidence interval [CI], 0.862-0.931). Pooled weighted estimates of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.828 (95% CI, 0.801-0.853), 0.801 (95% CI, 0.753-0.843), 4.01 (95% CI, 2.78-5.80), and 0.20 (95% CI, 0.15-0.27), respectively. Heterogeneity was found to have stemmed primarily from study design (retrospective or prospective study). Subgroup analysis showedthat diagnostic performance (sensitivity, 0.88; 95% CI, 0.82-0.92 and specificity, 0.89; 95% CI, 0.79-0.96) of retrospectively designed studies was significantly higher than that of prospectively designed studies. The Deeks' funnel plot indicated the absence of publication bias. With respect to the accuracy and DOR, DWI is useful for differentiation between malignant and benign pulmonary nodules or masses. Diagnostic test accuracy is not the be-all and end-all of diagnostic testing. Concerning PLR and NLR, DWI may not help to alter posttest probability compared to pretest probability to sufficiently alter physician's decision making. Future analyses should be conducted in large-scale, high-quality trials to evaluate its clinical value and establish standards of DWI measurement, analysis, and cutoff values of diagnosis.

Lung nodule detection in a high-risk population: Comparison of magnetic resonance imaging and low-dose computed tomography

ObjectiveTo investigate the potential of MRI for lung nodule detection in a high-risk population in comparison to low-dose CT. Methods49 participants (31 men, 18 women, 51–71 years) of the German Lung Cancer Screening and Intervention Trial (LUSI) with a cancer-suspicious lung lesion in CT were examined with non-contrast-enhanced MRI of the lung at 1.5T. Data were pseudonymized and presented at random order together with 30 datasets (23 in men, 7 in women, 18–64 years) from healthy volunteers. Two radiologists read the data for the presence of nodules. Sensitivity and specificity were calculated. Gold standard was either histology or long-term follow-up. Contrast-to-Noise-Ratio (CNR) was measured for all detected lesions in all MRI sequences. ResultsAverage maximum diameter of the lesions was 15mm. Overall sensitivity and specificity of MRI were 48% (26/54) and 88% (29/33) compared to low-dose CT. Sensitivity of MRI was significantly higher for malignant nodules (78% (12.5/16)) than for benign ones (36% (13.5/38); P=0.007). There was no statistically significant difference in sensitivity between nodules (benign and malignant) larger or smaller than 10mm (P=0.7). Inter observer agreement was 84% (κ=0.65). Lesion-to-background CNR of T2-weighted single-shot turbo-spin-echo was significantly higher for malignant nodules (89±27) than for benign ones (56±23; P=0.002). ConclusionThe sensitivity of MRI for detection of malignant pulmonary nodules in a high-risk population is 78%. Due to its inherent soft tissue contrast, MRI is more sensitive to malignant nodules than to benign ones. MRI may therefore represent a useful test for early detection of lung cancer.

The Role of PET/CT in Assessing Pulmonary Nodules in Children With Solid Malignancies

OBJECTIVE. The purpose of this article is to assess the feasibility and utility of PET/CT in distinguishing benign from malignant pulmonary nodules in patients with solid childhood malignancies. SUBJECTS AND METHODS. This prospective study was conducted between March 2008 and August 2010. We enrolled 25 subjects 21 years old or younger with solid childhood malignancies and at least one pulmonary nodule measuring 0.5-3.0 cm. PET/CT was performed within 3 weeks of diagnostic chest CT. Three panels of three reviewers each reviewed diagnostic CT only (panel 1), PET/CT only (panel 2), or diagnostic CT and PET/CT concurrently (panel 3) and predicted each nodule's histologic diagnosis as benign, malignant, or indeterminate. Interreviewer agreement was assessed with the kappa statistic. Using nodule biopsy or clinical follow-up as reference standards, the sensitivity, specificity, and accuracy for each panel was assessed. Logistic regression was used to assess the nodule's maximum standardized uptake value (SUVmax) association with its histologic diagnosis. RESULTS. There were 75 nodules with a median size of 0.74 cm (range, 0.18-2.38 cm); 48 nodules were malignant. Sensitivity was 85% (41/48) for panel 1, 60% (29/48) for panel 2, and 67% (32/48) for panel 3. All panels had poor specificities. Interreviewer agreement was moderate for panel 1 (0.43) and poor for panels 2 (0.22) and 3 (0.33). SUVmax was a significant predictor of histologic diagnosis (p = 0.004). CONCLUSION. PET/CT assessment of pulmonary nodules is feasible in children with solid malignancies but may not reliably improve our ability to predict a nodule's histologic diagnosis. The SUVmax may improve the performance of PET/CT in this setting.