Benign Solitary Pulmonary Nodules Research Articles

A case report of primary pulmonary meningioma masquerading as lung metastasis in a patient with rectal carcinoma: role of 18F-FDG PET/CT

BackgroundPrimary pulmonary meningioma (PPM) is an extremely rare disease, which is often misdiagnosed as lung metastasis. Previous studies indicated that PPM usually showed homogeneous enhancement on enhanced CT and high uptake of 18F-fluorodeoxyglucose (18F-FDG) on positron emission tomography/CT (PET/CT). In this study, we report a case of PPM with atypical enhanced CT and 18F-FDG PET/CT features in a patient with rectal carcinoma.Case presentationA 70-year-old male was demonstrated to have rectal carcinoma by biopsy while a solitary pulmonary nodule (SPN) with well-defined edges measuring 13 × 13 × 15 mm was almost simultaneously found in the right lower robe on chest CT scan. Contrast-enhanced CT and PET/CT revealed mild centripetal enhancement of the nodule without accumulation of 18F-FDG. A thoracoscopic wedge resection of the right lower lobe was finally performed and histopathologic examinations and PET/CT imaging showed that the nodule was a PPM.ConclusionPPM is a rare disease with heterogeneity not only in blood supply but also in glucose metabolism. 18F-FDG PET/CT may be an effective method for differentiating benign and malignant SPNs. The diagnosis of PPM depends on pathological and radiological examinations.

Value of circulating tumor cells in the diagnosis and treatment of solitary pulmonary nodules.

BackgroundLung cancer has become the most common malignant tumor worldwide, with the highest rates of morbidity and mortality. The detection of circulating tumor cells (CTCs) can be simple, rapid, and minimally invasive, thus endowing them with a high value in the diagnosis of malignant tumors. We aimed to explore the correlation between CTCs in peripheral blood and benign or malignant solitary pulmonary nodules (SPNs).MethodsA total of 223 patients with SPNs from January 2018 to May 2020 were recruited. During the same period, 20 healthy volunteers were recruited as controls. Venous blood samples were collected from participants for detecting CTCs using a folate receptor (FR)-positive cell detection kit, as well as tumor biomarkers.ResultsA significant difference in the level of CTCs were observed between the malignant SPNs group, the benign SPNs group, and the control group, which was markedly higher in the malignant SPNs group (10.48±3.49 FU/3 mL) than both the benign SPNs and control groups (6.38±0.53 and 4.45±1.21 FU/3 mL, respectively) (P<0.001). In addition, the level of CTCs was significantly higher in the benign SPNs group than in the control group (P=0.023). In particular, in the malignant SPNs group, patients older than 60 years (11.45±3.92 FU/3 mL) presented a notably higher level of CTCs than other patients (9.55±2.74 FU/3 mL). The patients were then classified according to the pathological subtypes of lung cancer. There was a significant difference in level of CTCs among patients with squamous cell carcinoma (9.10±1.94 FU/3 mL), adenocarcinoma (10.77±3.71 FU/3 mL), and adenosquamous cell carcinoma (11.78±2.61 FU/3 mL). Binary logistic regression analysis suggested that CTCs were an independent risk factor of malignant SPN (OR =3.698, 95% CI: 1.136–11.035, P=0.030). The sensitivity and specificity of CTCs in diagnosing malignant SPNs was significantly higher than tumor biomarkers (single or combined) [sensitivity =89.1%; specificity =92.3%; area under curve (AUC) (95% CI) =0.907 (0.861–0.942)].ConclusionsPeripheral blood CTCs can be used in the diagnosis of malignant SPNs and are recommended for clinical application.

A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population

BackgroundThis study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs).Methods Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort.ResultsA total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed − 0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models.ConclusionsThe novel model in our study had a high clinical value in diagnose of MSPNs.

The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification.

Differentiating between malignant solitary pulmonary nodules (SPNs) and other lung diseases remains a substantial challenge. The latest generation of dual-energy computed tomography (CT), which realizes dual-energy technology at the detector level, has clinical potential for distinguishing lung cancer from other benign SPNs. This study aimed to evaluate the performance of dual-layer spectral detector CT (SDCT) for the differentiation of SPNs. Spectral images of 135 SPNs confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP). Patients were classified into two groups [the malignant group (n=93) and the benign group (n=42)], with the malignant group further divided into small cell lung cancer (SCLC, n=30) and non-small cell lung cancer (NSCLC, n=63) subtypes. The slope of the spectral Hounsfield Unit (HU) curve (λHU), normalized iodine concentration (NIC), CT values of 40 keV monochromatic images (CT40keV), and normalized arterial enhancement fraction (NAEF) in contrast-enhanced images were calculated and compared between the benign and malignant groups, as well as between the SCLC and NSCLC subgroups. ROC curve analysis was performed to assess the diagnostic performance of the above parameters. Seventy cases were randomly selected and independently measured by two radiologists, and intraclass correlation coefficient (ICC) and Bland-Altman analyses were performed to calculate the reliability of the measurements. Except for NAEF (P=0.23), the values of the parameters were higher in the malignant group than in the benign group (all P<0.05). NIC, λHU, and CT40keV performed better in the VP (NICVP, λVPHU, and CTVP40keV) (P<0.001), with an area under the ROC curve (AUC) of 0.93, 0.89, and 0.89 respectively. With respective cutoffs of 0.31, 1.83, and 141.00 HU, the accuracy of NICVP, λVPHU, and CTVP40keV was 91.11%, 85.19%, and 88.15%, respectively. In the subgroup differentiating NSCLC and SCLC, the diagnostic performances of NICAP (AUC =0.89) were greater than other parameters. NICAP had an accuracy of 86.02% when the cutoff was 0.14. ICC and Bland-Altman analyses indicated that the measurement of SDCT has great reproducibility. Quantitative measures from SDCT can help to differentiate benign from malignant SPNs and may help with the further subclassification of malignant cancer into SCLC and NSCLC.

Prediction of malignancy for solitary pulmonary nodules based on imaging, clinical characteristics and tumor marker levels.

ObjectiveTo establish a prediction model of malignancy for solitary pulmonary nodules (SPNs) on the basis of imaging, clinical characteristics and tumor marker levels.MethodsTotally, 341 cases of SPNs were enrolled in this retrospective study, in which 70% were selected as the training group (n = 238) and the rest 30% as the verification group (n = 103). The imaging, clinical characteristics and tumor marker levels of patients with benign and malignant SPNs were compared. Influencing factors were identified using multivariate logistic regression analysis. The model was assessed by the area under the curve (AUC) of the receiver operating characteristic curve.ResultsDifferences were evident between patients with benign and malignant SPNs in age, gender, smoking history, carcinoembryonic antigen (CEA), neuron-specific enolase, nodule location, edge smoothing, spiculation, lobulation, vascular convergence sign, air bronchogram, ground-glass opacity, vacuole sign and calcification (all P < 0.05). Influencing factors for malignancy included age, gender, nodule location, spiculation, vacuole sign and CEA (all P < 0.05). The established model was as follows: Y = −5.368 + 0.055 × age + 1.012 × gender (female = 1, male = 0) + 1.302 × nodule location (right upper lobe = 1, others = 0) + 1.208 × spiculation (yes = 1, no = 0) + 2.164 × vacuole sign (yes = 1, no = 0) −0.054 × CEA. The AUC of the model with CEA was 0.818 (95% confidence interval, 0.763–0.865), with a sensitivity of 64.80% and a specificity of 84.96%, and the stability was better through internal verification.ConclusionsThe prediction model established in our study exhibits better accuracy and internal stability in predicting the probability of malignancy for SPNs.

Assessment of Solitary Pulmonary Nodules Based on Virtual Monochrome Images and Iodine-Dependent Images Using a Single-Source Dual-Energy CT with Fast kVp Switching.

With lung cancer being the most common malignancy diagnosed worldwide, lung nodule assessment has proved to be one of big challenges of modern medicine. The aim of this study was to examine the usefulness of Dual Energy Computed Tomography (DECT) in solitary pulmonary nodule (SPN) assessment. Between January 2017 and June 2018; 65 patients (42 males and 23 females) underwent DECT scans in the late arterial phase (AP) and venous phase (VP). We concluded that imaging at an energy level of 65 keV was the most accurate in detecting malignancy in solitary pulmonary nodules (SPNs) measuring ≤30 mm in diameter on virtual monochromatic maps. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. As for iodine concentration maps, the analysis of venous phase images resulted in the highest clinical usefulness. To summarize, DECT may be a useful tool in the differentiation of benign and malignant SPNs. A single-phase DECT examination with scans acquired 90 s after contrast media injection is recommended.

Predicting lung nodules malignancy

BackgroundIt is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting. MethodsThe authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy. ResultsFrom a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573. ConclusionsThe prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.

The value of four imaging modalities to distinguish malignant from benign solitary pulmonary nodules: a study based on 73 cohorts incorporating 7956 individuals.

Solitary pulmonary nodules (SPNs) frequently bother oncologists. The differentiation of malignant from benign nodules with non-invasive approach remains a tough challenge. This study was designed to assess the diagnostic accuracy of dynamic computed tomography (CT), dynamic magnetic resonance imaging (MRI), fluorine 18 fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), and technetium 99m (99mTc) depreotide single photon emission computed tomography (SPECT) for SPNs. Electronic databases of MEDLINE, PubMed, EMBASE, and Cochrane Library were searched to identify relevant trials. The primary evaluation index of diagnostic accuracy was areas under the summary receiver-operating characteristic (SROC) curve. The results were analyzed utilizing Stata 12.0 statistical software. Seventy-three trials incorporating 7956 individuals were recruited. Sensitivities, specificities, positive likelihood ratios, negative likelihood ratios, diagnostic score, diagnostic odds ratios, and areas under the SROC curve with 95% confidence intervals were, respectively, 0.92 (0.89-0.95), 0.64 (0.54-0.74), 2.60 (1.98-3.42), 0.12 (0.08-0.17), 3.10 (2.62-3.59), 22.24 (13.67-36.17), and 0.91 (0.88-0.93) for CT; 0.92 (0.86-0.95), 0.85 (0.77-0.90), 6.01 (3.90-9.24), 0.10 (0.06-0.17), 4.12 (3.41-4.82), 61.39 (30.41-123.93), and 0.94 (0.92-0.96) for MRI; 0.90 (0.86-0.93), 0.73 (0.65-0.79), 3.28 (2.56-4.20), 0.14 (0.10-0.19), 3.16 (2.69-3.64), 23.68 (14.74-38.05), and 0.90 (0.87-0.92) for 18F-FDG PET; and 0.93 (0.88-0.96), 0.70 (0.56-0.81), 3.12 (2.03-4.81), 0.10 (0.06-0.17), 3.43 (2.63-4.22), 30.74 (13.84-68.27), and 0.93 (0.91-0.95) for 99mTc-depreotide SPECT. The dynamic MRI, dynamic CT, 18F-FDG PET, and 99mTc-depreotide SPECT were favorable non-invasive approaches to distinguish malignant SPNs from benign. Moreover, from the viewpoint of cost-effectiveness and avoiding radiation, the dynamic MRI was recommendable for SPNs.

Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features

This study aimed to evaluate the diagnostic value of a support vector machine (SVM) model built with texture features based on standard 2-[F]fluoro-2-deoxy-D-glucose (F-FDG) PET in patients with solitary pulmonary nodules (SPNs) at a volume larger than 5 mL. The PET results of 82 patients diagnosed with SPNs between 2014 and 2018 were retrospectively analysed. The volumes of interest (VOIs) of the SPNs were automatically segmented using threshold techniques from the standard PET imaging. Then, a large number of texture features were extracted from the VOIs using texture-analysis software. Next, an optimized SVM machine-learning model that was trained on standard PET images using texture features was employed to identify the optimal discrimination between malignant and benign nodules. Diagnostic models based on the maximum standardized uptake value (SUVmax) and the metabolic tumour volume (MTV) were compared with the SVM model with regard to the SPN diagnostic power. Compared with the SUVmax and MTV models, the texture-based SVM model provided an improvement of approximately 20% in diagnostic accuracy, positive predictive value, negative predictive value and the area under the operating characteristic curve. The receiver operating characteristic curve of the SVM model showed a significant improvement compared with the MTV model (P = 0.0345 < 0.05) and the SUVmax model (P = 0.01 < 0.05). Standard F-FDG PET imaging can increase the differentiation of benign and malignant SPNs with volumes larger than 5 mL using an SVM model based on texture features.

A model of malignant risk prediction for solitary pulmonary nodules on 18 F-FDG PET/CT: Building and estimating.

BackgroundTo develop a model of malignant risk prediction of solitary pulmonary nodules (SPNs) using metabolic characteristics of lesions.MethodsA total of 362 patients who underwent PET/CT imaging from January 2013 to July 2017 were analyzed. Differences in the clinical and imaging characteristics were analyzed between patients with benign SPNs and those with malignant SPNs. Risk factors were screened by multivariate nonconditional logistic regression analysis. The self‐verification of the model was performed by receiver operating characteristic (ROC) curve analysis, and out‐of‐group verification was performed by k‐fold cross‐validation.ResultsThere were statistically significant differences in age, maximum standardized uptake value (SUVmax), size, lobulation, spiculation, pleural traction, vessel connection, calcification, presence of vacuoles, and emphysema between patients with benign nodules and those with malignant nodules (all P < 0.05). The risk factors for malignant nodules included age, SUVmax, size, lobulation, calcification and vacuoles. The logistic regression model was as follows: P = l/(1 + e‐x), x = − 5.583 + 0.039 × age + 0.477 × SUVmax + 0.139 × size + 1.537 × lobulation – 1.532 × calcification + 1.113 × vacuole. The estimated area under the curve (AUC) for the model was 0.915 (95% CI: 0.883–0.947), the sensitivity was 89.7%, and the specificity was 78.9%. K‐fold cross‐validation showed that the training accuracy was 0.899 ± 0.011, and the predictive accuracy was 0.873 ± 0.053.ConclusionsThe risk factors for malignant nodules included age, SUVmax, size, lobulation, calcification and vacuoles. After verification, the model has satisfactory accuracy, and it may assist clinics make appropriate treatment decisions.

Using neighborhood gray tone difference matrix texture features on dual time point PET/CT images to differentiate malignant from benign FDG-avid solitary pulmonary nodules

ObjectiveLung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone difference matrix (NGTDM) texture features.MethodsRetrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUVmax and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features.ResultsIn the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUVmax were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively.ConclusionCompared to SUVmax or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.

Comparing attenuations of malignant and benign solitary pulmonary nodule using semi-automated region of interest selection on contrast-enhanced CT.

The purpose of this study was to determine whether semi-automated region of interest (ROI) measurement of CT attenuations of solitary pulmonary nodule (SPN) is an accurate approach in differentiating malignant from benign SPN. Ninety cases of pathologically proven SPN were retrospectively reviewed. CT attenuations of SPN before and after contrast injection were measured using semi-automated ROI selection method. Attenuations within a range of -100 to 200 Hounsfield units (HU) as soft tissue density range were set. The ROI included the entire SPN regardless of its internal soft tissue contents after automatic elimination of airs, calcific, or bony densities. There were 42 (46.7%) malignant SPN and 48 (53.3%) benign SPN, which were grouped into A (18 tuberculoma, 13 fungus), B (5 focal organizing pneumonia, 5 abscess), and C (7 other benign tumors). The malignant SPN showed significantly higher mean attenuations of enhancement and net-enhancement than all benign SPN (P<0.001). Using the area under the receiver operating characteristic curve (AUC), the cut-off net-enhancement of 15 HU gave 83% sensitivity, 65% specificity and 73% accuracy for predicting malignancy. Malignant SPN (mean 67.9 HU) had significantly higher enhancement than group A (mean 52.6 HU, P<0.001, 95% CI: 8.73, 21.81) and group B (mean 57.0 HU, P=0.025, 95% CI: -1.43, 20.34) while group C showed no significant difference (mean 68.1 HU, P=0.97). Net enhancements were higher in group B (mean 18.8 HU) than in group A (mean 8.8 HU) (P<0.001, 95% CI: 11.8, 23.18). The semi-automated ROI measurement of SPN's attenuations on CT is an accurate approach in distinguishing indeterminate SPN.

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

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). 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. 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. 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.

Diagnostic value of diffusion kurtosis imaging in differentiating indeterminate solitary pulmonary nodules: comparison with diffusion weighted imaging

Objective To investigate the role of diffusion kurtosis imaging (DKI) in indeterminate solitary pulmonary nodules (SPN) diagnosis and to compare with conventional diffusion weighted imaging (DWI). Methods From March 2016 to Dec 2017, forty-three consecutive patients (30 male, 13 female, age: 56±11 years) with indeterminate SPNs were included. All patients underwent axial multi-b factor DWI (with b values=0, 50, 200, 400, 800, 1 400, 2 000 s/mm2) examination and were divided into benign group (19 cases) and malignant group (24 cases) according to pathological results of SPN. ADC Kurtosis (K) and Diffusivity (Dk) values were compared between malignant and benign group and among different subtypes of lung cancer using independent t test (normal distribution and homogeneity of variance) and Mann-Whitney U test (skewed distribution or variance). Receiver operating characteristic (ROC) curves were employed to evaluate the diagnostic performance. Results K values were significantly higher for malignant SPNs than for benign SPNs (0.839±0.197 vs. 0.718±0.120; t=2.359, P=0.023). ADC values were found to be significantly higher in benignity than malignant SPNs [(1.605±0.422) ×10-3mm2/s vs. (1.278±0.210)×10-3mm2/s; t=-3.089, P=0.005). No difference was observed in Dk between the two groups (P=0.922). All parameters cannot differentiate subtypes of lung cancer. The ADC value had higher AUC (area under ROC curve) than that of K value. The sensitivity (70.8%) and accuracy (72.1%) of ADC value was higher than K value, the specificity of both methods was equal. Conclusion DKI is a feasible non-invasive tool which has comparable capability of conventional DWI in SPNs differentiation, although with lower sensitivity and accuracy. DKI can provide additional information for SPNs characterization and has a potential to be a robust way in SPNs interpretation. Key words: Magnetic resonance imaging; Solitary pulmonary nodule

The value of 18F-FDG PET/CT in the diagnosis of different size of solitary pulmonary nodules.

This study aimed to evaluate the diagnostic value of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) for solitary pulmonary nodules (SPNs) with different diameters.One hundred eighty two consecutive patients with SPN who underwent 18F-FDG PET/CT examination were retrospectively studied. Patients were categorized into 3 groups according to the diameter of nodules: Group A with the diameter of greater than or equal to 6 mm and less than or equal to 10 mm; Group B with diameter greater than 10 mm and less than or equal to 20 mm; Group C with diameter greater than 20 mm and less than or equal to 30 mm. The efficiency of PET/CT, PET and CT in the diagnosis of benign and malignant SPNs and different subgroup of SPNs was calculated. Receiver operating characteristic curves (ROCs) were drawn and area under the curves (AUCs) were compared between different groups.The age, diameter, mean standardized uptake value (SUVmean) and maximum standardized uptake value (SUVmax) of benign and malignant nodules were significantly different (P < .05). For overall SPNs, the sensitivity, specificity, accuracy, PPV, and NPV of PET/CT were 98.35%, 77.05%, 91.21%, 89.47%, and 95.92%, respectively. The AUC of PET/CT was significantly larger than that of SUVmean, SUVmax, and CT (P < .05). For different size of SPNs, the AUC of PET/CT in group A was higher than that in group B and group C, but there was no significant difference with CT (P > .05). In group B, the accuracy of PET/CT in the diagnosis of SPN was significantly higher than that of CT (P < .05).18F-FDG PET/CT demonstrated excellent performance in identifying different size of SPNs, especially for those with diameter between 11 and 20 mm, the diagnostic value of 18F-FDG PET/CT is significantly higher than other methods.

Correlation study between dual source CT perfusion imaging and the microvascular composition of solitary pulmonary nodules

ObjectiveTo explore the correlation between dual source computed tomography perfusion imaging (CTPI) and microvascular parameters, and evaluate the value of CTPI in the differential diagnosis of solitary pulmonary nodule (SPN). Methods65 consecutive patients with SPN who successfully underwent pre-operative CT perfusion imaging with dual source CT and received a final diagnosis by postoperative pathology. The cases were divided into malignant, benign and inflammatory groups according to the pathological results. CT perfusion parameters, such as blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability surface (PMB) were obtained by performing CTPI of SPNs. The postoperative specimens of SPNs were immunohistochemically stained for CD34 and SMA to detect microvessel density (MVD) and luminal vascular parameters, such as luminal vascular number (LVN), luminal vascular area (LVA) and luminal vascular perimeter (LVP). The receiver operating characteristic (ROC) curve was used to assess the diagnostic efficiency of CT perfusion parameter in diagnosing malignant SPNs. ResultsIn these 65 cases, malignant, benign and inflammatory SPNs were respectively 39, 14 and 12 cases. Significant difference was observed in LVN/MVD, LVA and LVP among the three groups (P < 0.05). The correlation between CT perfusion parameters (BF, BV and PMB) and the luminal vascular parameters was stronger than that with MVD (P < 0.05). PMB has the strongest correlation with LVN/MVD. Using BF≥60ml/100ml/min, BV≥6.34ml/100ml and PMB≥13.35ml/100 ml/min for the diagnosis, the area under the curve (AUC) of the ROC curve was 0.760, the sensitivity was 82% and the specificity was 61%. ConclusionsThe main indicators reflecting blood perfusion of SPN are the degree of lumen or maturity of microvessels (LVN, LVA and LVP), not just the number of microvessels (e.g. MVD). CT perfusion imaging can be used as an important method to non-invasively evaluate tumour angiogenesis and help to distinguish malignant SPNs from benign and inflammatory SPNs.

Non-invasive diagnosis of early-stage lung cancer using high-throughput targeted DNA methylation sequencing of circulating tumor DNA (ctDNA).

Rational: LDCT screening can identify early-stage lung cancers yet introduces excessive false positives and it remains a great challenge to differentiate malignant tumors from benign solitary pulmonary nodules, which calls for better non-invasive diagnostic tools.Methods: We performed DNA methylation profiling by high throughput DNA bisulfite sequencing in tissue samples (nodule size < 3 cm in diameter) to learn methylation patterns that differentiate cancerous tumors from benign lesions. Then we filtered out methylation patterns exhibiting high background in circulating tumor DNA (ctDNA) and built an assay for plasma sample classification.Results: We first performed methylation profiling of 230 tissue samples to learn cancer-specific methylation patterns which achieved a sensitivity of 92.7% (88.3% - 97.1%) and a specificity of 92.8% (89.3% - 96.3%). These tissue-derived DNA methylation markers were further filtered using a training set of 66 plasma samples and 9 markers were selected to build a diagnostic prediction model. From an independent validation set of additional 66 plasma samples, this model obtained a sensitivity of 79.5% (63.5% - 90.7%) and a specificity of 85.2% (66.3% - 95.8%) for differentiating patients with malignant tumor (n = 39) from patients with benign lesions (n = 27). Additionally, when tested on gender and age matched asymptomatic normal individuals (n = 118), our model achieved a specificity of 93.2% (89.0% - 98.3%). Specifically, our assay is highly sensitive towards early‐stage lung cancer, with a sensitivity of 75.0% (55.0%-90.0%) in 20 stage Ia lung cancer patients and 85.7% (57.1%-100.0%) in 7 stage Ib lung cancer patients.Conclusions: We have developed a novel sensitive blood based non‐invasive diagnostic assay for detecting early stage lung cancer as well as differentiating lung cancers from benign pulmonary nodules.

Can spectral computed tomography imaging improve the differentiation between malignant and benign pulmonary lesions manifesting as solitary pure ground glass, mixed ground glass, and solid nodules?

BackgroundThis study quantitatively assessed the efficacy of spectral computed tomography (CT) imaging parameters for differentiating the malignancy and benignity of solitary pulmonary nodules (SPNs) manifesting as ground glass nodules (GGNs) and solid nodules (SNs).MethodsThe study included 114 patients with SPNs (61 GGNs, and 53 SNs) who underwent CT plain and enhanced scans in the arterial (a) and venous (v) phases using the spectral imaging mode. The spectral CT imaging parameters included: iodine concentrations (IC) of lesions in the arterial (ICLa) and venous (ICLv) phases; normalized IC (NICa/NICv, normalized to the IC in the aorta); the slope of the spectral Hounsfield unit (HU) curve (λHUa/λHUv); and monochromatic CT number (CT40keVa/v, CT70keVa/v) enhancement on 40 and 70 keV images. The two‐sample Mann–Whitney U test was used to compare quantitative parameters between malignant and benign SPNs, SNs, and GGNs.ResultsPathology revealed 75 lung cancer cases, 3 metastatic nodules, 14 benign nodules, and 22 inflammatory nodules. Among the 53 SNs there were 37 malignant and 16 benign nodules. Among the 61 GGNs there were 41 malignant and 20 benign nodules. Overall, the CT40keVa, λHUa, CT40keVv, λHUv, and ICLv of benign SPNs were all greater than those of malignant SPNs (all P < 0.05). For GGNs, CT40keVa/v, CT70keVa/v, λHUa/λHUv, and ICLv of malignant GGNs were all lower than those of benign GGNs.ConclusionSpectral CT imaging is a more promising method for distinguishing malignant from benign nodules, especially in nodules manifesting as GGNs in contrast‐enhanced scanning.

Comparison of Various Parameters of DWI in Distinguishing Solitary Pulmonary Nodules

In order to prospectively assess various parameters of diffusion weighted imaging (DWI) in differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs), 58 patients (40 men and 18 women, and mean age of 48.1±10.4 years old) with SPNs undergoing conventional MR, DWI using b=500 s/mm2 on a 1.5T MR scanner, were studied. Various DWI parameters [apparent diffusion coefficient (ADC), lesion-tospinal cord signal intensity ratio (LSR), signal intensity (SI) score] were calculated and compared between malignant and benign SPNs groups. A receiver operating characteristic (ROC) curve analysis was employed to compare the diagnostic capabilities of all the parameters for discrimination between benign and malignant SPNs. The results showed that there were 42 malignant and 16 benign SPNs. The ADC was significantly lower in malignant SPNs (1.40±0.44)×10-3 mm2/s than in benign SPNs (1.81±0.58)×10-3 mm2/s. The LSR and SI scores were significantly increased in malignant SPNs (0.90±0.37 and 2.8±1.2) as compared with those in benign SPNs (0.68±0.39 and 2.2±1.2). The area under the ROC curves (AUC) of all parameters was not significantly different between malignant SPNs and benign SPNs. It was suggested that as three reported parameters for DWI, ADC, LSR and SI scores are all feasible for discrimination of malignant and benign SPNs. The three parameters have equal diagnostic performance.

Circulating tumor cell levels and carcinoembryonic antigen: An improved diagnostic method for lung adenocarcinoma.

BackgroundThe aim of this study was to determine a correlation between benign and malignant lung solitary pulmonary nodules (SPN), and analyze the association between circulating tumor cell (CTC) levels and different subtypes of lung adenocarcinoma.MethodsA total of 200 patients (80 with SPNs and 120 diagnosed with lung cancer) were included in the study. The CTC levels were quantified by identifying the folate receptor on the surface of tumor cells; clinical tumor specific markers were detected by biochemical immunization. The content of peripheral blood CTCs in benign and malignant lung SPN patients was detected and the differences in preoperative CTC levels in different pathological subtypes were analyzed. Based on the collected data, receiver operating characteristic curves were calculated and the rate of lung cancer was predicted.ResultsThe peripheral blood CTC levels in patients with malignant lung SPNs were higher than in patients with benign SPNs. The maximum nodule diameter, carcinoembryonic antigen, and CTC levels were independent risk factors for malignant lung SPNs. The peripheral blood CTC levels in patients with stage III–IV lung adenocarcinoma were higher than in stage I–II patients. The peripheral blood CTC levels in patients with microinvasive and invasive adenocarcinoma were higher than in adenocarcinoma in situ patients. The CTC levels in the peripheral blood of patients with maximum tumor diameter > 2 cm were higher than in patients with tumors < 2 cm.ConclusionThe detection of CTCs can be used as a biomarker for screening SPNs and diagnosing early‐stage lung cancer. Using the combination of CTC levels and CEA significantly improves the efficacy of lung adenocarcinoma diagnosis.