Benign Solitary Pulmonary Nodules Research Articles

The study of plain CT combined with contrast-enhanced CT-based models in predicting malignancy of solitary solid pulmonary nodules

To compare the diagnostic performance between plain CT-based model and plain plus contrast CT-based modelin the classification of malignancy for solitary solid pulmonary nodules. Between January 2012 and July 2021, 527 patients with pathologically confirmed solitary solid pulmonary nodules were collected at dual centers with similar CT examinations and scanning parameters. Before surgery, all patients underwent both plain and contrast-enhanced chest CT scans. Two clinical characteristics, fifteen plain CT characteristics, and four enhanced characteristics were used to develop two logistic regression models: model 1 (plain CT only) and model 2 (plain + contrast CT). The diagnostic performance of the two models was assessed separately in the development and external validation cohorts using the AUC. 392 patients from Center A were included in the training cohort (median size, 20.0 [IQR, 15.0–24.0] mm; mean age, 55.8 [SD, 9.9] years; male, 53.3%). 135 patients from Center B were included in the external validation cohort (median size, 20.0 [IQR, 16.0–24.0] mm; mean age, 56.4 [SD, 9.6] years; male, 51.9%). Preoperative patients with 201 malignant (adenocarcinoma, 148 [73.6%]; squamous cell carcinoma, 35 [17.4%]; large cell carcinoma,18 [9.0%]) and 326 benign (pulmonary hamartoma, 118 [36.2%]; sclerosing pneumocytoma, 35 [10.7%]; tuberculosis, 104 [31.9%]; inflammatory pseudonodule, 69 [21.2%]) solitary solid pulmonary nodules were gathered from two independent centers. The mean sensitivity, specificity, accuracy, PPV, NPV, and AUC (95%CI) of model 1 (Plain CT only) were 0.79, 0.78, 0.79, 0.67, 0.87, and 0.88 (95%CI, 0.82–0.93), the model 2 (Plain + Contrast CT) were 0.88, 0.91, 0.90, 0.84, 0.93, 0.93 (95%CI, 0.88–0.98) in external validation cohort, respectively. A logistic regression model based on plain and contrast-enhanced CT characteristics showed exceptional performance in the evaluation of malignancy for solitary solid lung nodules. Utilizing this contrast-enhanced CT model would provide recommendations concerning follow-up or surgical intervention for preoperative patients presenting with solid lung nodules.

WITHDRAWN: Diffusion kurtosis imaging (DKI) in characterization in indetermined solitary pulmonary nodules (SPNs): comparison with conventional DWI and quantitative

We contrasted traditional DWI with quantitative imaging to better understand the function of diffusion kurtosis imaging (DKI) in characterising solitary pulmonary nodules (SPNs).Forty-seven consecutive patients with SPNs (30 males, 17 females, median age: 61 years; range: 29 to 86 years) were included in this paper between March 2018 and July 2020. Quantitative DCE-MRI and multi-b factor DWI (b values ranging from 0 to 2000 sec/mm2) were obtained. We compared the ADC, Kapp, Dapp, Ktrans, Kep, Ve, and iAUC values between the malignant and benign groups, as well as between the various LC subtypes. The purpose of the ROC curves was to evaluate the diagnostic value.Kapp, Ktrans, Ve and iAUC values were obviously higher for malignant SPNs compared to that of benign SPNs (P < 0.035). ADC was obviously higher in benignity compared to malignant SPNs (P = 0.001). There exist no obvious difference in Dapp and Kep between the two groups (P = 0.06). Kapp value has highest sensitivity (81.8%) and accuracy (75.7%), and ADC value has highest specificity (80.0%). Combination of ADC and iAUC enhanced the sensitivity to 81.8%, specificity to 86.7% and precision to 83.8%.

Decreased levels of sex hormones in females with solitary pulmonary nodules were risk factors for malignancy

ObjectiveThe purpose of this research was to detect the relationship between the levels of sex hormones in females with solitary pulmonary nodules (SPNs) and their potential malignancies.MethodsA total of 187 consecutive patients with pathologically confirmed SPNs by chest CT were enrolled in our study. They were divided into two groups based on the pathologic findings of SPNs after surgery: benign and malignant SPNs. Progesterone (P), estradiol (E2), and testosterone (T) levels in the two groups were measured. Meanwhile, we used binary logistic regression analysis to analyze the risk factors for SPNs.ResultsOf these 187 patients, 73 had benign SPNs, while 114 had malignant SPNs. We found that the levels of progesterone (P), estradiol (E2), and testosterone (T) were decreased significantly in patients with malignant SPNs compared to patients with benign SPNs (all P < 0.05). Multivariate logistic regression analysis revealed that second-hand smoke, burr sign, lobulation sign, pleural traction sign, vascular convergence sign, vacuole sign, and ≥ 1 cm nodules were independent risk factors for malignant pulmonary nodules in females.ConclusionsDecreased levels of sex hormones in females were associated with malignant pulmonary nodules, suggesting that they can contribute to the diagnosis of lung cancer.

Diagnosis and Management for Pulmonary Tuberculoma

Pulmonary tuberculoma, prevalent particularly in tuberculosis (TB)-endemic regions, often appears as a solitary nodule of less than 30 mm, or a tumor of more than 30 mm on radiological examinations. It may also present with a combination of abnormalities, such as multiple nodules with infiltration or pleuritis. Benign solitary pulmonary nodules represent up to 25% of all resected solitary pulmonary nodules, with approximately 5-24% of these identified as pulmonary tuberculoma post-surgery. This condition is prevalent particularly in TB-endemic regions, and must be considered while determining the diagnosis, especially for patients at high risk for lung cancer. Modalities for diagnosing pulmonary tuberculoma include chest radiography, USG, CT scan, PET scan and bronchoscopy with transbronchial biopsy. The treatments for pulmonary tuberculoma are anti-TB drugs and surgery. Pulmonary tuberculoma responds poorly to anti-TB drugs and requires long-term treatment. Surgery is performed when the diameter of tuberculoma still increases after adequate anti-TB treatment.

Serum lncRNA THRIL predicts benign and malignant pulmonary nodules and promotes the progression of pulmonary malignancies

BackgroundThis project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of THRIL/miR-99a in malignant SPNs.MethodsThe study groups consisted of 169 patients with SPN and 74 healthy subjects. The differences in THRIL levels were compared between the two groups and the healthy group. The receiver operating characteristic curve (ROC) was utilized to analyze the THRIL’s significance in detecting benign and malignant SPN. Pearson correlation and binary regression coefficients represented the association between THRIL and SPN. CCK-8 assay, Transwell assay, and flow cytometry were utilized to detect the regulatory effect of THRIL silencing. The interaction between THRIL, miR-99a, and IGF1R was confirmed by the double luciferase reporter gene.ResultsThere were differences in THRIL expression in the healthy group, benign SPN group, and malignant SPN group. High accuracy of THRIL in the diagnosis of benign SPN and malignant SPN was observed. THRIL was associated with the development of SPN. The expression of THRIL was upregulated and miR-99a was downregulated in lung cancer cells. The double luciferase report experiment confirmed the connections between THRIL/miR-99a/IGF1R. Silencing THRIL could suppress cell proliferation, migration, and invasion and promote cell apoptosis by binding miR-99a.ConclusionThe detection of THRIL in serum is useful for the assessment of malignant SPN. THRIL can regulate the expression of IGF1R through miR-99a, thereby promoting the growth of lung cancer cells and inhibiting apoptosis.

Value of spectral computed tomography-derived quantitative parameters based on full volume analysis in the diagnosis of benign/malignant and pathological subtypes of solitary pulmonary nodules.

Conventional dynamic computed tomography (CT) has a low specificity for the distinction between benign and malignant solitary pulmonary nodules (SPNs), and spectral CT has been proposed as a potential alternative. We aimed to investigate the role of quantitative parameters based on full-volume spectral CT in the differential diagnosis of SPNs. This retrospective study included spectral CT images of 100 patients with pathologically confirmed SPNs (78 and 22 in the malignant and benign groups, respectively). All cases were confirmed by postoperative pathology, percutaneous biopsy, and bronchoscopic biopsy. Multiple quantitative parameters derived from spectral CT were extracted from whole-tumor volume and standardized. Differences in quantitative parameters between groups were statistically analyzed. Diagnostic efficiency was evaluated by generating a receiver operating characteristic (ROC) curve. Between-group differences were evaluated using an independent sample t-test or Mann-Whitney U test. Interobserver repeatability was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. Spectral CT-derived quantitative parameters, except attenuation difference between the SPN in the 70 keV and arterial enhancement [ΔS-A(70 keV)], were significantly higher for malignant SPNs than for benign nodules (P<0.05). In the subgroup analysis, most parameters could distinguish between the benign and adenocarcinoma groups, and between the benign and squamous cell carcinoma groups (P<0.05). Only 1 parameter could differentiate the adenocarcinoma and squamous cell carcinoma groups (P=0.020). ROC curve analysis indicated that normalized arterial enhancement fraction in the 70 keV (NEF70 keV), normalized iodine concentration (NIC), and Δ70 keV had high diagnostic efficacy for differentiating SPNs between the benign and malignant SPNs [area under the curve (AUC): 0.867, 0.866, and 0.848, respectively] and between the benign and adenocarcinoma groups (AUC: 0.873, 0.872, and 0.874, respectively). The multiparameters derived from spectral CT exhibited satisfactory interobserver repeatability (ICC: 0.856-0.996). Our study suggests that quantitative parameters derived from whole-volume spectral CT may be useful to improve discrimination of SPNs.

Preoperative diagnosis of solitary pulmonary nodules with a novel hematological index model based on circulating tumor cells.

Preoperative noninvasive diagnosis of the benign or malignant solitary pulmonary nodule (SPN) is still important and difficult for clinical decisions and treatment. This study aimed to assist in the preoperative diagnosis of benign or malignant SPN using blood biomarkers. A total of 286 patients were recruited for this study. The serum FR+CTC, TK1, TP, TPS, ALB, Pre-ALB, ProGRP, CYFRA21-1, NSE, CA50, CA199, and CA242 were detected and analyzed. In the univariate analysis, age, FR+CTC, TK1, CA50, CA19.9, CA242, ProGRP, NSE, CYFRA21-1, and TPS showed the statistical significance of a correlation with malignant SPNs (P <0.05). The highest performing biomarker is FR+CTC (odd ratio [OR], 4.47; 95% CI: 2.57-7.89; P <0.001). The multivariate analysis identified that age (OR, 2.69; 95% CI: 1.34-5.59, P = 0.006), FR+CTC (OR, 6.26; 95% CI: 3.09-13.37, P <0.001), TK1 (OR, 4.82; 95% CI: 2.4-10.27, P <0.001), and NSE (OR, 2.06; 95% CI: 1.07-4.06, P = 0.033) are independent predictors. A prediction model based on age, FR+CTC, TK1, CA50, CA242, ProGRP, NSE, and TPS was developed and presented as a nomogram, with a sensitivity of 71.1% and a specificity of 81.3%, and the AUC was 0.826 (95% CI: 0.768-0.884). The novel prediction model based on FR+CTC showed much stronger performance than any single biomarker, and it can assist in predicting benign or malignant SPNs.

Total Lesion Glycolysis Obtained by FDG PET/CT in Diagnosing Solitary Pulmonary Nodules.

To evaluate the diagnostic performance of total lesion glycolysis (TLG) obtained by 18F-FDG PET/ CT to differentiate malignant solitary pulmonary nodules (SPNs) from benign ones. Observational study. Department of Nuclear Medicine, Istanbul Medeniyet University School of Medicine, Istanbul, Turkey, from January 2018 to April 2022. Eighty patients, who were found to have SPNs and underwent PET/CT imaging, were enrolled in this retrospective study. Parameters of PET-CT such as metabolic, volumetric, and metabolovolumetric were assessed concerning diagnostic value. Moreover, maximum standard uptake value (SUVmax) and TLG were combined and compared to improve diagnostic accuracy. The number of the detected benign and malignant SPNs was 38 and 42, respectively. Compared to the benign lesions, the malignant nodules presented significantly higher values in terms of SUVmax, TLG, and the volumes of metabolic tumour (MTV) and CT. Considering all parameters, the highest area under the curve (AUC) was occupied by TLG and SUVmax. The values for the sensitivity, specificity, and accuracy of SUVmax, TLG, and SUVmax combined with TLG were as follows respectively: 97.6%, 63.2%, and 81.2%; 85.7%, 92.1%, and 88.7%; and 85.7%, 94.7%, and 90%. The conventional value of SUVmax does not yield satisfactory results in order to separate the malignant nodules from the benign ones. The SUVmax value could be more valuable if it is used with TLG measurement in diagnosing SPNs. 18F-FDG PET/CT, SUVmax, TLG, Pulmonary nodule.

Qualitative (and Quantitative) Values of the Lung-RADS and Computed Tomography in Diagnosing Solitary Pulmonary Nodules.

Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.

Analysis of the Causes of Solitary Pulmonary Nodule Misdiagnosed as Lung Cancer by Using Artificial Intelligence: A Retrospective Study at a Single Center.

Artificial intelligence (AI) adopting deep learning technology has been widely used in the med-ical imaging domain in recent years. It realized the automatic judgment of benign and malig-nant solitary pulmonary nodules (SPNs) and even replaced the work of doctors to some extent. However, misdiagnoses can occur in certain cases. Only by determining the causes can AI play a larger role. A total of 21 Coronavirus disease 2019 (COVID-19) patients were diagnosed with SPN by CT imaging. Their Clinical data, including general condition, imaging features, AI re-ports, and outcomes were included in this retrospective study. Although they were confirmed COVID-19 by testing reverse transcription-polymerase chain reaction (RT-PCR) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), their CT imaging data were misjudged by AI to be high-risk nodules for lung cancer. Imaging characteristics included burr sign (76.2%), lobulated sign (61.9%), pleural indentation (42.9%), smooth edges (23.8%), and cavity (14.3%). The accuracy of AI was different from that of radiologists in judging the nature of be-nign SPNs (p < 0.001, κ = 0.036 < 0.4, means the two diagnosis methods poor fit). COVID-19 patients with SPN might have been misdiagnosed using the AI system, suggesting that the AI system needs to be further optimized, especially in the event of a new disease outbreak.

Comprehensive plasma metabolomics and lipidomics of benign and malignant solitary pulmonary nodules.

Solitary pulmonary nodules (SPNs) are commonly found in imaging technologies, but are plagued by high false-positive rates. We aimed to identify metabolic alterations in SPN etiology and diagnosis using less invasive plasma metabolomics and lipidomics. In total, 1160 plasma samples were obtained from healthy volunteers (n = 280), benign SPNs (n = 157) and malignant SPNs (stage I, n = 723) patients enrolled from 5 independent centers. Gas chromatography-triple quadrupole mass spectrometry (GC‒MS) and liquid chromatography-Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometry (LC‒MS) were used to analyze the samples for untargeted metabolomics and lipidomics. GC‒MS-based metabolomics revealed 1336 metabolic features, while LC‒MS-based lipidomics revealed 6088 and 2542 lipid features in the positive and negative ion modes, respectively. The metabolic and lipidic characteristics of healthy vs. benign or malignant SPNs exhibited substantial pattern differences. Of note, benign and malignant SPNs had no significant variations in circulating metabolic and lipidic markers and were validated in four other centers. This study demonstrates evidence of early metabolic alterations that can possibly distinguish SPNs from healthy controls, but not between benign and malignant SPNs.

Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis.

PurposeThis study aims to evaluate the accuracy of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in distinguishing malignant and benign solitary pulmonary nodules and masses.MethodsStudies investigating the diagnostic accuracy of IVIM-DWI in lung lesions published through December 2020 were searched. The standardized mean differences (SMDs) of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The sensitivity, specificity, area under the curve (AUC), publication bias, and heterogeneity were then summarized, and the source of heterogeneity and the reliability of combined results were explored by meta-regression and sensitivity analysis.ResultsA total of 16 studies including 714 malignant and 355 benign lesions were included. Significantly lower ADC, D, and f values were found in malignant pulmonary lesions compared to those in benign lesions. The D value showed the best diagnostic performance (sensitivity = 0.90, specificity = 0.71, AUC = 0.91), followed by ADC (sensitivity = 0.84, specificity = 0.75, AUC = 0.88), f (sensitivity = 0.70, specificity = 0.62, AUC = 0.71), and D* (sensitivity = 0.67, specificity = 0.61, AUC = 0.67). There was an inconspicuous publication bias in ADC, D, D* and f values, moderate heterogeneity in ADC, and high heterogeneity in D, D*, and f values. Subgroup analysis suggested that both ADC and D values had a significant higher sensitivity in “nodules or masses” than that in “nodules.”ConclusionsThe parameters derived from IVIM-DWI, especially the D value, could further improve the differential diagnosis between malignant and benign solitary pulmonary nodules and masses.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier: CRD42021226664

Development of a diagnostic model for malignant solitary pulmonary nodules based on radiomics features

BackgroundThis study proposed a precise diagnostic model for malignant solitary pulmonary nodules (SPNs). This model can be used to identify objective and quantifiable image features and guide the clinical treatment strategy adopted for SPNs. This model will help clinicians optimize management strategies for SPN.MethodsIn this retrospective study, the clinical data of 455 patients of SPN with defined pathological diagnosis between September 2016 and August 2019 were collected and analyzed. The data included pathological diagnosis, preoperative computed tomography (CT) diagnosis, gender, age, smoking history, family history of tumor, previous history, and contact history data. The quantitative image features and radiomic information of the SPNs were provided using computer-aided detection (CAD) “digital lung” software. The Chi-squared test was used to assess the accuracy between CAD and conventional CT in the diagnosis of SPNs. The diagnostic model for benign or malignant SPNs was developed using a multivariate logistic regression analysis that comprises 6 radiomic factors (irregularity, average diameter, COPD910, proportion of emphysema, proportion of fat, and average density of related blood vessels). The area under the receiver operating characteristic curve was used to evaluate the performance of the model in determining SPN risk of malignancy.ResultsThere was a statistical difference in the accuracy of CAD and conventional CT in diagnosing SPNs. According to the golden standard pathological diagnosis, the diagnostic accuracy of CAD (81%) was higher than that of conventional CT (63.7%) (P<0.05). Six variables (i.e., irregularity, the mean diameter, COPD910, the proportion of emphysema, the proportion of fat, and the vascular density) were identified using multivariable logistic regression to establish the diagnostic model for distinguish benign or malignant SPNs. The area under the receiver operating characteristic (ROC) curve (AUC) of the diagnostic model was 0.876 (95% CI: 0.8445–0.9076), and its sensitivity and specificity were 81.25% and 82.56% respectively.ConclusionsThe proposed diagnostic model, which comprises 6 radiomic factors, is accurate and effective at diagnosing benign or malignant SPNs.

Detection of circulating rare cells benefitted the diagnosis of malignant solitary pulmonary nodules.

Solitary pulmonary nodules (SPNs) are challenging in differentiating between benignancy and malignancy. Therefore, more effective non-invasive biomarkers are urgently needed. The purpose of this investigation was to examine whether circulating rare cells (CRCs) could facilitate the differentiation between benign and malignant SPNs as well as its sensitivity and specificity. 164 patients diagnosed with SPNs, 24 healthy volunteers, and 25 patients diagnosed with advanced-stage lung cancer were included. CT/PET-CT images, serum tumor markers, and biopsy results were collected. The CRCs were examined using subtraction enrichment and immunostaining-fluorescence in situ hybridization (SE-iFISH) and their relationship with malignant or benign SPNs was analyzed. The total CRC numbers from patients with malignant SPNs diagnosed by biopsy were significantly greater compared to those with benign SPNs (P < 0.0001), but not significantly different from patients with advanced lung cancer (P > 0.05). The total CRCs, with a cut-off value of 21.5 units, showed 67.6% sensitivity and 73.3% specificity [area under curve (AUC) 95% CI, 0.778 (0.666-0.889)] in discriminating benign and malignant SPNs and the triploid CRCs exhibited a high positive likelihood ratio of 8.4, which suggested that CRCs appeared to have a distinct advantage in discriminating benign and malignant SPNs compared to CT/PET-CT images and serum tumor markers and could be a potential screening indicator for lung cancer in the high-risk population. SE-iFISH could effectively detect CRCs including circulating tumor cells (CTCs) and circulating tumor-derived endothelial cells (CTECs) and the detection of CRCs could benefit the differentiation of patients with benign and malignant SPNs.

Clinical-radiological predictive model in differential diagnosis of small (\u2264\u200920\xa0mm) solitary pulmonary nodules

BackgroundThere is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs.Materials and methodsBetween January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified.ResultsIn the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = − 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828–0.913) than the other two models.ConclusionsWe established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.

Clinical Analysis of Video-Assisted Thoracoscopic Surgery for Resection of Solitary Pulmonary Nodules and Influencing Factors in the Diagnosis of Benign and Malignant Nodules.

Purpose This is a retrospective research comparing the clinical outcomes of single-hole versus multi-hole video-assisted thoracoscopic surgical (VATS) resection for solitary pulmonary nodules (SPN) and examining the factors influencing the diagnosis of benign and malignant pulmonary nodules. Method We collected the clinical data, surgical status, outcomes, and corresponding imaging features of 317 patients with SPN who were surgically resected by VATS and diagnosed as benign or malignant by pathology in our hospital from January 2019 to December 2021. Result Among the 317 patients, 124 (39.12%) underwent single-port VATS and 193 (60.88%) underwent multiple-hole VATS. All patients were grouped according to the different surgical methods, and their postoperative indicators were statistically analyzed. The results showed that neither the single-port VATS group nor the multi-port VATS group had any serious adverse events such as death during the perioperative period. The average operation time, intraoperative blood loss, drainage tube indwelling time, and postoperative hospital stay were significantly lower in the two groups. Statistics of postoperative pathological diagnosis showed that 98 cases (30.91%) of all nodules were benign nodules and 219 cases (69.09%) were malignant nodules, and a further single-multivariate analysis showed that age, nodule maximum diameter, lobular sign, burr sign, vascular cluster sign, and pleural depression sign were independent relevant factors for the diagnosis of benign and malignant nodules. Conclusion VATS is less invasive and has fewer complications and is of great clinical value for both diagnosis and treatment of benign and malignant SPN. Age, maximum nodal diameter, lobar sign, burr sign, vascular set sign, and pleural depression sign were independent correlates affecting the diagnosis of benign and malignant SPN, which reminds that great attention should be paid to patients who are older and have risk factors on imaging, and early and timely active treatment or close follow-up should be carried out.

Evaluation of dual‐energy and perfusion CT parameters for diagnosing solitary pulmonary nodules

BackgroundTo evaluate the correlation and accuracy of dual‐energy CT (DECT) (70/Sn150) and low‐dose volume perfusion CT (VPCT) parameters for the diagnosis of solitary pulmonary nodules (SPN).MethodsA total of 15 patients with benign SPN (mean age 56 ± 7 years) and 34 patients with malignant SPN and clinical indication for surgery (mean age 58 ± 6 years) were enrolled from July 2017 to September 2019 at a single institution. All the patients underwent low‐dose VPCT with a scan volume of 114 mm on the z‐axis and a venous phase enhancement DECT (70/150 Sn) scan just before surgery on the same day. All CT findings were studied in comparison with the pathological results after surgery. Perfusion and dual‐energy CT parameters such as blood flow (BF), blood volume (BV), mean transit time (MTT), flow extraction product (FED), pulmonary nodule enhancement peak (PPnod) and iodine concentration (IC) were evaluated as well as t‐test, chi‐square test, Pearson correlation analysis, and ROC curve analysis to determine the significance of study parameters.ResultsThe effective radiation dosage of the VPCT and DECT scans were 4.67 ± 0.26 mSv and 0.32 ± 0.10 mSv, respectively. Significant correlations were found between iodine concentration from DECT and VPCT parameters (r = 0.376–0.533, p < 0.05). The sensitivity and specificity of IC to differentiate the SPN were 86.67% and 72.73%, which was slightly lower than that of BV (94.44%, 73.33%), FED (88.89%, 80.00%) and PPnod (94.44%, 80.00%).ConclusionsVPCT scans have low radiation dosage achieved by shortening the z‐axis scan range for assessment of SPN. IC from DECT is significantly correlated with VPCT parameters, and VPCT parameters have better diagnostic performance for SPN than DECT parameters.

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule.

This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P < 0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.

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.