The Solitary Pulmonary Nodule

The role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography SUVMax in deciding on a computed tomography-guided lung biopsy in solid solitary pulmonary nodules

OBJECTIVE

The aim of this study was to calculate a useful cut-off point of the 18F-fluorodeoxyglucose positron emission tomography/computed tomography SUVMax value to decide on a computed tomography-guided percutaneous transthoracic needle biopsy for solitary pulmonary nodules of sizes between 11 and 20 mm.

METHODS

Between January 2015 and April 2020, patients with solitary pulmonary nodules who underwent computed tomography-guided percutaneous transthoracic needle biopsy were retrospectively reviewed, and those with solitary pulmonary nodules of 11-20 mm in diameter, who had undergone an 18F-fluorodeoxyglucose positron emission tomography/computed tomography examination before computed tomography-guided percutaneous transthoracic needle biopsy, were included in the study. A total of 76 patients who met the inclusion criteria were evaluated.

RESULTS

There was no distinguishing finding on the computed tomography examination (p>0.05). The SUVMax values of the malignant solid solitary pulmonary nodules were higher than the benign solitary pulmonary nodules (p<0.05).

CONCLUSION

The benign and malignant solid solitary pulmonary nodules between 11 and 20 mm have similar computed tomography features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography is a useful imaging technique for distinguishing benign and malignant solitary pulmonary nodules. Notably, 4.85 SUVMax value can be used to decide on a computed tomography-guided percutaneous transthoracic needle biopsy procedure in solid solitary pulmonary nodules between 11 and 20 mm with excellent sensitivity and moderate specificity rates.

Integrative multi-omics analysis of the microbiome and metabolome in bronchoalveolar lavage fluid from patients with early-stage lung cancer

Lung cancer is a significant health concern that poses a considerable threat to human health and quality of life. In order to enhance the prognosis of patients with lung cancer, we conducted a combined analysis of 16S rDNA gene sequencing of alveolar lavage fluid and LC-MS metabolomics research, with the objective of identifying biomarkers in patients with early-stage lung cancer presenting as SPN. A comparison of the benign nodule group and the early-stage lung cancer patients revealed that the phylum-level Bacteroidetes and the genus-level Chryseobacterium and Delftia were more abundant in the latter group. Additionally, the Fusobacteriales might serve as a predictive marker for the diagnosis of early-stage lung cancer. In the context of metabolomics, the early-stage lung cancer was found to be characterised by elevated levels of specific metabolites, including Alternariol, dTMP, Oxymatrine, Gedunin, PC 36:4. Conversely, reductions in other metabolites, such as LPC O-24:0, PC 18:2_18:3, PC 19:2_19:2, Cholecalciferol and T-2 Triol, were also observed. Correlation analyses demonstrated that alveolar lavage microorganisms were closely associated with differential metabolites. Specifically, reductions in Cholecalciferol were associated with a variety of high-abundance flora and involved in vitamin digestion and absorption pathways. Furthermore, reductions in cholecalciferol may serve as a robust predictor of early-stage lung cancer. These findings provide new predictive biomarkers for early-stage lung cancer manifested by SPN, which is clinically important and requires further study of the potential mechanisms of action and function of the targets.

Diagnostic performance of metagenomic next-generation sequencing based on alveolar lavage fluid in unexplained lung shadows

BACKGROUND

Unexplained lung shadows are challenging in respiratory medicine, with both infectious and non-infectious etiologies. Lung biopsy is definitive but invasive, prompting a need for non-invasive alternatives. Metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF) is emerging as a promising diagnostic tool.

METHODS

We retrospectively analyzed 105 patients with unexplained lung shadows, collecting general information, mNGS results from BALF, and clinical diagnosis. We evaluated mNGS's diagnostic performance by comparing with final diagnosis.

RESULTS

mNGS showed good diagnostic performance in differentiating infectious from non-infectious causes. The specificity and accuracy for bacteria and fungi exceeded 90%, while the sensitivity and precision for fungi were lower than for bacteria. Atypical pathogens were frequently identified, especially in mixed infections.

CONCLUSIONS

mNGS of BALF is efficient in diagnosing infectious and non-infectious causes of unexplained lung shadows. While effective for bacteria and fungi detection, the sensitivity and precision for fungi are lower.

Inconsistencies in predictive models based on exhaled volatile organic compounds for distinguishing between benign pulmonary nodules and lung cancer: a systematic review

BACKGROUND

There is a general rise in incidentally found pulmonary nodules (PNs) requiring follow-up due to increased CT use. Biopsy and repeated CT scan are the most useful methods for distinguishing between benign PNs and lung cancer, while they are either invasive or involves radiation exposure. Therefore, there has been increasing interest in the analysis of exhaled volatile organic compounds (VOCs) to distinguish between benign PNs and lung cancer because it's cheap, noninvasive, efficient, and easy-to-use. However, the exact value of breath analysis in this regard remains unclear.

METHODS

A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-oriented systematic search was performed to include studies that established exhaled VOC-based predictive models to distinguish between benign PNs and lung cancer and reported the exact VOCs used. Data regarding study characteristics, performance of the models, which predictors were incorporated, and methodologies for breath collection and analysis were independently extracted by two researchers. The exhaled VOCs incorporated into the predictive models were narratively synthesized, and those compounds that were reported in > 2 studies and reportedly exhibited consistent associations with lung cancer were considered key breath biomarkers. A quality assessment was independently performed by two researchers using both the Newcastle-Ottawa Scale (NOS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

A total of 11 articles reporting on 46 VOC-based predictive models were included. The majority relied solely on exhaled VOCs (n = 44), while two incorporated VOCs, demographical factors, and radiological signs. The variation in the sensitivity, specificity, and AUC indicators of the models that incorporated multiple factors was lower compared with those of the models that relied solely on exhaled VOCs. A total of 84 VOCs were incorporated. Of these, 2-butanone, 3-hydroxy-2-butanone, and 2-hydroxyacetaldehyde were identified as key predictors that had significantly higher concentrations in the exhaled breath samples of patients with lung cancer. Substantial heterogeneity was observed in terms of the modeling and validation methods used, as well as the approaches to breath collection and analysis. Many of the reports were missing certain key pieces of clinical and methodological information.

CONCLUSIONS

Although exhaled VOC-based models for predicting cancer risk might be a conceivable role as monitoring tools for PNs risk, there has been little overall change in the accuracy of these tests over time, and their role in routine clinical practice has not yet been established.

CLINICAL TRIAL NUMBER

PROSPERO registration number CRD42023381458.

Effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction strength level on the detection and characterization of pulmonary nodules in ultra-low-dose chest CT

OBJECTIVE

To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT).

MATERIALS AND METHODS

An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared.

RESULTS

Image noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP.

CONCLUSION

Detection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules.

Amide proton transfer-weighted imaging and stretch-exponential model DWI based 18F-FDG PET/MRI for differentiation of benign and malignant solitary pulmonary lesions

OBJECTIVES

To differentiate benign and malignant solitary pulmonary lesions (SPLs) by amide proton transfer-weighted imaging (APTWI), mono-exponential model DWI (MEM-DWI), stretched exponential model DWI (SEM-DWI), and 18F-FDG PET-derived parameters.

METHODS

A total of 120 SPLs patients underwent chest 18F-FDG PET/MRI were enrolled, including 84 in the training set (28 benign and 56 malignant) and 36 in the test set (13 benign and 23 malignant). MTRasym(3.5 ppm), ADC, DDC, α, SUVmax, MTV, and TLG were compared. The area under receiver-operator characteristic curve (AUC) was used to assess diagnostic efficacy. The Logistic regression analysis was used to identify independent predictors and establish prediction model.

RESULTS

SUVmax, MTV, TLG, α, and MTRasym(3.5 ppm) values were significantly lower and ADC, DDC values were significantly higher in benign SPLs than malignant SPLs (all P < 0.01). SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors. Within the training set, the prediction model based on these independent predictors demonstrated optimal diagnostic efficacy (AUC, 0.976; sensitivity, 94.64%; specificity, 92.86%), surpassing any single parameter with statistical significance. Similarly, within the test set, the prediction model exhibited optimal diagnostic efficacy. The calibration curves and DCA revealed that the prediction model not only had good consistency but was also able to provide a significant benefit to the related patients, both in the training and test sets.

CONCLUSION

The SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors for differentiation of benign and malignant SPLs, and the prediction model based on them had an optimal diagnostic efficacy.

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients' DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.

Preoperative pulmonary nodule localization: A comparison of hook wire and Lung-pro-guided surgical markers

In minimally invasive thoracoscopic surgery, for solitary pulmonary nodules (SPNs) far from the pleura, it is difficult to resected by only relying on imaging data, and effective preoperative localization can significantly improve the success rate of surgery. Therefore, preoperative localization is particularly important for accurate resection. Here, we compare the value of a novel Lung-pro-guided localization technique with Hook-wire localization in video-assisted thoracoscopic surgery.

METHOD

In this study, 70 patients who underwent CT-guided Hook-wire localization and Lung-pro guided surgical marker localization before VATS-based SPNs resection between May 2020 and March 2021 were analyzed, and the clinical efficacy and complication rate of the two groups were compared.

RESULT

Thirty-five patients underwent Lung-pro guided surgical marker localization, and 35 patients underwent CT-guided Hook-wire localization. The localization success rates were 94.3% and 88.6%, respectively (p = 0.673). Compared with the puncture group, the locating time in the Lung-pro group was significantly shorter (p = 0.000), and the wedge resection time was slightly shorter than that in the puncture group (P = 0.035). There were no significant differences in the success rate of localization, localization complications, intraoperative blood loss, postoperative hospital stay, and the number of staplers used.

CONCLUSION

The above studies show that the Lung-pro guided surgical marker localization and the CT-guided Hook-wire localization have shown good safety and effectiveness. However, the Lung-pro guided surgical marker localization may show more safety than the Hook-wire and can improve the patient's perioperative experience.

Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm

Background

At present, how to identify the benign or malignant nature of small (≤ 2 cm) solitary pulmonary nodules (SPN) are an urgent clinical challenge. This retrospective study aimed to develop a clinical prediction model combining clinical and radiological characteristics for assessing the probability of malignancy in SPNs measuring ≤ 2 cm.

Method

In this study, we included patients with SPNs measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to December 2021. Clinical features, preoperative biomarker results, and computed tomography characteristics were collected. The enrolled patients were randomized at a ratio of 7:3 into a training cohort of 775 and a validation cohort of 331. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. The receiver operating characteristic (ROC) curve was used to evaluate the identification ability of the model. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curve. The clinical utility of the nomogram was also assessed by decision curve analysis (DCA).

Result

A total of 1,106 patients were included in this study. Among them, the malignancy rate of SPNs was 85.08% (941/1,106). We finally identified the following six independent risk factors by logistic regression: age, carcinoembryonic antigen, nodule shape, calcification, maximum diameter, and consolidation-to-tumor ratio. The area under the ROC curve (AUC) for the training cohort was 0.764 (95% confidence interval [CI]: 0.714-0.814), and the AUC for the validation cohort was 0.729 (95% CI: 0.647-0.811), indicating that the prediction accuracy of nomogram was relatively good. The calibration curve of the predictive model also demonstrated a good calibration in both cohorts. DCA proved that the clinical prediction model was useful in clinical practice.

Conclusion

We developed and validated a predictive model and nomogram for estimating the probability of malignancy in SPNs measuring ≤ 2 cm. With the application of predictive models, thoracic surgeons can make more rational clinical decisions while avoiding overtreatment and wasting medical resources.

Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules

It has been proved that Raman spectral intensities could be used to diagnose lung cancer patients. However, the application of Raman spectroscopy in identifying the patients with pulmonary nodules was barely studied. In this study, we revealed that Raman spectra of serum samples from healthy participants and patients with benign and malignant pulmonary nodules were significantly different. A support vector machine (SVM) model was developed for the classification of Raman spectra with wave points, according to ANOVA test results. It got a good performance with a median area under the curve (AUC) of 0.89, when the SVM model was applied in discriminating benign from malignant individuals. Compared with three common clinical models, the SVM model showed a better discriminative ability and added more net benefits to participants, which were also excellent in the small-size nodules. Thus, the Raman spectroscopy could be a less-invasive and low-costly liquid biopsy.

Correlation between CT imaging characteristics and pathological diagnosis for subcentimeter pulmonary nodules

Background

Advances in chest computed tomography (CT) have resulted in more frequent detection of subcentimeter pulmonary nodules (SCPNs), some of which are non‐benign and may represent invasive lung cancer. The present study aimed to explore the correlation between pathological diagnosis and the CT imaging manifestations of SCPNs.

Methods

This retrospective study included patients who underwent pulmonary resection for SCPNs at Shandong Provincial Hospital in China. Lesions were divided into five categories according to their morphological characteristics on CT: cotton ball, solid‐filled with spiculation, solid‐filled with smooth edges, mixed‐density ground‐glass, and vacuolar. We further analyzed lesion size, enhancement patterns, vascular aggregation, and SCPN traversing. Chi‐square tests, Fisher's exact tests, and Welch's one‐way analysis of variance were used to examine the correlation between CT imaging characteristics and pathological type.

Results

There were statistically significant differences in the morphological distributions of SCPNs with different pathological types, including benign lesions and malignant lesions at different stages (p < 0.01). The morphological distributions of the four subtypes of invasive lung adenocarcinoma also exhibited significant differences (p < 0.01). In addition, size and enhancement patterns differed significantly among different pathological types of SCPNs.

Conclusion

Different pathological types of SCPNs exhibit significant differences based on their morphological category, size, and enhancement pattern on CT imaging. These CT characteristics may assist in the qualitative diagnosis of SCPNs.

The Cole Relaxation Frequency as a Parameter to Identify Cancer in Lung Tissue: Preliminary Animal and Ex Vivo Patient Studies

BACKGROUND

Lung cancer is the world's leading cause of cancer deaths, and diagnosis remains challenging. Lung cancer starts as small nodules; early and accurate diagnosis allows timely surgical resection of malignant nodules while avoiding unnecessary surgery in patients with benign nodules.

OBJECTIVE

The Cole relaxation frequency (CRF) is a derived electrical bioimpedance signature, which may be utilized to distinguish cancerous tissues from normal tissues.

METHODS

Human testing ex vivo was conducted with NoduleScan in freshly resected lung tissue from 30 volunteer patients undergoing resection for nonsmall cell lung cancer. The CRF of the tumor and the distant normal lung tissue relative to the tumor were compared to histopathology specimens to establish a potential algorithm for point-of-care diagnosis. For animal testing in vivo, 20 mice were implanted with xenograft human lung cancer tumor cells injected subcutaneously into the right flank of each mouse. Spectral impedance measurements were taken on the tumors on live animals transcutaneously and on the tumors after euthanasia. These CRF measurements were compared to healthy mouse lung tissue. For porcine lung testing ex vivo, porcine lungs were received with the trachea. After removal of the vocal box, a ventilator was attached to pressurize the lung and simulate breathing. At different locations of the lobes, the lung's surface was cut to produce a pocket that could accommodate tumors obtained from in vivo animal testing. The tumors were placed in the subsurface of the lung, and the electrode was placed on top of the lung surface directly over the tumor but with lung tissue between the tumor and the electrode. Spectral impedance measurements were taken when the lungs were in the deflated state, inflated state, and also during the inflation-deflation process to simulate breathing.

RESULTS

Among 60 specimens evaluated in 30 patients, NoduleScan allowed ready discrimination in patients with clear separation of CRF in tumor and distant normal tissue with a high degree of sensitivity (97%) and specificity (87%). In the 25 xenograft small animal model specimens measured, the CRF aligns with the separation observed in the human in vivo measurements. The CRF was successfully measured of tumors implanted into ex vivo porcine lungs, and CRF measurements aligned with previous tests for pressurized and unpressurized lungs.

CONCLUSIONS

As previously shown in breast tissue, CRF in the range of 1kHz-10MHz was able to distinguish nonsmall cell lung cancer versus normal tissue. Further, as evidenced by in vivo small animal studies, perfused tumors have the same CRF signature as shown in breast tissue and human ex vivo testing. Inflation and deflation of the lung have no effect on the CRF signature. With additional development, CRF derived from spectral impedance measurements may permit point-of-care diagnosis guiding surgical resection.

The Value of Computed Tomography-Guided Percutaneous Lung Biopsy Combined With Rapid On-Site Evaluation in Diagnosis of Peripheral Pulmonary Nodules

Objective: To investigate the value of computed tomography-guided percutaneous lung biopsy (CT-PLB) combined with rapid on-site evaluation (ROSE) in the diagnosis of peripheral pulmonary lesions (PPLs). Methods: A total of 108 patients who diagnosed with PPLs by chest CT examination were prospectively collected and randomly divided into ROSE group (n = 56) and No-ROSE group (n = 52). Both groups received CT-PLB and pathological examination. The smear submitted for ROSE was stained using Diff Quik dye. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), number of punctures, puncture time and incidence of complications were compared between the two groups. Results: The accuracy, sensitivity, specificity, PPV, and NPV of the ROSE group were 89.29%, 87.50%, 91.67%, 93.33%, and 84.62%, respectively. The number of punctures in the ROSE group was significantly lower than that in the No-ROSE group (P < .05). The incidence of pneumothorax and hemoptysis in the ROSE group were lower than those in the No-ROSE group, but there was no statistical difference between the two groups (P > .05). ROSE has good concordance with routine pathological examination in the diagnosis of unidentified PPLs (Kappa = 0.786, P < .01). Conclusions: CT-PLB combined with ROSE is a safe and effective method for the diagnosis of PPLs.

The Prevalence of Benign Pathology Following Major Pulmonary Resection for Suspected Malignancy

BACKGROUND

In the era of lung cancer screening with low-dose computed tomography, there is concern that high false-positive rates may lead to an increase in nontherapeutic lung resection. The aim of this study is to determine the current rate of major pulmonary resection for ultimately benign pathology.

MATERIALS AND METHODS

A single-institution, retrospective analysis of all patients > 18 y who underwent major pulmonary resection between 2013 and 2018 for suspected malignancy and had benign final pathology was performed.

RESULTS

Of 394 major pulmonary resections performed for known or presumed malignancy, 10 (2.5%) were benign. Of these 10, the mean age was 61.1 y (SD 14.6). Most were current or former smokers (60%). Ninety percent underwent a fluorodeoxyglucose positron emission tomography scan. Median nodule size was 27 mm (IQR 21-35) and most were in the right middle lobe (50%). Preoperative biopsy was performed in four (40%) but were nondiagnostic. Video-assisted thoracoscopic lobectomy (70%) was the most common surgical approach. Final pathology revealed three (30%) infectious, three (30%) inflammatory, two (20%) fibrotic, and two (20%) benign neoplastic nodules. Two (20%) patients had perioperative complications, both of which were prolonged air leaks, one (10%) patient was readmitted within 30 d, and there was no mortality.

CONCLUSIONS

A small percentage of patients (2.5% in our series) may undergo major pulmonary resection for unexpectedly benign pathology. Knowledge of this rate is useful to inform shared decision-making models between surgeons and patients and evaluation of thoracic surgery program performance.

Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules

BACKGROUND

There 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 METHODS

Between 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.

RESULTS

In 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.

CONCLUSIONS

We 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.

Use of PET/CT to aid clinical decision-making in cases of solitary pulmonary nodule: a probabilistic approach

Objective

To determine the frequency with which ¹⁸F-FDG-PET/CT findings change the probability of malignancy classification of solitary pulmonary nodules.

Materials and Methods

This was a retrospective analysis of all ¹⁸F-FDG-PET/CT examinations performed for the investigation of a solitary pulmonary nodule between May 2016 and May 2017. We reviewed medical records and PET/CT images to collect the data necessary to calculate the pre-test probability of malignancy using the Swensen model and the Herder model. The probability of malignancy was classified as low if < 5%, intermediate if 5-65%, and high if > 65%. Cases classified as intermediate in the Swensen model were reclassified by the Herder model.

Results

We reviewed the records for 33 patients, of whom 17 (51.5%) were male. The mean age was 68.63 ± 12.20 years. According to the Swensen model, the probability of malignancy was intermediate in 23 cases (69.7%). Among those, the application of the Herder model resulted in the probability of malignancy being reclassified as low in 6 (26.1%) and as high in 8 (34.8%).

Conclusion

¹⁸F-FDG-PET/CT was able to modify the probability of malignancy classification of a solitary pulmonary nodule in more than 50% of the cases evaluated.

Diagnostic Accuracy of Maximum Intensity Projection in Diagnosis of Malignant Pulmonary Nodules

Introduction Pulmonary nodules are frequently encountered during chest imaging, and its evaluation is usually done by chest radiograph and computed tomography (CT) scan of chest. High resolution of multidetector CT (MDCT) has improved the nodule detection. Post processing techniques such as maximum intensity projection (MIP) can further improve the sensitivity of MDCT for nodule detection. Failure to diagnose malignancy in pulmonary nodules can delay the treatment. Therefore, the aim of this study was to determine the diagnostic accuracy of MIP in the diagnosis of malignant pulmonary nodules taking histopathology findings as gold standard. Materials and methods A retrospective cross-sectional study was conducted at Dow Institute of Radiology, Dow University of Health Sciences, from 1 December 2018 till 30 June 2019. Both male and female patients aged 18 years and above who underwent CT scan of chest with suspicion of pulmonary nodules were included. Patients already diagnosed with malignant pulmonary nodules and presenting for follow-up were excluded. Contrast-enhanced CT chest was performed on a multi-slice scanner. MIP reconstruction and evaluation was performed on the workstation. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of MIP were calculated taking histopathology findings as gold standard. Results A total of 202 patients were included in this study. The mean age of the patients was 55.87 ± 13.08 years. A total of 103 patients (51.0%) were males and 99 patients (49.0%) were females. There were 131 (64.9%) nodules with smooth margins and 71 (35.1%) nodules with irregular margins. The mean size of nodule was 3.1 ± 0.7 cm. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of MIP in diagnosing malignant pulmonary nodules were found to be 85.82%, 82.35%, 90.55%, 74.67%, and 84.65%, respectively, taking histopathology findings as gold standard. The nodules >3 cm in size had a higher sensitivity for diagnosing malignant pulmonary nodules. Smooth margin nodule had high sensitivity, specificity, and diagnostic accuracy for diagnosing malignant pulmonary nodules. Conclusion MIP images have high sensitivity, specificity, and diagnostic accuracy in the diagnosis of malignant pulmonary nodules. The utilization of MIP images can aid in the detection of malignant pulmonary nodules and help in formulating early treatment strategies for the patients. Other post processing techniques such as volume rendering and computer-aided detection can help in further improving patient care.

Computed tomography-guided percutaneous transthoracic needle biopsy for solitary pulmonary nodules in diameter less than 20 mm

To evaluate the diagnostic value of computed tomography (CT)-guided percutaneous lung biopsy for solitary pulmonary nodules (SPN) < 20 mm.A total of 248 patients who were diagnosed a SPN of < 20 mm underwent CT-guided percutaneous transthoracic needle biopsy were reviewed.Specimens of 248 patients were obtained successfully. Around 174 cases were proved to be malignancies and 74 cases of benign lesions by biopsy. About 178 malignancies (71.8%) and 70 benign lesions were proved by surgery and clinical course. The diagnostic accuracy was 96.8%. The diagnostic accuracy of large nodules group (>10 and < 20 mm) was 99.3%, higher than 93.5% of small nodules group (≤10 mm) with statistical significance. The incidence of phenmothorax and hemorrhage was 16.1% and 6.8%, respectively. No death-related complications happened. The incidence of phenmothorax was related to puncture times (P = .013) and the length of puncture needle in lung tissues (P = .019).CT-guided percutaneous lung biopsy for SPN of < 20 mm is an efficient and safe diagnostic method.

Solitary Pulmonary Lesion in Patients with History of Malignancy: Primary Lung Cancer or Metastatic Cancer?

BACKGROUND

Defining the status of solitary pulmonary lesion (SPL) in patients with history of malignancy is important because primary lung cancer (PLC) or intrapulmonary metastasis might indicate different surgical strategies. The aim of this study is to identify factors related to the status of these lesions and construct a clinical model to estimate the pretest probability of PLC.

METHODS

From January 2005 to January 2016, 104 patients with previous malignancy and suitable for surgery were retrospectively studied. Univariate and multivariate analyses were performed to identify possible factors related to SPLs. A nomogram was constructed to differentiate PLC from intrapulmonary metastasis.

RESULTS

Ninety-seven (93.3%) patients were diagnosed as malignant postoperatively, including 61 patients with intrapulmonary metastasis and 36 patients with PLC. Multivariate analysis showed that site of primary tumor [head and neck squamous cell cancer: odds ratio (OR) = 28.509, P = 0.006; genitourinary cancer: OR = 23.928, P = 0.012], negative lymph node status of primary tumor (OR = 3.154, P = 0.038), spiculation of SPL (OR = 3.972, P = 0.022), and central location of SPL (OR = 4.679, P = 0.026) were four independent factors differentiating PLC from intrapulmonary metastasis. All of these were included in the nomogram. The C-index of the nomogram for predicting probability was 0.82.

CONCLUSIONS

Incidence of malignant SPLs was fairly high in patients with history of malignancy. A nomogram including site and lymph node status of primary tumor, and spiculation and location of SPL might be a good tool for differentiating PLC from intrapulmonary metastasis preoperatively and guiding treatment.

Integrated Metabolomics and Proteomics Highlight Altered Nicotinamide- and Polyamine Pathways in Lung Adenocarcinoma

Lung cancer is the leading cause of cancer mortality in the United States with non-small cell lung cancer (NSCLC) adenocarcinoma being the most common histological type. Early perturbations in cellular metabolism are a hallmark of cancer, but the extent of these changes in early stage lung adenocarcinoma remains largely unknown. In the current study, an integrated metabolomics and proteomics approach was utilized to characterize the biochemical and molecular alterations between malignant and matched control tissue from 27 subjects diagnosed with early stage lung adenocarcinoma. Differential analysis identified 71 metabolites and 1102 proteins that delineated tumor from control tissue. Integrated results indicated four major metabolic changes in early stage adenocarcinoma: (1) increased glycosylation and glutaminolysis; (2) elevated Nrf2 activation; (3) increase in nicotinic and nicotinamide salvaging pathways; and (4) elevated polyamine biosynthesis linked to differential regulation of the SAM/nicotinamide methyl-donor pathway. Genomic data from publicly available databases were included to strengthen proteomic findings. Our findings provide insight into the biochemical and molecular biological reprogramming that may accompanies early stage lung tumorigenesis and highlight potential therapeutic targets.

[Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules]

BACKGROUND

The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs.

METHODS

We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model.

RESULTS

Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%.

CONCLUSIONS

Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.

Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography

The aim of the present study was to analyze the value of applying dual-source 64-layer spiral computed tomography (CT) in the differential diagnosis of solitary pulmonary nodules (SPNs). Mediastinal windows from 45 cases were selected to study SPNs (maximum diameter, ≤3 cm), and the pathological nature of lesions was determined by clinical and pathological diagnosis. Conventional 64-layer spiral CT scanning, local enhancement and 3D recombination technologies were used to determine the occurrence rate, lesion diameter, degree of enhancement, lobular sign, spicule sign, pleural indentation sign, vessel convergence sign and bronchus sign. The final diagnoses indicated 34 cases of malignant SPNs (75.6%) and 11 benign cases (24.4%). When the nodule diameter in the malignant group was compared with that of the benign group, the difference was not statistically significant (P>0.05). Nodules in the malignant group showed inhomogeneous enhancement while nodules in the benign group showed homogeneous enhancement. The enhanced CT values in the malignant group were higher than those in the benign group, and the difference was statistically significant (P<0.05). The proportion of nodules with lobular sign in the malignant group was significantly higher than that in the benign group (P<0.05). The proportion of nodules with calcification, vessel convergence sign and bronchus sign in the malignant group were significantly higher than those in the benign group, and the differences were statistically significant (P<0.05). A comparison of vacuole sign, pleural indentation sign, spiculate protuberance and fat occurrence between the two groups yielded no statistically significant differences (P>0.05). The sensitivity of CT enhancement was 85.6%, specificity was 79.6%, positive predicated value was 92.3%, and the negative predicted value was 85.2%. In conclusion, SPNs diagnosed by CT enhancement manifested with enhancement degree, lobular sign, calcification, vessel convergence sign and bronchus sign with high diagnostic accuracy.

Bronchoscopy in lung cancer: navigational modalities and their clinical use

INTRODUCTION

More than 150,000 solitary pulmonary nodules are reported each year in the United States. As the popularity of low dose CT scan use for screening grows, these numbers are likely to increase substantially over the coming years. More than 10 million CT scans of the chest were performed in the United States alone in 2012, highlighting the potential for this clinical scenario. Detecting a nodule on either a chest radiograph or a CT scan frequently leads to a cascade of further investigations to establish a definite diagnosis. Thoracic surgery is frequently performed to obtain a tissue diagnosis in these cases, however, for what often turns out to be a benign abnormality.

AREAS COVERED

During the last decade, several new technologies have been introduced to the field of bronchoscopy. As experience with these technologies has increased, the body of published literature has grown. Expert commentary: In this review, we assess the most important and interesting articles in the field. The value and the limitations of the various options will be discussed based on the actual evidence.