Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules.

Background: This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs). Method: We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA). Result: The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively. Conclusion: Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.

Artificial intelligence's contribution to early pulmonary lesion detection in chest X-rays: insights from two retrospective studies on a Czech population.

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI's role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions. We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists. In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.

Integrating Machine Learning in Clinical Practice for Characterizing the Malignancy of Solitary Pulmonary Nodules in PET/CT Screening.

The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules' (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital of Patras, in Greece. A dataset comprising 456 SPN characteristics extracted from CT scans, the SUVmax score from the PET examination, and the ultimate outcome (benign/malignant), determined by patient follow-up or biopsy, was used to build the ML classifier. Two medical experts provided their malignancy likelihood scores, taking into account the patient's clinical condition and without prior knowledge of the true label of the SPN. Incorporating human assessments into ML model training improved diagnostic efficiency by approximately 3%, highlighting the synergistic role of human judgment alongside ML. Under the latter setup, the ML model had an accuracy score of 95.39% (CI 95%: 95.29-95.49%). While ML exhibited swings in probability scores, human readers excelled in discerning ambiguous cases. ML outperformed the best human reader in challenging instances, particularly in SPNs with ambiguous probability grades, showcasing its utility in diagnostic grey zones. The best human reader reached an accuracy of 80% in the grey zone, whilst ML exhibited 89%. The findings underline the collaborative potential of ML and human expertise in enhancing SPN characterization accuracy and confidence, especially in cases where diagnostic certainty is elusive. This study contributes to understanding how integrating ML and human judgement can optimize SPN diagnostic outcomes, ultimately advancing clinical decision-making in PET/CT screenings.

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

BACKGROUND: Some solitary pulmonary nodules (SPNs) as early manifestations of lung cancer, it is difficult to determine its nature, which brings great trouble to clinical diagnosis and treatment. Radiomics can deeply explore the essence of images and provide clinical decision support for clinicians. The purpose of our study was to explore the effect of positron emission tomography (PET) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose integrated with computed tomography (CT; 18F-FDG-PET/CT) combined with radiomics for predicting probability of malignancy of SPNs. METHODS: We retrospectively enrolled 190 patients with SPNs confirmed by pathology from January 2013 to December 2019 in our hospital. SPNs were benign in 69 patients and malignant in 121 patients. Patients were randomly divided into a training or testing group at a ratio of 7:3. Three-dimensional regions of interest (ROIs) were manually outlined on PET and CT images, and radiomics features were extracted. Synthetic minority oversampling technique (SMOTE) method was used to balance benign and malignant samples to a ratio of 1:1. In the training group, least absolute shrinkage and selection operator (LASSO) regression analyses and Spearman correlation analyses were used to select the strongest radiomics features. Three models including PET model, CT model, and joint model were constructed using multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were plotted to evaluate diagnostic efficiency, calibration degree, and clinical usefulness of all models in training and testing groups. RESULTS: The estimative effectiveness of the joint model was superior to the CT or PET model alone in the training and testing groups. For the joint model, CT model, and PET model, area under the ROC curve was 0.929, 0.819, 0.833 in the training group, and 0.844, 0.759, 0.748 in the testing group, respectively. Calibration and decision curves showed good fit and clinical usefulness for the joint model in both training and testing groups. CONCLUSION: Radiomics models constructed by combining PET and CT radiomics features are valuable for distinguishing benign and malignant SPNs. The combined effect is superior to qualitative diagnoses with CT or PET radiomics models alone.

Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm.

Objective: In this study, we aimed to utilize computed tomography (CT)-derived radiomics and various machine learning approaches to differentiate between invasive mucinous adenocarcinoma (IMA) and invasive non-mucinous adenocarcinoma (INMA) preoperatively in solitary pulmonary nodules (SPN) ≤3 cm. Methods: A total of 538 patients with SPNs measuring ≤3 cm were enrolled, categorized into either the IMA group (n = 50) or INMA group (n = 488) based on postoperative pathology. Radiomic features were extracted from non-contrast-enhanced CT scans and identified using the least absolute shrinkage and selection operator (LASSO) algorithm. In constructing radiomics-based models, logistic regression, support vector machines, classification and regression trees, and k-nearest neighbors were employed. Additionally, a clinical model was developed, focusing on CT radiological features. Subsequently, this clinical model was integrated with the most effective radiomic model to create a combined model. Performance assessments of these models were conducted, utilizing metrics such as the area under the receiver operating characteristic curve (AUC), DeLong's test, net reclassification index (NRI), and integrated discrimination improvement (IDI). Results: The support vector machine approach showed superior predictive efficiency, with AUCs of 0.829 and 0.846 in the training and test cohorts, respectively. The clinical model had AUCs of 0.760 and 0.777 in the corresponding cohorts. The combined model had AUCs of 0.847 and 0.857 in the corresponding cohorts. Furthermore, compared to the radiomic model, the combined model significantly improved performance in both the training (DeLong test P = 0.045, NRI 0.206, IDI 0.024) and test cohorts (P = 0.029, NRI 0.125, IDI 0.032), as well as compared to the clinical model in both the training (P = 0.01, NRI 0.310, IDI 0.09) and test cohorts (P = 0.047, NRI 0.382, IDI 0.085). Conclusion: the combined model exhibited excellent performance in distinguishing between IMA and INMA in SPNs ≤3 cm.

Does obstructive sleep apnea-induced intermittent hypoxia increase the incidence of solitary pulmonary nodules, thyroid nodules, and other disorders? A retrospective study based on 750 cardiovascular disease patients.

BACKGROUND: Obstructive sleep apnea (OSA) has been shown to be an important risk factor for cardiovascular disease (CVD), and intermittent hypoxia is an important pathogenetic factor for it. In the clinic, it was found that most CVD patients combined with OSA were also combined with solitary pulmonary nodules (SPN) or thyroid nodules (TN). Are these disorders related to intermittent hypoxia? One study showed that intermittent hypoxia is a pathogenic factor for lung cancer in mice, but there have been no clinical reports. So we conducted a retrospective study to explore whether intermittent hypoxia caused by OSA increases the incidence of SPN, TN, and other disorders. METHODS: We selected 750 patients with cardiovascular disease (CVD), who were divided into the control group and the OSA group according to the result of portable sleep monitoring. Retrospectively analyzed the comorbidities that patients with OSA are prone to and explored the correlation between OSA and those comorbidities. RESULTS: The incidence of SPN, TN, cervical spondylosis, and carotid-artery plaques was higher in the OSA group than in the control group. These diseases are significantly associated with OSA (p < 0.05), and their incidence increased with an elevated apnea-hypopnea index. After excluding interference from age, gender, BMI, smoking history, history of lung disease, and history of tumors, OSA showed a significant correlation with SPN. After excluding age, gender, BMI, and thyroid disease, OSA was associated with TN. Patients with comorbidities have lower nocturnal oxygen saturation and more extended periods of apnea. Logistic multiple regression results revealed that male, advanced age, obesity, CS, and nasal septum deviation were independent risk factors for OSA. CONCLUSIONS: Patients combined with OSA may further develop more comorbidities, such as SPN, TN, and carotid-artery plaques. It may be related to intermittent hypoxia caused by OSA.

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

OBJECTIVE: The 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. METHODS: A 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. RESULTS: Of 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. CONCLUSIONS: Decreased levels of sex hormones in females were associated with malignant pulmonary nodules, suggesting that they can contribute to the diagnosis of lung cancer.

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.

Value of MSCT perfusion imaging parameters in the differential diagnosis of benign and malignant solitary pulmonary nodules / 沈阳医学院学报

Objective:To investigate the diagnostic value of multi-slice spiral CT(MSCT)perfusion imaging parameters in the differential diagnosis of benign and malignant solitary pulmonary nodules(SPN).Methods:A total of 80 patients with SPN admitted to our hospital from Oct 2021 to Oct 2022 were selected.All patients underwent MSCT perfusion imaging and pathological examination after admission.According to the histopathological examination results,the patients were divided into benign nodule group and malignant nodule group.MSCT perfusion imaging parameters(blood volume,mean transit time,blood flow,surface permeability coefficient)of the two groups were compared.Receiver operating characteristic(ROC)curve was used to analyze the value of MSCT perfusion imaging parameters in the differential diagnosis of benign and malignant SPN.Results:Among the 80 patients with SPN,47 were diagnosed as malignant nodules and 33 as benign nodules by pathological examination.There was no significant difference in mean transit time between 2 groups(P＞0.05).The blood volume,blood flow and surface permeability coefficient in malignant nodule group were higher than those of benign nodule group(P＜0.05).The results of ROC curve showed that the area under the curve(AUC)of blood volume,blood flow and surface permeability coefficient separately and in combination were 0.823(95% CI:0.721-0.926),0.855(95% CI:0.761-0.949),0.850(95% CI:0.752-0.948)and 0.963(95% CI:0.924-1.000)for the diagnosis of benign and malignant SPN,all of which had certain diagnostic value.When blood volume,blood flow and surface permeability coefficient were 4.405 ml/100 g,51.325 ml/(min·100 g)and 21.115 ml/(min·100 g),respectively,the best diagnostic efficiency could be obtained,and the combined diagnosis value was higher.Conclusion:The combination of blood volume,blood flow and surface permeability coefficient of MSCT perfusion imaging parameters have high value in the differential diagnosis of benign and malignant SPN,which can provide effective basis for the early diagnosis and treatment of benign and malignant SPN.

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.

Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules.

Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.

The effect of combining different sampling tools on the performance of electromagnetic navigational bronchoscopy for the evaluation of peripheral lung lesions and factors associated with its diagnostic yield.

BACKGROUND: We assessed the performance of Electromagnetic navigational bronchoscopy (ENB) as a standalone diagnostic technique and the performance of different sampling tools used during the procedure. METHODS: We recruited 160 consecutive patients who underwent ENB for peripheral lung lesions (PLL) at a tertiary care centre. The diagnostic performance of ENB and sampling tools was assessed using a logistic regression model and a ROC-curve in which the dependent variable was diagnostic success. A multivariate model was built to predict diagnostic success before performing ENB to select the best candidates for the procedure. RESULTS: Most patients with PLLs in the study were male (65%), with a mean age of 67.9 years. The yield was 66% when the most common techniques were used together as suction catheter + transbronchial biopsy forceps (TBBx) + bronchoalveolar lavage + bronchial washing (p < 0.001) and increased to 69% when transbronchial needle aspiration (TBNA) and cytology brush were added (p < 0.001). Adding diagnostic techniques such as TBBx and TBNA resulted in an increase in diagnostic performance, with a statistically significant trend (p = 0.002). The logistic model area-under the ROC-curve for diagnostic success during ENB was 0.83 (95% CI:0.75-0.90; p < 0.001), and a logit value ≥ 0.12 was associated with ≥ 50% probability of diagnostic success. CONCLUSIONS: ENB, as a stand-alone diagnostic tool for the evaluation of PLLs when performed by experienced operators using a multi-modality technique, has a good diagnostic yield. The probability of having a diagnostic ENB could be assessed using the proposed model.

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.

The Safety of CT-Guided Percutaneous Lung Biopsy in Elderly Patients With Solitary Pulmonary Nodules.

OBJECTIVE: Computed tomography (CT)-guided percutaneous lung biopsy is an effective diagnostic procedure for patients with solitary pulmonary nodules (SPN). The aim of this study is to evaluate the safety of this procedure for elderly patients with SPN. METHODS: A total of 125 patients with SPN who received a CT-guided percutaneous lung biopsy were retrospectively analyzed. Patients were divided into elderly (age 65 and above) and non-elderly groups. The patients' characteristics and procedure-related complications were compared between the two groups. RESULTS: The elderly and non-elderly groups included 74 and 51 patients, respectively. The success rate of a CT-guided percutaneous lung biopsy was 100%. The diagnosis rate of lung cancer in the elderly group was significantly higher than that in the non-elderly group (83.78% vs. 64.70%, p = 0.014). The incidence of pulmonary hemorrhage after lung biopsy in the elderly group (44, 59.45%) was significantly higher than that in the non-elderly group (21, 41.17%, p = 0.044), and moderate hemorrhage was the main contributor. The incidence rate of pneumothorax in the elderly group numerically increased, but the difference did not reach statistical significance. CONCLUSION:  Computed tomography-guided percutaneous lung biopsy was an efficient procedure for diagnosing SPN in elderly patients. Although complication rates were relatively higher in elderly patients, the safety of this procedure was acceptable.

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.

Background: 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. Methods: 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. Results: 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). Conclusions: Our study suggests that quantitative parameters derived from whole-volume spectral CT may be useful to improve discrimination of SPNs.

Diagnostic yield of radial probe endobronchial ultrasonography-guided transbronchial biopsy without fluoroscopy in peripheral pulmonary lesions: A systematic review and meta-analysis.

PURPOSE: Although radial probe endobronchial ultrasound (R-EBUS) has been used to investigate peripheral pulmonary lesions (PPLs), its diagnostic performance without fluoroscopy remains unclear. We sought to determine the diagnostic yield of R-EBUS-guided transbronchial biopsy (TBB) without fluoroscopy. METHODS: We performed a systematic literature review using Pubmed, Embase, and the Cochrane Central Register. Then, we performed a proportional meta-analysis to determine the diagnostic yield of this modality. Subgroup and meta-regression analyses were used to identify factors affecting the performance of R-EBUS-guided TBB without fluoroscopy. RESULTS: We identified 31 studies consisting of a total of 6491 patients. Pooled overall diagnostic yield of R-EBUS-guided TBB without fluoroscopy was 0.70 (95% confidence interval [CI], 0.67-0.74). There was significant heterogeneity across studies (I2  = 89.45%, p < 0.001). In subgroup and meta-regression analyses, air bronchus sign on chest computed tomography scans, larger size PPLs, probe location within lesions, and heterogeneous echogenicity were associated with significantly higher diagnostic yield. Diagnostic yield from the upper lobe was statistically lower than that from the middle and lower lobes. Pooled pneumothorax rate was 0.01 (95% CI, 0.01-0.01, I2  = 63.51%, p < 0.001). CONCLUSIONS: R-EBUS-guided TBB without fluoroscopy appears to be a relatively useful tool with a low pneumothorax rate for the diagnosis of PPLs. Factors mentioned above may affect the diagnostic yield of this tool. Because of substantial between-study heterogeneity, our results should be interpreted with caution.

Not all Nodules Are Cancer and not all Fungal Infections Are Aspergillus.

Pithomyces, a dematiaceous fungus, is a common colonizer of dead leaves and stems of many different plants and is associated with facial eczema in some animals. We report a case of invasive fungal pulmonary disease by Pithomyces chartarum in a healthy, nonimmunocompromised patient. We aim to demonstrate our diagnostic and therapeutic approach and focus on the major challenges arising from the lack of scientific evidence regarding infection by this fungus in humans.

Pithomyces, um fungo demáceo, é um colonizador comum de folhas e caules de diferentes plantas e está associado a eczema facial em alguns animais. Neste trabalho, descrevemos um caso de infeção fúngica invasiva pelo fungo Pithomyces chartarum, numa mulher não imunocomprometida. O nosso objetivo é descrever a abordagem diagnóstica e terapêutica deste caso, realçando os principais desafios que surgem devido à falta de evidência científica relativamente à infeção deste fungo em humanos.

Low-dose CT-guided Hookwire puncture for localization of lung nodules in patients with body mass index≤22 kg/m2 / 中国介入影像与治疗学

Objective To explore the feasibility of low-dose CT-guided Hookwire localization of lung nodules in patients with body mass index(BMI)≤22 kg/m2.Methods Totally 53 BMI≤22 kg/m2 patients with 66 lung nodules were prospectively enrolled,CT-guided Hookwire localization of lung nodules were performed.According to the dose of CT scanning,the patients were divided into conventional dose group(group A,25 patients with 32 lung nodules)or low dose group(group B,28 patients with 34 lung nodules),while previous conventional dose CT scanning data of patients in group B were taken as control(group C).The image quality of group A and group B were scored using a 5-point scale,and the displaying of lung nodules in group B and group C were scored using a 3-point scale.The basic information of patients,lung nodules location,type,CT value,the maximum diameter,image quality score,puncture times,complications,as well as volume CT dose index(CTDIvol),dose-length product(DLP)and effective dose(ED)were compared between group A and B,so were score of lung nodules display,the maximum diameter and CT value of nodules between group B and C.Results No significant difference of basic information,the location,type,CT value,the maximum diameter,puncture times of lung nodules nor complications was found between group A and B(all P＞0.05).The quality score of group B(4[3,4])was lower than that of group A(5[4,5])(P＜0.05)but all greater than 3 and met the needs of puncturing.CTDIvol,DLP and ED of group B were lower than those of group A(all P＜0.05).There was no significant difference of the maximum diameter,CT value nor score of lung nodules display between group B and C(all P＞0.05).Conclusion Low-dose CT-guided Hookwire localization of lung nodules was feasible in patients with BMI≤22 kg/m2.

Comparison of qualitative diagnostic value of 3 methods based on 18F-FDG PET-CT for solitary pulmonary nodules / 肿瘤研究与临床

Objective:To compare the value of 3 methods (threshold method, visual method and CT method) based on 18F-FDG PET-CT for qualitative diagnosis of solitary pulmonary nodules (SPN). Methods:The clinical characteristics, pathological results, CT signs and maximum standardized uptake value (SUV max) of 226 SPN patients who underwent 18F-FDG PET-CT and met lung imaging reporting and data system (Lung-RADS1.1) grading criteria grade 2-4 in Shanxi Province Cancer Hospital from January 2015 to January 2019 were retrospectively analyzed, and the diagnostic efficacy of threshold method (according to SUV max), visual method [according to the degree of fluorodeoxyglucose (FDG) uptake in the mediastinum or liver blood pool] and CT method (according to Lung-RADS1.1 grading criteria) for SPN were compared by using receiver operating characteristic (ROC) curve. The highest diagnostic accuracy of CT method and the highest diagnostic accuracy of threshold method and visual method were combined as PET-CT comprehensive diagnosis method, and the diagnostic efficiency of which was compared with the other methods. Results:Among the 226 patients with SPN, 174 cases were malignant and 52 cases were benign in pathology. ROC curve analysis showed that the area under the curve (AUC) of CT method 2 (defined Lung-RADS1.1 grade 4A and below as benign, grade 4B and above as malignant) in CT methods for qualitative diagnosis of SPN was 0.622, the sensitivity was 87.93%, and the accuracy was 76.11%, the diagnostic efficiency was higher than method 2. The AUC of the threshold method 1 (defined solid nodules that SUV max≥2.5 and ground-glass nodules that SUV max≥1.14 as malignant, the others as benign) in threshold methods for qualitative diagnosis of SPN was 0.675, the sensitivity was 85.06%, and the accuracy was 76.99%, the diagnostic efficiency was higher than other methods in the threshold methods and visual methods. The AUC of PET-CT comprehensive diagnosis method (combination of CT method 2 and threshold method 1) for qualitative diagnosis of SPN was 0.652, the sensitivity was 97.70%, and the accuracy was 82.74%, the diagnostic efficiency was higher than other methods. Conclusions:There is no significant difference among threshold method, visual method and CT method based on 18F-FDG PET-CT in qualitative diagnosis of SPN. The diagnostic efficiency of combining CT method with threshold method is significantly improved.

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.