Diagnosis Of Pulmonary Nodules Research Articles

Efficient diagnosis of benign and malignant pulmonary nodules based on nano-zero-valent iron enhanced serum metabolic fingerprinting

Efficient diagnosis of benign and malignant pulmonary nodules based on nano-zero-valent iron enhanced serum metabolic fingerprinting

Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.

The aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging. A data set of Centers 1 (training set: 2633; internal testing set: 1129); Center 2 and Center 3 (external testing set: 218) of patients with pulmonary nodule cases was retrospectively collected. Handcrafted features were extracted from noncontrast chest CT scans by three senior radiologists. A total of 22 clinically handcrafted parameters (age, gender, L-RADS, and PNI-GARS etal.) were used to construct machine learning models (random forest, gradient boosting, and explainable boosting) for the classification of preoperative pulmonary nodules, and the parameters of the model were adjusted to achieve optimal performance. To evaluate the prediction capacity of each model. Both 5-fold cross-validation and 10-fold cross-validation were used to test the robustness of the models. The explainable boosting model had the best performance on our constructed data. The model achieves an accuracy of 89.9%, a precision of 97.48%, a specificity of 89.5%, a sensitivity of 91.1%, and an AUC of 90.3%. In human-machine comparison, the AUC of machine learning models (90.4%, 95% CI: 85.5%-94.8%) was significantly improved compared to radiologists (60%, 95% CI: 50%-71.4%). The explainable boosting model exhibited superior performance on our dataset, achieving high accuracy and precision in the diagnosis of pulmonary nodules compared to experienced radiologists.

PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges.

Lung cancer is currently the leading cause of cancer-related deaths, and early diagnosis and screening can significantly reduce its mortality rate. Since some early-stage lung cancers lack obvious clinical symptoms and only present as pulmonary nodules (PNs) in imaging examinations, accurately determining the benign or malignant nature of PNs is crucial for improving patient survival rates. 18F-FDG PET/CT is important in diagnosing PNs, but its specificity needs improvement. Radiomics can provide information beyond traditional visual assessment, overcoming its limitations by extracting high-throughput quantitative features from medical images. Radiomics features based on 18F-FDG PET/CT and deep learning methods have shown great potential in the noninvasive diagnosis of PNs. This paper reviews the latest advancements in these methods and discusses their contributions to improving diagnostic accuracy and the challenges they face.

A Novel Scale for Diagnosis of Pulmonary Ground-Glass Nodules: A Multicenter and Ambispective Cohort Study.

A screening tool was devised to aid the diagnosis and treatment of ground-glass nodules (GGNs). The current ambispective cohort study included retrospective collation of 20 variables synthesizing a patient's clinical characteristics, serum tumor markers, and CT results, which allowed division into noninvasive (benign, atypical adenomatous hyperplasia, and adenocarcinoma insitu) and invasive (minimally invasive and invasive adenocarcinomas) tumors to build a prediction nomogram and GGN screening scale. The model was verified internally. A prospective cohort of patients was randomly divided by envelope method into those assessed by the GGN screening scale and those assessed via CT values. The diagnostic efficiencies were compared to allow external verification of the model. A total of 223 patients with 225 GGNs were recruited into the retrospective cohort between January 2021 and December 2022. Multivariable analysis showed sex, diameter, air bronchogram, and vessel convergence sign to be independent factors for prediction of noninvasive and invasive GGNs. Internal verification showed the model had a sensitivity of 70.7% and specificity of 75.0% with the Youden index at 0.457 and area under the curve (AUC) of 0.793 (95% CI: 0.734-0.852). Calibration curves indicated good internal stability (p = 0.357). Between January 2023 and March 2023, 147 patients with 148 GGNs were recruited into the prospective cohort. External verification showed the model had a sensitivity of 92.4% and specificity of 40.0% with the Youden index at 0.324 and AUC of 0.678 (95% CI: 0.509-0.847). Calibration curves indicated good external stability (p = 0.088). The scale was shown to have a sensitivity of 75.00%, specificity of 37.50%, positive predictive value of 91.53%, negative predictive value of 14.29%, and accuracy of 71.25%. The GGN screening scale has high sensitivity and accuracy, making it suitable for diagnosis of GGNs.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BackgroundAccurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.MethodsBlood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.ResultsOur results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5–10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.ConclusionsOur study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.Trial registration: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.clinicaltrials.gov/ct2/show/NCT05432128.

Yield of Navigational Ultrathin Bronchoscopy with Fluoroscopy in Diagnosis of Pulmonary Nodules

Yield of Navigational Ultrathin Bronchoscopy with Fluoroscopy in Diagnosis of Pulmonary Nodules

MA08.07 Transthoracic Versus Transbronchial Approaches for Diagnosis of Pulmonary Nodules Located in the Middle Lung Zone

MA08.07 Transthoracic Versus Transbronchial Approaches for Diagnosis of Pulmonary Nodules Located in the Middle Lung Zone

An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning

The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorization of grade and texture for nodules is particularly significant since it can aid radiologists and clinicians to make better-informed decisions concerning the management of nodules. However, currently existing nodule classification techniques have a singular function of nodule classification and rely on an extensive amount of high-quality annotation data, which does not meet the requirements of clinical practice. To address this issue, we develop an anthropomorphic diagnosis system of pulmonary nodules (PN) based on deep learning (DL) that is trained by weak annotation data and has comparable performance to full-annotation based diagnosis systems. The proposed system uses DL models to classify PNs (benign vs. malignant) with weak annotations, which eliminates the need for time-consuming and labor-intensive manual annotations of PNs. Moreover, the PN classification networks, augmented with handcrafted shape features acquired through the ball-scale transform technique, demonstrate capability to differentiate PNs with diverse labels, including pure ground-glass opacities, part-solid nodules, and solid nodules. Through 5-fold cross-validation on two datasets, the system achieved the following results: (1) an Area Under Curve (AUC) of 0.938 for PN localization and an AUC of 0.912 for PN differential diagnosis on the LIDC-IDRI dataset of 814 testing cases, (2) an AUC of 0.943 for PN localization and an AUC of 0.815 for PN differential diagnosis on the in-house dataset of 822 testing cases. In summary, our system demonstrates efficient localization and differential diagnosis of PNs in a resource limited environment, and thus could be translated into clinical use in the future.

NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search

In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.

Cone-beam computed tomography-guided shape-sensing robotic bronchoscopy vs. electromagnetic navigation bronchoscopy for pulmonary nodules.

Electromagnetic navigation bronchoscopy (ENB) and shape-sensing robotic-assisted bronchoscopy (ssRAB) are minimally invasive technologies for the diagnosis of pulmonary nodules. Cone-beam computed tomography (CBCT) has shown to increase diagnostic yield by allowing real-time confirmation of position of lesion and biopsy tool. There is a lack of comparative studies of such platforms using CBCT guidance to overcome computed tomography to body divergence. The aim of this study was to compare the diagnostic yield of ENB- and ssRAB-guided CBCT for biopsy of pulmonary nodules. We conducted a retrospective comparative study of consecutive patients undergoing ENB-CBCT and ssRAB-CBCT. Navigational success was defined as biopsy tool within lesion confirmed during CBCT. Diagnostic yield was assessed using two methods: (I) presence of malignancy or benign histological findings that lead to a specific diagnosis at the time of bronchoscopy, and (II) longitudinal follow-up of patients with nonspecific benign finding during bronchoscopy. ENB-CBCT was used to biopsy 97 nodules and ssRAB-CBCT was used to biopsy 111 nodules. Median size of the lesion for the ENB-CBCT group was 16.5 mm [interquartile range (IQR), 12-22 mm] as compared to 12 mm (IQR, 9-16 mm) in the ssRAB-CBCT group (P<0.001). Navigational success was 70.1% in ENB-CBCT arm as compared to 83% in ssRAB-CBCT arm respectively (P=0.03). Diagnostic yield was 66% for ENB-CBCT and 89.2% for ssRAB-CBCT (P<0.001) following bronchoscopy; 79.4% for ENB-CBCT and 95.4% for ssRAB-CBCT (P<0.001) with longitudinal follow-up data respectively. Following multivariate regression analysis adjusting for the size of the lesion, distance from the pleura, presence of bronchus sign, number of CBCT spins, and number of nodules, the odds ratio for the diagnostic yield was 4.72 [95% confidence interval (CI): 2.05-10.85; P<0.001] in the ssRAB-CBCT group as compared with ENB-CBCT. The overall rate of adverse events was similar in both groups (P=0.77). ssRAB-CBCT showed increased navigational success and diagnostic yield as compared to ENB-CBCT for pulmonary nodule biopsies.

Cone Beam Computed Tomography-Guided Navigation Bronchoscopy with Augmented Fluoroscopy for the Diagnosis of Peripheral Pulmonary Nodules: A Step-by-Step Guide.

Cone beam computed tomography-guided navigation bronchoscopy (CBCT-NB) with augmented fluoroscopy (AF) guidance represents a minimally invasive endobronchial technique for diagnosing small, peripheral pulmonary lesions. This approach is characterized by its high diagnostic accuracy and low complication risk. Current pilot trials are exploring the application of localized therapies using this innovative approach. This report aims to provide a detailed procedural guide for performing CBCT-NB with AF guidance as the only tool for navigation and image guided biopsy. We outline the procedural steps involved in the CBCT-NB procedure for diagnosing peripheral pulmonary lesions, supported by specific intra-procedural clinical video footage. The steps include (1) preprocedural considerations, (2) a detailed procedural workflow encompassing navigation to the target lesion, (3) position confirmation and tissue acquisition, and (4) postprocedural follow-up. CBCT-NB with AF guidance is a safe and precise stand-alone navigation modality that offers high-resolution real-time 3D imaging, enhancing the diagnosis and potential treatment of peripheral pulmonary nodules.

Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers

ObjectiveTo analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).MethodsThe case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.ResultsOf the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.ConclusionCombining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.

A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules

Accurate, non-invasive, and cost-effective tools are needed to assist pulmonary nodule diagnosis and management due to increasing detection by low-dose computed tomography (LDCT). We perform genome-wide methylation sequencing on malignant and non-malignant lung tissues and designed a panel of 263 differential DNA methylation regions, which is used for targeted methylation sequencing on blood cell-free DNA (cfDNA) in two prospectively collected and retrospectively analyzed multicenter cohorts. We develop and optimize an integrative model for risk stratification of pulmonary nodules based on 40 cfDNA methylation biomarkers, age, and five simple computed tomography (CT) imaging features using machine learning approaches and validate its good performance in two cohorts. Using the two-threshold strategy can effectively reduce unnecessary invasive surgeries, overtreatment costs, and injury for patients with benign nodules while advising immediate treatment for patients with lung cancer, which can potentially improve the overall diagnosis of lung cancer following LDCT/CT screening.

Evaluating the effectiveness of artificial intelligence imaging in the qualitative diagnosis of pulmonary nodules

Our study aimed to evaluate the effectiveness of artificial intelligence (AI) image diagnostic systems in the qualitative diagnosis of pulmonary nodules. We analyzed 291 cases from June 2023 to January 2024 at Chongqing University Three Gorges Hospital. All patients in the study underwent low-dose chest computed tomography scans, which identified lung nodules, followed by thoracic surgery for pathological confirmation. We compared the predictive accuracy of AI-based diagnosis with that of physician-based diagnosis in distinguishing between benign and malignant lung nodules. Among the 291 lung nodules examined, 226 were cancerous, and 65 were benign. Receiver operating characteristic (ROC) curves, plotted based on the malignancy probabilities predicted by both methods, revealed that the AI group achieved an area under the ROC curve (AUC) of 0.727, with a sensitivity of 90.27% and a specificity of 58.46%. In comparison, the physician-reading group had an AUC of 0.737, with a sensitivity of 83.19% and a specificity of 66.15%. Our findings demonstrate that the AI diagnostic system effectively calculates malignancy probabilities for lung nodules, highlighting its significant predictive potential. This system can serve as a valuable adjunct tool for clinicians and imaging physicians in the diagnostic process.

Hybrid Deep Learning Approach with Feature Engineering for Enhanced Pulmonary Nodule Diagnosis

Hybrid Deep Learning Approach with Feature Engineering for Enhanced Pulmonary Nodule Diagnosis

SSANet—Novel Residual Network for Computer‐Aided Diagnosis of Pulmonary Nodules in Chest Computed Tomography

ABSTRACTThe manifestations of early lung cancer in medical imaging often appear as pulmonary nodules, which can be classified as benign or malignant. In recent years, there has been a gradual application of deep learning‐based computer‐aided diagnosis technology to assist in the diagnosis of lung nodules. This study introduces a novel three‐dimensional (3D) residual network called SSANet, which integrates split‐based convolution, shuffle attention, and a novel activation function. The aim is to enhance the accuracy of distinguishing between benign and malignant lung nodules using convolutional neural networks (CNNs) and alleviate the burden on doctors when interpreting the images. To fully extract pulmonary nodule information from chest CT images, the original residual network is expanded into a 3D CNN structure. Additionally, a 3D split‐based convolutional operation (SPConv) is designed and integrated into the feature extraction module to reduce redundancy in feature maps and improve network inference speed. In the SSABlock part of the proposed network, ACON (Activated or Not) function is also introduced. The proposed SSANet also incorporates an attention module to capture critical characteristics of lung nodules. During the training process, the PolyLoss function is utilized. Once SSANet generates the diagnosis result, a heatmap displays using Score‐CAM is employed to evaluate whether the network accurately identifies the location of lung nodules. In the final test set, the proposed network achieves an accuracy of 89.13%, an F1‐score of 84.85%, and a G‐mean of 86.20%. These metrics represent improvements of 5.43%, 5.98%, and 4.09%, respectively, compared with the original base network. The experimental results align with those of previous studies on pulmonary nodule diagnosis networks, confirming the reliability and clinical applicability of the diagnostic outcomes.

Conventional versus cone-beam computed tomography in lung biopsy: diagnostic performance, risks, and the advantages of tract embolization with gelfoam particle suspension.

With the widespread adoption of computed tomography (CT) technology, the number of detected pulmonary nodules has gradually increased. CT-guided percutaneous needle biopsy has become the primary method for qualitative diagnosis of pulmonary nodules. Benefiting from its three-dimensional (3D) reconstruction capability, cone-beam CT (CBCT) technology has also been widely adopted. Nevertheless, pneumothorax remains the most common complication of these diagnostic and therapeutic procedures. This study assessed the diagnostic accuracy of conventional CT (CCT)- and CBCT-guided coaxial core needle biopsy (CCNB) and the effectiveness of gelfoam particle suspension in reducing complications through tract embolization. A retrospective analysis was conducted on 320 patients who had undergone CCNB for nodules ≤3 cm from January 2020 to June 2022 at Zhongshan People's Hospital, comprising 325 biopsies (145 CCT-guided and 180 CBCT-guided). Gelfoam tract embolization was specifically used in biopsies of patients identified with a high risk of complications. Comparative statistics involved diagnostic outcomes (sensitivity, specificity, accuracy), procedural lengths, complication occurrences, and radiation doses. Diagnostically, both CCT (sensitivity 93.3%, specificity 100%, accuracy 94.1%) and CBCT (sensitivity 92.8%, specificity 100%, accuracy 93.8%) offered a similarly high performance. The CCT technique was preferable in terms of shorter median operational times (19 vs. 24 minutes; P<0.001) and greater radiation exposure (13.9 vs. 10.1 mSv; P<0.001). The complication rates of CBCT and CCT, such as those of pneumothorax (18.9% vs. 20.7%; P=0.69) and hemorrhage (23.9% vs. 18.6%; P=0.25), were comparable. Of note, the comparison of biopsies with and without gelfoam embolization revealed a marked reduction in postoperative pneumothorax incidence (1.24% vs. 7.9%; P=0.004) and the requirement for drainage (0% vs. 4.27%; P=0.02), indicating the effectiveness of this procedure. CCT- and CBCT-guided lung biopsies demonstrate equivalent diagnostic capacities, with CCT providing shorter median operational times. Importantly, gelfoam embolization substantially diminishes the risk of postoperative pneumothorax, underscoring its value in high-risk patients.

Lung adenocarcinoma concurrent with pulmonary cryptococcosis: a case report and literature review

Pulmonary cryptococcosis (PC) is a common opportunistic fungal infection caused by Cryptococcus neoformans or Cryptococcus gattii. PC primarily invades the respiratory system, followed by the central nervous system. Few clinical reports have examined the coexistence of PC and lung cancer. This study reports the case of a 54-year-old immunocompetent PC patient with lung adenocarcinoma. Chest CT revealed multiple nodules in the right lung, with the largest nodule located in the dorsal segment of the right lower lobe. 18 F‑FDG positron emission tomography-computed tomography (PET-CT) revealed elevated glucose metabolism in the dorsal segment of the right lower lobe, which suggested lung cancer. The metabolism level of the nodule in the basal segment of the right lower lobe and the anterior segment of the right upper lobe was not abnormally increased, but the possibility of a malignant tumour could not be excluded. The pulmonary nodules in the dorsal segment and the basal segment of the right lower lobe were simultaneously resected via video-assisted thoracic surgery (VATS), and the final histopathology revealed primary lung adenocarcinoma and pulmonary cryptococcal infection, respectively. After surgery, antifungal treatment was administered for 3 months. Over the 3-year follow-up, contrast-enhanced computed tomography (CT) revealed no recurrence of either disease. This case study highlights the possibility of dualism in the diagnosis of multiple pulmonary nodules on chest CT, such as the coexistence of lung cancer and PC. Surgical resection is recommended for micronodules that are not easy to diagnose via needle biopsy; in addition, early diagnosis and treatment are helpful for ensuring a good prognosis. This paper reports the clinical diagnosis and treatment of one patient with pulmonary cryptococcal infection of the right lung complicated with lung adenocarcinoma, including 3 years of follow-up, providing a reference for clinical practice.

Diagnostic efficacy of endobronchial ultrasound-guided transbronchoscopic lung biopsy for identifying tuberculous nodules

BackgroundMicrobiological diagnosis of pulmonary tuberculosis (PTB) is hampered by a low pathogen burden, low compliance and unreliable sputum sampling. Although endobronchial ultrasound-guided transbronchoscopic lung biopsy (EBUS-TBLB) has been found to be useful for the assessment of intrapulmonary nodules in adults, few data are available for the clinical diagnosis of pulmonary tuberculosis. Here, we evaluated EBUS-TBLB as a diagnostic procedure in adult patients with radiologically suspected intrapulmonary tuberculous nodules.MethodsThis was a retrospective analysis of patients admitted with pulmonary nodules between January 2022 and January 2023 at Hangzhou Red Cross Hospital. All patients underwent EBUS-TBLB, and lung biopsy samples were obtained during hospitalization. All samples were tested for Mycobacterium tuberculosis using acid‒fast smears, Bactec MGIT 960, Xpert MTB/RIF, next-generation sequencing (NGS), and DNA (TB‒DNA) and RNA (TB‒RNA). The concordance between different diagnostic methods and clinical diagnosis was analysed via kappa concordance analysis. The diagnostic efficacy of different diagnostic methods for PTB was analysed via ROC curve.ResultsA total of 107 patients were included in this study. Among them, 86 patients were diagnosed by EBUS-TBLB, and the overall diagnostic rate was 80.37%. In addition, 102 enrolled patients had benign lesions, and only 5 were diagnosed with lung tumours. Univariate analysis revealed that the diagnostic rate of EBUS-TBLB in pulmonary nodules was related to the location of the probe. The consistency analysis and ROC curve analysis revealed that NGS had the highest concordance with the clinical diagnosis results (agreement = 78.50%, κ = 0.558) and had the highest diagnostic efficacy for PTB (AUC = 0.778). In addition, Xpert MTB/RIF + NGS had the highest concordance with the clinical diagnosis results (agreement = 84.11%, κ = 0.667) and had the highest efficacy in the diagnosis of PTB (AUC = 0.826).ConclusionEBUS-TBLB is a sensitive and safe method for the diagnosis of pathological pulmonary nodules. Xpert MTB/RIF combined with NGS had the highest diagnostic efficacy and can be used in the initial diagnosis of PTB.

Chinese expert consensus on diagnosis and treatment of pulmonary nodules(2024)

Lung cancer is the leading cause of the incidence and mortality of malignant tumors in China. The 5-year survival rate released for China in 2018 was 19.7%. The 5-year survival rate for stage Ⅰ patients is 77%-92%. Early diagnosis and treatment of lung cancer are key to improving the 5-year overall survival rate and prognosis of lung cancer patients. Therefore, experts from the Academic Group of Lung Cancer in Chinese Thoracic Society and Chinese Alliance Against Lung Cancer Expert Group formulated the Chinese Expert Consensus on the Diagnosis and Treatment of Lung Nodules in 2015 to standardize the diagnosis and treatment of lung nodules. In 2018, this consensus was updated to formulate the Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2018 edition), and widely applied in multiple branch centers of Chinese Alliance Against Lung Cancer, proposing the Intelligent Treatment of Million Early Lung Cancer Project. Based on applied experience of the expert consensus in recent years, with reference to the latest evidence has been updated, Chinese Expert Consensus on Diagnosis and Treatment of Lung Nodules (2024 edition) was formulated. The updated content of this consensus mainly includes the following aspects: (1) Define the screening age of high-risk lung cancer populations in China based on the national conditions; (2) Propose definition of "difficult-to-determine pulmonary nodules" to avoid delaying the diagnosis and treatment; (3) Evaluate pulmonary nodules assisted by artificial intelligence (AI) imaging-assisted diagnostic system and propose human-machine MDT to avoid the limitations of AI; (4) Evaluate pulmonary nodules by routine and individualized evaluations for different populations, and make recommendations based on evidence-based management guidelines for different types and sizes of pulmonary nodules. In the updated consensus, 18 consensus points were recommended as a reference for clinical management of pulmonary nodules to improve the 5-year overall survival rate and the prognosis of lung cancer in China.