UNASSIGNED: Pulmonary nodules are small, focal lesions often identified via computed tomography (CT) scans. Although the majority are benign, a small percentage of them may be malignant or potentially become malignant, underscoring the importance of early detection and effective management. This study systematically reviews the epidemiology, risk factors, and management strategies for pulmonary nodules, comparing findings across Chinese and non-Chinese populations to better inform the actuarial calculations for predicting the demand of medical services for patients with pulmonary nodules.
UNASSIGNED: We performed a systematic analysis of the PubMed and China Knowledge Infrastructure (CNKI) databases for studies reporting the detection rate of pulmonary nodules through CT scans. Both cross-sectional studies and the baseline data from longitudinal studies were included. A modified version of the Newcastle-Ottawa Scale was used to assess the risk of bias and random effect models were used to estimate the overall prevalence.
UNASSIGNED: We identified 32 studies and included 24 of them in our meta-analysis. Pooled analysis showed that the overall prevalence of pulmonary nodules was 0.27 (95% confidence interval: 0.25-0.29) after outliers removal. Subgroup analysis showed that there was no significant difference for prevalence between Chinese and non-Chinese populations. Males (0.38) were shown to have slightly higher prevalence compared to females (0.36), but not significant (P=0.88). Age and smoking are the most frequently reported risk factors by studies.
UNASSIGNED: Overall, 27% of participants were positive for pulmonary nodules. Advancing age and smoking were consistently identified as a key risk factor for the incidence of pulmonary nodules. Although the management strategies are different across studies, recent guidelines recommend personalized management strategies, prioritizing nodule size, characteristics, and individual risk factors to optimize outcomes.

OBJECTIVE: This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice.
METHODS: A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software.
RESULTS: The selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93).
CONCLUSIONS: Circulating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.

BACKGROUND: There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules.
METHODS: An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used.
RESULTS: Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively.
CONCLUSIONS: DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.

Lung cancer is the malignancy with the highest global mortality rate and imposes a substantial burden on society. The increasing popularity of lung cancer screening has led to increasing number of patients being diagnosed with pulmonary nodules due to their potential for malignancy, causing considerable distress in the affected population. However, the diagnosis and treatment of sub-centimeter grade pulmonary nodules remain controversial. The evolution of genetic detection technology and the development of targeted drugs have positioned the diagnosis and treatment of lung cancer in the precision medicine era, leading to a marked improvement in the survival rate of patients with lung cancer. It has been established that lung cancer driver genes serve a key role in the development and progression of sub-centimeter lung cancer. The present review aimed to consolidate the findings on genes associated with sub-centimeter lung cancer, with the intent of serving as a reference for future studies and the personalized management of sub-centimeter lung cancer through genetic testing.

BACKGROUND: Pulmonary nodules are an early imaging indication of lung cancer, and early detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the applications of machine learning, the convolutional neural network (CNN) applied to computed tomography (CT) imaging data improves the accuracy of diagnosis, but the results could be more consistent.
OBJECTIVE: To evaluate the diagnostic performance of CNN in assisting in detecting pulmonary nodules in CT images.
METHODS: PubMed, Cochrane Library, Web of Science, Elsevier, CNKI and Wanfang databases were systematically retrieved before 30 April 2023. Two reviewers searched and checked the full text of articles that might meet the criteria. The reference criteria are joint diagnoses by experienced physicians. The pooled sensitivity, specificity and the area under the summary receiver operating characteristic curve (AUC) were calculated by a random-effects model. Meta-regression analysis was performed to explore potential sources of heterogeneity.
RESULTS: Twenty-six studies were included in this meta-analysis, involving 2,391,702 regions of interest, comprising segmented images with a few wide pixels. The combined sensitivity and specificity values of the CNN model in detecting pulmonary nodules were 0.93 and 0.95, respectively. The pooled diagnostic odds ratio was 291. The AUC was 0.98. There was heterogeneity in sensitivity and specificity among the studies. The results suggested that data sources, pretreatment methods, reconstruction slice thickness, population source and locality might contribute to the heterogeneity of these eligible studies.
CONCLUSIONS: The CNN model can be a valuable diagnostic tool with high accuracy in detecting pulmonary nodules.

Pulmonary nodules with intermediate to high risk of malignancy should preferably be diagnosed with image guide minimally invasive diagnostics before treatment. Several technological innovations have been developed to endobronchially navigate to these lesions and obtain tissue for diagnosis. This review addresses these technological advancements in navigation bronchoscopy in three basic steps: navigation, position confirmation and acquisition, with a specific focus on cone-beam computed tomography (CBCT). For navigation purposes ultrathin bronchoscopy combined with virtual bronchoscopy navigation, electromagnetic navigation and robotic assisted bronchoscopy all achieve good results as a navigation guidance tool, but cannot confirm location or guide biopsy positioning. Diagnostic yield has seen improvement by combining these techniques with a secondary imaging tool like radial endobronchial ultrasound (rEBUS) and fluoroscopy. For confirmation of lesion access, rEBUS provides local detailed ultrasound-imaging and can be used to confirm lesion access in combination with fluoroscopy, measure nodule-contact area length and determine catheter position for sampling. CBCT is the only technology that can provide precise 3D positioning confirmation. When focusing on tissue acquisition, there is often more than 10% difference between reaching the target and getting a diagnosis. This discrepancy is multifactorial and caused by breathing movements, small samples sizes, instrument tip displacements by tool rigidity and tumour inhomogeneity. Yield can be improved by targeting fluorodeoxyglucose (FDG)-avid regions, immediate feedback of rapid onsite evaluation, choosing sampling tools with different passive stiffnesses, by increasing the number biopsies taken and (future) catheter modifications like (robotic assisted-) active steering. CBCT with augmented fluoroscopy (CBCT-AF) based navigation bronchoscopy combines navigation guidance with 3D-image confirmation of instrument-in-lesion positioning in one device. CBCT-AF allows for overlaying the lesion and navigation pathway and the possibility to outline trans-parenchymal pathways. It can help guide and verify sampling in 3D in near real-time. Disadvantages are the learning curve, the inherent use of radiation and limited availability/access to hybrid theatres. A mobile C-arm can provide 3D imaging, but lower image quality due to lower power and lower contrast-to-noise ratio is a limiting factor. In conclusion, a multi-modality approach in experienced hands seems the best option for achieving a diagnostic accuracy >85%. Either adequate case selection or detailed 3D imaging are essential to obtain high accuracy. For current and future transbronchial treatments, high-resolution (CBCT) 3D-imaging is essential.

The development of robotic-assisted bronchoscopy has empowered bronchoscopists to access the periphery of the lung with more confidence and promising accuracy. This is due in large to the superior maneuverability, further reach, and stability of these technologies. Despite the advantages of robotic bronchoscopy, there are some drawbacks to using these technologies, such as the loss of tactile feedback, the need to overcome computed tomography (CT)-to-body divergence, and the potential for overreliance on the navigation software. There are currently two robotic bronchoscopy platforms on the US market, the MonarchTM Platform by Auris Health© (Redwood City, CA, USA) and the IonTM endoluminal robotic bronchoscopy platform by Intuitive Surgical© (Sunnyvale, CA, USA). In this clinical practice review, we highlight the evidence and strategies for successful clinical use of both robotic bronchoscopy platforms for pulmonary lesion sampling. Specifically, we will review pre-procedural considerations, such as procedural mapping, room set-up and anesthesia considerations. We will also review the technical aspects of using the robotic bronchoscopy platforms, such as how to compensate for the loss of tactile feedback, optimize visualization, use of ancillary technology to accommodate for CT-to-body divergence, employ best practices for sampling techniques, and utilize information from rapid on-site evaluation (ROSE) to aid in improving diagnostic yield.

Head and neck squamous cell carcinoma (HNSCC) often presents with synchronous nodules of the lung (sNL), which may be benign nodules, second primary malignancies or metastases of HNSCC. We sought to gain an insight into the incidence of sNL and synchronous second primary of the lung (sSPML) in HNSCC patients and current opinions on useful diagnostic and therapeutic approaches. We conducted a systematic search of the PubMed database for articles that reported the simultaneous detection of HNSCC and sNL/sPML, within the timeframe of diagnosis and staging. Only studies involving humans were included, without restrictions for sex, age, ethnicity, or smoking history. All articles were categorised according to the Oxford Centre of Evidence-Based Medicine levels and their data collected. Data from 24 studies were analysed. Amongst HNSCC, the mean overall incidence rate of sNL and sSPML was 11.4% (range: 1.3-27%) and 2.95% (range: 0.4-7.4%), respectively. The possibility of a sNL to be a sSPML cannot be ignored (mean: 35.2%). Studies investigating smoking habits showed that the majority (98-100%) of HNSCC patients with sSPML were previous or active smokers. Detection of human papillomavirus through DNA analysis, p16 immunohistochemistry, and identification of clonal evolution were useful in differentiating metastasis from sSPML. 18FDG-PET scan was the most reliable method to diagnose sSPML (sensitivity: 95%; specificity: 96%; positive predictive value: 80%). With early sSPML detection and curative treatment, the 5-year overall survival rate is 34-47%. However, the proposed advantage of early detection warrants further evidence-based justification.

Navigation bronchoscopy has seen rapid development in the past decade in terms of new navigation techniques and multi-modality approaches utilizing different techniques and tools. This systematic review analyses the diagnostic yield and safety of navigation bronchoscopy for the diagnosis of peripheral pulmonary nodules suspected of lung cancer.
An extensive search was performed in Embase, Medline and Cochrane CENTRAL in May 2022. Eligible studies used cone-beam CT-guided navigation (CBCT), electromagnetic navigation (EMN), robotic navigation (RB) or virtual bronchoscopy (VB) as the primary navigation technique. Primary outcomes were diagnostic yield and adverse events. Quality of studies was assessed using QUADAS-2. Random effects meta-analysis was performed, with subgroup analyses for different navigation techniques, newer versus older techniques, nodule size, publication year, and strictness of diagnostic yield definition. Explorative analyses of subgroups reported by studies was performed for nodule size and bronchus sign.
A total of 95 studies (n = 10,381 patients; n = 10,682 nodules) were included. The majority (n = 63; 66.3%) had high risk of bias or applicability concerns in at least one QUADAS-2 domain. Summary diagnostic yield was 70.9% (95%-CI 68.4%-73.2%). Overall pneumothorax rate was 2.5%. Newer navigation techniques using advanced imaging and/or robotics(CBCT, RB, tomosynthesis guided EMN; n = 24 studies) had a statistically significant higher diagnostic yield compared to longer established techniques (EMN, VB; n = 82 studies): 77.5% (95%-CI 74.7%-80.1%) vs 68.8% (95%-CI 65.9%-71.6%) (p < 0.001).Explorative subgroup analyses showed that larger nodule size and bronchus sign presence were associated with a statistically significant higher diagnostic yield. Other subgroup analyses showed no significant differences.
Navigation bronchoscopy is a safe procedure, with the potential for high diagnostic yield, in particular using newer techniques such as RB, CBCT and tomosynthesis-guided EMN. Studies showed a large amount of heterogeneity, making comparisons difficult. Standardized definitions for outcomes with relevant clinical context will improve future comparability.

Early diagnosis of lung cancer in pulmonary nodules identified by computed tomography (CT) may be critical in reducing the epidemiological burden of the disease, particularly in countries where such a burden is considerably high and risk factors for lung cancer very prevalent. The establishment and operation of pulmonary nodule clinics (PNCs), ie, multidisciplinary services that watch and evaluate nodules found through deliberate screening efforts or as incidental findings, is increasingly becoming a key tool to implement such early-intervention, cancer-risk management policies elsewhere in the world. This review aims to research and present in a structured manner findings from published sources on options and considerations for setting up a PNC in a country such as Greece. These refer to the type of services a PNC would provide to optimize diagnosis of suspect pulmonary nodules, its structure and organization, including processes, human resources and technology infrastructure, its target audience, ie, who would be eligible to use its services, and the expected outcomes of its operation, in terms of a set of key performance indicators. Our review also revealed critical key success factors that should be considered when designing the introduction of a PNC in a health care setting, including optimal referral pathways, aligned clinical decision making and patient preferences and participation/empowerment. Our findings may inform health care systems with a high lung cancer burden and no available PNC service on options and considerations before introducing such a service in their respective settings.