Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. METHODS: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. RESULTS: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. CONCLUSION: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Shared decision-making in the management of pulmonary nodules: a systematic review of quantitative and qualitative studies.

OBJECTIVE: The objective of this systematic review was to explore the evidence regarding shared decision-making (SDM) in the management of pulmonary nodules. DESIGN: Systematic review of quantitative and qualitative studies. DATA SOURCE: Studies published in English or Chinese up to April 2022 were extracted from nine databases: PubMed, PsycINFO, EMBASE, Cochrane Library, Web of Science and CINAHL, China National Knowledge Infrastructure, Wanfang Data and SinoMed Data. ELIGIBILITY CRITERIA: Studies were eligible if patients or healthcare providers are faced with pulmonary nodule management options or the interventions or experiences were focused on the patient-healthcare provider relationship or health education to make, increase or support shared decisions. All types of studies were included, including quantitative and qualitative studies. Grey literature and literature that had not been peer reviewed were excluded. Poster abstracts and non-empirical publications such as editorials, letters, opinion papers and review articles were excluded. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened abstracts and full texts, assessed quality using Joanna Briggs Institute's critical appraisal tools, and extracted data from included studies. Thematic syntheses were used to identify prominent themes emerging from the data. RESULTS: A total of 12 studies met the inclusion criteria, 11 of which were conducted in USA. These included six qualitative studies and six quantitative studies (including both survey and quasi-experimental designs). Three major themes with specific subthemes emerged: (1) Opportunity (uncertainty in the diagnosis and treatment of pulmonary nodules, willingness to participate in decision-making); (2) Ability (patient's lack of knowledge, physician's experience); and (3) Different worldview (misconception, distress among patients, preference for diagnosis and treatment). CONCLUSIONS: Uncertainty in the management of pulmonary nodules is the opportunity to implement SDM. Patients' lack of knowledge, distress, and misunderstandings between healthcare providers and patients are both the main obstacles and the causes of the application of SDM.

Bronchoscopy with and without needle-based confocal laser endomicroscopy for peripheral lung nodule diagnosis: protocol for a multicentre randomised controlled trial (CLEVER trial).

INTRODUCTION: Despite many technological advances, the diagnostic yield of bronchoscopic peripheral lung nodule analysis remains limited due to frequent mispositioning. Needle-based confocal laser endomicroscopy (nCLE) enables real-time microscopic feedback on needle positioning, potentially improving the sampling location and diagnostic yield. Previous studies have defined and validated nCLE criteria for malignancy, airway and lung parenchyma. Larger studies demonstrating the effect of nCLE on diagnostic yield are lacking. We aim to investigate if nCLE-imaging integrated with conventional bronchoscopy results in a higher diagnostic yield compared with conventional bronchoscopy without nCLE. METHODS AND ANALYSIS: This is a parallel-group randomised controlled trial. Recruitment is performed at pulmonology outpatient clinics in universities and general hospitals in six different European countries and one hospital in the USA. Consecutive patients with a for malignancy suspected peripheral lung nodule (10-30 mm) with an indication for diagnostic bronchoscopy will be screened, and 208 patients will be included. Web-based randomisation (1:1) between the two procedures will be performed. The primary outcome is diagnostic yield. Secondary outcomes include diagnostic sensitivity for malignancy, needle repositionings, procedure and fluoroscopy duration, and complications. Pathologists will be blinded to procedure type; patients and endoscopists will not. ETHICS AND DISSEMINATION: Primary approval by the Ethics Committee of the Amsterdam University Medical Center. Dissemination involves publication in a peer-reviewed journal. SUPPORT: Financial and material support from Mauna Kea Technologies. TRIAL REGISTRATION NUMBER: NCT06079970.

Comparative Analysis of Clinical Characteristics and Prognosis Between Primary Lung Cancers Identified as Screening Detected and Incidentally Detected Pulmonary Nodules.

BACKGROUND/AIM: Although the importance of low-dose computed tomography (LDCT) screening is increasingly emphasized and implemented, many lung cancers continue to be incidentally detected during routine medical practices, and data on incidentally detected lung cancer (IDLC) remain scarce. This study aimed to investigate the clinical characteristics and prognosis of IDLCs by comparing them with screening-detected lung cancers (SDLCs). PATIENTS AND METHODS: In this retrospective study, subjects with cT1 (≤3 cm) pulmonary nodules detected on baseline computed tomography (CT), later pathologically confirmed as primary lung cancer in 2015, were included. Patients were categorized into IDLC and SDLC groups based on the setting of the first pulmonary nodule detection. RESULTS: Out of 457 subjects, 129 (28.2%) were IDLCs and 328 (71.8%) were SDLCs. The IDLC group, consisted of older individuals with a higher prevalence of smokers and underlying pulmonary disease, compared to the SDLC group. Adenocarcinomas were more frequently detected in SDLCs (87.5%) than in IDLCs (76.7%, p<0.001). The time to treatment initiation (TTI) and 5-year overall survival (OS) rates were similar. Multivariate analyses revealed underlying interstitial lung disease, DLCO, solidity of nodules and TNM stage as independent risk factors associated with mortality. Less than 30% of study participants would have been eligible for the current lung cancer screening program. CONCLUSION: The IDLC group was associated with older age, higher rate of smokers, underlying pulmonary disease, and non-adenocarcinoma histology. However, prognosis was similar to that of the SDLC group, attributable to the similarity in TNM stage, strict adherence to guidelines, and short TTI. Furthermore, less than 30% of the participants would have been suitable for the existing lung cancer screening program, indicating a potential need to reconsider the scope for screening candidates.

Non-malignant pathological results from CT-guided biopsy for pulmonary nodules: a predictive model for identifying false-negative results.

BACKGROUND: Computed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, it is critical to correctly determine false-negative results. Therefore, the current study was designed to construct a predictive model for predicting false-negative cases among patients receiving CTB for PNs who receive non-malignant results. MATERIALS AND METHODS: From January 2016 to December 2020, consecutive patients from two centers who received CTB-based non-malignant pathology results while undergoing evaluation for PNs were examined retrospectively. A training cohort was used to discover characteristics that predicted false negative results, allowing the development of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy. RESULTS: The training cohort included 102 patients with PNs who showed non-malignant pathology results based on CTB. Each patient underwent CTB for a single nodule. Among these patients, 85 and 17 patients, respectively, showed true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUVmax, P = 0.001) and CTB-based findings of suspected malignant cells (P = 0.043) were identified as being predictive of false negative results. Following that, these two predictors were combined to produce a predictive model. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.945. Furthermore, it demonstrated sensitivity and specificity values of 88.2% and 87.1% respectively. The testing cohort included 62 patients, each of whom had a single PN. When the developed model was used to evaluate this testing cohort, this yielded an AUC value of 0.851. CONCLUSIONS: In patients with PNs, the predictive model developed herein demonstrated good diagnostic effectiveness for identifying false-negative CTB-based non-malignant pathology data.

How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape?

INTRODUCTION: Implementation of lung cancer screening, with its subsequent findings, is anticipated to change the current diagnostic and surgical lung cancer landscape. This review aimed to identify and present the most updated expert opinion and discuss relevant evidence regarding the impact of lung cancer screening and lung nodule management on the diagnostic and surgical landscape of lung cancer, as well as summarise points for clinical practice. METHODS: This article is based on relevant lectures and talks delivered during the European Society of Thoracic Surgeons-European Respiratory Society Collaborative Course on Thoracic Oncology (February 2023). Original lectures and talks and their relevant references were included. An additional literature search was conducted and peer-reviewed studies in English (December 2022 to June 2023) from the PubMed/Medline databases were evaluated with regards to immediate affinity of the published papers to the original talks presented at the course. An updated literature search was conducted (June 2023 to December 2023) to ensure that updated literature is included within this article. RESULTS: Lung cancer screening suspicious findings are expected to increase the number of diagnostic investigations required therefore impacting on current capacity and resources. Healthcare systems already face a shortage of imaging and diagnostic slots and they are also challenged by the shortage of interventional radiologists. Thoracic surgery will be impacted by the wider lung cancer screening implementation with increased volume and earlier stages of lung cancer. Nonsuspicious findings reported at lung cancer screening will need attention and subsequent referrals where required to ensure participants are appropriately diagnosed and managed and that they are not lost within healthcare systems. CONCLUSIONS: Implementation of lung cancer screening requires appropriate mapping of existing resources and infrastructure to ensure a tailored restructuring strategy to ensure that healthcare systems can meet the new needs.

Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.

BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PNs) and to compare with three published models. METHODS: Nine hundred fourteen patients with PNs were collected from four medical institutions (A, B, C and D), which were organized into tables containing clinical features, radiologic features and laboratory test features. Patients were divided into benign lesion (BL), precursor lesion (PL) and malignant lesion (ML) groups according to pathological diagnosis. Approximately 80% of patients in A (total/male: 632/269, age: 57.73 ± 11.06) were randomly selected as a training set; the remaining 20% were used as an internal test set; and the patients in B (total/male: 94/53, age: 60.04 ± 11.22), C (total/male: 94/47, age: 59.30 ± 9.86) and D (total/male: 94/61, age: 62.0 ± 11.09) were used as an external validation set. Logical regression (LR), decision tree (DT), random forest (RF) and support vector machine (SVM) were used to establish prediction models. Finally, the Mayo model, Peking University People's Hospital (PKUPH) model and Brock model were externally validated in our patients. RESULTS: The AUC values of RF model for MLs, PLs and BLs were 0.80 (95% CI: 0.73-0.88), 0.90 (95% CI: 0.82-0.99) and 0.75 (95% CI: 0.67-0.88), respectively. The weighted average AUC value of the RF model for the external validation set was 0.71 (95% CI: 0.67-0.73), and its AUC values for MLs, PLs and BLs were 0.71 (95% CI: 0.68-0.79), 0.98 (95% CI: 0.88-1.07) and 0.68 (95% CI: 0.61-0.74), respectively. The AUC values of the Mayo model, PKUPH model and Brock model were 0.68 (95% CI: 0.62-0.74), 0.64 (95% CI: 0.58-0.70) and 0.57 (95% CI: 0.49-0.65), respectively. CONCLUSIONS: The RF model performed best, and its predictive performance was better than that of the three published models, which may provide a new noninvasive method for the risk assessment of PNs.

[Clinical Application of Robotic Assisted Bronchoscopy 
in Peripheral Pulmonary Nodule Biopsy].

With the popularization of chest computed tomography (CT) lung cancer screening, the detection rate of peripheral pulmonary nodules is increasing day by day. Some patients could make clear diagnoses and receive early treatment by obtaining biopsy specimens. Transbronchial lung biopsy (TBLB) is one of the non-surgical biopsy methods for peripheral pulmonary nodules, which has less trauma and lower incidence of complications compared to percutaneous thoracic needle biopsy (PTNB). However, the diagnostic rate of TBLB is about 70%, which is still inferior to that of PTNB, which is about 90%. Since 2018, robot assisted bronchoscopy systems have been applied in clinical practice. This article reviews their application in further improving the diagnostic rate of peripheral pulmonary nodules by TBLB.
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Attention pyramid pooling network for artificial diagnosis on pulmonary nodules.

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914.

Value of turbo spin echo-based diffusion-weighted imaging in the differential diagnosis of benign and malignant solitary pulmonary lesions.

To quantitatively assess the diagnostic efficacy of multiple parameters derived from multi-b-value diffusion-weighted imaging (DWI) using turbo spin echo (TSE)-based acquisition techniques in patients with solitary pulmonary lesions (SPLs). A total of 105 patients with SPLs underwent lung DWI using single-shot TSE-based acquisition techniques and multiple b values. The apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) parameters, and lesion-to-spinal cord signal intensity ratio (LSR), were analyzed to compare the benign and malignant groups using the Mann-Whitney U test and receiver operating characteristic analysis. The Dstar values observed in lung cancer were slightly lower than those observed in pulmonary benign lesions (28.164 ± 31.950 versus 32.917 ± 34.184; Z = -2.239, p = 0.025). The LSR values were significantly higher in lung cancer than in benign lesions (1.137 ± 0.581 versus 0.614 ± 0.442; Z = - 4.522, p < 0.001). Additionally, the ADC800, ADCtotal, and D values were all significantly lower in lung cancer than in the benign lesions (Z = - 5.054, -5.370, and -6.047, respectively, all p < 0.001), whereas the f values did not exhibit any statistically significant difference between the two groups. D had the highest area under the curve (AUC = 0.887), followed by ADCtotal (AUC = 0.844), ADC800 (AUC = 0.824), and LSR (AUC = 0.789). The LSR, ADC800, ADCtotal, and D values did not differ statistically significantly in diagnostic effectiveness. Lung DWI using TSE is feasible for differentiating SPLs. The LSR method, conventional DWI, and IVIM have comparable diagnostic efficacy for assessing SPLs.

Diagnostic yield using electromagnetic navigation bronchoscopy for peripheral pulmonary nodules <2 cm.

BACKGROUND: Although electromagnetic navigation bronchoscopy (ENB) is highly sensitive in the diagnosis of peripheral pulmonary nodules (PPNs), its diagnostic yield for subgroups of smaller PPNs is under evaluation. OBJECTIVES: Diagnostic yield evaluation of biopsy using ENB for PPNs <2 cm. DESIGN: The diagnostic yield, sensitivity, specificity, positive predictive value, and negative predictive value of the ENB-mediated biopsy for PPNs were evaluated. METHODS: Patients who had PPNs with diameters <2 cm and underwent ENB-mediated biopsy between May 2015 and February 2020 were consecutively enrolled. The final diagnosis was made via pathological examination after surgery. RESULTS: A total of 82 lesions from 65 patients were analyzed. The median tumor size was 11 mm. All lesions were subjected to ENB-mediated biopsy, of which 29 and 53 were classified as malignant and benign, respectively. Subsequent segmentectomy, lobectomy, or wedge resection, following pathological examinations were performed on 64 nodules from 57 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value for nodules <2 cm were 53.3%, 91.7%, 92.3%, and 51.2%, respectively. The receiver operating curve showed an area under the curve of 0.721 (p < 0.001). Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value were 62.5%, 100%, 100%, and 42.9%, respectively, for nodules with diameters equal to or larger than 1 cm; and 30.8%, 86.7%, 66.7%, and 59.1%, respectively, for nodules less than 1 cm. In the subgroup analysis, neither the lobar location nor the distance of the PPNs to the pleura affected the accuracy of the ENB diagnosis. However, the spiculated sign had a negative impact on the accuracy of the ENB biopsy (p = 0.010). CONCLUSION: ENB has good specificity and positive predictive value for diagnosing PPNs <2 cm; however, the spiculated sign may negatively affect ENB diagnostic accuracy. In addition, the diagnostic reliability may only be limited to PPNs equal to or larger than 1 cm.

Subsolid Nodules: Significance and Current Understanding.

Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.

The use of diagnostic complex robotic-assisted segmentectomy in the management of incidental and screen-detected pulmonary nodules.

OBJECTIVES: Robotic-assisted thoracoscopic surgery (RATS) facilitates complex pulmonary segmentectomy which offers one-stage diagnostic and therapeutic management of small pulmonary nodules. We aimed to explore the potential advantages of a faster, simplified pathway and earlier diagnosis against the disadvantages of unnecessary morbidity in benign cases. METHODS: In an observational study, patients with small, solitary pulmonary nodules deemed suspicious of malignancy by a multidisciplinary team were offered surgery without a pre or intraoperative biopsy. We report our initial experience with RATS complex segmentectomy (using >1 parenchymal staple line) to preserve as much functioning lung tissue as possible. RESULTS: Over a 4-year period, 245 RATS complex segmentectomies were performed; 140 right: 105 left. A median of 2 (1-4) segments was removed. There was no in-hospital mortality and no requirement for postoperative ventilation. Complications were reported in 63 (25.7%) cases, of which 36 (57.1%) were hospital-acquired pneumonia. A malignant diagnosis was found in 198 (81%) patients and a benign diagnosis in 47 (19%). The malignant diagnoses included: adenocarcinoma in 136, squamous carcinoma in 31 and carcinoid tumour in 15. The most frequent benign diagnosis was granulomatous inflammation in 18 cases. CONCLUSIONS: RATS complex segmentectomy offers a precise, safe and effective one-stop therapeutic biopsy in incidental and screen-detected pulmonary nodules.

[The role of endoscopy in the management of peripheral pulmonary nodules, part 2: Treatment]. / Place de l'endoscopie dans la gestion des nodules pulmonaires périphériques, partie 2 : traitement.

The management of peripheral lung nodules is challenging, requiring specialized skills and sophisticated technologies. The diagnosis now appears accessible to advanced endoscopy (see Part 1), which can also guide treatment of these nodules; this second part provides an overview of endoscopy techniques that can enhance surgical treatment through preoperative marking, and stereotactic radiotherapy treatment through fiduciary marker placement. Finally, we will discuss how, in the near future, these advanced endoscopic techniques will help to implement ablation strategy.

Shape-Sensing Robotic-Assisted Bronchoscopy versus Computed Tomography-Guided Transthoracic Biopsy for the Evaluation of Subsolid Pulmonary Nodules.

INTRODUCTION: Lung cancer remains the leading cause of cancer death worldwide. Subsolid nodules (SSN), including ground-glass nodules (GGNs) and part-solid nodules (PSNs), are slow-growing but have a higher risk for malignancy. Therefore, timely diagnosis is imperative. Shape-sensing robotic-assisted bronchoscopy (ssRAB) has emerged as reliable diagnostic procedure, but data on SSN and how ssRAB compares to other diagnostic interventions such as CT-guided transthoracic biopsy (CTTB) are scarce. In this study, we compared diagnostic yield of ssRAB versus CTTB for evaluating SSN. METHODS: A retrospective study of consecutive patients who underwent either ssRAB or CTTB for evaluating GGN and PSN with a solid component less than 6 mm from February 2020 to April 2023 at Mayo Clinic Florida and Rochester. Clinicodemographic information, nodule characteristics, diagnostic yield, and complications were compared between ssRAB and CTTB. RESULTS: A total of 66 nodules from 65 patients were evaluated: 37 PSN and 29 GGN. Median size of PSN solid component was 5 mm (IQR: 4.5, 6). Patients were divided into two groups: 27 in the ssRAB group and 38 in the CTTB group. Diagnostic yield was 85.7% for ssRAB and 89.5% for CTTB (p = 0.646). Sensitivity for malignancy was similar between ssRAB and CTTB (86.4% vs. 88.5%; p = 0.828), with no statistical difference. Complications were more frequent in CTTB with no significant difference (8 vs. 2; p = 0.135). CONCLUSION: Diagnostic yield for SSN was similarly high for ssRAB and CTTB, with ssRAB presenting less complications and allowing mediastinal staging within the same procedure.

[Suspected pulmonary nodule in a male heavy smoker: Is biopsy really necessary?] / Nodule pulmonaire suspect chez un grand tabagique : faut-il faire une biopsie ?

INTRODUCTION: Sclerotherapy is a widely used as a lifesaving therapeutic option in cases of upper gastrointestinal bleeding (UGB) due to ruptured gastro-esophageal varices (GOV) in cirrhotic patients, especially when there exists a portosystemic shunt. This endoscopic technique can entail many complications, including systemic and non-thrombotic pulmonary embolism (PE). While multiple pulmonary parenchymal manifestations have been described following sclerotherapy of GOV, to our knowledge no solitary suspicious pulmonary nodule has been described. CASE PRESENTATION: We report the case of 55-year-old man with heavy smoking history who was referred to our pulmonary clinic for work-up of a solitary pulmonary nodule. He was known to have liver cirrhosis with history of massive UGB due to rupture of GOV two months before. He was treated with sclerotherapy by injecting a 3 cc of Histoacryl/lipiodole solution. The post- endoscopic phase was unremarkable. An enhanced CT scan of chest and abdomen performed two months later showed a right upper lobe nodule, even though at that point, the patient was completely asymptomatic. This was ascribed to non-thrombotic PE secondary to sclerotherapy due to complete resolution of the nodule on a CT scan carried out at 2-month follow-up. At that point, his condition did not require any further treatment. CONCLUSION: Solitary pulmonary nodule is one of the radiologic manifestations of PE subsequent to sclerotherapy of GOV. Awareness and radiologic follow-up of this unusual radiologic presentation may prevent unnecessary biopsies.

Developing a multi-institutional nomogram for assessing lung cancer risk in patients with 5-30 mm pulmonary nodules: a retrospective analysis.

Background: The diagnosis of benign and malignant solitary pulmonary nodules based on personal experience has several limitations. Therefore, this study aims to establish a nomogram for the diagnosis of benign and malignant solitary pulmonary nodules using clinical information and computed tomography (CT) results. Methods: Retrospectively, we collected clinical and CT characteristics of 1,160 patients with pulmonary nodules in Guang'an People's Hospital and the hospital affiliated with North Sichuan Medical College between 2019 and 2021. Among these patients, data from 773 patients with pulmonary nodules were used as the training set. We used the least absolute shrinkage and selection operator (LASSO) to optimize clinical and imaging features and performed a multivariate logistic regression to identify features with independent predictive ability to develop the nomogram model. The area under the receiver operating characteristic curve (AUC), C-index, decision curve analysis, and calibration plot were used to evaluate the performance of the nomogram model in terms of predictive ability, discrimination, calibration, and clinical utility. Finally, data from 387 patients with pulmonary nodules were utilized for validation. Results: In the training set, the predictors for the nomogram were gender, density of the nodule, nodule diameter, lobulation, calcification, vacuole, vascular convergence, bronchiole, and pleural traction, selected through LASSO and logistic regression analysis. The resulting model had a C-index of 0.842 (95% CI [0.812-0.872]) and AUCs of 0.842 (95% CI [0.812-0.872]). In the validation set, the C-index was 0.856 (95% CI [0.811-0.901]), and the AUCs were 0.844 (95% CI [0.797-0.891]). Results from the calibration curve and clinical decision curve analyses indicate that the nomogram has a high fit and clinical benefit in both the training and validation sets. Conclusion: The establishment of a nomogram for predicting the benign or malignant diagnosis of solitary pulmonary nodules by this study has shown good efficacy. Such a nomogram may help to guide the diagnosis, follow-up, and treatment of patients.

Novel electromagnetic navigation bronchoscopy system for the diagnosis of peripheral pulmonary nodules: a prospective, multicentre study.

BACKGROUND: Traditional electromagnetic navigation bronchoscopy (ENB) is a real-time image-guided system and used with thick bronchoscopes for the diagnosis of peripheral pulmonary nodules (PPNs). A novel ENB that could be used with thin bronchoscopes was developed. This study aimed to evaluate the diagnostic yield and the experience of using this ENB system in a real clinical scenario. METHODS: This multicentre study enrolled consecutive patients with PPNs adopting ENB from March 2019 to August 2021. ENB was performed with different bronchoscopes, ancillary techniques and sampling instruments according to the characteristics of the nodule and the judgement of the operator. The primary endpoint was the diagnostic yield. The secondary endpoints included the diagnostic yield of subgroups, procedural details and complication rate. RESULTS: In total, 479 patients with 479 nodules were enrolled in this study. The median lesion size was 20.9 (IQR, 15.9-25.9) mm. The overall diagnostic yield was 74.9% (359/479). A thin bronchoscope was used in 96.2% (461/479) nodules. ENB in combination with radial endobronchial ultrasound (rEBUS), a guide sheath (GS) and a thin bronchoscope was the most widely used guided method, producing a diagnostic yield of 74.1% (254/343). The median total procedural time was 1325.0 (IQR, 1014.0-1676.0) s. No severe complications occurred. CONCLUSION: This novel ENB system can be used in combination with different bronchoscopes, ancillary techniques and sampling instruments with a high diagnostic yield and safety profile for the diagnosis of PPNs, of which the combination of thin bronchoscope, rEBUS and GS was the most common method in clinical practice. TRIAL REGISTRATION NUMBER: NCT03716284.