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.

Diagnostic utility of adding needle aspiration (using PeriView FLEX needle) to radial endobronchial ultrasound guide sheath transbronchial lung biopsy: a single center retrospective study.

Background: Radial endobronchial ultrasound (rEBUS) guide sheath (GS) transbronchial lung biopsy (TBLB) improves the diagnostic yield of peripheral lung lesions (PLL). However, its diagnostic yield is approximately 60%. We aimed to evaluate the diagnostic utility of adding rEBUS GS transbronchial needle aspiration (TBNA) using PeriView FLEX needle (Olympus, Tokyo, Japan) to rEBUS GS TBLB. Methods: In this retrospective study, we initially screened 124 PLLs in 123 patients who underwent rEBUS GS procedures for PLLs from December 2020 to August 2021. The analysis was performed on 74 PLLs in 73 patients who underwent both rEBUS GS TBLB and TBNA. Results: PLLs showed the following characteristics: lesion size [mean ± standard deviation (SD)], 24±12 mm; nature (solid vs. subsolid), 59 (79.7%) vs. 15 (20.3%); distance from the pleura (mean ± SD), 14±14 mm; rEBUS visualization type (probe within PLL vs. probe adjacent to PLL), 56 (75.7%) vs. 18 (24.3%). Among 74 PLLs, 47 (63.5%) were successfully diagnosed by rEBUS GS TBLB. In 27 PLLs not diagnosed by rEBUS GS TBLB, 5 (18.5%) were further diagnosed by rEBUS GS TBNA [overall diagnostic yield: 70.3% (52/74)]. EBUS visualization type of "probe adjacent to PLL" was a significant factor associated with the diagnostic yield of additional rEBUS GS TBNA. Conclusions: In rEBUS GS procedures for PLLs, the diagnostic yield might be improved by implementing TBNA in addition to TBLB. In particular, additional TBNA is preferable if the probe is adjacent to the lesion rather than within the lesion on rEBUS.

Preoperative computed tomography-guided localization for pulmonary nodules: a randomized controlled trial of coil and anchored needle localization.

Introduction: In patients with pulmonary nodules (PNs), computed tomography (CT)-guided localization is commonly performed prior to the resection of these nodules through video-assisted thoracic surgery (VATS). Aim: To evaluate the relative clinical efficacy of coil and anchored needle (AN) insertion as approaches to preoperative CT-guided PN localization. Material and methods: This single-center, prospective, open-label, randomized controlled trial (registration number: NCT05183945) enrolled consecutive patients from January 2022 to July 2022, assigning these patients at random to undergo either coil or AN localization prior to VATS. Efficacy and safety outcomes in these two groups were then compared. Results: This study enrolled in total 100 patients with 120 PNs who were assigned at random to the coil (patients = 50; PNs = 60) and AN (patients = 50; PNs = 60) localization groups. The respective technical success rates for coil and AN localization were 98.3% (59/60) and 100% (60/60), with no significant difference between the groups (p = 1.000). The coil group had a significantly longer median duration of localization relative to the AN group (16.0 min vs. 8.0 min, p < 0.001). Similar rates of localization-related pneumothorax (8.3% vs. 5.0%, p = 0.715) and pulmonary hemorrhage (5.0% vs. 13.3%, p = 0.110) were observed in both groups. In addition, the VATS resection procedures achieved 100% technical success rates in both of these localization groups. Conclusions: Both coil- and AN-based localization approaches can be successfully employed to localize PNs prior to VATS resection, with the AN localization procedure requiring less time to complete on average as compared to the coil-based approach.

Preoperative computed tomography-guided localization for pulmonary nodules: comparison between hook-wire and anchored needle localization.

Introduction: Both hook-wire (HW) and anchored needle (AN) techniques can be used for preoperative computed tomography (CT)-guided localization for pulmonary nodules (PNs). But the outcomes associated with these two materials remain unclear. Aim: To assess the relative safety and efficacy of preoperative CT-guided HW and AN localization for PNs. Material and methods: This was a retrospective analysis of data collected from two institutions. Consecutive patients with PNs between January 2020 and December 2021 who underwent preoperative CT-guided HW or AN localization followed by video-assisted thoracoscopic surgery (VATS) procedures were included in these analyses, which compared the safety and clinical efficiency of these two localization strategies. Results: In total, 98 patients (105 PNs) and 93 patients (107 PNs) underwent CT-guided HW and AN localization procedures, respectively. The HW and AN groups exhibited similar rates of successful PN localization (95.2% vs. 99.1%, p = 0.117), but the dislodgement rate in the HW group was significantly higher than that for the AN group (4.8% vs. 0.0%, p = 0.029). The mean pain score of patients in the HW group was significantly higher than that for the AN group (p = 0.001). HW and AN localization strategies were associated with comparable pneumothorax (21.4% vs. 16.1%, p = 0.349) and pulmonary hemorrhage (29.6% vs. 23.7%, p = 0.354) rates. All patients other than 1 individual in the HW group successfully underwent VATS-guided limited resection. Conclusions: These data suggest that AN represents a safe, well-tolerated, feasible preoperative localization strategy for PNs that may offer value as a replacement for HW localization.

Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study.

BACKGROUND: The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. PURPOSE: To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models. METHODS: Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL-based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack-years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL-based policy models with rule-based follow-up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). RESULTS: We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack-years, beyond those considered in guideline protocols) and the selection of follow-up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL-based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule-based protocols, the three best-performing RL-based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. CONCLUSIONS: This study highlights the potential of using an RL-based approach that is both clinically interpretable and performance-robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.

Pulmonary nodules and the psychological harm they can cause: A scoping review.

More than 1.6 million pulmonary nodules are diagnosed in the United States each year. Although the majority of nodules are found to be benign, nodule detection and the process of ruling out malignancy can cause patients psychological harm to varying degrees. The present study undertakes a scoping review of the literature investigating pulmonary nodule-related psychological harm as a primary or secondary outcome. Online databases were systematically searched to identify papers published through June 30, 2023, from which 19 publications were reviewed. We examined prevalence by type, measurement, associated factors, and behavioral or clinical consequences. Of the 19 studies reviewed, 11 studies investigated distress, anxiety (n = 6), and anxiety and depression (n = 4). Prevalence of distress was 24.0 %-56.7 %; anxiety 9.9 %-42.1 %, and 14.6 %-27.0 % for depression. A wide range of demographic and social characteristics and clinical factors were associated with nodule-related psychological harm. Outcomes of nodule-related harms included experiencing conflict when deciding about treatment or surveillance, decreased adherence to surveillance, adoption of more aggressive treatment, and lower health-related quality of life. Our scoping review demonstrates that nodule-related psychological harm is common. Findings provide evidence that nodule-related psychological harm can influence clinical decisions and adherence to treatment recommendations. Future research should focus on discerning between nodule-related distress and anxiety; identifying patients at risk; ascertaining the extent of psychological harm on patient behavior and clinical decisions; and developing interventions to assist patients in managing psychological harm for better health-related quality of life and treatment outcomes.

3D airway geometry analysis of factors in airway navigation failure for lung nodules.

BACKGROUND: This study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure. METHODS: We retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit ( http://www.vmtk.org ), simple insight toolkit ( https://sitk.org ), and sci-kit image ( https://scikit-image.org ). We used a machine learning-based approach to explore the utility of these significant factors. RESULTS: Of the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803. CONCLUSIONS: Geometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study.

Purpose: To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making. Methods: A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. Preoperative neuron-specific enolase (NSE) levels were collected as a clinical predictor. Results: In the training set, the AUCs of the radiomics model (0.878 [95% CI: 0.836, 0.915]) and merged model (0.884 [95% CI: 0.844, 0.919]) significantly outperformed the semantic model (0.718 [95% CI: 0.663, 0.769], p both<.001). In the external test set, the AUCs of the radiomics model (0.787 [95% CI: 0.696, 0.871]), merged model (0.807 [95%CI: 0.720, 0.889]) and semantic model (0.729 [95% CI: 0.631, 0.811]) did not exhibit statistical differences. The radiomics model outperformed NSE in sensitivity in the training set (85.3% vs 20.0%; p <.001) and external test set (88.9% vs 40.7%; p = .002). Conclusion: The CT radiomics model could non-invasively, effectively and sensitively predict LNEN and LADC presenting as a PSN to assist in treatment strategy selection.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. CLINICAL RELEVANCE STATEMENT: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. KEY POINTS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors' evaluation in lung cancer screening.

OBJECTIVE: To evaluate the consistency between doctors and artificial intelligence (AI) software in analysing and diagnosing pulmonary nodules, and assess whether the characteristics of pulmonary nodules derived from the two methods are consistent for the interpretation of carcinomatous nodules. MATERIALS AND METHODS: This retrospective study analysed participants aged 40-74 in the local area from 2011 to 2013. Pulmonary nodules were examined radiologically using a low-dose chest CT scan, evaluated by an expert panel of doctors in radiology, oncology, and thoracic departments, as well as a computer-aided diagnostic(CAD) system based on the three-dimensional(3D) convolutional neural network (CNN) with DenseNet architecture(InferRead CT Lung, IRCL). Consistency tests were employed to assess the uniformity of the radiological characteristics of the pulmonary nodules. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy. Logistic regression analysis is utilized to determine whether the two methods yield the same predictive factors for cancerous nodules. RESULTS: A total of 570 subjects were included in this retrospective study. The AI software demonstrated high consistency with the panel's evaluation in determining the position and diameter of the pulmonary nodules (kappa = 0.883, concordance correlation coefficient (CCC) = 0.809, p = 0.000). The comparison of the solid nodules' attenuation characteristics also showed acceptable consistency (kappa = 0.503). In patients diagnosed with lung cancer, the area under the curve (AUC) for the panel and AI were 0.873 (95%CI: 0.829-0.909) and 0.921 (95%CI: 0.884-0.949), respectively. However, there was no significant difference (p = 0.0950). The maximum diameter, solid nodules, subsolid nodules were the crucial factors for interpreting carcinomatous nodules in the analysis of expert panel and IRCL pulmonary nodule characteristics. CONCLUSION: AI software can assist doctors in diagnosing nodules and is consistent with doctors' evaluations and diagnosis of pulmonary nodules.

Pulmonary endometriosis presenting as multiple nodules and pseudocavities.

Pulmonary endometriosis is a rare disease of uncertain pathogenesis which generally presents with the cyclic clinical symptoms and catamenial changes noticed on computer tomography during menstruation. We report a case of a 33-year-old woman with recurrent hemoptysis for 1 year. The patient did not exhibit a temporal relationship between her periods and the onset of hemoptysis. A chest computed tomography scan showed multiple pseudocavities in the lower lobe of the right lung and multiple nodules in both lower lobes of the lungs. The right lower lobe wedge resection was performed. Postoperative pathological examination showed pulmonary endometriosis which is a rare cause of hemoptysis.

Subpleural pulmonary nodule marking with patent blue V dye prior to surgical resection.

Background and objective: Subpleural located pulmonary nodules are perioperatively invisible to the surgeon. Their precise identification is conventionally possible by palpation, but often at the cost of performing a thoracotomy. The aim of the study was to evaluate the success rate and feasibility of the pre-operative CT-guided marking subpleural localized nodule using a mixture of Patent Blue V and an iodine contrast agent prior to the extra-anatomical video-assisted thoracoscopic surgery (VATS) resection in patients for whom the primary anatomical resection in terms of segmentectomy or lobectomy was not indicated. Methods: The data of consecutive patients with pulmonary nodules located ≤ 30 mm from the parietal pleura, who were indicated for VATS extra-anatomical resection between 2017 to 2023, were retrospectively reviewed and analyzed. All patients indicated for VATS resection underwent color marking of the area with the pulmonary lesion under CT-guided control immediately before the surgery. The primary outcome was the marking success. Morphological lesion characteristics, time from marking to the surgery, procedure related complications, final histology findings and 30day mortality were analyzed. Additionally, we assessed the association of the successful marking and the patient's smoking history. Results: A total of 62 lesions were marked. The successful marking was observed in 56/62 (90.3%) patients. The median time from the lesion marking to the beginning of surgery was 75.0 (IQR 65.0-85.0) minutes. The procedure related pneumothorax was observed in 6 (9.7%) patients, intraparenchymal hematoma in 1 (1.6%) patient. No statistically significant association of the depth of the subpleural lesion's location, occurrence of complications or time from the marking to surgery and the successful marking was observed. The 30day mortality was zero. No association of smoking and successful marking was observed. Conclusions: The method of marking the subpleural pulmonary lesions under CT-guided control with a mixture of Patent Blue V and iodine contrast agent is a safe and effective method with minimal complications. It provides surgeons the precise visualization of the affected pulmonary parenchyma before the planned extra-anatomical VATS resection.

First-in-human use of a new robotic electromagnetic navigation bronchoscopic platform with integrated Tool-in-Lesion Tomosynthesis (TiLT) technology for peripheral pulmonary lesions: The FRONTIER study.

BACKGROUND AND OBJECTIVE: As the presentation of pulmonary nodules increases, the importance of a safe and accurate method of sampling peripheral pulmonary nodules is highlighted. First-generation robotic bronchoscopy has successfully assisted navigation and improved peripheral reach during bronchoscopy. Integrating tool-in-lesion tomosynthesis (TiLT) may further improve yield. METHODS: We performed a first-in-human clinical trial of a new robotic electromagnetic navigation bronchoscopy system with integrated digital tomosynthesis technology (Galaxy System, Noah Medical). Patients with moderate-risk peripheral pulmonary nodules were enrolled in the study. Robotic bronchoscopy was performed using electromagnetic navigation with TiLT-assisted lesion guidance. Non-specific results were followed up until either a clear diagnosis was achieved or repeat radiology at 6 months demonstrated stability. RESULTS: Eighteen patients (19 nodules) were enrolled. The average lesion size was 20 mm, and the average distance from the pleura was 11.6 mm. The target was successfully reached in 100% of nodules, and the biopsy tool was visualized inside the target lesion in all cases. A confirmed specific diagnosis was achieved in 17 nodules, 13 of which were malignant. In one patient, radiological monitoring confirmed a true non-malignant result. This translates to a yield of 89.5% (strict) to 94.7% (intermediate). Complications included one pneumothorax requiring observation only and another requiring an overnight chest drain. There was one case of severe pneumonia following the procedure. CONCLUSION: In this first-in-human study, second-generation robotic bronchoscopy using electromagnetic navigation combined with integrated digital tomosynthesis was feasible with an acceptable safety profile and demonstrated a high diagnostic yield for small peripheral lung nodules.

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.

Efficiency of pulmonary nodule risk scoring systems in Turkish population.

Malignancy risk calculation models were developed using the clinical and radiological features. It was aimed to compare pulmonary nodule risk calculation models and evaluate their effectiveness and applicability for the Turkish population. Between 2014 and 2019, 351 patients who were operated on for pulmonary nodules were evaluated with the following data: age, gender, smoking history, family history of lung cancer, extrapulmonary malignancy and granulomatous disease, nodule diameter, attenuation character, side, localization, spiculation, nodule count, presence of pulmonary emphysema, FDG uptake in PET/CT of the nodule, and definitive pathology data. Malignancy risk scores were calculated using the equations of the Brock, Mayo, and Herder models. The results were evaluated statistically. The mean age of the 351 patients (236 men, 115 women) was 57.84 ± 10.87 (range 14-79) years, and 226 malignant and 125 benign nodules were observed. Significant correlations were found between malignancy and age (p < 0.001), nodule diameter (p < 0.001), gender (p < 0.009), speculation (p < 0.001), emphysema (p < 0.05), FDG uptake (p < 0.001). All three models were found effective in the differentiation (p < 0.001). The ideal threshold value was determined for the Brock (19.5%), Mayo (23.1%), and Herder (56%) models. All models were effective for nodules of > 10 mm, but none of them were for 0-10 mm. Brock was effective in ground-glass nodules (p = 0.02) and all models were effective for semi-solid and solid nodules. None of the groups could provide AUC values as high as those achieved in the original studies. This suggests the need to optimize models and malignancy risk thresholds for Turkish population.

Recommendations to Improve Management of Incidental Pulmonary Nodules in Canada: Expert Panel Consensus.

Introduction: Incidental pulmonary nodules (IPN) are common radiologic findings, yet management of IPNs is inconsistent across Canada. This study aims to improve IPN management based on multidisciplinary expert consensus and provides recommendations to overcome patient and system-level barriers. Methods: A modified Delphi consensus technique was conducted. Multidisciplinary experts with extensive experience in lung nodule management in Canada were recruited to participate in the panel. A survey was administered in 3 rounds, using a 5-point Likert scale to determine the level of agreement (1 = extremely agree, 5 = extremely disagree). Results: Eleven experts agreed to participate in the panel; 10 completed all 3 rounds. Consensus was achieved for 183/217 (84.3%) statements. Panellists agreed that radiology reports should include a standardized summary of findings and follow-up recommendations for all nodule sizes (ie, <6, 6-8, and >8 mm). There was strong consensus regarding the importance of an automated system for patient follow-up and that leadership support for organizational change at the administrative level is of utmost importance in improving IPN management. There was no consensus on the need for standardized national referral pathways, development of new guidelines, or establishing a uniform picture archiving and communication system. Conclusion: Canadian IPN experts agree that improved IPN management should include standardized radiology reporting of IPNs, standardized and automated follow-up of patients with IPNs, guideline adherence and implementation, and leadership support for organizational change. Future research should focus on the implementation and long-term effectiveness of these recommendations in clinical practice.

Advances in the localization of pulmonary nodules: a comprehensive review.

In recent years, with the widespread use of chest CT, the detection rate of pulmonary nodules has significantly increased (Abtin and Brown, J Clin Oncol 31:1002-8, 2013). Video-assisted thoracoscopic surgery (VATS) is the most commonly used method for suspected malignant nodules. However, for nodules with a diameter less than 1 cm, or located more than 1.5 cm from the pleural edge, especially ground-glass nodules, it is challenging to achieve precise intraoperative localization by manual palpation (Ciriaco et al., Eur J Cardiothorac Surg 25:429-33, 2004). Therefore, preoperative accurate localization of such nodules becomes a necessary condition for precise resection. This article provides a comprehensive review and analysis of the research progress in pulmonary nodule localization, focusing on four major localization techniques: Percutaneous puncture-assisted localization, Bronchoscopic preoperative pulmonary nodule localization, 3D Printing-Assisted Localization, and intraoperative ultrasound-guided pulmonary nodule localization.

[Differential diagnosis of cystic and nodular lung diseases]. / Differenzialdiagnose zystischer und nodulärer Lungenkrankheiten.

BACKGROUND: Cystic and nodular lung diseases encompass a broad spectrum of diseases with different etiologies and clinicoradiological presentations. Their differentiation is crucial for patient management but can be complex due to diseases with features of both categories and overlapping radiological patterns. OBJECTIVE: This study aims to describe the imaging features of cystic and nodular lung diseases in high-resolution computed tomography (CT) in detail-primarily based on their etiology-in order to allow a more accurate differential diagnosis of these diseases. MATERIALS AND METHODS: A narrative review based on current literature on the topic was conducted from a clinicoradiological perspective. RESULTS: This paper systematically categorizes the differential diagnosis of cystic and nodular lung disease and provides insights into their radiological patterns and etiologies. It highlights the role of CT in the diagnosis of these diseases and emphasizes the importance of multidisciplinary panels combining expertise from radiology, pulmonology, rheumatology, and pathology. CONCLUSION: Reliable differential diagnosis of cystic and nodular lung diseases, particularly based on their radiological features alone, remains difficult due to their overlapping and dynamic nature. Multidisciplinary boards should be the clinical standard for accurate work-up of these diseases, as they combine the medical history, symptoms, radiological findings, and, if necessary, histopathological examinations, thus providing a more robust framework for diagnosis and management.

[A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning].

Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.

Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study.

OBJECTIVES: Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low-dose computed tomography (LDCT) screening for the high-risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real-world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick-slice LDCT scanning. METHODS: A total of 29 296 subjects who underwent low-dose thick-slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low-dose thin-slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups. RESULTS: The positive rate of LDCT screening in the thin-slice scanning group was significantly higher than that in the thick-slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin-slice LDCT screening positive group was significantly higher than that in the thick-slice scanning group (78.0% vs. 52.9%, p < 0.001). CONCLUSIONS: The screening positive rate of low-dose thin-slice CT scanning is higher and more early-stage lung cancer (IA1 stage) can be detected in the screen-positive group.