Search

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., Competing Interests: Declaration of interests J.T., Z.Y., S.W., L.T., and J.-B.F. are current employees of AnchorDx Medical or AnchorDx., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Both microcoils and hook-wires are commonly utilized for preoperative pulmonary nodule localization due to their convenience, but it remains unclear which one should be prioritized for recommendation., Aims: To compare the safety and efficacy of microcoils and hook-wires for pulmonary nodule localization., Methods: From January 2021 to December 2021, 310 consecutive patients (113 males and 197 females) with 341 pulmonary nodules who underwent CT-guided microcoil or hook-wire localization prior to video-assisted thoracoscopic surgery (VATS) at our center were retrospectively included in this study. There were 161 patients in the microcoil group and 149 patients in the hook-wire group. The successful localization rate, complication rate, radiation exposure, and medical costs were compared between the two groups., Results: A total of 341 pulmonary nodules were localized, with a success rate of 99% (180/184) in the microcoil group and 93% (146/157) in the hook-wire group, respectively. All patients successfully underwent VATS. Multivariate analysis revealed that hook-wire localization, shorter needle depth into the lung tissue and the longer waiting time from localization to VATS were the risk factors for the localization failure. The incidences of pneumothorax in the microcoil group and hook-wire group were 34.8% (56/161) and 34.9% (52/149), respectively (P = 0.983). The incidences of pneumorrhagia were 13% (24/184) and 46.5% (73/157), respectively (P = 0.000). Multivariate analysis revealed that hook-wire localization and greater depth of needle penetration into lung tissue were risk factors for pneumorrhagia., Conclusion: Microcoil localization of pulmonary nodules is superior to hook-wire localization in terms of efficacy and safety. This finding provides insight into priority and broader promotion of microcoil localization., (© 2024. The Author(s).)

Background: In recent years, clinicians often encounter patients with multiple pulmonary nodules in their clinical practices. As most of these ground glass nodules (GGNs) are small in volume and show no spicule sign, it is difficult to use Mayo Clinic Model to make early diagnosis of lung cancer accurately, especially in large numbers of nonsmoking women who have no tumor history. Other clinical models are disadvantaged by a relatively high false-positive or false-negative rate. Therefore, there is an urgent need to establish a new model of predicting malignancy or benignity of pulmonary GGNs for the sake of making accurate and early diagnosis of lung cancer., Methods: Included in this study were GGNs surgically resected from patients who were admitted to Yiwu Central Hospital from January 2018 to March 2024, including both male and female patients, there is no gender specific issue. The nature of all these GGN tissues was confirmed pathologically. The case data were statistically analyzed to establish a mathematical prediction model, the prediction performance of which was verified by the pathological results., Results: Altogether 261 GGN patients met the inclusion criteria. Using the results of logistic regression analysis, a mathematical prediction equation was established as follows: Malignant probability (mP) = e x / (1 + e x ); when mP was > 0.5, the GGN was considered as malignant, and when mP was ≤ 0.5, it was considered as benign. x= -2.46 + 1.032*gender + 1.85*mGGN + 1.40*VCS-0.0027*mean CT value of the nodule + 0.078*maximum diameter of the nodule, where e represents the natural logarithm; if the patient was a female, gender = 1 (otherwise = 0); if the pulmonary nodule was a mixed GGN, mGGN = 1 (otherwise = 0); if the pulmonary nodule had vascular convergence sign, VCS = 1 (otherwise = 0). The prediction performance of the mathematical prediction model was verified as follows: the negative prediction value was 0.97156 and the positive prediction value was 0.3800 in the model group versus 1 and 0.25 in the verification group., Conclusion: In this study, we identified female gender, mGGN, VCS, mean CT value and maximum nodule diameter as five key factors for predicting malignancy or benignity of pulmonary nodules, based on which we established a mathematical prediction model. This novel innovation may provide a useful auxiliary tool for predicting malignancy and benignity of pulmonary nodules, especially in women patients., (© 2024. The Author(s).)

Background: There are no standard treatment options for bilateral multiple pulmonary nodules requiring resection. This study aimed to summarize the experience of simultaneous bilateral uniportal video-assisted thoracoscopic surgery for the treatment of bilateral multiple primary pulmonary nodules., Methods: The clinical data of 65 cases of simultaneous bilateral uniportal thoracoscopic surgery for bilateral multiple primary pulmonary nodules treated were retrospectively analyzed. These cases were treated within The Ninth Medical Center of PLA General Hospital between January 2018 and November 2020. Parameters related to the surgery, perioperative aspects, surgical techniques, pathology results, and postoperative complications were examined., Results: All surgeries were conducted through uniportal video-assisted thoracoscopic surgery, with no instances of intraoperative conversion to thoracotomy. Fifty-three patients further underwent CT-guided Hookwire localization for the localization of pulmonary nodules. A total of 189 nodules were resected using multiple surgical procedures, with a malignancy rate of 86.2%. The average operation time was 226 ± 77.4 min, the average thoracic drainage duration was 3.1 ± 1.5 days, the average 24 h pleural drainage was 385.9 ± 157.4 mL, the average postoperative hospital stay was 8.6 ± 2.4 days, and the average blood loss was 77.2 ± 33.8 mL. Post-surgery, all patients were transferred to the ward safely within 12 h. 15.38% of patients have prolonged drainage time, and 12.31% of patients experience complications such as lung infection, arrhythmia, and venous thrombosis., Conclusion: The selected cases undergoing simultaneous bilateral uniportal video-assisted thoracoscopic surgery for the management of bilateral multiple primary pulmonary nodules demonstrated favorable outcomes. Our observations indicate the safety and feasibility of this procedure, providing an individualized and precise treatment approach for affected patients., (© 2024. The Author(s).)

Objective: To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules., Methods and Materials: In this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 malignant) collected between 2004 and 2012. DL performance was measured using area under the ROC curve (AUC) for the full set of nodules, for the certain cases and for the uncertain cases. Additionally, nodule characteristics were compared to identify trends for inducing uncertainty., Results: The DL algorithm performed significantly worse in the uncertain group compared to the certain group of DLCST (AUC 0.62 (95% CI: 0.49, 0.76) vs 0.93 (95% CI: 0.88, 0.97); p < .001) and the clinical dataset (AUC 0.62 (95% CI: 0.50, 0.73) vs 0.90 (95% CI: 0.86, 0.94); p < .001). The uncertain group included larger benign nodules as well as more part-solid and non-solid nodules than the certain group., Conclusion: The integrated uncertainty estimation showed excellent performance for identifying uncertain cases in which the DL-based nodule malignancy risk estimation algorithm had significantly worse performance., Clinical Relevance Statement: Deep Learning algorithms often lack the ability to gauge and communicate uncertainty. For safe clinical implementation, uncertainty estimation is of pivotal importance to identify cases where the deep learning algorithm harbors doubt in its prediction., Key Points: • Deep learning (DL) algorithms often lack uncertainty estimation, which potentially reduce the risk of errors and improve safety during clinical adoption of the DL algorithm. • Uncertainty estimation identifies pulmonary nodules in which the discriminative performance of the DL algorithm is significantly worse. • Uncertainty estimation can further enhance the benefits of the DL algorithm and improve its safety and trustworthiness., (© 2024. The Author(s).)

Background:  The possibility of coil dislocation in computed tomography (CT)-guided microcoil localization of superficial pulmonary nodules is relatively high. The aim of the study is to investigate the outcomes of deeper localization technique during CT-guided microcoil localization of superficial pulmonary nodules before video-assisted thoracoscopic surgery (VATS)., Methods:  Fifty-seven identified superficial pulmonary nodules (nodule-pleural distance ≤ 1 cm on CT image) from 51 consecutive patients underwent CT-guided microcoil localization, and subsequent VATSs were included. The rate of technical success, complications, and excised lung volume were compared between deeper localization technique group and conventional localization technique group., Results:  The technical success rate of the localization procedure was 100% (25/25) in the deeper localization group and 81.3% (26/32) in the conventional localization group ( p  = 0.030). Excluding one case of lobectomy, the excised lung volume in the deeper localization group and the conventional localization group was 39.3 ± 23.5 and 37.2 ± 16.2 cm 3 , respectively ( p  = 0.684). The incidence of pneumothorax was similar between the deeper localization group and the conventional localization group (24.0 vs. 21.9%, respectively, p  = 0.850). The incidence of intrapulmonary hemorrhage in the deeper localization group was higher (16.0%) than that in the conventional localization group (6.3%), but the difference was not statistically significant ( p  = 0.388)., Conclusion:  CT-guided microcoil localization of superficial pulmonary nodules prior to VATS using a deeper localization technique is feasible. Deeper localization technique reduced the occurrence of dislocation but did not increase excised lung volume., Competing Interests: None declared., (Thieme. All rights reserved.)

Purpose: This study was designed to conduct pooled comparisons of the relative clinical efficacy and safety of computed tomography (CT)-guided localization for pulmonary nodules (PNs) using either coil- or liquid material-based approaches., Material and Methods: Relevant articles published as of July 2023 were identified in the Web of Science, PubMed, and Wanfang databases, and pooled analyses of relevant endpoints were then conducted., Results: Six articles that enrolled 287 patients (341 PNs) and 247 patients (301 PNs) that had respectively undergone CT-guided localization procedures using coil- and liquid material-based approaches prior to video-assisted thoracic surgery (VATS) were included in this meta-analysis. The liquid material group exhibited a significantly higher pooled successful localization rate as compared to the coil group ( p  = 0.01), together with significantly lower pooled total complication rates ( p  = 0.0008) and pneumothorax rates ( p  = 0.01). Both groups exhibited similar rates of pulmonary hemorrhage ( p  = 0.44) and successful wedge resection ( p  = 0.26). Liquid-based localization was also associated with significant reductions in pooled localization and VATS procedure durations ( p  = 0.004 and 0.007)., Conclusions: These data are consistent with CT-guided localization procedures performed using liquid materials being safer and more efficacious than coil-based localization in patients with PNs prior to VATS resection.

Rationale and Objectives: Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses., Materials and Methods: A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis., Results: Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05)., Conclusion: The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background:  Nondisappearing subsolid nodules requiring follow-up are often detected during lung cancer screening, but changes in their invasiveness can be overlooked owing to slow growth. We aimed to develop a method for automatic identification of invasive tumors among subsolid nodules during multiple health checkups using radiomics technology based on low-dose computed tomography (LD-CT) and examine its effectiveness., Methods:  We examined patients who underwent LD-CT screening from 2014 to 2019 and had lung adenocarcinomas resected after 5-year follow-ups. They were categorized into the invasive or less-invasive group; the annual growth/change rate (Δ) of the nodule voxel histogram using three-dimensional CT (e.g., tumor volume, solid volume percentage, mean CT value, variance, kurtosis, skewness, and entropy) was assessed. A discriminant model was designed through multivariate regression analysis with internal validation to compare its efficacy with that of a volume doubling time of < 400 days., Results:  The study included 47 tumors (23 invasive, 24 less invasive), with no significant difference in the initial tumor volumes. Δskewness was identified as an independent predictor of invasiveness (adjusted odds ratio, 0.021; p  = 0.043), and when combined with Δvariance, it yielded high accuracy in detecting invasive lesions (88% true-positive, 80% false-positive). The detection model indicated surgery 2 years earlier than the volume doubling time, maintaining accuracy (median 3 years vs.1 year before actual surgery, p  = 0.011)., Conclusion:  LD-CT radiomics showed promising potential in ensuring timely detection and monitoring of subsolid nodules that warrant follow-up over time., Competing Interests: None declared., (Thieme. All rights reserved.)

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., Conclusion: 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., (© 2024. The Author(s).)

Purpose: Many large radiographic datasets of lung nodules are available, but the small and hard-to-detect nodules are rarely validated by computed tomography. Such difficult nodules are crucial for training nodule detection methods. This lack of difficult nodules for training can be addressed by artificial nodule synthesis algorithms, which can create artificially embedded nodules. This study aimed to develop and evaluate a novel cost function for training networks to detect such lesions. Embedding artificial lesions in healthy medical images is effective when positive cases are insufficient for network training. Although this approach provides both positive (lesion-embedded) images and the corresponding negative (lesion-free) images, no known methods effectively use these pairs for training. This paper presents a novel cost function for segmentation-based detection networks when positive-negative pairs are available., Methods: Based on the classic U-Net, new terms were added to the original Dice loss for reducing false positives and the contrastive learning of diseased regions in the image pairs. The experimental network was trained and evaluated, respectively, on 131,072 fully synthesized pairs of images simulating lung cancer and real chest X-ray images from the Japanese Society of Radiological Technology dataset., Results: The proposed method outperformed RetinaNet and a single-shot multibox detector. The sensitivities were 0.688 and 0.507 when the number of false positives per image was 0.2, respectively, with and without fine-tuning under the leave-one-case-out setting., Conclusion: To our knowledge, this is the first study in which a method for detecting pulmonary nodules in chest X-ray images was evaluated on a real clinical dataset after being trained on fully synthesized images. The synthesized dataset is available at https://zenodo.org/records/10648433 ., (© 2024. The Author(s).)

The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents' mean detection rate increased significantly from 64 to 77% with AI assist, while seniors' detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67-77% vs. 80-86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates., (© 2024. The Author(s).)

Rationale: Pulmonary ground-glass nodules (GGNs) pose challenges in intraoperative localization due to their primarily nonsolid composition. This report highlights a novel approach using H-marker deployment guided by LungPro navigation combined with cone-beam computed tomography (CBCT) for precise localization of multiple GGNs., Patient Concerns: A 55-year-old female patient presented at Sir-Run-Run-Shaw Hospital, Zhejiang University School of Medicine, in June 2021, requiring thoracoscopic surgery for the management of multiple GGNs in her right lung. She had a recent history of thoracoscopic wedge resection for a lesion in her lower left lung 3 months prior., Diagnoses: Computed tomography scans revealed the presence of 3 mixed GGNs in the right lung, with further confirmation identifying these as solitary pulmonary nodules, necessitating surgical intervention., Interventions: The patient underwent thoracoscopic surgery, during which the multiple nodules in her right lung were precisely localized utilizing an H-marker implanted bronchoscopically under the guidance of LungPro navigation technology, with CBCT providing additional confirmation of nodule positioning. This innovative combination of technologies facilitated accurate targeting of the lesions., Outcomes: Postoperative histopathological analysis confirmed the nodules to be microinvasive adenocarcinomas. Radiographic examination with chest X-rays demonstrated satisfactory lung expansion, indicating effective lung function preservation following the procedure. Follow-up assessments have shown no evidence of tumor recurrence, suggesting successful treatment., Lessons: The employment of H-marker implantation guided by the LungPro navigation system with CBCT confirmation presents a feasible and efficacious strategy for localizing multiple pulmonary GGNs. To further validate its clinical utility and safety, large-scale, multicenter, prospective studies are warranted. This approach holds promise in enhancing the precision and outcomes of surgeries involving GGNs., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: This study aimed to evaluate the efficacy and safety between electromagnetic navigational bronchoscopy (ENB) and computed tomography (CT)-guided percutaneous localization before resection of pulmonary nodules., Methods: Pubmed, Embase, Web of Science, and the Cochrane Library databases were searched from January 1, 2000 to April 30, 2022, for relevant studies. Two reviewers conducted the search, selection, and extraction of data from eligible studies. The risk of bias was assessed using the Newcastle-Ottawa Scale. The primary outcome was the localization success rate, and the secondary outcomes were the pneumothorax incidence and localization time. The meta-analysis was performed by Review Manager 5.4. The protocol for the meta-analysis was registered on PROSPERO (Registration number: CRD42022345972)., Results: Five cohort studies comprising 441 patients (ENB group: 185, CT group: 256) were analyzed. Compared with the CT-guided group, the ENB-guided group was associated with lower pneumothorax incidence (relative ratio = 0.16, 95% confidence interval [CI]: 0.04-0.65, P = .01). No significant differences were found in location success rates (relative ratio = 1.01, 95% CI: 0.98-1.05, P = .38) and localization time (mean difference = 0.99, 95% CI: -5.73 to 7.71, P = .77) between the ENB group and CT group., Conclusion: Both ENB and CT-guided are valuable technologies in localizing lung nodules before video-assisted thoracoscopic surgery based on current investigations. ENB achieved a lower pneumothorax rate than the CT-guided group. In our opinion, there is no perfect method, and decision-making should be given the actual circumstances of each institute. Future prospective studies in the form of a randomized trial are needed to confirm their clinical value., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Purpose: Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tackle these concerns, this investigation introduces a novel approach: the 3D Global Coordinated Attention Wide Inverted ResNet Network (GC-WIR). This network aims to achieve precise classification of benign and malignant pulmonary nodules, leveraging its merits of heightened efficiency, parsimonious parameterization, and robust stability., Methods: Within this framework, a 3D Global Coordinate Attention Mechanism (3D GCA) is designed to compute the features of the input images by converting 3D channel information and multi-dimensional positional cues. By encompassing both global channel details and spatial positional cues, this approach maintains a judicious balance between flexibility and computational efficiency. Furthermore, the GC-WIR architecture incorporates a 3D Wide Inverted Residual Network (3D WIRN), which augments feature computation by expanding input channels. This augmentation mitigates information loss during feature extraction, expedites model convergence, and concurrently enhances performance. The utilization of the inverted residual structure imbues the model with heightened stability., Results: Empirical validation of the GC-WIR method is performed on the LUNA 16 dataset, yielding predictions that surpass those generated by previous models. This novel approach achieves an impressive accuracy rate of 94.32%, coupled with a specificity of 93.69%. Notably, the model's parameter count remains modest at 5.76M, affording optimal classification accuracy., Conclusion: Furthermore, experimental results unequivocally demonstrate that, even under stringent computational constraints, GC-WIR outperforms alternative deep learning methodologies, establishing a new benchmark in performance., (© 2024. The Author(s).)

Objective: To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT)., Materials and Methods: An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared., Results: Image noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP., Conclusion: Detection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules., (© 2024. The Author(s).)

Objective: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm., Methods: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed., Results: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%., Conclusion: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system., (© 2024. The Author(s).)

Background: A 3.0-mm ultrathin bronchoscope (UTB) with a 1.7-mm working channel provides better accessibility to peripheral bronchi. A 4.0-mm thin bronchoscope with a larger 2.0-mm working channel facilitates the use of a guide sheath (GS), ensuring repeated sampling from the same location. The 1.1-mm ultrathin cryoprobe has a smaller diameter, overcoming the limitation of the size of biopsy instruments used with UTB. In this study, we compared the endobronchial ultrasound localization rate and diagnostic yield of peripheral lung lesions by cryobiopsy using UTB and thin bronchoscopy combined with GS., Methods: We retrospectively evaluated 133 patients with peripheral pulmonary lesions with a diameter less than 30 mm who underwent bronchoscopy with either thin bronchoscope or UTB from May 2019 to May 2023. A 3.0-mm UTB combined with rEBUS was used in the UTB group, whereas a 4.0-mm thin bronchoscope combined with rEBUS and GS was used for the thin bronchoscope group. A 1.1-mm ultrathin cryoprobe was used for cryobiopsy in the two groups., Results: Among the 133 patients, peripheral pulmonary nodules in 85 subjects were visualized using r-EBUS. The ultrasound localization rate was significantly higher in the UTB group than in the thin bronchoscope group (96.0% vs. 44.6%, respectively; P < 0.001). The diagnostic yield of cryobiopsy specimens from the UTB group was significantly higher compared to the thin bronchoscope group (54.0% vs. 30.1%, respectively; p = 0.006). Univariate analysis demonstrated that the cryobiopsy diagnostic yields of the UTB group were significantly higher for lesions ≤ 20 mm, benign lesions, upper lobe lesions, lesions located lateral one-third from the hilum, and lesions without bronchus sign., Conclusions: Ultrathin bronchoscopy combined with cryobiopsy has a superior ultrasound localization rate and diagnostic yield compared to a combination of cryobiopsy and thin bronchoscopy., (© 2024. The Author(s).)

Objective: To intelligently evaluate the invasiveness of pure ground-glass nodules with multiple classifications using deep learning., Methods: pGGNs in 1136 patients were pathologically confirmed as lung precursor lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Four different models [EfficientNet-b0 2D, dual-head ResNet&#95;3D, a 3D model combining three features (3D&#95;3F), and a 3D model combining 19 features (3D&#95;19F)] were constructed to evaluate the invasiveness of pGGNs using the EfficientNet and ResNet networks. The Obuchowski index was used to evaluate the differences in diagnostic efficiency among the four models., Results: The patients with pGGNs (360 men, 776 women; mean age, 54.63 ± 12.36 years) included 235 cases of AAH + AIS, 332 cases of MIA, and 569 cases of IAC. In the validation group, the areas under the curve in detecting the invasiveness of pGGNs as a three-category classification (AAH + AIS, MIA, IAC) were 0.8008, 0.8090, 0.8165, and 0.8158 for EfficientNet-b0 2D, dual-head ResNet&#95;3D, 3D&#95;3F, and 3D&#95;19F, respectively, whereas the accuracies were 0.6422, 0.6158, 0.651, and 0.6364, respectively. The Obuchowski index revealed no significant differences in the diagnostic performance of the four models., Conclusions: The dual-head ResNet&#95;3D&#95;3F model had the highest diagnostic efficiency for evaluating the invasiveness of pGGNs in the four models., (© 2024. The Author(s).)

Pulmonary nodules are common incidental findings requiring surveillance. Follow-up recommendations vary depending on risk factors, size and solid or subsolid characteristics. This review aimed to evaluate the prevalence of clinically significant nodules detected on noncancer-dedicated imaging and the prevalence of part-solid and ground-glass nodules. We conducted a systematic search of literature and screened texts for eligibility. Clinically significant nodules were noncalcified nodules >4-6 mm. Prevalence estimates were calculated for all studies and risk of bias was assessed by one reviewer. Twenty-four studies were included, with a total of 30 887 participants, and 21 studies were cross-sectional in design. Twenty-two studies used computed tomography (CT) imaging with cardiac-related CT being the most frequent. Prevalence of significant nodules was highest in studies with large field of view of the chest and low size thresholds for reporting nodules. The prevalence of part-solid and ground-glass nodules was only described in two cardiac-related CT studies. The overall risk of bias was low in seven studies and moderate in 17 studies. While current literature frequently reports incidental nodules on cardiovascular-related CT, there is minimal reporting of subsolid characteristics. Unclear quantification of smoking history and heterogeneity of imaging protocol also limits reliable evaluation of nodule prevalence in nonscreening cohorts., (© 2024 The Author(s). Internal Medicine Journal published by John Wiley & Sons Australia, Ltd on behalf of Royal Australasian College of Physicians.)

Objective: Lung cancers that present as radiographic subsolid nodules represent a subtype with distinct biological behavior and outcomes. The objective of this document is to review the existing literature and report consensus among a group of multidisciplinary experts, providing specific recommendations for the clinical management of subsolid nodules., Methods: The American Association for Thoracic Surgery Clinical Practice Standards Committee assembled an international, multidisciplinary expert panel composed of radiologists, pulmonologists, and thoracic surgeons with established expertise in the management of subsolid nodules. A focused literature review was performed with the assistance of a medical librarian. Expert consensus statements were developed with class of recommendation and level of evidence for each of 4 main topics: (1) definitions of subsolid nodules (radiology and pathology), (2) surveillance and diagnosis, (3) surgical interventions, and (4) management of multiple subsolid nodules. Using a modified Delphi method, the statements were evaluated and refined by the entire panel., Results: Consensus was reached on 17 recommendations. These consensus statements reflect updated insights on subsolid nodule management based on the latest literature and current clinical experience, focusing on the correlation between radiologic findings and pathological classifications, individualized subsolid nodule surveillance and surgical strategies, and multimodality therapies for multiple subsolid lung nodules., Conclusions: Despite the complex nature of the decision-making process in the management of subsolid nodules, consensus on several key recommendations was achieved by this American Association for Thoracic Surgery expert panel. These recommendations, based on evidence and a modified Delphi method, provide guidance for thoracic surgeons and other medical professionals who care for patients with subsolid nodules., Competing Interests: Conflict of Interest Statement A full list of author’s disclosures is provided in Appendix E1. The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To determine the risk of lung cancer in incidental pulmonary nodules, as well as the time until cancer growth is detected., Patients and Methods: This retrospective study examined patients with incidental nodules detected on chest computed tomography (CT) in 2017. Characteristics of the dominant nodule were automatically extracted from CT reports, and cancer diagnoses were manually verified by a thoracic radiologist. Nodules were categorized per Fleischner Society guideline categories: solid <6 mm, solid 6 to 8 mm, solid >8 mm, subsolid <6 mm, ground glass nodules ≥6 mm, and part-solid nodules ≥6 mm. The time to nodule growth was determined by CT reports., Results: A total of 3180 patients (nodules) were included, of which 155 (5%) were diagnosed with lung cancer. By category, 7/1601 (0.4%) solid nodules <6 mm, 11/713 (1.5%) solid nodules 6 to 8 mm, 71/446 (15.9%) solid nodules >8 mm, 1/124 (0.8%) subsolid nodules <6 mm, 29/202 (14.4%) ground glass nodules ≥6 mm, and 36/94 (37.9%) part-solid nodules ≥6 mm were malignant. Of solid lung cancers <6 mm, growth was observed in 1/4 imaged by 1 year and 2/5 by 2 years; of solid lung cancers 6 to 8 mm, growth was observed in 3/10 imaged by 1 year and 6/10 by 2 years., Conclusion: Solid nodules <6 mm have a very low risk of malignancy and may not require routine follow-up. However, when malignant, growth is often not observed until 2 or more years later; therefore, stability at 1 to 2 years does not imply benignity., Competing Interests: The author declares no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The risk of malignancy in pulmonary nodules incidentally detected on computed tomography (CT) in patients who are aged younger than 35 years is unclear., Objective: The aim of this study was to evaluate the incidence of lung cancer in incidental pulmonary nodules in patients who are 15-34 years old., Methods: This retrospective study included patients aged 15-34 years who had an incidental pulmonary nodule on chest CT from 2010 to 2018 at our hospital. Patients with prior, current, or suspected malignancy were excluded. A chart review identified patients with diagnosis of malignancy. Incidental pulmonary nodule was deemed benign if stable or resolved on a follow-up CT at least 2 years after initial or if there was a medical visit in our health care network at least 2 years after initial CT without diagnosis of malignancy.Receiver operating characteristic curve analysis was performed with nodule size. Association of categorical variables with lung cancer diagnosis was performed with Fisher exact test, and association of continuous variables was performed with logistic regression., Results: Five thousand three hundred fifty-five chest CTs performed on patients aged 15-34 years between January 2010 and December 2018. After excluding patients without a reported pulmonary nodule and prior or current malignancy, there were a total of 779 patients. Of these, 690 (89%) had clinical or imaging follow-up after initial imaging. Of these, 545 (70% of total patients) patients had imaging or clinical follow-up greater than 2 years after their initial imaging.A malignant diagnosis was established in 2/779 patients (0.3%; 95% confidence interval, 0.1%-0.9%). Nodule size was strongly associated with malignancy ( P = 0.007), with area under the receiver operating characteristic curve of 0.97. There were no malignant nodules that were less than 10 mm in size. Smoking history, number of nodules, and nodule density were not associated with malignancy., Conclusions: Risk of malignancy for incidentally detected pulmonary nodules in patients aged 15-34 years is extremely small (0.3%). There were no malignant nodules that were less than 10 mm in size. Routine follow-up of subcentimeter pulmonary nodules should be carefully weighed against the risks., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC)., Methods: A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysis RESULTS: The pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P < 0.05). The AUC of the GTV and GTV + 5/10/15-mm PTV were 0.869, 0.910, 0.951, and 0.872 in the training cohort and 0.700, 0.715, 0.745, and 0.724 in the validation cohort, respectively., Conclusions: The radiomic features we extracted from the GTV and PTV of pGGNs could effectively be used to differentiate grade-1 and grade-2 tumors. In particular, the radiomic features from the PTV increased the efficiency of the diagnostic model, with GTV + 10 mm PTV exhibiting the highest efficacy., (© 2024. The Author(s).)

Background: Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant., Methods: Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods., Results: Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year)., Conclusions: In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules., (© 2024. The Author(s).)

With the development of chest CT screening, surgically resected lung tumors have shifted from predominantly large masses to predominantly small nodules. The intraoperative frozen diagnosis of pulmonary small nodules faces many challenges, such as the accurate understanding about the concepts of adenocarcinoma in situ, minimally invasive adenocarcinoma and lepidic adenocarcinoma, as well as their differential diagnosis with small size invasive adenocarcinoma, benign tumors (such as bronchiolar adenoma, sclerosing pneumocytoma, etc.), metastatic tumors and so on. This study summarizes some common problems encountered in the intraoperative frozen diagnosis of small pulmonary nodules in daily practice, focusing on the diagnosis and differential diagnosis of adenocarcinoma, in order to make the accurate intraoperative frozen diagnosis of small pulmonary nodules and diminish misdiagnosis.

Background: The ION system is a shape-sensing robotic-assisted bronchoscopy (SS-RAB) platform developed to biopsy peripheral pulmonary nodules (PPNs). There is a lack of data describing the use of this system in the Chinese population. The study aimed to assess the feasibility and safety of using SS-RAB to diagnose PPNs across multiple centers within China., Methods: This prospective, multicenter study used SS-RAB in consecutive patients with solid or sub-solid PPNs 8 to 30 mm in largest diameter. Primary endpoints were diagnostic yield and the rates of procedure- or device-related complications. Radial endobronchial ultrasound (rEBUS) was to confirm lesion localization, followed by sampling, using the Flexision biopsy needle, biopsy forceps, and cytology brush. Subjects with nonmalignant index biopsy results were followed up to 6 months., Results: A total of 90 PPNs were biopsied from 90 subjects across 3 centers using SS-RAB. The median nodule size was 19.4 mm (IQR: 19.3, 24.6) in the largest dimension. In all (100%) cases, the catheter successfully reached the target nodule with tissue samples obtained. The diagnostic yield was 87.8% with a sensitivity for malignancy of 87.7% (71/81). In a univariate analysis, nodule lobar location, presence of bronchus sign, and rEBUS view were associated with a diagnostic sample, but only rEBUS view showed an association in a multivariate analysis. The overall pneumothorax rate was 1.1% without pneumothorax requiring intervention, and there was no periprocedural bleeding., Conclusion: As an emerging technology in the Chinese population, SS-RAB can safely biopsy PPNs with strong diagnostic performance., Competing Interests: Disclosure: M.J.S. is a pulmonologist at Henry Ford Hospital, Wayne State University School of Medicine, and serves as the associate medical officer of Ion Robotic Systems, Intuitive Surgical, Inc. as well. For the remaining authors, there are no conflicts of interest or other disclosures., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Case Presentation: A 62-year-old woman came to our hospital with worsening cough and dyspnea over the preceding week, during which time she had been treated with azithromycin and prednisone for suspected pneumonia. She had no fever, chills, or sweats, but her cough had become productive of clear to blood-tinged phlegm during the interval. Medical history was significant for insulin-dependent diabetes mellitus and OSA. She had quit smoking 44 years earlier and had no history of lung disease. She was a bank teller residing in southeastern Minnesota and described no relevant inhalational or environmental exposures, drug use, aspiration, or travels preceding her illness., Competing Interests: Financial/Nonfinancial Disclosures None declared., (Copyright © 2024 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: To develop a Dual generative-adversarial-network (GAN) Cascaded Network (DGCN) for generating super-resolution computed tomography (SRCT) images from normal-resolution CT (NRCT) images and evaluate the performance of DGCN in multi-center datasets., Materials and Methods: This retrospective study included 278 patients with chest CT from two hospitals between January 2020 and June 2023, and each patient had all three NRCT (512×512 matrix CT images with a resolution of 0.70 mm, 0.70 mm,1.0 mm), high-resolution CT (HRCT, 1024×1024 matrix CT images with a resolution of 0.35 mm, 0.35 mm,1.0 mm), and ultra-high-resolution CT (UHRCT, 1024×1024 matrix CT images with a resolution of 0.17 mm, 0.17 mm, 0.5 mm) examinations. Initially, a deep chest CT super-resolution residual network (DCRN) was built to generate HRCT from NRCT. Subsequently, we employed the DCRN as a pre-trained model for the training of DGCN to further enhance resolution along all three axes, ultimately yielding SRCT. PSNR, SSIM, FID, subjective evaluation scores, and objective evaluation parameters related to pulmonary nodule segmentation in the testing set were recorded and analyzed., Results: DCRN obtained a PSNR of 52.16, SSIM of 0.9941, FID of 137.713, and an average diameter difference of 0.0981 mm. DGCN obtained a PSNR of 46.50, SSIM of 0.9990, FID of 166.421, and an average diameter difference of 0.0981 mm on 39 testing cases. There were no significant differences between the SRCT and UHRCT images in subjective evaluation., Conclusion: Our model exhibited a significant enhancement in generating HRCT and SRCT images and outperformed established methods regarding image quality and clinical segmentation accuracy across both internal and external testing datasets., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background Most of the data regarding prevalence and size distribution of solid lung nodules originates from lung cancer screening studies that target high-risk populations or from Asian general cohorts. In recent years, the identification of lung nodules in non-high-risk populations, scanned for clinical indications, has increased. However, little is known about the presence of solid lung nodules in the Northern European nonsmoking population. Purpose To study the prevalence and size distribution of solid lung nodules by age and sex in a nonsmoking population. Materials and Methods Participants included nonsmokers (never or former smokers) from the population-based Imaging in Lifelines study conducted in the Northern Netherlands. Participants (age ≥ 45 years) with completed lung function tests underwent chest low-dose CT scans. Seven trained readers registered the presence and size of solid lung nodules measuring 30 mm 3 or greater using semiautomated software. The prevalence and size of lung nodules (≥30 mm 3 ), clinically relevant lung nodules (≥100 mm 3 ), and actionable nodules (≥300 mm 3 ) are presented by 5-year categories and by sex. Results A total of 10 431 participants (median age, 60.4 years [IQR, 53.8-70.8 years]; 56.6% [ n = 5908] female participants; 46.1% [ n = 4812] never smokers and 53.9% [ n = 5619] former smokers) were included. Of these, 42.0% ( n = 4377) had at least one lung nodule (male participants, 47.5% [2149 of 4523]; female participants, 37.7% [2228 of 5908]). The prevalence of lung nodules increased from age 45-49.9 years (male participants, 39.4% [219 of 556]; female participants, 27.7% [236 of 851]) to age 80 years or older (male participants, 60.7% [246 of 405]; female participants, 50.9% [163 of 320]). Clinically relevant lung nodules were present in 11.1% (1155 of 10 431) of participants, with prevalence increasing with age (male participants, 8.5%-24.4%; female participants, 3.7%-15.6%), whereas actionable nodules were present in 1.1%-6.4% of male participants and 0.6%-4.9% of female participants. Conclusion Lung nodules were present in a substantial proportion of all age groups in the Northern European nonsmoking population, with slightly higher prevalence for male participants than female participants. © RSNA, 2024 Supplemental material is available for this article.

Background: Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the 'suspicious' pulmonary nodules identified by LDCT are later confirmed benign through resection surgery, adding to patients' discomfort and the burden on the healthcare system. In this study, we aim to develop a noninvasive liquid biopsy assay for distinguishing pulmonary malignancy from benign yet 'suspicious' lung nodules using cell-free DNA (cfDNA) fragmentomics profiling., Methods: An independent training cohort consisting of 193 patients with malignant nodules and 44 patients with benign nodules was used to construct a machine learning model. Base models using four different fragmentomics profiles were optimized using an automated machine learning approach before being stacked into the final predictive model. An independent validation cohort, including 96 malignant nodules and 22 benign nodules, and an external test cohort, including 58 malignant nodules and 41 benign nodules, were used to assess the performance of the stacked ensemble model., Results: Our machine learning models demonstrated excellent performance in detecting patients with malignant nodules. The area under the curves reached 0.857 and 0.860 in the independent validation cohort and the external test cohort, respectively. The validation cohort achieved an excellent specificity (68.2%) at the targeted 90% sensitivity (89.6%). An equivalently good performance was observed while applying the cut-off to the external cohort, which reached a specificity of 63.4% at 89.7% sensitivity. A subgroup analysis for the independent validation cohort showed that the sensitivities for detecting various subgroups of nodule size (<1 cm: 91.7%; 1-3 cm: 88.1%; >3 cm: 100%; unknown: 100%) and smoking history (yes: 88.2%; no: 89.9%) all remained high among the lung cancer group., Conclusions: Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives., Competing Interests: Disclosure WT, HB, SC, and XW are employees of Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, China. The remaining authors have declared no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: To compare the image quality and pulmonary nodule detectability between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in ultra-low-dose CT (ULD-CT)., Methods: 142 participants required lung examination who underwent simultaneously ULD-CT (UL-A, 0.57 ± 0.04 mSv or UL-B, 0.33 ± 0.03 mSv), and standard CT (SDCT, 4.32 ± 0.33 mSv) plain scans were included in this prospective study. SDCT was the reference standard using ASIR-V at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective scores were measured. The presence and accuracy of nodules were analyzed using a combination of a deep learning-based nodule evaluation system and a radiologist., Results: A total of 710 nodules were detected by SDCT, including 358 nodules in UL-A and 352 nodules in UL-B. DLIR-H exhibited superior noise, SNR, and CNR performance, and achieved comparable or even higher subjective scores compared to 50%ASIR-V in ULD-CT. Nodules sensitivity detection of 50%ASIR-V, DLIR-M, and DLIR-H in ULD-CT were identical (96.90%). In multivariate analysis, body mass index (BMI), nodule diameter, and type were independent predictors for the sensitivity of nodule detection (p<.001). DLIR-H provided a lower absolute percent error (APE) in volume (3.10% ± 95.11% vs 8.29% ± 99.14%) compared to 50%ASIR-V of ULD-CT (P<.001)., Conclusions: ULD-CT scanning has a high sensitivity for detecting pulmonary nodules. Compared with ASIR-V, DLIR can significantly reduce image noise, and improve image quality, and accuracy of the nodule measurement in ULD-CT., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Purpose: The detection of small lung nodules in thoracoscopic procedure is difficult when the lesions are not located within the outer border of the lung. In the case of ground-glass opacities, it is often impossible to palpate the lesion. Marking lung nodules using a radiotracer is a known technique. We analysed the accuracy and safety of the technique and the potential benefits of operating in a hybrid operating room., Methods: 57 patients, including 33 (58%) females with a median age of 67 years (range 21-82) were included. In 27 patients, we marked and resected the lesion in a hybrid room. In 30 patients, the lesion was marked at the department of radiology the day before resection. [ 99m Tc]Tc-Macrosalb (Pulmocis ® ) was used at an activity of 1 MBq in the hybrid room and at an activity of 3 MBq the day before to get technical feasible results. Radioactivity was detected using the Neoprobe ® detection system., Results: Precise detection and resection of the nodules was possible in 95% of the lesions and in 93% of the patients. Complete thoracoscopic resection was possible in 90% of the patients. Total conversion rate was 10%, but conversion due to failure of the marking of the nodule was observed in only 5% of the patients. Histology revealed 28 (37%) primary lung cancers, 24 (32%) metastases and 21 (28%) benign lesions. In 13 (23%) patients, minor complications were observed. None of them required additional interventions., Conclusion: The radio-guided detection of small pulmonary nodules is very accurate and safe after CT-guided injection of [ 99m Tc]Tc-Macrosalb. Performing the operation in a hybrid room has several logistic advantages and allows using lower technetium-99m activities. The technique allows minimally invasive lung sparing resection and prevents overtreatment of benign and metastatic lesions., (© 2023. The Author(s).)

We present a non-smoker woman in her 40s with PLCH who presented with atypical imaging findings of multiple pulmonary noncavitary nodules without air cysts with repeated waxing and waning., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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., (© 2024 The Author(s). Respirology published by John Wiley & Sons Australia, Ltd on behalf of Asian Pacific Society of Respirology.)

This study aimed to evaluate the value of low-dose dual-input computed tomography perfusion (CTP) imaging in the differential diagnosis of benign and malignant pulmonary ground-glass opacity nodules (GGO). A retrospective study was conducted in patients with GGO who underwent CTP in our hospital from January 2021 to October 2023. All nodules were confirmed via pathological analysis or disappeared during follow-up. Postprocessing analysis was conducted using the dual-input perfusion mode (pulmonary artery and bronchial artery) of the body perfusion software to measure the perfusion parameters of the pulmonary GGOs. A total of 101 patients with pulmonary GGOs were enrolled in this study, including 43 benign and 58 malignant nodules. The dose length product of the CTP (348 mGy.cm) was < 75% of the diagnostic reference level of the unenhanced chest CT (470 mGy.cm). The effective radiation dose was 4.872 mSV. The blood flow (BF), blood volume (BV), mean transit time (MTT), and flow extraction product (FEP) of malignant nodules were higher than those of the benign nodules (p < 0.05). The FEP had the highest accuracy for the diagnosis of malignant nodules (area under the curve [AUC] = 0.821, 95% confidence interval [CI]: 0.735-0.908) followed by BV (AUV = 0.713, 95% CI 0.608-0.819), BF (AUC = 0.688, 95% CI 0.587-0.797), and MTT (AUC = 0.616, 95% CI 0.506-0.726). When the FEP was ≥ 19.12 mL/100 mL/min, the sensitivity was 91.5% and the specificity was 62.8%. To distinguish between benign nodules and malignant nodules, the AUC of the combination of BV and FEP was 0.816 (95% CI 0.728-0.903), whereas the AUC of the combination of BF, BV, MTT, and FEP was 0.814 (95% CI 0.729-0.900). Low-dose dual-input perfusion CT was extremely effective in distinguishing between benign from malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability., (© 2024. The Author(s).)

Background: This study aimed to explore the efficacy of hookwire for computed tomography (CT)-guided pulmonary nodule (PN) localization before video-assisted thoracoscopic surgery (VATS) resection and determine the risk factors for localization-related complications., Methods: We enrolled 193 patients who underwent preoperative CT-guided PN hookwire localization. The patients were categorized into groups A (103 patients had no complications) and B (90 patients had complications) according to CT and VATS. Uni- and multivariate logistic regression analyses were used to identify risk factors for localization-related complications. A numerical rating scale was used to evaluate hookwire localization-induced pain., Results: We successfully performed localization in 173 (89.6%) patients. Pneumothorax was the main complication in 82 patients (42.5%). Patient gender, age, body mass index, tumor diameter, consolidation tumor ratio, pathologic diagnosis, position adjustment during location, lesion location, waiting time for surgery, and pleural adhesions were not significantly different between the two groups. The number of nodules, number of punctures, scapular rest position, and depth of insertion within the lung parenchyma were significant factors for successful localization. Multivariate regression analysis further validated the number of nodules, scapular rest position, and depth of insertion within the lung parenchyma as risk factors for hookwire-localization-related complications. Hookwire localization-induced pain is mainly mild or moderate pre- and postoperatively, and some patients still experience pain 7 days postoperatively., Conclusions: Hookwire preoperative PN localization has a high success rate, but some complications remain. Thus, clinicians should be vigilant and look forward to further improvement., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Rationale and Objective: To investigate the influence of the deep learning image reconstruction (DLIR) on the image quality and quantitative analysis of pulmonary nodules under ultra-low dose lung CT conditions., Materials and Methods: This was a prospective study with patient consent and included 56 patients with suspected pulmonary nodules. Patients were examined by both standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with adaptive statistical iterative reconstruction-V 40% (ASIR-V40%) (group A), while ULDCT images were reconstructed using ASIR-V40% (group B) and high-strength DLIR (DLIR-H) (group C). The three image sets were analyzed using a commercial computer aided diagnosis (CAD) software. Parameters such as nodule length, width, density, volume, risk, and classification were measured. The CAD quantitative data of different nodule types (solid, calcified, and subsolid nodules) and nodule image quality scores evaluated by two physicians on a 5-point scale were compared., Result: The radiation dose in ULDCT was 0.25 ± 0.08mSv, 7.2% that of the 3.48 ± 1.08mSv in SDCT (P < 0.001). 104 pulmonary nodules were detected (51/53 solid, 26/24 calcified and 27/27 subsolid in Groups A and (B&C), respectively). Group B had lower density for solid, calcified nodules, and lower volume and risk for subsolid nodules than Group A, while Group C had lower density for calcified nodules (P < 0.05), There were no significant differences in other parameters among the three groups (P > 0.05). Group A and C had similar image quality for nodules and were higher than Group B (P < 0.05)., Conclusion: DLIR-H significantly improves image quality than ASIR-V40% and maintains similar nodule detection and characterization with CAD in ULDCT compared to SDCT., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Detection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing this task. However, existing deep convolutional neural networks exhibit limited long-range dependency capabilities and neglect crucial contextual information, resulting in reduced performance on detecting small-size nodules in CT scans. In this work, we propose a novel end-to-end framework called LGDNet for the detection of suspicious pulmonary nodules in lung CT scans by fusing local features and global representations. To overcome the limited long-range dependency capabilities inherent in convolutional operations, a dual-branch module is designed to integrate the convolutional neural network (CNN) branch that extracts local features with the transformer branch that captures global representations. To further address the issue of misalignment between local features and global representations, an attention gate module is proposed in the up-sampling stage to selectively combine misaligned semantic data from both branches, resulting in more accurate detection of small-size nodules. Our experiments on the large-scale LIDC dataset demonstrate that the proposed LGDNet with the dual-branch module and attention gate module could significantly improve the nodule detection sensitivity by achieving a final competition performance metric (CPM) score of 89.49%, outperforming the state-of-the-art nodule detection methods, indicating its potential for clinical applications in the early diagnosis of lung diseases., (© 2024. International Federation for Medical and Biological Engineering.)

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., (Copyright © 2024 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Objective: This study aims to explore the efficacy of multi-slice spiral computed tomography (MSCT) and rapid on-site evaluation (ROSE) in diagnosing pulmonary nodules, thereby providing more diagnostic information for clinical diagnosis, and improving the diagnostic efficiency of pulmonary nodules., Methods: With the means of a retrospective study, 103 patients with pulmonary nodules in our hospital from January 2019 to December 2021 were analyzed. The included patients had no history of lung surgery, and had no cognitive, audio-visual, language communication and physical activity disorders, with visual lesions in bronchoscopy. All patients underwent MSCT scans and ROSE. In the process of cell puncture or tissue biopsy, cell fluid smears or tissue prints were directly used to make cytological specimens. In the operation site, real-time production, staining and real-time cell analysis were carried out to determine whether the material was qualified. The diagnostic efficacy of MSCT, ROSE, and the combination of the two for pulmonary nodules was analyzed., Results: Of the 103 patients, there were finally 68 cases diagnosed with solitary nodules (66.02%) and 35 cases with multiple nodules (33.98%), with 196 pulmonary nodules in total; 25 of them were peripheral lung cancer (24.27%) and 78 were benign nodules (75.73%); and based on the results of clinical diagnosis, they were divided into the malignant group and the benign group separately. Diagnosis of MSCT showed that the probabilities of calcification, spicular sign, lobulation sign, vacuolar sign, and spinous process in the malignant group were significantly higher than those in the benign group (P = .000). 30 positive cases and 73 negative cases were detected by MSCT, including 13 false positives and 8 false negatives. ROSE detected 29 positive cases and 74 negative cases, of which 5 positives were diagnosed as negatives, and the 9 negatives were diagnosed as positives. There were 28 positive cases and 75 negative cases detected by the combination of MSCT and ROSE, including 5 false positives and 2 false negatives. The combined diagnosis of MSCT and ROSE demonstrated an accuracy of 93.20%, sensitivity of 92.00%, specificity of 93.59%, positive predictive value of 82.14%, and negative predictive value of 97.33%. The accuracy, sensitivity, specificity, positive and negative predictive values of MSCT diagnosis were 79.61%, 68.00%, 83.33%, 56.67% and 89.04%, respectively. In ROSE diagnosis, the accuracy, sensitivity, specificity, positive and negative predictive values were 86.41%, 80.00%, 88.46%, 68.97% and 93.24%. The combined diagnosis of MSCT and ROSE had a significantly higher diagnosis rate than the single diagnosis of MSCT and ROSE (P = .000). Through ROC analysis, the area under the curve (AUC) of combined diagnosis was overtly larger than that of single diagnosis of MSCT and ROSE (P = .000). The AUC of MSCT diagnosis and ROSE diagnosis were 0.757 (95%CI: 0.639-0.875) and 0.842 (95%CI: 0.742-0.943) respectively, and the AUC of the combined diagnosis of MSCT and ROSE was 0.928 (95%CI: 0.859-0.997)., Conclusion: The combination of MSCT and ROSE contributes to the advances in the diagnostic efficacy for pulmonary nodules in order to reduce the damage caused by ineffective biopsy, which is of great clinically instructional value to the early diagnosis of this disease. This method is convenient to provide reasonable reference materials for the formulation of scientific clinical treatment plan and accurate judgment of prognosis, thereby promoting the good prognosis of patients.

Introduction: Electromagnetic navigation bronchoscopy (ENB) and radial probe endobronchial ultrasound (RP-EBUS) are essential bronchoscopic procedures for diagnosing peripheral lung lesions. Despite their individual advantages, the optimal circumstances for their combination remain uncertain., Methods: This single-center retrospective study enrolled 473 patients with 529 pulmonary nodules who underwent ENB and/or RP-EBUS biopsies between December 2021 and December 2022. Diagnostic yield was calculated using strict, intermediate, and liberal definitions. In the strict definition, only malignant and specific benign lesions were deemed diagnostic at the time of the index procedure. The intermediate and liberal definitions included additional results from the follow-up period., Results: The diagnostic yield of the strict definition was not statistically different among the three groups (ENB/Combination/RP-EBUS 63.8%/64.2%/62.6%, p = 0.944). However, the diagnostic yield was superior in the ENB + RP-EBUS group for nodules with a bronchus type II or III and a solid part <20 mm (odds ratio 1.96, 95% confidence interval 1.09-3.53, p = 0.02). In terms of complications, bleeding was significantly higher in the ENB + RP-EBUS group (ENB/Combination/RP-EBUS 3.7% /6.2/0.6%, p = 0.002), but no major adverse event was observed., Conclusion: The combination of ENB and RP-EBUS enhanced the diagnostic yield for nodules with bronchus type II or III and solid part <20 mm, despite a slightly elevated risk of bleeding. Careful patient selection based on nodule characteristics is important to benefit from this combined approach., (© 2024 The Author(s). Thoracic Cancer published by John Wiley & Sons Australia, Ltd.)

Objectives: Distinguishing solid nodules from nodules with ground-glass lesions in lung cancer is a critical diagnostic challenge, especially for tumors ≤2 cm. Human assessment of these nodules is associated with high inter-observer variability, which is why an objective and reliable diagnostic tool is necessary. This study focuses on artificial intelligence (AI) to automatically analyze such tumors and to develop prospective AI systems that can independently differentiate highly malignant nodules., Materials and Methods: Our retrospective study analyzed 246 patients who were diagnosed with negative clinical lymph node metastases (cN0) using positron emission tomography-computed tomography (PET/CT) imaging and underwent surgical resection for lung adenocarcinoma. AI detected tumor sizes ≤2 cm in these patients. By utilizing AI to classify these nodules as solid (AI&#95;solid) or non-solid (non-AI&#95;solid) based on confidence scores, we aim to correlate AI determinations with pathological findings, thereby advancing the precision of preoperative assessments., Results: Solid nodules identified by AI with a confidence score ≥0.87 showed significantly higher solid component volumes and proportions in patients with AI&#95;solid than in those with non-AI&#95;solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI&#95;solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI&#95;solid nodules represented high-grade malignancies., Conclusion: In small-sized lung cancer diagnosed as cN0, AI automatically identifies tumors as solid nodules ≤2 cm and evaluates their malignancy preoperatively. The AI classification can inform lymph node assessment necessity in sublobar resections, reflecting metastatic potential., Competing Interests: Disclosure The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings., Materials and Methods: Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians., Results: There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician., Conclusion: The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules., Clinical Relevance Statement: The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis., Key Points: • According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images. • The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%). • The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: To determine the frequency of malignancy of nonperifissural juxtapleural nodules (JPNs) measuring 6 to < 10 mm in a subset of low-dose chest computed tomographies from the National Lung Cancer Screening Trial and the rate of down-classification of such nodules in Lung-Reporting and Data System (RADS) 2.0 compared with Lung-RADS 1.1., Materials and Methods: A secondary analysis of a subset of the National Lung Screening Trial was performed. An exemption was granted by the Institutional Review Board. The dominant noncalcified nodule measuring 6 to <10 mm was identified on all available prevalence computed tomographies. Nodules were categorized as pleural or nonpleural. Benign or malignant morphology was recorded. Initial and updated categories based on Lung-RADS 1.1 and Lung-RADS 2.0 were assigned, respectively. The impact of the down-classification of JPN was assessed. Both classification schemes were compared using the McNemar test ( P < 0.01)., Results: A total of 2813 patients (62 ± 5 y, 1717 men) with 4408 noncalcified nodules were studied. One thousand seventy-three dominant nodules measuring 6 to <10 mm were identified. Three hundred forty-eight (32.4%) were JPN. The updated scheme allowed down-classification of 310 JPN from categories 3 (n = 198) and 4A (n = 112) to category 2. We, therefore, estimate a 4.8% rate of down-classification to category 2 in the entire National Lung Screening Trial screening group. Two/348 (0.57%) JPN were malignant, both nonbenign in morphology. The false-positive rate decreased in the updated classification ( P < 0.01)., Conclusion: This study demonstrates the low malignant potential of benign morphology JPN measuring 6 mm to <10 mm. The Lung-RADS 2.0 approach to JPN is estimated to reduce short-term follow-ups and false-positive results., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Aim: To evaluate the motion amplitude of lung nodules in different locations during preoperative computed tomography (CT)-guided localization, and the influence of respiratory movement on CT-guided percutaneous lung puncture., Materials and Methods: A consecutive cohort of 398 patients (123 men and 275 women with a mean age of 53.9 ± 10.7 years) who underwent preoperative CT-guided lung nodule localization from May 2021 to Apr 2022 were included in this retrospective study. The respiratory movement-related nodule amplitude in the cranial-caudal direction during the CT scan, characteristics of patients, lesions, and procedures were statistically analyzed. Univariate and multivariate logistic regression analyses were used to evaluate the influence of these factors on CT-guided localization., Results: The nodule motion distribution showed a statistically significant correlation within the upper/middle (lingular) and lower lobes (p<0.001). Motion amplitude was an independent risk factor for CT scan times (p=0.011) and procedure duration (p=0.016), but not for the technical failure rates or the incidence of complications. Puncture depth was an independent risk factor for the CT scan times, procedure duration, technical failure rates, and complications (p<0.01). Female, prone, and supine (as opposed to lateral) positions were significant protective factors for pneumothorax, while the supine position was an independent risk factor for parenchymal hemorrhage (p=0.025)., Conclusion: Respiratory-induced motion amplitude of nodules was greater in the lower lobes, resulting in more CT scan times/radiation dose and longer localization duration, but showed no statistically significant influence on the technical success rates or the incidence of complications during preoperative CT-guided localization., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)