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.

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.

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.

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.

[18F]FDG PET/CT is useful in discriminating invasive adenocarcinomas among pure ground-glass nodules: comparison with CT findings-a bicenter retrospective study.

PURPOSE: Predicting the malignancy of pure ground-glass nodules (GGNs) using CT is challenging. The optimal role of [18F]FDG PET/CT in this context has not been clarified. We compared the performance of [18F]FDG PET/CT in evaluating GGNs for predicting invasive adenocarcinomas (IACs) with CT. METHODS: From June 2012 to December 2020, we retrospectively enrolled patients with pure GGNs on CT who underwent [18F]FDG PET/CT within 90 days. Overall, 38 patients with 40 ≥ 1-cm GGNs were pathologically confirmed. CT images were analyzed for size, attenuation, uniformity, shape, margin, tumor-lung interface, and internal/surrounding characteristics. Visual [18F]FDG positivity, maximum standardized uptake value (SUVmax), and tissue fraction-corrected SUVmax (SUVmaxTF) were evaluated on PET/CT. RESULTS: The histopathology of the 40 GGNs were: 25 IACs (62.5%), 9 minimally invasive adenocarcinomas (MIA, 22.5%), and 6 adenocarcinomas in situ (AIS, 15.0%). No significant differences were found in CT findings according to histopathology, whereas visual [18F]FDG positivity, SUVmax, and SUVmaxTF were significantly different (P=0.001, 0.033, and 0.018, respectively). The size, visual [18F]FDG positivity, SUVmax, and SUVmaxTF showed significant diagnostic performance to predict IACs (area under the curve=0.693, 0.773, 0.717, and 0.723, respectively; P=0.029, 0.001, 0.018, and 0.013, respectively). In the multivariate logistic regression analysis, visual [18F]FDG positivity discriminated IACs among GGNs among various CT and PET findings (P=0.008). CONCLUSIONS: [18F]FDG PET/CT demonstrated superior diagnostic performance compared to CT in differentiating IAC from AIS/MIA among pure GGNs, thus it has the potential to guide the proper management of patients with pure GGNs.

A Self-supervised Learning-Based Fine-Grained Classification Model for Distinguishing Malignant From Benign Subcentimeter Solid Pulmonary Nodules.

RATIONALE AND OBJECTIVES: Diagnosing subcentimeter solid pulmonary nodules (SSPNs) remains challenging in clinical practice. Deep learning may perform better than conventional methods in differentiating benign and malignant pulmonary nodules. This study aimed to develop and validate a model for differentiating malignant and benign SSPNs using CT images. MATERIALS AND METHODS: This retrospective study included consecutive patients with SSPNs detected between January 2015 and October 2021 as an internal dataset. Malignancy was confirmed pathologically; benignity was confirmed pathologically or via follow-up evaluations. The SSPNs were segmented manually. A self-supervision pre-training-based fine-grained network was developed for predicting SSPN malignancy. The pre-trained model was established using data from the National Lung Screening Trial, Lung Nodule Analysis 2016, and a database of 5478 pulmonary nodules from the previous study, with subsequent fine-tuning using the internal dataset. The model's efficacy was investigated using an external cohort from another center, and its accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. RESULTS: Overall, 1276 patients (mean age, 56 ± 10 years; 497 males) with 1389 SSPNs (mean diameter, 7.5 ± 2.0 mm; 625 benign) were enrolled. The internal dataset was specifically enriched for malignancy. The model's performance in the internal testing set (316 SSPNs) was: AUC, 0.964 (95% confidence interval (95%CI): 0.942-0.986); accuracy, 0.934; sensitivity, 0.965; and specificity, 0.908. The model's performance in the external test set (202 SSPNs) was: AUC, 0.945 (95% CI: 0.910-0.979); accuracy, 0.911; sensitivity, 0.977; and specificity, 0.860. CONCLUSION: This deep learning model was robust and exhibited good performance in predicting the malignancy of SSPNs, which could help optimize patient management.

Mixed invasive mucinous and non-mucinous adenocarcinoma of the lung with hematogenous metastases to multiple organs.

Mixed invasive mucinous and non-mucinous adenocarcinoma is a rare variant of lung adenocarcinoma. In pure invasive mucinous adenocarcinoma, multilobar and bilateral involvement are common, and extrathoracic metastasis is rare. Here, we report a case of mixed invasive mucinous and non-mucinous adenocarcinoma with distant metastasis to multiple organs without marked enlargement of the primary lung lesion. The pathological findings indicated high tumor invasiveness and the patient died 10 months after diagnosis despite chemoimmunotherapy. Further investigations are necessary to elucidate the clinical characteristics and appropriate management of mixed invasive mucinous and non-mucinous adenocarcinoma.

Does FDG-PET/CT for incidentally found pulmonary lesions lead to a cascade of more incidental findings?

OBJECTIVE: To determine the frequency, nature, and downstream healthcare costs of new incidental findings that are found on whole-body FDG-PET/CT in patients with a non-FDG-avid pulmonary lesion ≥10 mm that was incidentally found on previous imaging. MATERIALS AND METHODS: This retrospective study included a consecutive series of patients who underwent whole-body FDG-PET/CT because of an incidentally found pulmonary lesion ≥10 mm. RESULTS: Seventy patients were included, of whom 23 (32.9 %) had an incidentally found pulmonary lesion that proved to be non-FDG-avid. In 12 of these 23 cases (52.2 %) at least one new incidental finding was discovered on FDG-PET/CT. The total number of new incidental findings was 21, of which 7 turned out to be benign, 1 proved to be malignant (incurable metastasized cancer), and 13 whose nature remained unclear. One patient sustained permanent neurologic impairment of the left leg due to iatrogenic nerve damage during laparotomy for an incidental finding which turned out to be benign. The total costs of all additional investigations due to the detection of new incidental findings amounted to €9903.17, translating to an average of €141.47 per whole-body FDG-PET/CT scan performed for the evaluation of an incidentally found pulmonary lesion. CONCLUSION: In many patients in whom whole-body FDG-PET/CT was performed to evaluate an incidentally found pulmonary lesion that turned out to be non-FDG-avid and therefore very likely benign, FDG-PET/CT detected new incidental findings in our preliminary study. Whether the detection of these new incidental findings is cost-effective or not, requires further research with larger sample sizes.

Slowly Growing Pulmonary Glandular Papilloma with Air Bronchogram: A Case Report.

Pulmonary glandular papilloma is a rare benign neoplasm that has not been studied extensively. This neoplasm presents as a solid nodule, consolidation, or mass, with or without atelectasis, and assessing the correlation between these findings and the risk of malignancy is challenging. A 60-year-old woman presented a solitary pulmonary nodule on screening chest radiography and chest computed tomography (CT). During the subsequent 2-year follow-up, CT showed a progressive increase in nodule size and an air bronchogram, suggesting malignancy. The patient underwent a right upper lobectomy, and the final diagnosis was glandular papilloma. Teaching point: Pulmonary glandular papilloma with growth and an air bronchogram.

A solitary pulmonary nodule in a former glass blower mimicking a primary lung adenocarcinoma.

INTRODUCTION: Inhalation of silicon dioxide causes silicosis, a condition that may occur in various industries and work settings. Radiologic findings typically show numerous nodular opacities, while solitary pulmonary nodules are atypical for silicosis. PRESENTATION OF CASE: A 68-year-old woman, a former glassblower, presented with a left solitary pulmonary nodule (13 mm) on chest computed tomography. The nodule enlarged to 23 mm over 6 months, exhibiting an irregular shape, spiculated margin, and rapid growth with a doubling time of 186.4 days. She underwent a left upper lobectomy with a suspicion of lung cancer. The histopathological findings revealed peribronchial lymphocytic infiltration and granulomatous-like structures containing multinucleated giant cells and phagocytic crystalline foreign bodies. These findings were consistent with a foreign body reaction to the glass fragments. DISCUSSION: Inhaled glass fragments may present as a solitary pulmonary nodule after the retirement of a glass blower. Its behavior and radiological features mimicked a primary lung adenocarcinoma. CONCLUSION: Solitary pulmonary nodules due to inhaled glass fragments may mimic a primary lung adenocarcinoma. A definitive diagnosis requires a histological examination in this rare condition.

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules.

BACKGROUND: We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs). METHODS: A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value. CONCLUSIONS: The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs. RELEVANCE STATEMENT: The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work. KEY POINTS: • We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.

Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer.

Background: The accurate assessment of lymph node metastasis (LNM) is crucial for the staging, treatment, and prognosis of lung cancer. In this study, we explored the potential value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters in the prediction of LNM in non-small cell lung cancer (NSCLC). Methods: In total, 91 patients presenting with solid solitary pulmonary nodules (8 mm < diameter ≤30 mm) with pathologically confirmed NSCLC (57 without LNM, and 34 with LNM) were enrolled in the study. The patients' basic clinical data and the SDCT morphological features were analyzed using the chi-square test or Fisher's exact test. The Mann-Whitney U-test and independent sample t-test were used to analyze the differences in multiple SDCT quantitative parameters between the non-LNM and LNM groups. The diagnostic efficacy of the corresponding parameters in predicting LNM in NSCLC was evaluated by plotting the receiver operating characteristic (ROC) curves. A multivariate logistic regression analysis was conducted to determine the independent predictive factors of LNM in NSCLC. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results: There were no significant differences between the non-LNM and LNM groups in terms of age, sex, and smoking history. Lesion size and vascular convergence sign differed significantly between the two groups (P<0.05), but there were no significant differences in the six tumor markers. The SDCT quantitative parameters [SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, normalized iodine concentration (NIC) and NZeff] were significantly higher in the non-LNM group than the LNM group (P<0.05). The ROC analysis showed that CER40keV, NIC, and CER70keV had higher diagnostic efficacy than other quantitative parameters in predicting LNM [areas under the curve (AUCs) =0.794, 0.791, and 0.783, respectively]. The multivariate logistic regression analysis showed that size, λ, and NIC were independent predictive factors of LNM. The combination of size, λ, and NIC had the highest diagnostic efficacy (AUC =0.892). The interobserver repeatability of the SDCT quantitative and derived quantitative parameters in the study was good (ICC: 0.801-0.935). Conclusions: The SDCT quantitative parameters combined with the clinical data have potential value in predicting LNM in NSCLC. The size + λ + NIC combined parameter model could further improve the prediction efficacy of LNM.

Performance of Lung-RADS in different target populations: a systematic review and meta-analysis.

OBJECTIVES: Multiple lung cancer screening studies reported the performance of Lung CT Screening Reporting and Data System (Lung-RADS), but none systematically evaluated its performance across different populations. This systematic review and meta-analysis aimed to evaluate the performance of Lung-RADS (versions 1.0 and 1.1) for detecting lung cancer in different populations. METHODS: We performed literature searches in PubMed, Web of Science, Cochrane Library, and Embase databases on October 21, 2022, for studies that evaluated the accuracy of Lung-RADS in lung cancer screening. A bivariate random-effects model was used to estimate pooled sensitivity and specificity, and heterogeneity was explored in stratified and meta-regression analyses. RESULTS: A total of 31 studies with 104,224 participants were included. For version 1.0 (27 studies, 95,413 individuals), pooled sensitivity was 0.96 (95% confidence interval [CI]: 0.90-0.99) and pooled specificity was 0.90 (95% CI: 0.87-0.92). Studies in high-risk populations showed higher sensitivity (0.98 [95% CI: 0.92-0.99] vs. 0.84 [95% CI: 0.50-0.96]) and lower specificity (0.87 [95% CI: 0.85-0.88] vs. 0.95 (95% CI: 0.92-0.97]) than studies in general populations. Non-Asian studies tended toward higher sensitivity (0.97 [95% CI: 0.91-0.99] vs. 0.91 [95% CI: 0.67-0.98]) and lower specificity (0.88 [95% CI: 0.85-0.90] vs. 0.93 [95% CI: 0.88-0.96]) than Asian studies. For version 1.1 (4 studies, 8811 individuals), pooled sensitivity was 0.91 (95% CI: 0.83-0.96) and specificity was 0.81 (95% CI: 0.67-0.90). CONCLUSION: Among studies using Lung-RADS version 1.0, considerable heterogeneity in sensitivity and specificity was noted, explained by population type (high risk vs. general), population area (Asia vs. non-Asia), and cancer prevalence. CLINICAL RELEVANCE STATEMENT: Meta-regression of lung cancer screening studies using Lung-RADS version 1.0 showed considerable heterogeneity in sensitivity and specificity, explained by the different target populations, including high-risk versus general populations, Asian versus non-Asian populations, and populations with different lung cancer prevalence. KEY POINTS: • High-risk population studies showed higher sensitivity and lower specificity compared with studies performed in general populations by using Lung-RADS version 1.0. • In non-Asian studies, the diagnostic performance of Lung-RADS version 1.0 tended to be better than in Asian studies. • There are limited studies on the performance of Lung-RADS version 1.1, and evidence is lacking for Asian populations.

Local imaging to interpret tumor size in F18 fluorodeoxyglucose positron emission tomography/CT in lung cancers

SUMMARY OBJECTIVE: This study aimed to determine the thoracic and extra-thoracic extension of the disease in patients diagnosed with lung cancer and who had whole-body F18-fluorodeoxyglucose positron emission tomography/CT imaging and to investigate whether there is a relationship between tumor size and extrathoracic spread. METHODS: A total of 308 patients diagnosed with lung cancer were included in this study. These 308 patients were first classified as group 1 (SPN 30 mm>longest lesion diameter ≥10 mm) and group 2 (lung mass (longest lesion diameter ≥30 mm), and then the same patients were classified as group 3 (nodular diameter of ≤20 mm) and group 4 (nodular size of >20 mm). Group 1 was compared with group 2 in terms of extrathoracic metastases. Similarly, group 3 was compared with group 4 in terms of frequency of extrathoracic metastases. F18 fluorodeoxyglucose positron emission tomography/CT examination was used to detect liver, adrenal, bone, and supraclavicular lymph node metastasis, besides extrathoracic metastasis. RESULTS: Liver, bone, and extrathoracic metastasis in group 1 was statistically lower than in group 2 (p<0.001, p<0.01, and p=0.03, respectively). Liver, extrathoracic, adrenal, and bone metastasis in group 3 was statistically lower than that in group 4 (p<0.001, p=0.01, and p=0.04, p<0.01, respectively). The extrathoracic extension was observed in only one patient in group 3. In addition, liver, adrenal, and bone metastases were not observed in group 3 patients. CONCLUSION: Positron emission tomography/CT may be more appropriate for cases with a nodule diameter of ≤20 mm. Performing local imaging in patients with a nodule diameter of ≤20 mm could reduce radiation exposure and save radiopharmaceuticals used in positron emission tomography/CT imaging.

Challenging Diagnosis: Unmasking the Enigma of Imaging Findings with Lung Schwannomas.

Schwannomas are benign slow-growing tumors arising from the embryonic neural crest cells of the nerve sheaths of peripheral and cranial nerves, and they are a rare type of soft tissue mass that is usually always solitary. Generally, it grows in the head, neck, and flexor portions of the limbs, where many nerves are located. Schwannomas of the lung are extremely rare. Fewer than ten cases of schwannomas in this organ region have been reported in the existing literature. In this case report, a 40-year-old male non-smoker was hospitalized with occasional chest pain. His chest computed tomographic scan revealed a 3.8 cm space occupying lesion in the upper lobe of the right lung. This lesion has clear boundaries and uneven internal density. And it was concluded as a benign lesion possibility, it is preferred to be considered as a haematoma. This space occupying lesion was eventually confirmed as a schwannoma by needle biopsy tissues. Due to the presence in rare locations, such as the lung, the clinical presentation of this space occupying lesion is non-specific, making diagnosis difficult. The data presented in this case report can help clinicians to obtain information on the identification of this disease, which highlighted lung schwannoma as a differential diagnosis for patients with intermittent pain. It can also alert clinicians and radiologists to observe every detail of the radiology imaging findings.

ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule.

Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Growth characteristics of early-stage (IA) lung adenocarcinoma and its value in predicting lymph node metastasis.

BACKGROUND: We aim to compare the differences in growth characteristics between part-solid and solid lung adenocarcinoma, and to investigate the value of volume doubling time (VDT) or mass doubling time (MDT) in predicting lymph node (LN) metastasis and preoperative evaluation in patients of early-stage (IA) non-small cell lung cancer (NSCLC). METHOD: We reviewed 8,653 cases of surgically resected stage IA lung adenocarcinoma between 2018 and 2022, with two follow-up visits at least 3 months apart, comparing diameter, volume, and mass growth of pSN and SN. VDT and MDT calculations for nodules with a volume change of at least 25%. Univariable or multivariable analysis was used to identify the risk factors. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic value. RESULTS: A total of 144 patients were included 114 with solid nodules (SN) and 25 with part-solid nodules (pSN). During the follow-up period, the mean VDTt and MDTt of SN were shorter than those of pSN, 337 vs. 541 days (p = 0.005), 298 vs. 458 days (p = 0.018), respectively. Without considering the ground-glass component, the mean VDTc and MDTc of SN were shorter than the solid component of pSN, 337 vs. 498 days (p = 0.004) and 298 vs. 453 days (p = 0.003), respectively. 27 nodules were clinically and pathologically diagnosed as N1/N2. Logistic regression identified initial diameter (p < 0.001), consolidation increase (p = 0.019), volume increase (p = 0.020), mass increase (p = 0.021), VDTt (p = 0.002), and MDTt (p = 0.004) were independent factors for LN metastasis. The ROC curves showed that the AUC for VDTt was 0.860 (95% CI, 0.778-0.943; p < 0.001) and for MDTt was 0.848 (95% CI, 0.759-0.936; p < 0.001). CONCLUSIONS: Our study showed significant differences in the growth characteristics of pSN and SN, and the application of VDT and MDT could be a valid predictor LN metastasis in patients with early-stage NSCLC.

Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (≤10 mm) solid pulmonary nodules.

Background: This study assessed the diagnostic performance of a deep learning (DL)-based model for differentiating malignant subcentimeter (≤10 mm) solid pulmonary nodules (SSPNs) from benign ones in computed tomography (CT) images compared against radiologists with 10 and 15 years of experience in thoracic imaging (medium-senior seniority). Methods: Overall, 200 SSPNs (100 benign and 100 malignant) were retrospectively collected. Malignancy was confirmed by pathology, and benignity was confirmed by follow-up or pathology. CT images were fed into the DL model to obtain the probability of malignancy (range, 0-100%) for each nodule. According to the diagnostic results, enrolled nodules were classified into benign, malignant, or indeterminate. The accuracy and diagnostic composition of the model were compared with those of the radiologists using the McNemar-Bowker test. Enrolled nodules were divided into 3-6-, 6-8-, and 8-10-mm subgroups. For each subgroup, the diagnostic results of the model were compared with those of the radiologists. Results: The accuracy of the DL model, in differentiating malignant and benign SSPNs, was significantly higher than that of the radiologists (71.5% vs. 38.5%, P<0.001). The DL model reported more benign or malignant deterministic results and fewer indeterminate results. In subgroup analysis of nodule size, the DL model also yielded higher performance in comparison with that of the radiologists, providing fewer indeterminate results. The accuracy of the two methods in the 3-6-, 6-8-, and 8-10-mm subgroups was 75.5% vs. 28.3% (P<0.001), 62.0% vs. 28.2% (P<0.001), and 77.6% vs. 55.3% (P=0.001), respectively, and the indeterminate results were 3.8% vs. 66.0%, 8.5% vs. 66.2%, and 2.6% vs. 35.5% (all P<0.001), respectively. Conclusions: The DL-based method yielded higher performance in comparison with that of the radiologists in differentiating malignant and benign SSPNs. This DL model may reduce uncertainty in diagnosis and improve diagnostic accuracy, especially for SSPNs smaller than 8 mm.

Corrigendum: Convolutional neural network-based diagnostic model for a solid, indeterminate solitary pulmonary nodule or mass on computed tomography.

[This corrects the article DOI: 10.3389/fonc.2021.792062.].