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Background: Widely used computed tomography (CT) screening increases the detection of pulmonary pure ground-glass nodules (pGGNs), often classified as the second category of Lung Imaging Reporting and Data System (Lung-RADS 2). Despite their low malignancy risk, these nodules pose significant challenges and necessitate accurate assessment to minimize the risk of long-term follow-ups. This study investigated the detection efficacy of zero echo time (ZTE) magnetic resonance imaging (MRI) and thin-slice fat-saturated T2-weighted imaging (T2WI-FS) on 3.0 T MRI on the predictive accuracy of invasiveness for Lung-RADS 2 pGGNs. Methods: This prospective study enrolled 83 consecutive patients with 110 pGGNs who underwent preoperative CT and MRI scans. All CT images were assessed by artificial intelligence (AI) software and confirmed by a thoracic radiologist. Another two radiologists blind to pathology results assessed MRI for image quality (objective and subjective evaluations) and detection of pGGNs. Differences in nodule diameter, CT density and detection rate were compared within different pathological groups. The objective and subjective image quality scores were compared using the Wilcoxon signed rank test between ZTE and T2WI-FS. Interobserver agreement was calculated using the kappa coefficient. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic accuracy for distinguishing invasiveness. Results: Among the 110 pGGNs evaluated, T2WI-FS demonstrated a higher detection rate (80.0%) compared to ZTE (51.8%). ZTE showed a superior signal-to-noise ratio (SNR) in the lung parenchyma, aorta, and peripheral lung structures, whereas T2WI-FS more effectively delineated tracheal walls and pulmonary nodules. Both observers rated ZTE higher for vascular and bronchial visibility, while T2WI-FS was better in terms of lower noise and fewer artifacts. Notably, ZTE visibility varied with pathological results, exhibiting a range from 0% in atypical adenomatous hyperplasia (AAH) to 94.1% in invasive adenocarcinoma (IAC). The key indicators for distinguishing invasive pGGNs from non-invasive ones were nodule diameter [area under the curve (AUC) =0.874], ZTE visibility (AUC =0.740), followed by CT values (AUC =0.682) and T2WI-FS visibility (AUC =0.678). Conclusions: MRI has the potential to detect and predict the invasiveness of pGGN. Both T2WI-FS and ZTE demonstrate reliable image quality in pulmonary imaging, each displaying strengths in visualizing pGGN. Thin-slice T2WI-FS has a superior detection rate, while ZTE better predicts histological invasiveness.

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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_3D, a 3D model combining three features (3D_3F), and a 3D model combining 19 features (3D_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_3D, 3D_3F, and 3D_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_3D_3F model had the highest diagnostic efficiency for evaluating the invasiveness of pGGNs in the four models.

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BACKGROUND: A primary pulmonary meningioma is an extremely rare entity. Primary pulmonary meningiomas manifested with a ground glass nodule are a very rare occurrence in clinical practice. CASE PRESENTATION: In this study, we report a case of a primary pulmonary meningioma with atypical computed tomography features. A 59-year-old Han Chinese female came to our hospital for treatment and reported that her physical examination revealed a ground glass nodule in the right lung for over 3 months. The histologic result revealed a primary pulmonary meningioma. The patient underwent a thoracoscopic lung wedge resection of the right upper lobe for a ground glass nodule. After 1 year of follow-up, the patient is still alive without evidence of metastasis or recurrence. CONCLUSIONS: Primary pulmonary meningiomas could have a variety of radiological findings. As there are no specific radiologic features for the diagnosis of primary pulmonary meningiomas, complete resection of the lesion is required for both diagnosis and treatment. It is necessary to note the imaging features of primary pulmonary meningiomas, presenting as a ground glass nodule; this rare tumor should be considered in differential diagnoses.

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BACKGROUND AND OBJECTIVE: Radiofrequency ablation (RFA) is an emerging treatment of lung cancer, yet it is accompanied by certain safety concerns and operational limitations. This first multi-centre, large-scale clinical trial aimed to investigate the technical performance, efficacy and safety of an innovative transbronchial RFA system for lung tumours. METHODS: The study enrolled patients with malignant lung tumours who underwent transbronchial RFA using an automatic saline microperfusion system between January 2021 and December 2021 across 16 medical centres. The primary endpoint was the complete ablation rate. The performance and safety of the technique, along with the 1-year survival rates, were evaluated. RESULTS: This study included 126 patients (age range: 23-85 years) with 130 lung tumours (mean size: 18.77 × 14.15 mm) who had undergone 153 transbronchial RFA sessions, with a technique success rate of 99.35% and an average ablation zone size of 32.47 mm. At the 12-month follow-up, the complete ablation rate and intrapulmonary progression-free survival rates were 90.48% and 88.89%, respectively. The results of patients with ground-glass nodules (GGNs) were superior to those of the patients with solid nodules (12-month complete ablation rates: solid vs. pure GGN vs. mixed GGN: 82.14% vs. 100% vs. 96.08%, p = 0.007). No device defects were reported. Complications such as pneumothorax, haemoptysis, pleural effusion, pulmonary infection and pleural pain were observed in 3.97%, 6.35%, 8.73%, 11.11% and 10.32% of patients, respectively. Two subjects died during the follow-up period. CONCLUSION: Transbronchial RFA utilizing an automatic saline microperfusion system is a viable, safe and efficacious approach for the treatment for lung tumours, particularly for patients with GGNs.

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OBJECTIVE: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant. METHODS: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model. RESULTS: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model. CONCLUSION: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

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Background: Ground-glass nodule (GGN) is the most common manifestation of lung adenocarcinoma on computed tomography (CT). Clinically, the success rate of preoperative diagnosis of GGN by puncture biopsy and other means is still low. The aim of this study is to investigate the clinical and radiomics characteristics of lung adenocarcinoma presenting as GGN on CT images using radiomics analysis methods, establish a radiomics model, and predict the classification of pathological tissue and instability of GGN type lung adenocarcinoma. Methods: This study retrospectively collected 249 patients with 298 GGN lesions who were pathologically confirmed of having lung adenocarcinoma. The images were imported into the Siemens scientific research prototype software to outline the region of interest and extract the radiomics features. Logistic model A (a radiomics model to identify the infiltration of lung adenocarcinoma manifesting as GGNs) was established using features after the dimensionality reduction process. The receiver operating characteristic (ROC) curve of the model on training set and the verification set was drawn, and the area under the curve (AUC) was calculated. Second, a total of 112 lesions were selected from 298 lesions originating from CT images of at least two occasions, and the time between the first CT and the preoperative CT was defined as not less than 90 days. The mass doubling time (MDT) of all lesions was calculated. According to the different MDT diagnostic thresholds instability was predicted. Finally, their AUCs were calculated and compared. Results: There were statistically significant differences in age and lesion location distribution between the "noninvasive" lesion group and the invasive lesion group (P<0.05), but there were no statistically significant differences in sex (P>0.05). Model A had an AUC of 0.89, sensitivity of 0.75, and specificity of 0.86 in the training set and an AUC of 0.87, sensitivity of 0.63, and specificity of 0.90 in the validation set. There was no significant difference statistically in MDT between "noninvasive" lesions and invasive lesions (P>0.05). The AUCs of radiomics models B1, B2 and B3 were 0.89, 0.80, and 0.81, respectively; the sensitivities were 0.71, 0.54, and 0.76, respectively; the specificities were 0.83, 0.77, and 0.60, respectively; and the accuracies were 0.78, 0.65, and 0.69, respectively. Conclusions: There were statistically significant differences in age and location of lesions between the "noninvasive" lesion group and the invasive lesion group. The radiomics model can predict the invasiveness of lung adenocarcinoma manifesting as GGNs. There was no significant difference in MDT between "noninvasive" lesions and invasive lesions. The radiomics model can predict the instability of lung adenocarcinoma manifesting as GGN. When the threshold of MDT was set at 813 days, the model had higher specificity, accuracy, and diagnostic efficiency.

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Background: Anxiety-driven clinical interventions have been queried due to the nondeterminacy of pure ground-glass nodules (pGGNs). Although radiomics and radiogenomics aid diagnosis, standardization and reproducibility challenges persist. We aimed to assess a risk score system for invasive adenocarcinoma in pGGNs. Methods: In a retrospective, multi-center study, 772 pGGNs from 707 individuals in The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital were grouped into training (509 patients with 558 observations) and validation (198 patients with 214 observations) sets consecutively from January 2017 to November 2021. An additional test set of 143 observations in Hainan Cancer Hospital was analyzed in the same period. Computed tomography (CT) signs and clinical features were manually collected, and the quantitative parameters were achieved by artificial intelligence (AI). The positive cutoff score was ≥3. Risk scores system 3 combined carcinoma history, chronic obstructive pulmonary disease (COPD), maximum diameters, nodule volume, mean CT values, type II or III vascular supply signs, and other radiographic characteristics. The evaluation included the area under the curves (AUCs), accuracy, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for both the risk score systems 1, 2, 3 and the AI model. Results: The risk score system 3 [AUC, 0.840; 95% confidence interval (CI): 0.789-0.890] outperformed the AI model (AUC, 0.553; 95% CI: 0.487-0.619), risk score system 1 (AUC, 0.802; 95% CI: 0.754-0.851), and risk score system 2 (AUC, 0.816; 95% CI: 0.766-0.867), with 88.0% (0.850-0.904) accuracy, 95.6% (0.932-0.972) PPV, 0.620 (0.535-0.702) NPV, 89.6% (0.864-0.920) sensitivity, and 80.6% (0.717-0.872) specificity in the training sets. In the validation and test sets, risk score system 3 performed best with AUCs of 0.769 (0.678-0.860) and 0.801 (0.669-0.933). Conclusions: An AI-based risk scoring system using quantitative image parameters, clinical features, and radiographic characteristics effectively predicts invasive adenocarcinoma in pulmonary pGGNs.

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Background: Radiomics models based on computed tomography (CT) can be used to differentiate invasive ground-glass nodules (GGNs) in lung adenocarcinoma to help determine the optimal timing of GGN resection, improve the accuracy of prognostic prediction, and reduce unnecessary surgeries. However, general radiomics does not fully utilize follow-up data and often lacks model interpretation. Therefore, this study aimed to build an interpretable model based on delta radiomics to predict GGN invasiveness. Methods: A retrospective analysis was conducted on a set of 303 GGNs that were surgically resected and confirmed as lung adenocarcinoma in Shanghai Chest Hospital between September 2017 and August 2022. Delta radiomics and general radiomics features were extracted from preoperative follow-up CT scans and combined with clinical features for modeling. The performance of the delta radiomics-clinical model was compared to that of the radiomics-clinical model. Additionally, Shapley additive explanations (SHAP) was employed to interpret and visualize the model. Results: Two models were constructed using a combination of 34 radiomic features and 10 delta radiomic features, along with 14 clinical features. The radiomics-clinical model and the delta radiomics-clinical model exhibited area under the curve (AUC) of 0.986 [95% confidence interval (CI): 0.977-0.995] and 0.974 (95% CI: 0.959-0.987) in the training set, respectively, and 0.949 (95% CI: 0.908-0.978) and 0.927 (95% CI: 0.879-0.966) in the test set, respectively. The DeLong test of the two models showed no statistical significance (P=0.10) in the test set. SHAP was used to output a summary plot for global interpretation, which showed that preoperative mass, three-dimensional (3D) length, mean diameter, volume, mean CT value, and delta radiomics feature original_firstorder_RootMeanSquared were the relatively more important features in the model. Waterfall plots for local interpretation showed how each feature contributed to the prediction output of a given GGN. Conclusions: The delta radiomics-based model proved to be a helpful tool for predicting the invasiveness of GGNs in lung adenocarcinoma. This approach offers a precise, noninvasive alternative in informing clinical decision-making. Additionally, SHAP provided insightful and user-friendly interpretations and visualizations of the model, enhancing its clinical applicability.

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OBJECTIVE: The genomic and molecular ecology involved in the stepwise continuum progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) remains unclear and requires further elucidation. We aimed to characterize gene mutations and expression landscapes, and explore the association between differentially expressed genes (DEGs) and significantly mutated genes (SMGs) during the dynamic evolution from AIS to IAC. METHODS: Thirty-five patients with ground-glass nodules (GGNs) lung adenocarcinomas were enrolled. Whole-exome sequencing (WES) and transcriptome sequencing (RNA-Seq) were conducted on all patients, encompassing both tumor samples and corresponding noncancerous tissues. Data obtained from WES and RNA-Seq were subsequently analyzed. RESULTS: The findings from WES delineated that the predominant mutations were observed in EGFR (49%) and ANKRD36C (17%). SMGs, including EGFR and RBM10, were associated with the dynamic evolution from AIS to IAC. Meanwhile, DEGs, including GPR143, CCR9, ADAMTS16, and others were associated with the entire process of invasive LUAD. We found that the signaling pathways related to cell migration and invasion were upregulated, and the signaling pathways of angiogenesis were downregulated across the pathological stages. Furthermore, we found that the messenger RNA (mRNA) levels of FAM83A, MAL2, DEPTOR, and others were significantly correlated with CNVs. Gene set enrichment analysis (GSEA) showed that heme metabolism and cholesterol homeostasis pathways were significantly upregulated in patients with EGFR/RBM10 co-mutations, and these patients may have poorer overall survival than those with EGFR mutations. Based on the six calculation methods for the immune infiltration score, NK/CD8+ T cells decreased, and Treg/B cells increased with the progression of early LUAD. CONCLUSIONS: Our findings offer valuable insights into the unique genomic and molecular features of LUAD, facilitating the identification and advancement of precision medicine strategies targeting the invasive progression of LUAD from AIS to IAC.

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Background: The preoperative differential diagnosis of nodular lung adenocarcinoma has long been a challenging issue for thoracic surgeons. This study aimed to explore differential diagnosis of nodular lung adenocarcinoma by comprehensively analyzing its clinical, computed tomography (CT) imaging, and postoperative pathological and genetic features. Methods: The clinical, CT imaging, and postoperative pathological features of different classifications of nodular lung adenocarcinoma were retrospectively analyzed through univariate and multivariate statistical methods. Results: There were 132 patients with nodular lung adenocarcinoma enrolled. Firstly, compared with ground-glass nodular lung adenocarcinoma, solid nodular lung adenocarcinoma was more common in women [odds ratio (OR), 3.662; 95% confidence interval (CI): 1.066-12.577] and older adults (OR, 1.061; 95% CI: 1.007-1.119), and CT signs were mostly lobulation (OR, 4.957; 95% CI: 1.714-14.337) and spiculation (OR, 8.214; 95% CI: 2.740-24.621); the mean CT (CTm) value of solid nodular lung adenocarcinoma was significantly higher than that of ground-glass nodular lung adenocarcinoma, and the optimal diagnostic threshold was -267.5 Hounsfield units (HU). Secondly, the maximum diameter of nodule size (NSmax) of invasive adenocarcinoma (IAC) was significantly greater than that of minimally IAC (MIA; OR, 6.306; 95% CI: 1.191-33.400) or atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS; OR, 189.539; 95% CI: 4.720-7,610.476), and the optimal diagnostic threshold between IAC and MIA was 1.35 cm; the CTm value of IAC was significantly higher than that of MIA, and the optimal diagnostic threshold was -460.75 HU. Thirdly, lepidic-predominant adenocarcinoma (LPA) manifest more commonly as pure ground-glass nodule (pGGN; OR, 6.252; 95% CI: 1.429-27.358) or mixed ground-glass nodule (mGGN; OR, 4.224; 95% CI: 1.223-14.585). Moreover, the mutation rate of epidermal growth factor receptor (EGFR) in IAC was 70.69% (41/58). The EGFR mutation rates of mGGNs (OR, 8.794; 95% CI: 1.489-51.933) and solid nodules (SNs; OR, 12.912; 95% CI: 1.597-104.383) were significantly higher than that of pGGNs. Furthermore, compared with those of micropapillary-predominant adenocarcinoma (MPA), solid-predominant adenocarcinoma (SPA), or invasive mucinous adenocarcinoma (IMA), there were significantly higher EGFR mutation rates in acinar-predominant adenocarcinoma/papillary-predominant adenocarcinoma (APA/PPA; OR, 55.925; 95% CI: 4.045-773.284) and LPA (OR, 38.265; 95% CI: 2.307-634.596). Conclusions: Different classifications of nodular lung adenocarcinoma have their own clinicopathological and CT imaging features, and the latter is the main predictor.

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Background: The threshold value of consolidation-to-tumor ratio (CTR) for distinguishing between ground-glass opacity (GGO)-predominant and solid-predominant ground-glass nodules (GGNs) needs to be clarified, as the lack of clarity has caused the prognostic implications to remain ambiguous. This study aimed to determine the threshold value of CTR for distinguishing between GGO-predominant GGNs and solid-predominant GGNs and elucidate the prognostic implications of the solid-predominant GGNs categorized by CTR on c-stage IA lung adenocarcinoma. Methods: Between January 2016 and October 2018, 764 c-stage IA lung adenocarcinoma cases were assembled from the First Affiliated Hospital of Chongqing Medical University. Of the 764 lesions, 515 (67.4%) were nodules with a GGO component, and 249 (32.6%) were solid nodules (SNs) on thin-section computed tomography (CT). We evaluated the correlation of the 3-dimensional (3D) consolidation component volume ratio with CTR based on the coefficient of determination, r. After receiver operating characteristic (ROC) analysis of 515 GGNs, we defined the nodule with CTR >0.750 as solid-predominant GGN and the nodule with CTR ≤0.750 as GGO-predominant GGN. Subsequently, the prognosis of 439 patients who had follow-up registration was evaluated. Survival curves were calculated using the Kaplan-Meier method, and the log-rank test was employed to compare survival rates among different groups. Cox proportional hazard regression models were applied to evaluate the independent risk factors for recurrence-free survival (RFS). Results: Among 764 patients, 515 (67.4%) were nodules with a GGO component, and 249 (32.6%) were SNs on thin-section CT. For 515 GGNs, the 3D consolidation component volume ratio correlated well with CTR (r=0.888). CTR tended to be slightly larger than the 3D consolidation component volume ratio. A 3D consolidation component volume ratio >50% was best predicted by CTR >0.750, followed by CTR >0.549. CTR >0.750 and CTR >0.549 predicted 3D consolidation component volume ratio >50% with 85% and 99.2% sensitivity and 91.6% and 57.2% specificity, respectively. The 5-year RFS and overall survival (OS) of patients with 0.750< CTR <1 were worse than those of patients with 0≤ CTR ≤0.750 (P<0.001 and P<0.001, respectively) but better than those of patients with CTR =1 (P=0.002 and P=0.03, respectively). Carcinoembryonic antigen (CEA) >2.1 [hazard ratio (HR) =12.516, 95% confidence interval (CI): 1.729-90.598], CTR >0.750 (HR =13.934, 95% CI: 3.341-58.123), larger consolidation component size with diameter more than 20 mm (HR =1.855, 95% CI: 1.242-2.770), poorly differentiated (HR =1.622, 95% CI: 1.056-2.491), lymph node metastasis (HR =2.473, 95% CI: 1.601-3.821), and sublobar resection (HR =2.596, 95% CI: 1.701-3.962) could predict the poor prognosis. Patients with 0≤ CTR ≤0.750 receiving sublobar resection had prognoses comparable to those receiving lobar resection, whether the tumor size ≤2 cm or consolidation component size ≤3 cm. Lobar resection was superior to sublobar resection for non-small cell lung cancer (NSCLC) ≤2 cm with CTR >0.750. Conclusions: Compared to CTR =0.5, the 2-dimensional (2D) CTR =0.750 found using the 3D consolidation component volume ratio as the gold standard better differentiated between solid-predominant GGNs and GGO-predominant GGNs. CTR >0.750 was an independent risk factor associated with the poor prognosis of patients with c-stage IA lung adenocarcinoma. Sublobar resection should be cautiously adopted in GGNs with 0.750< CTR ≤1.

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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_solid) or non-solid (non-AI_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_solid than in those with non-AI_solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI_solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI_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.

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Purpose The increasing use of computed tomography (CT) imaging has led to the detection of more ground-glass nodules (GGNs) and subsolid nodules (SSNs), which may be malignant and require a biopsy for proper diagnosis. Approximately 75% of persistent GGNs can be attributed to adenocarcinoma in situ or minimally invasive adenocarcinoma. A CT-guided biopsy has been proven to be a reliable procedure with high diagnostic performance. However, the diagnostic accuracy and safety of a CT-guided biopsy for GGNs and SSNs with solid components ≤6 mm are still uncertain. The aim of this study is to assess the diagnostic accuracy of a CT-guided core needle biopsy (CNB) for GGN and SSNs with solid components ≤6 mm. Methods This is a retrospective study of patients who underwent CT-guided CNB for the evaluation of GGNs and SSNs with solid components ≤6 mm between February 2020 and January 2023. Biopsy findings were compared to the final diagnosis determined by definite histopathologic examination and clinical course. Results A total of 22 patients were enrolled, with a median age of 74 years (IQR: 68-81). A total of 22 nodules were assessed, comprising 15 (68.2%) SSNs with a solid component measuring ≤6 mm and seven (31.8%) pure GGNs. The histopathological examination revealed that 12 (54.5%) were diagnosed as malignant, nine (40.9%) as benign, and one (4.5%) as non-diagnostic. The overall diagnostic accuracy and sensitivity for malignancy were 86.36% and 85.7%, respectively. Conclusion A CT-guided CNB for GGNs and SSNs with solid components measuring ≤6 mm appears to have a high diagnostic accuracy.

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Background: In patients with pulmonary nodules undergoing computed tomography (CT)-guided localization procedures, a range of liquid-based materials have been employed to date in an effort to guide video-assisted thoracoscopic surgery (VATS) procedures to resect target nodules. However, the relative performance of these different liquid-based localization strategies has yet to be systematically evaluated. Accordingly, this study was developed with the aim of examining the relative safety and efficacy of CT-guided indocyanine green (IG) and blue-stained glue (BSG) PN localization. Methods: Consecutive patients with PNs undergoing CT-guided localization prior to VATS from November 2021 - April 2022 were enrolled in this study. Safety and efficacy outcomes were compared between patients in which different localization materials were used. Results: In total, localization procedures were performed with IG for 121 patients (140 PNs), while BSG was used for localization procedures for 113 patients (153 PNs). Both of these materials achieved 100% technical success rates for localization, with no significant differences between groups with respect to the duration of localization (P = 0.074) or visual analog scale scores (P = 0.787). Pneumothorax affected 8 (6.6%) and 8 (7.1%) patients in the respective IG and BSG groups (P = 0.887), while 12 (9.9%) and 10 (8.8%) patients of these patients experienced pulmonary hemorrhage. IG was less expensive than BSG ($17.2 vs. $165). VATS sublobar resection procedure technical success rates were also 100% in both groups, with no instances of conversion to thoracotomy. Conclusions: IG and BSG both offer similarly high levels of clinical safety and efficacy when applied for preoperative CT-guided PN localization, with IG being less expensive than BSG.

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Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%CI=1.010-1.092,P=0.015) and mass (95%CI=1.002-1.067,P=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (χ2=4.515,P=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.

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Background: Patients with breast cancer have a higher risk of developing lung cancer than the general population. The study aimed to evaluate the prevalence of ground glass nodule (GGN) and risk factors for GGN growth in patients with breast cancer and to evaluate the prevalence and pathologic features of lung cancer. Methods: We retrospectively reviewed the clinical data and chest computed tomography (CT) of 1,384 patients diagnosed with breast cancer who underwent chest CT between January 2008 and December 2022. We evaluated the prevalence of GGNs and their size changes on follow-up chest CT with volume doubling time (VDT) and identified independent risk factors associated with the growth of GGN using multivariable logistic regression analyses. Furthermore, the prevalence and pathologic features of lung cancer were also evaluated. Results: We detected persistent GGNs in 69 of 1,384 (5.0%) patients. The initial diameter of GGNs was 6.3±3.6 mm on average, with primarily (85.5%) pure GGNs. Among them, 27 (39.1%) exhibited interval growth with a median VDT of 1,006.0 days (interquartile range, 622.0-1,528.0 days) during the median 959.0 days (interquartile range, 612.0-1,645.0 days) follow-up period. Older age (P=0.026), part-solid nodules (P=0.006), and total number of GGNs (≥2) (P=0.007) were significant factors for GGN growth. Lung cancer was confirmed in 13 of 1,384 patients (0.9%), all with adenocarcinoma, including one case of minimally invasive adenocarcinoma. The cancers demonstrated a high rate of epidermal growth factor receptor (EGFR) mutation (69.2%). Conclusions: Persistent GGNs in breast cancer patients with high-risk factors should be adequately monitored for early detection and treatment of lung cancer.

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PURPOSE: Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. MATERIALS AND METHODS: Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. RESULTS: In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. CONCLUSION: Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.

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Background: Pure ground glass nodules (GGNs) have been increasingly detected through lung cancer screening programs. However, there were limited reports about pathologic characteristics of pure GGN. Here we presented a meta-analysis of the histologic outcome and proportion analysis of pure GGN. Methods: This study included previous pathological reports of pure GGN published until June 14, 2022 following a systematic search. A meta-analysis estimated the summary effects and between-study heterogeneity for pathologic diagnosis of invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and atypical adenomatous hyperplasia (AAH). Results: This study incorporated 24 studies with 3,845 cases of pure GGN that underwent surgery. Among them, sublobar resection was undertaken in 60% of the patients [95% confidence interval (CI): 38-78%, I2=95%]. The proportion of IA in cases of resected pure GGN was 27% (95% CI: 18-37%, I2=95%), and 50% of IA had non-lepidic predominant patterns (95% CI: 35-65%, I2=91%). The pooled proportions of MIA, AIS, and AAH were 24%, 36%, and 11%, respectively. Among nine studies with available clinical outcomes, no recurrences or metastases was observed other than one study. Conclusions: The portion of IA in cases of pure GGN is significantly larger that expected. More than half of them owned invasiveness components if MIA and IA were combined. Furthermore, there were quite number of lesions with aggressive histologic patterns other than the lepidic subtype. Therefore, further attempts are necessary to differentiate advanced histologic subtype among radiologically favorable pure GGN.

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

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PURPOSE: Intraoperative frozen section pathology (FS) is widely used to guide surgical strategies while the accuracy is relatively low. Underestimating the pathological condition may result in inadequate surgical margins. This study aims to identify CT imaging features related to upgraded FS and develop a predictive model. METHODS: Collected data from 860 patients who underwent lung surgery from January to December 2019. We analyzed the consistency rate of FS and categorized the patients into three groups: Group 1 (n = 360) had both FS and Formalin-fixed Paraffin-embedded section (FP) as non-invasive adenocarcinoma (IAC); Group 2 (n = 128) had FS as non-IAC but FP as IAC; Group 3 (n = 372) had both FS and FP as IAC. Clinical baseline characteristics were compared and propensity score adjustment was used to mitigate the effects of these characteristics. Univariate analyses identified imaging features with inter-group differences. A multivariate analysis was conducted to screen independent risk factors for FS upgrade, after which a logistic regression prediction model was established and a receiver operating characteristic (ROC) curve was plotted. RESULTS: The consistency rate of FS with FP was 84.19%. 26.67% of the patients with non-IAC FS diagnosis were upgraded to IAC. The predictive model's Area Under Curve (AUC) is 0.785. Consolidation tumor ratio (CTR) ≤ 0.5 and smaller nodule diameter are associated with the underestimation of IAC in FS. CONCLUSION: CT imaging has the capacity to effectively detect patients at risk of upstaging during FS.