Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique.

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.

A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma.

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.

Clinical Outcomes of Resected Pure Ground-Glass, Heterogeneous Ground-Glass, and Part-Solid Pulmonary Nodules.

BACKGROUND. Increased (but not definitively solid) attenuation within pure ground-glass nodules (pGGNs) may indicate invasive adenocarcinoma and the need for resection rather than surveillance. OBJECTIVE. The purpose of this study was to compare the clinical outcomes among resected pGGNs, heterogeneous ground-glass nodules (GGNs), and part-solid nodules (PSNs). METHODS. This retrospective study included 469 patients (335 female patients and 134 male patients; median age, 68 years [IQR, 62.5-73.5 years]) who, between January 2012 and December 2020, underwent resection of lung adenocarcinoma that appeared as a subsolid nodule on CT. Two radiologists, using lung windows, independently classified each nodule as a pGGN, a heterogeneous GGN, or a PSN, resolving discrepancies through discussion. A heterogeneous GGN was defined as a GGN with internal increased attenuation not quite as dense as that of pulmonary vessels, and a PSN was defined as having an internal solid component with the same attenuation as that of the pulmonary vessels. Outcomes included pathologic diagnosis of invasive adenocarcinoma, 5-year recurrence rates (locoregional or distant), and recurrence-free survival (RFS) and overall survival (OS) over 7 years, as analyzed by Kaplan-Meier and Cox proportional hazards regression analyses, with censoring of patients with incomplete follow-up. RESULTS. Interobserver agreement for nodule type, expressed as a kappa coefficient, was 0.69. Using consensus assessments, 59 nodules were pGGNs, 109 were heterogeneous GGNs, and 301 were PSNs. The frequency of invasive adenocarcinoma was 39.0% in pGGNs, 67.9% in heterogeneous GGNs, and 75.7% in PSNs (for pGGNs vs heterogeneous GGNs, p < .001; for pGGNs vs PSNs, p < .001; and for heterogeneous GGNs vs PSNs, p = .28). The 5-year recurrence rate was 0.0% in patients with pGGNs, 6.3% in those with heterogeneous GGNs, and 10.8% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .06; for pGGNs vs PSNs, p = .02; and for heterogeneous GGNs vs PSNs, p = .18). At 7 years, RFS was 97.7% in patients with pGGNs, 82.0% in those with heterogeneous GGNs, and 79.4% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .02; for pGGNs vs PSNs, p = .006; and for heterogeneous GGNs vs PSNs, p = .40); OS was 98.0% in patients with pGGNs, 84.6% in those with heterogeneous GGNs, and 82.9% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .04; for pGGNs vs PSNs, p = .01; and for heterogeneous GGNs vs PSNs, p = .50). CONCLUSION. Resected pGGNs had excellent clinical outcomes. Heterogeneous GGNs had relatively worse outcomes, more closely resembling outcomes for PSNs. CLINICAL IMPACT. The findings support surveillance for truly homogeneous pGGNs versus resection for GGNs showing internal increased attenuation even if not having a true solid component.

Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.

BACKGROUND. Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropriate. OBJECTIVE. The purpose of this study was to develop and validate an automated deep learning model for differentiation of pGGNs on chest CT representing IAC from those representing atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA). METHODS. This retrospective study included 402 patients (283 women, 119 men; mean age, 53.2 years) with a total of 448 pGGNs on noncontrast chest CT that were resected from January 2019 to June 2022 and were histologically diagnosed as AAH (n = 29), AIS (n = 83), MIA (n = 235), or IAC (n = 101). Lung-PNet, a 3D deep learning model, was developed for automatic segmentation and classification (probability of IAC vs other entities) of pGGNs on CT. Nodules resected from January 2019 to December 2021 were randomly allocated to training (n = 327) and internal test (n = 82) sets. Nodules resected from January 2022 to June 2022 formed a holdout test set (n = 39). Segmentation performance was assessed with Dice coefficients with radiologists' manual segmentations as reference. Classification performance was assessed by ROC AUC and precision-recall AUC (PR AUC) and compared with that of four readers (three radiologists, one surgeon). The code used is publicly available (https://github.com/XiaodongZhang-PKUFH/Lung-PNet.git). RESULTS. In the holdout test set, Dice coefficients for segmentation of IACs and of other lesions were 0.860 and 0.838, and ROC AUC and PR AUC for classification as IAC were 0.911 and 0.842. At threshold probability of 50.0% or greater for prediction of IAC, Lung-PNet had sensitivity, specificity, accuracy, and F1 score of 50.0%, 92.0%, 76.9%, and 60.9% in the holdout test set. In the holdout test set, accuracy and F1 score (p values vs Lung-PNet) for individual readers were as follows: reader 1, 51.3% (p = .02) and 48.6% (p = .008); reader 2, 79.5% (p = .75) and 75.0% (p = .10); reader 3, 66.7% (p = .35) and 68.3% (p < .001); reader 4, 71.8% (p = .48) and 42.1% (p = .18). CONCLUSION. Lung-PNet had robust performance for segmenting and classifying (IAC vs other entities) pGGNs on chest CT. CLINICAL IMPACT. This automated deep learning tool may help guide selection of surgical strategies for pGGN management.

Safety and efficacy of a novel transbronchial radiofrequency ablation system for lung tumours: One year follow-up from the first multi-centre large-scale clinical trial (BRONC-RFII).

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.

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

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

Cone-Beam Computed Tomography-Guided Cryobiopsy Combined with Conventional Biopsy for Ground Glass Opacity-Predominant Pulmonary Nodules.

INTRODUCTION: Cryobiopsy (CB) using a 1.1-mm cryoprobe under fluoroscopic guidance is feasible and safe for diagnosis of ground glass opacity (GGO) lesions. However, the efficacy of CB combined with cone-beam CT (CBCT) for GGO-predominant pulmonary nodules remains elusive. METHODS: We retrospectively studied patients who underwent CB combined with conventional biopsy under CBCT guidance for GGO-predominant pulmonary nodules with a consolidation-to-tumour ratio <50.0%. RESULTS: A total of 32 patients with GGO-predominant pulmonary nodules were enrolled: 17 pure GGOs and 15 mixed GGOs. The mean lesion diameter was 15.81 ± 5.52 mm and the overall diagnostic yield was 71.9%. Seven lesions were diagnosed by CB alone, which increased the diagnostic outcomes by 21.9%. Diagnostic yields for CB, forceps biopsy (FB), brushing, and guide sheath flushing were 65.6%, 46.9%, 15.6%, and 14.3%, respectively. Univariate analysis revealed that positive computed tomography (CT) bronchus sign (p = 0.035), positive CBCT sign (p < 0.01), and CB-first biopsy sequence (p = 0.036) were significant predictive factors for higher diagnostic yield. Specimens obtained by CB had larger mean sample size (p < 0.01), lower blood cell area (p < 0.01), and fewer crush artefacts (p < 0.01) than specimens from FB. No severe bleeding or other complications occurred. CONCLUSION: CB using a 1.1-mm cryoprobe under CBCT guidance increased diagnostic yield for GGO-predominant pulmonary nodules based on conventional biopsy. Further, it provided larger and nearly intact samples compared with forceps.

Presurgical computed tomography-guided localization of lung ground glass nodules: comparing hook-wire and indocyanine green.

BACKGROUND: Presurgical computed tomography (CT)-guided localization is frequently employed to reduce the thoracotomy conversion rate, while increasing the rate of successful sublobar resection of ground glass nodules (GGNs) via video-assisted thoracoscopic surgery (VATS). In this study, we compared the clinical efficacies of presurgical CT-guided hook-wire and indocyanine green (IG)-based localization of GGNs. METHODS: Between January 2018 and December 2021, we recruited 86 patients who underwent CT-guided hook-wire or IG-based GGN localization before VATS resection in our hospital, and compared the clinical efficiency and safety of both techniques. RESULTS: A total of 38 patients with 39 GGNs were included in the hook-wire group, whereas 48 patients with 50 GGNs were included in the IG group. There were no significant disparities in the baseline data between the two groups of patients. According to our investigation, the technical success rates of CT-based hook-wire- and IG-based localization procedures were 97.4% and 100%, respectively (P = 1.000). Moreover, the significantly longer localization duration (15.3 ± 6.3 min vs. 11.2 ± 5.3 min, P = 0.002) and higher visual analog scale (4.5 ± 0.6 vs. 3.0 ± 0.5, P = 0.001) were observed in the hook-wire patients, than in the IG patients. Occurrence of pneumothorax was significantly higher in hook-wire patients (27.3% vs. 6.3%, P = 0.048). Lung hemorrhage seemed higher in hook-wire patients (28.9% vs. 12.5%, P = 0.057) but did not reach statistical significance. Lastly, the technical success rates of VATS sublobar resection were 97.4% and 100% in hook-wire and IG patients, respectively (P = 1.000). CONCLUSIONS: Both hook-wire- and IG-based localization methods can effectively identified GGNs before VATS resection. Furthermore, IG-based localization resulted in fewer complications, lower pain scores, and a shorter duration of localization.

Morphological Predictors of Primary Lung Cancer among Part-Solid Ground-Grass Nodules on High-Resolution CT.

Recent advancements in computed tomography (CT) scanning have improved the detection rates of peripheral pulmonary nodules, including those with ground-glass opacities (GGOs). This study focuses on part-solid pure ground-glass nodules (GGNs) and aims to identify imaging predictors that can reliably differentiate primary lung cancer from nodules with other diagnoses among part-solid GGNs on high-resolution CT (HRCT). A retrospective study was conducted on 609 patients who underwent surgical treatment or observation for lung nodules. Radiological findings from pre-operative HRCT scans were reviewed and several CT imaging features of part-solid GGNs were examined for their positive predictive value to identify primary lung cancer. The proportions of the nodules with a final diagnosis of primary lung cancer were significantly higher in part-solid GGNs (91.9%) compared with solid nodules (70.3%) or pure GGNs (66.7%). Among CT imaging features of part-solid GGNs that were evaluated, consolidation-to-tumor ratio (CTR) < 0.5 (98.1%), pleural indentation (96.4%), and clear tumor border (96.7%) had high positive predictive value to identify primary lung cancer. When two imaging features were combined, the combination of CTR < 0.5 and a clear tumor border was identified to have 100% positive predictive values with a sensitivity of 40.8%. Thus we conclude that part-solid GGNs with a CTR < 0.5 accompanied by a clear tumor border evaluated by HRCT are very likely to be primary lung cancers with an acceptable sensitivity. Preoperative diagnostic procedures to obtain a pathological diagnosis may potentially be omitted in patients harboring such part-solid GGNs.

[A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters].

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.

Comparative immunological landscape between pre- and early-stage LUAD manifested as ground-glass nodules revealed by scRNA and scTCR integrated analysis.

BACKGROUND: Mechanism underlying the malignant progression of precancer to early-stage lung adenocarcinoma (LUAD) as well as their indolence nature remains elusive. METHODS: Single-cell RNA sequencing (scRNA) with simultaneous T cell receptor (TCR) sequencing on 5 normal lung tissues, 3 precancerous and 4 early-stage LUAD manifested as pulmonary ground-glass nodules (GGNs) were performed. RESULTS: Through this integrated analysis, we have delineated five key modules that drive the malignant progression of early-stage LUAD in a disease stage-dependent manner. These modules are related to cell proliferation and metabolism, immune response, mitochondria, cilia, and cell adhesion. We also find that the tumor micro-environment (TME) of early-stage LUAD manifested as GGN are featured with regulatory T (Tregs) cells accumulation with three possible origins, and loss-functional state (decreased clonal expansion and cytotoxicity) of CD8 + T cells. Instead of exhaustion, the CD8 + T cells are featured with a shift to memory phenotype, which is significantly different from the late stage LUAD. Furthermore, we have identified monocyte-derived macrophages that undergo a lipid-phenotype transition and may contribute to the suppressive TME. Intense interaction between stromal cells, myeloid cells including lipid associated macrophages and LAMP3 + DCs, and lymphocytes were also characterized. CONCLUSIONS: Our work provides new insight into the molecular and cellular mechanism underlying malignant progression of LUAD manifested as GGN, and pave way for novel immunotherapies for GGN. Video Abstract.

Evaluation of the radiofrequency identification lung marking system: a multicenter study in Japan.

BACKGROUND: The radiofrequency identification (RFID) lung marking system is a novel technique using near-field radio-communication technology. The purpose of this study was to investigate the utility and feasibility of this system in the resection of small pulmonary nodules. METHODS: We retrospectively reviewed clinical records of 182 patients who underwent sublobar resection with the RFID marking system between March 2020 and November 2021 in six tertial hospitals in Japan. Target markings were bronchoscopically made within 3 days before surgery. The contribution of the procedure to the surgery and safety was evaluated. RESULTS: Target nodule average diameter and depth from the lung surface were 10.9 ± 5.4 mm and 14.6 ± 9.9 mm, respectively. Radiologically, one third of nodules appeared as pure ground-glass nodules (GGNs) on CT. The average distance from target nodule to RFID tag was 8.9 ± 7.1 mm. All surgical procedures were completed by video-assisted thoracoscopic surgery. Planned resection was achieved in all cases without any complications. The surgeons evaluated this system as helpful in 93% (necessary: 67%, useful; 26%) of cases. Nodule radiological features (p < 0.001) and type of surgery (p = 0.0013) were associated with the degree of contribution. In most cases, identification of the RFID tag was required within 1 min despite adhesion (p = 0.27). CONCLUSION: The RFID lung marking system was found to be safe and effective during successful sublobar resection. Patients with pure GGNs are the best candidates for the system.

Diagnostic Yield of Shape-Sensing Robotic-Assisted Bronchoscopy for Ground-Glass Nodules and Subsolid Nodules with a Solid Component Less than 6 mm.

BACKGROUND: Ground-glass pulmonary nodules (GGNs) are most commonly sampled by percutaneous transthoracic biopsy. Diagnostic yield for ground-glass nodules using robotic-assisted bronchoscopy has been scarcely described, with a reported yield of 70.6%. OBJECTIVES: The aim of this study is to assess diagnostic yield for GGNs using shape-sensing robotic-assisted bronchoscopy (ssRAB). METHOD: A retrospective study of patients who underwent ssRAB for evaluation of GGNs, from September 2021 to April 2023. Primary outcome was diagnostic yield of ssRAB for GGNs, secondary outcomes were sensitivity for malignancy, and complications that required admission or intervention. RESULTS: A total of 23 nodules were biopsied from 22 patients. Median age was 71 years (IQR 66-81), 63.6% were female, and 40.9% had a previous history of cancer. Forty-three percent of nodules were in the right upper lobes, and the median lesion size was 1.8 × 1.21. Twelve were subsolid nodules (SSNs), and 11 were pure GGNs. Overall diagnostic yield was 87%, with a sensitivity for malignancy of 88.9%. Adenocarcinoma was the most common malignancy diagnosed (70%). No procedure-related complications were reported. CONCLUSION: The use of ssRAB shows a high diagnostic yield for diagnosing GGN and SSN with less than 6 mm solid component with a low risk for complications.

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study.

PURPOSE: Persistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population. MATERIAL AND METHODS: We retrospectively selected 60 NSNs with an axial diameter of 6-30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses. RESULTS: In multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth. CONCLUSION: According to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.

Utility of whole exome sequencing analysis in differentiating intrapulmonary metastatic multiple ground-glass nodules (GGNs) from multiple primary GGNs.

PURPOSE: Clinical evidence of metastasis with ground-glass nodules (GGNs) has been reported, including pulmonary metastasis and distant metastasis. However, the clonal relationships of multiple GGNs at the genetic level remain unclear. EXPERIMENTAL DESIGN: Sixty tissue specimens were obtained from 19 patients with multiple GGN lung cancer who underwent surgery in 2019. Whole exome sequencing (WES) was performed on tissue samples, and genomic profiling and clone evolution analysis were conducted to investigate the genetic characteristics and clonality of multiple GGNs. RESULTS: A total of 15,435 nonsynonymous mutations were identified by WES, and GGNs with shared nonsynonymous mutations were observed in seven patients. Copy number variant (CNV) analysis showed that GGNs in ten patients had at least one shared arm-level CNV. Mutational spectrum analysis showed that GGNs in three patients had similar six substitution profiles and GGNs in fou patients had similar 96 substitution profiles. According to the clone evolution analysis, we found that GGNs in five patients had shared clonal driver gene mutations. Taken together, we identified that 5 patients may have multiple primary GGNs without any similar genetic features, 2 patients may have intrapulmonary metastatic GGNs with ≥ 3 similar genetic features, and the other 12 patients cannot be determined due to insufficient evidences in our cohort. CONCLUSIONS: Our findings suggest that the intrapulmonary metastasis exist in multiple GGNs, but the number of GGNs was not associated with the probability of metastasis. Application of genomic profiling may prove to be important to precise management of patients with multiple GGNs.

[Progress in screening and follow-up studies of pulmonary ground glass nodules].

With the wide application of high-resolution chest CT in health check-up, the ground glass nodule(GGN) has been increasingly detected. GGNs have a complex etiology and image features, which can develop fast or very slowly. Therefore, whether to follow up or to resect it is usually very difficult to be determined. Overdiagnosis or overtreatment frequently happens. According to the development of GGNs, the process can be clinically divided into four stages: biological onset stage (pre-detection stage), observational stage, clinical treatment stage and postoperative follow-up stage. This review summarizes the progress on the natural development process, imaging monitoring and differentiation, the optimal time of surgical treatment for GGNs based on the decision of multidisciplinary team. This revie wmay be helpful for clinicians to understand the rule of GGN development in the follow-up, and find an optimal time to give surgical intervention for improving the prognosis of and life quality of the GGN patients.

[Segmentation of ground glass pulmonary nodules using full convolution residual network based on atrous spatial pyramid pooling structure and attention mechanism].

Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.

HRCT features between lepidic-predominant type and other pathological subtypes in early-stage invasive pulmonary adenocarcinoma appearing as a ground-glass nodule.

BACKGROUND: Different pathological subtypes of invasive pulmonary adenocarcinoma (IPA) have different surgical methods and heterogeneous prognosis. It is essential to clarify IPA subtypes before operation and high-resolution computed tomography (HRCT) plays a very important role in this regard. We aimed to investigate the HRCT features of lepidic-predominant type and other pathological subtypes of early-stage (T1N0M0) IPA appearing as a ground-glass nodule (GGN). METHODS: We performed a retrospective analysis on clinical data and HRCT features of 630 lesions in 589 patients with pathologically confirmed IPA (invasive foci > 5 mm) appearing as pure GGN (pGGN) and mixed GGN (mGGN) with consolidation-to-tumor ratio (CTR) ≤0.5 from January to December 2019. All GGNs were classified as lepidic-predominant adenocarcinoma (LPA) and nonlepidic-predominant adenocarcinoma (n-LPA) groups. Univariate analysis was performed to analyze the differences of clinical data and HRCT features between the LPA and n-LPA groups. Multivariate analysis was conducted to determine the variables to distinguish the LPA from n-LPA group independently. The diagnostic performance of different parameters was compared using receiver operating characteristic curves. RESULTS: In total, 367 GGNs in the LPA group and 263 GGNs in the n-LPA group were identified. In the univariate analysis, the CTR, mean CT values, and mean diameters as well as mixed GGN, deep lobulation, spiculation, vascular change, bronchial change, and tumor-lung interface were smaller in the LPA group than in the n-LPA group (P <  0.05). Logistic regression model was reconstructed including the mean CT value, CTR, deep lobulation, spiculation, vascular change, and bronchial change (P <  0.05). Area under the curve of the logistic regression model for differentiating LPA and n-LPA was 0.840 (76.4% sensitivity, 78.7% specificity), which was significantly higher than that of the mean CT value or CTR. CONCLUSIONS: Deep lobulation, spiculation, vascular change, and bronchial change, CT value > - 472.5 HU and CTR > 27.4% may indicate nonlepidic predominant invasive pulmonary adenocarcinoma in GGNs.

Natural Progression of Ground-glass Nodules after Curative Resection for Non-small Cell Lung Cancer.

BACKGROUND: This retrospective study investigated the natural course of synchronous ground-glass nodules (GGNs) that remained after curative resection for non-small-cell lung cancer (NSCLC). METHODS: Prospectively collected retrospective data were reviewed concerning 2,276 patients who underwent curative resection for NSCLC between 2008 and 2017. High-resolution computed tomography or thin-section computed tomography data of 82 patients were included in the study. Growth in size was considered the most valuable outcome, and patients were grouped according to GGN size change. Patient demographic data (e.g., age, sex, and smoking history), perioperative data (e.g., GGN characteristics, histopathology and pathological stage of the resected tumours), and other medical history were evaluated in a risk factor analysis concerning GGN size change. RESULTS: The median duration of follow-up was 36.0 months (interquartile range, 23.0-59.3 months). GGN size decreased in 6 patients (7.3%), was stationary in 43 patients (52.4%), and increased in 33 patients (40.2%). In univariate analysis, male sex, the GGN size on initial CT, part-solid GGN and smoking history (≥ 10 pack-years) were significant risk factors. Among them, multivariate analysis revealed that lager GGN size, part-solid GGN and smoking history were independent risk factors. CONCLUSION: During follow-up, 40.2% of GGNs increased in size, emphasising that patients with larger GGNs, part-solid GGN or with a smoking history should be observed.

Thoracoscopic Localization of Small Peripheral Pulmonary Lesions Using Percutaneous Computed Tomography-guided Pleural Dye Marking: A Retrospective Analysis.

Small pulmonary lesions are often difficult to localize during thoracoscopic surgery. We describe a new com-puted tomography (CT)-guided pleural dye-marking method for small peripheral pulmonary lesions that does not involve a visceral pleural puncture. We used this technique for 23 lesions (22 patients) who underwent tho-racoscopic partial lung resection (Nov. 2016-Jan. 2018). With the patient in the lateral decubitus position, pre-operative CT-guided marking on the skin over the lesion was performed. During the surgery, we marked the visceral pleura with a skin marker directly or with an infant-size nutrition catheter with crystal violet at the tip through a venous indwelling needle inserted perpendicular to the skin marking. We localized and resected the lesions in all cases, without complications. The median nodule size measured histopathologically was 8 (4-20) mm overall, and 7 (0-20) mm of the solid part; the median distance from the visceral pleura to the nodule was 9 (1-33) mm. The median operation time was 67 (37-180) min. The median postoperative hospital stay was 3 (3-11) days. Our CT-guided pleural dye-marking method is useful and safe for the localization of small periph-eral pulmonary lesions in thoracoscopic partial lung resections.