Pure Ground-glass Nodules Research Articles

Predicting the malignancy of pulmonary nodules using baseline chest CT: an application study of deep learning model

Objective To investigate whether a deep learning-based model using unenhanced computed tomography (CT) at baseline could predict the malignancy of pulmonary nodules. Methods A deep learning model was trained and applied for the discrimination of pulmonary nodule in Dr. Wise Lung Analyzer. This study retrospectively recruited 130 consecutive participants with pulmonary nodules detected on CT who undergoing biopsy or surgery from May 2009 to June 2017 in Jinling hospital. A total of 136 pulmonary nodules were included in this study, including 86 malignant nodules and 50 benign ones. All patients underwent CT scans 2 times at least, the first scan was defined as baseline and the last scan before the pathological results was defined as final scan. The ROC curve of deep learning model was plotted and the AUCs were calculated. Delong test was used to examine the difference of AUCs baseline and final scan. The nodules were further divided into subsolid nodule group (pure ground-glass nodule and part solid nodule) (n=87) and solid nodule group (n=49). The difference of AUCs at baseline and final scans was evaluated intra two groups. Results The AUCs of the deep learning model at final and baseline scans were 0.876 and 0.819, respectively. There was no significant difference between them (P=0.075). The result indicated that the model could predict the consequences of pulmonary nodules well at baseline. In small nodules (longest diameter ≤10mm), the AUC at final scan (0.847) was better than it at baseline scan (0.734), but there was no significant difference between them (P=0.058). In solid nodule group, The AUC at final scan (0.932) was better than it at baseline scan (0.835), but there was no significant difference between them (P=0.066). In subsolid nodule group, the deep learning model exhibited consistent performance at final scan (AUC, 0.759) with the baseline scan (AUC, 0.728, P=0.580). Conclusions The deep learning model could predict the malignancy of pulmonary nodules including small ones at baseline, and the model exhibited consistent performance between baseline and final scans in subsolid nodules. Key words: Pulmonary nodule; Deep learning; Follow up

Genomic characterisation of pulmonary subsolid nodules: mutational landscape and radiological features.

Lung adenocarcinomas (LUADs) that display radiologically as subsolid nodules (SSNs) exhibit more indolent biological behaviour than solid LUADs. SSNs, commonly encompassing pre-invasive and invasive yet early-stage adenocarcinomas, can be categorised as pure ground-glass nodules and part-solid nodules. The genomic characteristics of SSNs remain poorly understood. We subjected 154 SSN samples from 120 treatment-naïve Chinese patients to whole-exome sequencing. Clinical parameters and radiological features of these SSNs were collected. The genomic landscape of SSNs and differences from that of advanced-stage LUADs were defined. In addition, we investigated the intratumour heterogeneity and clonal relationship of multifocal SSNs and conducted radiogenomic analysis to link imaging and molecular characteristics of SSNs. Fisher's exact and Wilcoxon rank sum tests were used in the statistical analysis. The median somatic mutation rate across the SSN cohort was 1.12 mutations per Mb. Mutations in EGFR were the most prominent and significant variation, followed by those in RBM10, TP53, STK11 and KRAS. The differences between SSNs and advanced-stage LUADs at a genomic level were unravelled. Branched evolution and remarkable genomic heterogeneity were demonstrated in SSNs. Although multicentric origin was predominant, we also detected early metastatic events among multifocal SSNs. Using radiogenomic analysis, we found that higher ratios of solid components in SSNs were accompanied by significantly higher mutation frequencies in EGFR, TP53, RBM10 and ARID1B, suggesting that these genes play roles in the progression of LUADs. Our study provides the first comprehensive description of the mutational landscape and radiogenomic mapping of SSNs.

Qualitative and quantitative imaging features of pulmonary subsolid nodules: differentiating invasive adenocarcinoma from minimally invasive adenocarcinoma and preinvasive lesions

To explore the role of qualitative and quantitative imaging features of pulmonary subsolid nodules (SSNs) in differentiating invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) and preinvasive lesions. We reviewed the clinical records of our institute from October 2010 to December 2015 and included 316 resected SSNs from 287 patients: 260 pure ground-glass nodules, 47 part-solid nodules with solid components ≤5 mm, and 9 ground-glass nodules (GGNs) with cystic airspaces. According to the pathologic review results, 307 SSNs in addition to nine GGNs with cystic airspaces were divided into two groups: A, including atypical adenomatous hyperplasia (AAH) (n=15), adenocarcinoma in situ (AIS) (n=56), and MIA (n=41); B, including 195 IACs. Univariate and binary logistic regression analyses were conducted to identify independent risk factors for IAC. Univariate analysis showed significant differences between groups regarding patient age, mean diameter, mean and relative computed tomography (CT) values, volume, mass (all P<0.001), and morphological features including lobulated sign (P<0.001), spiculated sign (P=0.028), vacuole sign/air bronchogram (P<0.001), and pleural retraction (P=0.017). Binary logistic regression and receiver operating characteristic analysis indicated the SSN mass as the only independent risk factor of IAC (odds ratio, 1.007; P<0.001), with an optimal cutoff value of 283.2 mg [area under curve (AUC): 0.859; sensitivity: 68.7%; specificity: 92.9%]. Among lepidic, acinar, and papillary adenocarcinomas, we found significant differences for the vacuole sign/air bronchogram (P=0.032) and mean and relative CT values (P<0.001). All nine GGNs with cystic airspaces were IACs. The SSN mass with an optimal cutoff value of 283.2 mg may be reliable for differentiating IAC from MIA and preinvasive lesions.

Three-dimensional mean CT attenuation value of pure and part-solid ground-glass lung nodules may predict invasiveness in early adenocarcinoma

This study evaluated the relationship between three-dimensional (3D) mean computed tomography (CT) attenuation values of ground-glass nodules (GGN) and pathological invasiveness in early lung adenocarcinoma. The diagnostic accuracy of 3D CT attenuation values was compared with that of two-dimensional (2D) CT attenuation values and standardised uptake value on positron-emission tomography (PET). Surgical and radiological data from 96 pure or part-solid GGNs of <20 mm were analysed retrospectively. Mean 2D and 3D CT attenuation values of the tumours were obtained with semi-automated volumetric software. Pathological invasiveness was diagnosed according to the International Association for the Study of Lung Cancer (IASLC))/American Thoracic Society (ATS)/European Respiratory Society (ERS) classification. Pre-invasive lesions and minimally invasive adenocarcinomas were classified as non-invasive adenocarcinoma. Univariate and multivariate analyses determined relationships between pathological invasiveness and clinical/radiological findings. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-off value for detecting invasive adenocarcinoma. A total of 66 non-invasive and 30 invasive adenocarcinoma cases between 2010 and 2016 were analysed. Univariate analysis revealed four tumour invasiveness-associated predictors: maximum diameter, SUVmax, mean 2D CT attenuation value, and mean 3D CT attenuation value (p<0.05). Multivariate analysis revealed that the maximum diameter, SUVmax, and mean 3D CT attenuation value were significant predictors of pathological invasiveness (p=0.023, 0.022, 0.004). The area under the ROC curve to predict invasive adenocarcinoma for mean 3D CT attenuation value was 0.838 and the cut-off value was -489 HU. The mean 3D CT attenuation value could distinguish pre-invasive lesions and minimally invasive adenocarcinoma from invasive adenocarcinoma.

Analysis of CT morphologic features and attenuation for differentiating among transient lesions, atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive and invasive adenocarcinoma presenting as pure ground-glass nodules

Thin-section computed tomography (TSCT) imaging biomarkers are uncertain to distinguish progressive adenocarcinoma from benign lesions in pGGNs. The purpose of this study was to evaluate the usefulness of TSCT characteristics for differentiating among transient (TRA) lesions, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) presenting as pure ground-glass nodules (pGGNs). Between January 2016 and January 2018, 255 pGGNs, including 64 TRA, 22 AAH, 37 AIS, 108 MIA and 24 IAC cases, were reviewed on TSCT images. Differences in TSCT characteristics were compared among these five subtypes of pGGNs. Logistic analysis was performed to identify significant factors for predicting MIA and IAC. Progressive pGGNs were more likely to be round or oval in shape, with clear margins, air bronchograms, vascular and pleural changes, creep growth, and bubble-like lucency than were non-progressive pGGNs. The optimal cut-off values of the maximum diameter for differentiating non-progressive from progressive pGGNs and IAC from non-IAC were 6.5 mm and 11.5 mm, respectively. For the prediction of IAC vs. non-IAC and non-progressive vs. progressive adenocarcinoma, the areas under the receiver operating characteristics curves were 0.865 and 0.783 for maximum diameter and 0.784 and 0.722 for maximum CT attenuation, respectively. The optimal cut-off values of maximum CT attenuation were −532 HU and −574 HU for differentiating non-progressive from progressive pGGNs and IAC from non-IAC, respectively. Maximum diameter, maximum attenuation and morphological characteristics could help distinguish TRA lesions from MIA and IAC but not from AAH. So, CT morphologic characteristics, diameter and attenuation parameters are useful for differentiating among pGGNs of different subtypes.

MA10.03 Quantitative Computed Tomography (CT) Based Texture Analysis; Could We Predict the Future Growth of the Pure Ground Glass Nodules?

MA10.03 Quantitative Computed Tomography (CT) Based Texture Analysis; Could We Predict the Future Growth of the Pure Ground Glass Nodules?

Synchronous multiple lung cancers presenting as multifocal pure ground glass nodules: are whole-body positron emission tomography/computed tomography and brain enhanced magnetic resonance imaging necessary?

Multifocal ground glass nodules (GGNs) represent a special radiological pattern indicative of synchronous multiple lung cancers (SMLCs), especially adenocarcinoma. However, the necessity of performing whole-body positron emission tomography/computed tomography (PET-CT) scanning and brain enhanced magnetic resonance imaging (MRI) as a staging workup for multifocal pure GGN (pGGN) patients remains unclear. The purpose of this study was to determine the utility of these two imaging scans for patients with multifocal pGGNs. This retrospective study was reviewed and approved by the ethics committee of the Cancer Hospital of the Chinese Academy of Medical Sciences. The study cohort was retrospectively selected from patients with multifocal pGGNs who underwent whole-body PET-CT examinations and/or brain enhanced MRIs between January 2010 and February 2019 at our institution. The additional value of the two exams for detecting nodal and distant metastases was evaluated. In total, 73 patients (male-to-female ratio, 20:53; median age, 57 years) with multifocal pGGNs who underwent whole-body PET-CT (55 patients) and/or brain enhanced MRI (25 patients) were enrolled. No clearly metastatic lesions were detected. Among the enrolled patients, 53 (128 pGGNs) underwent complete surgical resection. All pGGNs were adenocarcinomas and/or preneoplasias, and no lymph node metastases were found on final pathology. Whole-body PET-CT and brain enhanced MRI added no definite benefit compared with chest CT alone before surgery. Whole-body PET-CT scans and brain enhanced MRIs are not necessary for patients with multifocal pGGNs.

MA10.01 Invasive Adenocarcinoma in Screen Detected Pure Ground-Glass Nodules (GGN)

MA10.01 Invasive Adenocarcinoma in Screen Detected Pure Ground-Glass Nodules (GGN)

Computed tomography findings, clinicopathological features, genetic characteristics and prognosis of in situ and minimally invasive lung adenocarcinomas

To investigate the computed tomography findings, clinicopathological features, genetic characteristics and prognosis of in situ adenocarcinoma (AIS) and minimally invasive adenocarcinoma (MIA) of the lung. We retrospectively analyzed the data including computed tomography (CT) images, histopathological findings, Ki-67 immunostaining, and genetic mutations in patients with lung adenocarcinoma undergoing surgery at our hospital between 2014 and 2019. Of the total of 480 patients with lung adenocarcinoma we reviewed, 73 (15.2%) had AIS (n=28) or MIA (n=45) tumors. The age of the patients with MIA was significantly younger than that of patients with AIS (P &lt; 0.02). CT scans identified pure ground-glass nodules in 46.4% of AIS cases and in 44.4% of MIA cases. Multiple GGOs were more common in MIA than in AIS cases (P &lt; 0.05), and bluured tumor margins was less frequent in AIS cases (P &lt; 0.05). No significant difference was found in EGFR mutations between MIA and AIS cases. A Ki-67 labeling index (LI) value ≥2.8% did not differentiate MIA from AIS. The follow-up time in MIA group was significantly shorter than that in AIS group, but no recurrence or death occurred. Despite similar surgical outcomes and favorable survival outcomes, the patients with AIS and MIA show differences in terms of age, CT findings, EGFR mutations and Ki-67 LI.

Plasma extracellular vesicle microRNAs for pulmonary ground-glass nodules

ABSTRACTIn this study, we evaluated the diagnostic value and molecular characteristics of plasma extracellular vesicles (EVs)-derived miRNAs for patients with solitary pulmonary nodules (SPNs), particularly ground-glass nodules (GGNs). This study was registered at www.clinicaltrials.gov under registration number NCT03230019. Small RNA sequencing was performed to assess plasma EVs miRNAs in 59 patients, including 12 patients with benign nodules (2017, training set). MiRNA profiles of 40 an additional individuals were sequenced (2018, validation set). Overall, 16 pure GGNs, 21 mixed GGNs, and 42 solid nodules were included, with paired post-operative plasma samples available for 20 patients. The target miRNA/reference miRNA ratio was used to construct a support vector machine (SVM) model. The SVM model with the best specificity showed 100% specificity in both the training and validation sets independently. The model with the best sensitivity showed 100% and 96.9% sensitivity in the training and validation sets, respectively. Principal component analysis revealed that pure GGN distributions were distinct from those of solid nodules, and mixed GGNs had a diffuse distribution. Among differentially expressed miRNAs, miR-500a-3p, miR-501-3p, and miR-502-3p were upregulated in tumor tissues and enhanced overall survival. The SVM classifier accurately distinguished malignant GGNs and benign nodules. The distinct profile characteristics of miRNAs provided insights into the feasibility of EVs miRNAs as prognostic factors in lung cancer.

Cancer Risk in Subsolid Nodules in the National Lung Screening Trial.

Background Subsolid pulmonary nodules, comprising pure ground-glass nodules (GGNs) and part-solid nodules (PSNs), have a high risk of indolent malignancy. Lung Imaging Reporting and Data System (Lung-RADS) nodule management guidelines are based on expert opinion and lack independent validation. Purpose To evaluate Lung-RADS estimates of the malignancy rates of subsolid nodules, using nodules from the National Lung Screening Trial (NLST), and to compare Lung-RADS to the NELSON trial classification as well as the Brock University calculator. Materials and Methods Subsets of GGNs and PSNs were selected from the NLST for this retrospective study. A thoracic radiologist reviewed the baseline and follow-up CT images, confirmed that they were true subsolid nodules, and measured the nodules. The primary outcome for each nodule was the development of malignancy within the follow-up period (median, 6.5 years). Nodules were stratified according to Lung-RADS, NELSON trial criteria, and the Brock model. For analyses, nodule subsets were weighted on the basis of frequency in the NLST data set. Nodule stratification models were tested by using receiver operating characteristic curves. Results A total of 622 nodules were evaluated, of which 434 nodules were subsolid. At baseline, 304 nodules were classified as Lung-RADS category 2, with a malignancy rate of 3%, which is greater than the 1% in Lung-RADS (P = .004). The malignancy rate for GGNs smaller than 10 mm (two of 129, 1.3%) was smaller than that for GGNs measuring 10-19 mm (11 of 153, 6%) (P = .01). The malignancy rate for Lung-RADS category 3 was 14% (13 of 67), which is greater than the reported 2% in Lung-RADS (P < .001). The Brock model predicted malignancy better than Lung-RADS and the NELSON trial scheme (area under the receiver operating characteristic curve = 0.78, 0.70, and 0.67, respectively; P = .02 for Brock model vs NELSON trial scheme). Conclusion Subsolid nodules classified as Lung Imaging Reporting and Data System (Lung-RADS) categories 2 and 3 have a higher risk of malignancy than reported. The Brock risk calculator performed better than measurement-based classification schemes such as Lung-RADS. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Kauczor and von Stackelberg in this issue.

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation.

To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation. Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth. The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339-8640) days, and their median MDT was 1332 (range, 290-38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth. Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow. • The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448days (range, 339-8640days), and their median mass doubling time (MDT) was 1332days (range, 290-38,912days). The mean time to growth in volume was 854 ± 675days (range, 116-2856days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.

Clinical Characteristics and Prognosis of Sub-centimeter Lung Adenocarcinoma

背景与目的随着肺癌筛查的逐渐推广，越来越多的患者被确诊为亚厘米(直径≤1 cm)肺腺癌。亚厘米肺腺癌多为早期肺癌，但目前关于亚厘米肺腺癌的研究仍不充分。本研究针对亚厘米肺腺癌患者临床特征及预后进行分析，为该类患者的诊疗提供依据。方法回顾性分析2012年1月-2016年12月北京大学人民医院经胸腔镜手术病理确诊为亚厘米肺腺癌患者的临床及预后资料。根据结节影像学特征将患者分为纯磨玻璃结节(pure ground-glass nodules, pGGN)、混杂性磨玻璃结节(mixed ground-glass nodules, mGGN)和实性结节(solid nodules, SN)组，对比三组患者临床特征并对不同直径结节行亚组分析。此外，通过多因素分析筛选亚厘米肺浸润性腺癌的独立危险因素。结果本组共182例患者，中位年龄54(27-75)岁。男性57例，女性125例。女性亚厘米肺腺癌患者无吸烟史比例显著高于男性(P < 0.001)。所有1 mm-10 mm pGGN、1 mm-5 mm mGGN及1 mm-5 mm SN患者术后病理除原发灶外无其他阳性发现。46例6 mm-10 mm mGGN患者中有3例侵犯胸膜，1例发现脉管癌栓。39例6 mm-10 mm SN患者中有5例侵犯胸膜，2例发现脉管癌栓，2例出现淋巴结转移。侵犯胸膜、发现脉管癌栓或淋巴结转移的患者其病理类型均为浸润性腺癌。多因素Logistic回归分析发现吸烟史、既往肿瘤病史、mGGN、SN和肿瘤直径 > 5 mm是病理为肺浸润性腺癌的独立危险因素。中位随访时间44(22-82)个月，全组患者5年无复发生存率100.0%，总生存率98.9%。结论亚厘米肺腺癌患者发病年龄相对较小。影像学表现为6 mm-10 mm mGGN和6 mm-10 mm SN的亚厘米肺浸润性腺癌患者存在侵犯胸膜或淋巴结转移可能。吸烟史、既往肿瘤病史、mGGN、SN和肿瘤直径 > 5 mm为亚厘米肺浸润性腺癌的独立危险因素。对于亚厘米肺腺癌患者，早期发现并采取合适且有效的外科干预可获得良好的预后。

The new 8th TNM staging system of lung cancer and its potential imaging interpretation pitfalls and limitations with CT image demonstrations.

The tumor, node, metastasis (TNM) staging system approved by International Association for the Study of Lung Cancer (IASLC) and the American Joint Committee on Cancer (AJCC) to stage lung cancer was recently revised. The latest revision is the 8th edition published in January, 2017. This new edition made some important changes to the previous edition, including modification of the T classification based on 1 cm increment, downstage of T descriptor including endobronchial tumor disregarding its distance from carina (T2), merging total and partial atelectasis/pneumonitis into the same T category (T2), upstage diaphragmatic invasion to T4, new classification concept of adenocarcinoma in situ and minimally invasive adenocarcinoma for pure and part-solid ground-glass nodules, and further division of extrathoracic metastasis into M1b and M1c based on the number and sites of extrathoracic metastases. Consensus is reached for debating situations not covered in the previous edition of staging system, such as the classification of pancoast tumor based on its invasion depth and staging tumors that extend directly across the fissure as T2a. Classification of multiple sites of pulmonary involvement, including multiple primary lung cancer, separate lung cancer nodules, multiple ground-glass or lepidic lesions, and consolidation, is also discussed. Even though the 8th edition of the TNM lung staging system provides us with more precise classification based on prognostic analysis of each TNM descriptors, there are still some potential limitations and clinical situations that have not yet been clarified in terms of clinical staging by imaging. It is important for radiologists to understand the major changes introduced in the 8th edition of TNM staging and to recognize the potential pitfalls and limitations of imaging interpretation to precisely classify the clinical stage of lung cancer.

Potential value of FDG PET-CT in predicting occult lymph node metastasis in clinical stage ⅠA lung adenocarcinoma

Objective: To investigate the predictive value of (18)F-FDG PET-CT scan for occult lymph node metastasis in patients with stage ⅠA lung adenocarcinoma. Methods: The image and pathological data of 272 patients with stage ⅠA lung adenocarcinoma from October 2006 to September 2015 were retrospectively analyzed. All patients underwent preoperative (18)F-FDG PET-CT scan followed by lobectomy and systematic lymph node dissection. The correlation between occult lymph node metastasis and the maximum standardized uptake value (SUV(max)) of primary tumor as well as other clinicopathological factors was analyzed to screen the risk factors of occult lymph node metastasis in stage ⅠA lung adenocarcinoma. Results: Occult lymph node metastasis was detected in 50 patients (18.4%), with 24 (8.8%) patients of pN1 involvement and 26 (9.6%) of pN2 involvement. Among the 272 patients enrolled, 39 had pure ground glass nodule, 59 had part-solid nodule and 174 had solid nodule. All patients with pure ground glass nodule or nodule≤1 cm were pN0. For the 233 patients with part-solid and solid nodule, no lymph node metastasis was found in T1a stage (tumor length ≤1 cm). Primary tumor SUV(max) (Z=-5.663, P<0.001), nodule type (χ(2)=21.586, P<0.001), tumor location (χ(2)= 12.790, P< 0.001), histological grade (χ(2)= 22.784, P< 0.001) and visceral pleural invasion (χ(2)=5.357, P=0.021) showed significant differences between occult lymph node metastasis group (pN+ ) and non-lymph node metastasis group (pN0). With SUV(max)=2.405 as cut-off value, the sensitivity and specificity for predicting occult lymph node metastasis were 90.0% and 61.7%, the area under curve was 0.761(95%CI=0.700~0.823), and the negative predictive value was 95.8%. Multivariate analysis revealed that SUV(max) >2.405 (P<0.001), central location (P=0.030) and higher histological grade (P=0.024) were independent predictors of occult lymph node metastasis. Conclusions: For clinical stage ⅠA adenocarcinoma, primary tumor SUV(max) > 2.405, central location and higher histological grade were independent risk factors for occult lymph node metastasis. Systematic lymph node dissection may be avoided in lung adenocarcinoma with pure ground glass density, tumor length ≤1 cm or SUV(max) ≤ 2.405, due to the very low probability of nodal involvement.

Value of TSCT Features for Differentiating Preinvasive and Minimally Invasive Adenocarcinoma From Invasive Adenocarcinoma Presenting as Subsolid Nodules Smaller Than 3 cm

To distinguish preinvasive (adenocarcinoma in situ/atypical adenomatous hyperplasia) and minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IA) appearing as solitary subsolid nodules (SSNs) less than 3 cm based on thin-section computed tomography (TSCT) features to guide therapeutic approaches. A total of 154 lesions that were histopathologically confirmed to have pre/minimally invasive adenocarcinoma (hereafter pre/MIA) and IA presenting as part-solid nodules (PSNs) or pure ground-glass nodules (pGGNs) were retrospectively reviewed. The TSCT features, including diameter, area, CT value, shape, air bronchogram, margins, and location, were compared and assessed. Receiver operating characteristic analyses were conducted to determine the cut-off values for the qualitative variables and their diagnostic performances. Of 154 nodules, 89 IA, 53 MIA, eight adenocarcinoma in situ, and four atypical adenomatous hyperplasia lesions were found. Univariate and multivariate logistic regression of the pre/MIA and IA lesions were compared and analyzed among PSNs and pGGNs. Among pGGNs, a significant difference was found in the area (p = 0.004, odds ratio [OR] = 0.124, 95% confidence interval [CI] = 0.300-0.515) between the pre/MIA and IA groups. In PSNs, significant differences were found in the diameter (p = 0.001, OR = 0.171, 95% CI = 0.063-0.467) and CT value (p = 0.001, OR = 0.996, 95% CI = 0.993-0.998) between the pre/MIA and IA groups. According to the corresponding receiver operating characteristic curves, the optimal cut-off tumor area in pGGNs to differentiate pre/MIA from IA was 0.595 cm2. A higher CT value of the lesion (≥ -298.500 HU) and a larger diameter (≥1.450 cm) in PSNs were significantly associated with IA. Imaging features from TSCT contribute to distinguishing pre/MIA from IA in solitary subsolid nodules and may contribute to guide the clinical management of these lesions.

Invasive Pulmonary Adenocarcinomas Versus Preinvasive Lesions Appearing as Pure Ground-Glass Nodules: Differentiation Using Enhanced Dual-Source Dual-Energy CT.

OBJECTIVE. The objective of our study was to investigate the potentials of enhanced dual-source dual-energy CT (DECT) and three-planar measurements for differentiating invasive pulmonary adenocarcinomas (IPAs) from preinvasive lesions appearing as pure ground-glass nodules (pGGNs). MATERIALS AND METHODS. Thirty-nine patients with 53 pGGNs who underwent enhanced dual-source DECT were included in this retrospective study. All pGGNs were pathologically confirmed and categorized into two groups: preinvasive lesions or IPAs. The traditional CT features of the pGGNs were evaluated on unenhanced images. Quantitative parameters were measured on iodine-enhanced images of dual-source DECT in three planes, and both intra- and interobserver reproducibility analyses were performed to assess the measurement reproducibility of quantitative parameters. To identify significant factors for differentiating IPAs from preinvasive lesions, we performed logistic regression analysis and ROC curve analysis. RESULTS. For traditional CT features, only lesion size and unenhanced CT attenuation value showed significant differences between preinvasive lesions and IPAs (p < 0.05). Preinvasive lesions and IPAs exhibited significant differences in attenuation on virtual images, so-called "virtual HU" or "VHU," and the modified normalized iodine concentration (NIC) (p < 0.05), and both intra- and interobserver agreement for the quantitative measurements were excellent. Multivariate logistic regression analysis revealed that larger lesion size (adjusted odds ratio [OR], 3.65) and higher modified NIC (adjusted OR, 19.01) were significant differentiators of IPAs from preinvasive lesions (p < 0.05). ROC curve analysis revealed that modified NIC showed excellent performance (AUC, 0.924) and significantly higher performance than lesion size (AUC, 0.711) for differentiating IPAs from preinvasive lesions. CONCLUSION. In pGGNs, a lesion with a modified NIC value of more than 0.29 can be a very specific discriminator of IPAs from preinvasive lesions, and IPAs can be accurately and reliably differentiated from preinvasive lesions using enhanced dual-source DECT and three-planar measurements.

Persistent pulmonary subsolid nodules: How long should they be observed until clinically relevant growth occurs?

During the first two decades of 21 st century, there has been remarkable advance in the knowledge about lung cancer. One of the most important advances is persistent pulmonary subsolid nodules (SSNs) including pure ground-glass nodules (GGNs) and part-solid nodules (PSNs). Pulmonary SSNs, when persistent, have high probability of being pulmonary adenocarcinomas spectrum pathologically, and ironically show very indolent clinical courses over follow-ups (1-3). Many researchers investigated the natural history of persistent pulmonary SSNs, which included the frequency and pattern of interval growth, and the growth rate, to determine optimal management plan of patients with these nodules (4-8). However, uncertainty still remains on the natural course of persistent SSNs, especially with respect to the long-term consequences and ideal timing for surgical resection.

70O - Radiomic-based quantitative CT analysis to predict the invasiveness of pure ground-glass lung adenocarcinoma

Background: Lung cancer is till one of the leading causes of cancer-related deaths and lung adenocarcinoma is the most common type. Compared with invasive adenocarcinoma, adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are considered as indolent lung adenocarcinoma with good prognosis. However, AIS, MIA and even some early-stage invasive adenocarcinoma can be shown as pure ground-glass nodules on computed tomorgraphy images, which is quite difficult for clinicians to make a precise diagnosis and a suitable treatment plan. Thus, we aim to investigate the performance of radiomic-based quantitative analysis on CT images in identifying invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules. Methods: 275 lung adenocarcinoma cases with 322 pure ground-glass nodules from January 2015 to October 2017 were enrolled in this retrospective study. All lesions were resected surgically and confirmed pathologically. Clinical data like age, gender, smoking status of all cases were collected from digital medical records. Radiomic feature extraction was performed using Python with semi-automatically segmented tumor regions on CT scans which was contoured with an in-house developed plugin for 3D-Slicer. The predictive performance of the prediction models was evaluated through the receiver operating characteristic curve (ROC). Results: Among 322 nodules, 48(15%) were Adenocarcinoma in situ (AIS), 102(32%) were minimally invasive adenocarcinoma (MIA) and 172(53%) were invasive adenocarcinoma. All nodules were divided into training and validation cohort randomly with a ratio of 2:1 to establish prediction models. The values of the area under the curve were 0.716 (95%CI:0.600 0.832) and 0.827 (95%CI:0.729 0.925) with the diagnostic accuracy of 59.3% and 69.1% for radiomic and combined models, respectively. Conclusions: Radiomic model built via quantitative CT analysis can help to identify the invasiveness of lung adenocarcinoma represented as pure ground-glass nodules. Combining this model with clinical features can significantly improve its prediction performance. Legal entity responsible for the study: Fangyi Xv. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.

Erratum to quantitative features can predict further growth of persistent pure ground-glass nodule.

[This corrects the article DOI: 10.21037/qims.2019.01.04.].