Invasive Adenocarcinoma Groups Research Articles

Genomic Landscape Features of Minimally Invasive Adenocarcinoma and Invasive Lung Adenocarcinoma

Abstract Background The widespread implementation of computed tomography has significantly increased the detection of small pulmonary nodules, including atypical adenomatous hyperplasia, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). Few studies have focused on the genomic differences between MIA and IAC. Methods We retrospectively analyzed patients with lung adenocarcinoma (LUAD) who underwent surgery from January 2020 to December 2023. Patients were categorized into MIA and IAC groups. The mutation status of common driver genes was assessed using next-generation sequencing. Results A total of 422 LUAD patients were included in the study, comprising 119 MIA cases and 303 IAC cases. MIA patients were younger and predominantly female compared with IAC patients. EGFR mutations were detected in 251 patients (59.5%), with the frequency of EGFR mutations increasing from 37.0% in MIA to 68.3% in IAC (p < 0.001). TP53 mutations were found in 108 patients (25.6%), with 7 patients (5.9%) in MIA and 101 patients (33.3%) in IAC (p < 0.001). ERBB2 mutations were identified in 23 MIA patients (19.3%) and 20 IAC patients (6.6%) (p < 0.001). Additionally, CDKN2A mutations were detected in 23 IAC patients (7.6%), while no mutations in this gene were found in the MIA group. Moreover, ALK and RET gene fusions were identified in 11 patients, respectively. Conclusion ERBB2 mutations and RET fusions are early genomic events in LUAD, while TP53 and CDKN2A mutations and ALK fusions occur later. Genomic intratumor heterogeneity likely arises early, before invasive characteristics develop.

The differential diagnosis of lung precursor glandular lesions, micro-invasive adenocarcinoma, and invasive adenocarcinoma using low dose spectral computed tomography perfusion imaging.

Few studies about the association between computed tomography (CT) perfusion imaging parameters and invasiveness in lung adenocarcinoma (LUAD) have been conducted using low dose spectral CT perfusion imaging. The purpose of this study was to investigate application of spectral revolution CT low-dose perfusion imaging in the differential diagnosis of different pathological subtypes of LUAD. This was a cross-sectional study based on historical data from January 2018 to May 2019 in Peking University Cancer Hospital & Institute. A total of 62 cases were enrolled, including 2 cases of atypical adenomatous hyperplasia (AAH), 3 cases of adenocarcinoma in situ (AIS), 4 cases of minimally invasive adenocarcinoma (MIA), and 53 cases of invasive adenocarcinoma (IAC), all confirmed with pathology. The inclusion and exclusion criteria were regulated. Using Revolution low-dose CT perfusion imaging (GE, USA), the CT perfusion parameters of hemodynamics were obtained: blood flow (BF), blood volume (BV), impulse residue function time of arrival (IRF TO), maximum slope of increase (MSI), mean transit time (MTT), permeability surface area product (PS), positive enhancement integral (PEI), and maximum enhancement time (Tmax). Univariate analysis of variance (ANOVA) or Kruskal-Wallis test was used to compare the differences of CT perfusion quantitative parameters among AAH, AIS, MIA, and IAC. Mann-Whitney test was used to compare the difference of CT perfusion imaging parameters between preinvasive lesions (AAH and AIS) and invasive lung cancer (MIA and IAC). Statistically significant differences in IRF TO were observed in LUAD with different invasiveness, namely, among AIS, MIA, and IAC groups (0.56±0.74 vs. 0.54±1.08 vs. 4.39±2.19, P=0.004). Statistically significant differences in IRF TO were also observed between pre-invasive lesions group (AAH and AIS) and invasive lung cancer group (MIA and IAC) (1.12±1.27 vs. 3.75±2.79, P=0.031), and between AAH + AIS + MIA groups and IAC group (0.83±1.13 vs. 4.12±2.69, P<0.001). There were no statistically significant differences in other CT perfusion parameters of hemodynamics among different pathological subtypes of LUAD (P>0.05). The low-dose perfusion parameter IRF TO of revolution CT has the potential to be employed in the differential diagnosis of different pathological subtypes of LUAD.

Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules.

Mixed ground-glass lung nodules are a high-risk factor for lung adenocarcinoma. This study aimed to analyze the value of SDCT electron density imaging in the detection of mixed ground-glass lung nodules (GGNs). 150 patients with GGNs confirmed by chest SDCT and surgical pathology were retrospectively analyzed.GGNs were screened by two senior radiologists by the double-blind method based on conventional CT and SDCT electron density images.Average CT values and electron density (ED) values of GGNs were measured for all, solid and ground-glass. Thirty pGGN cases determined by conventional CT were found to be mGGN on electron density images, including 23 in the invasive adenocarcinoma group (detection rate of 35.38%), which was significantly higher than that of the PGL group (14.89%, P < 0.05). In electron density images, average CT values and ED values in the PGL and invasive adenocarcinoma groups with pGGNs were no difference.The average CT value and ED value were significantly higher in the mGGN invasive adenocarcinoma group compared with the PGL group (P < 0.05). Meanwhile, ROC curve analysis of average CT value and ED value revealed AUC values for mGGN infiltration of 0.759 and 0.752. SDCT can improve GGN visualization and increase the detection rate of mGGN compared with conventional CT.Attention should be paid to invasive adenocarcinoma for lung GGNs detected as mGGNs with high average CT value or ED value.

CT-Based Radiomic Analysis for Preoperative Prediction of Tumor Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodule.

It remains a challenge to preoperatively forecast whether lung pure ground-glass nodules (pGGNs) have invasive components. We aimed to construct a radiomic model using tumor characteristics to predict the histologic subtype associated with pGGNs. We retrospectively reviewed clinicopathologic features of pGGNs resected in 338 patients with lung adenocarcinoma between 2011-2016 at a single institution. A radiomic prediction model based on forward sequential selection and logistic regression was constructed to differentiate adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma. The study cohort included 133 (39.4%), 128 (37.9%), and 77 (22.8%) patients with AIS, MIA, and invasive adenocarcinoma (acinar 55.8%, lepidic 33.8%, papillary 10.4%), respectively. The majority (83.7%) underwent sublobar resection. There were no nodal metastases or tumor recurrence during a mean follow-up period of 78 months. Three radiomic features-cluster shade, homogeneity, and run-length variance-were identified as predictors of histologic subtype and were selected to construct a prediction model to classify the AIS/MIA and invasive adenocarcinoma groups. The model achieved accuracy, sensitivity, specificity, and AUC of 70.6%, 75.0%, 70.0%, and 0.7676, respectively. Applying the developed radiomic feature model to predict the histologic subtypes of pGGNs observed on CT scans can help clinically in the treatment selection process.

Clinical value of a differentiation prediction model for invasive lung adenocarcinoma

Objective: To investigate the value of predicting the degree of differentiation of pulmonary invasive adenocarcinoma (IAC) based on CT image radiomics model and the expression difference of immunohistochemical factors between different degrees of differentiation of lesions. Methods: The clinicopathological data of patients with pulmonary IAC confirmed by surgical pathology in the Affiliated Huai'an First People's Hospital to Nanjing Medical University from December 2017 to September 2018 were collected. High-throughput feature acquisition was performed for all outlined regions of interest, and prediction models were constructed after dimensionality reduction by the minimum absolute shrinkage operator. Receiver operating characteristic curve was used to assess the predictive efficacy of clinical characteristic model, radiomics model and individualized prediction model combined with both to identify the degree of pulmonary IAC differentiation, and immunohistochemical expressions of Ki-67, NapsinA and TTF-1 were compared between groups with different degrees of IAC differentiation using rank sum test. Results: A total of 396 high-throughput features were extracted from all IAC lesions, and 10 features with high generalization ability and correlation with the degree of IAC differentiation were screened. The mean radiomics score of poorly differentiated IAC in the training group (1.206) was higher than that of patients with high and medium differentiation (0.969, P=0.001), and the mean radiomics score of poorly differentiated IAC in the test group (1.545) was higher than that of patients with high and medium differentiation (-0.815, P<0.001). The differences in gender (P<0.001), pleural stretch sign (P=0.005), and burr sign (P=0.033) were statistically significant between patients in the well and poorly differentiated IAC groups. Multifactorial logistic regression analysis showed that gender and pleural stretch sign were related to the degree of IAC differentiation (P<0.05). The clinical feature model consisted of age, gender, pleural stretch sign, burr sign, tumor vessel sign, and vacuolar sign, and the individualized prediction model consisted of gender, pleural stretch sign, and radiomic score, and was represented by a nomogram. The Akaike information standard values of the radiomics model, clinical feature model and individualized prediction model were 54.756, 82.214 and 53.282, respectively. The individualized prediction model was most effective in identifying the degree of differentiation of pulmonary IAC, and the area under the curves (AUC) of the individualized prediction model in the training group and the test group were 0.92 (95% CI: 0.86-0.99) and 0.88 (95% CI: 0.74-1.00, respectively). The AUCs of the radiomics group model for predicting the degree of differentiation of pulmonary IAC in the training group and the test group were 0.91 (95% CI: 0.83-0.98) and 0.87 (95% CI: 0.72-1.00), respectively. The AUCs of the clinical characteristics model for predicting the degree of differentiation of pulmonary IACs in the training and test groups were 0.75 (95% CI: 0.63-0.86) and 0.76 (95% CI: 0.59-0.94), respectively. The expression level of Ki-67 in poorly differentiated IAC was higher than that in well-differentiated IAC (P<0.001). The expression levels of NapsinA, TTF-1 in poorly differentiated IAC were higher than those in well-differentiated IAC (P<0.05). Conclusions: Individualized prediction model consisted of gender, pleural stretch sign and radiomics score can discriminate the differentiation degree of IAC with the best performance in comparison with clinical feature model and radiomics model. Ki-67, NapsinA and TTF-1 express differently in different degrees of differentiation of IAC.

Dysbiosis of the Gut Microbiome Is Associated With Histopathology of Lung Cancer.

Lung cancer is a malignancy with high incidence and mortality worldwide. Previous studies have shown that the gut microbiome plays an important role in the development and progression of metabolic cancers. However, data on the characteristics of the gut microbiome with different histopathology types of lung cancer remain scant. We collected stool samples from 28 healthy people (HP) and 61 lung cancer patients. The lung cancer patients were classified into three types according to their histopathology: Atypical Adenomatous Hyperplasia/Adenocarcinoma in situ (AAH/AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA). In addition, we employed 16S rRNA gene amplicon sequencing to analyze the characteristics of the gut microbiome in these patients. Our analysis revealed that the categorized cancer patients had unique intestinal flora characteristics, and had lower density and flora diversity compared to healthy people. Besides, the structure of the flora families and genera was more complex, and each group presented specific pathogenic microbiota. The patients in the AAH/AIS group and HP group had relatively similar flora structure compared with the IA and MIA groups. In addition, we identified several flora markers that showed significant changes with the development of lung cancer. Lung cancer gut microbiota showed a decrease in short-chain fatty acids (SCFAs) producing and anti-inflammatory bacteria compared to healthy people, while some pathogenic bacteria such as proinflammatory or tumor-promoting bacteria were more abundant in lung cancer patients. On the other hand, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Group (COG) annotation demonstrated suppression of some dominant metabolism-related pathways in lung cancer. These findings provide new biomarkers for the diagnosis and prognostic assessment of lung cancer and lay the basis for novel targeted therapeutic strategies for the prevention and treatment of lung cancer.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT03244605].

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma

ObjectiveTo develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs). Materials and MethodsIn total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network. Deep learning signature (DLS) was developed via the least absolute shrinkage and selection operator (LASSO). New DLN integrating clinical variables, subjective CT findings, and DLS was constructed. The diagnostic efficiency and discriminative capability were analyzed using the receiver operating characteristic method and decision curve analysis (DCA). ResultsIn total, 18 deep learning features with non-zero coefficients were enrolled to develop the DLS, which was statistically different between the MIA and IAC groups. Independent predictors of DLS and lobulated sharp were used to build the DLN. The areas under the curves of the DLN were 0.889 (95% confidence interval (CI): 0.824–0.936), 0.915 (95% CI: 0.846–0.959), and 0.914 (95% CI: 0.848–0.958) in the training, internal validation, and external validation cohorts, respectively. After stratification analysis and DCA, the DLN showed potential generalization ability. ConclusionThe DLN incorporating the DLS and subjective CT findings have strong potential to distinguish MIA from IAC in patients with SSPNs, and will facilitate the suitable treatment method selection for the management of SSPNs.

Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact

BackgroundPure ground-glass nodules (pGGNs) with pleural contact (P-pGGNs) comprise not only invasive adenocarcinoma (IAC), but also minimally invasive adenocarcinoma (MIA). Radiomics recognizes complex patterns in imaging data by extracting high-throughput features of intra-tumor heterogeneity in a non-invasive manner. In this study, we sought to develop and validate a radiomics signature to identify IAC and MIA presented as P-pGGNs.MethodsIn total, 100 patients with P-pGGNs (69 training samples and 31 testing samples) were retrospectively enrolled from December 2012 to May 2018. Imaging and clinical findings were also analyzed. In total, 106 radiomics features were extracted from the 3D region of interest (ROI) using computed tomography (CT) imaging. Univariate analyses were used to identify independent risk factors for IAC. The least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation was used to generate predictive features to build a radiomics signature. Receiver-operator characteristic (ROC) curves and calibration curves were used to evaluate the predictive accuracy of the radiomics signature. Decision curve analyses (DCA) were also conducted to evaluate whether the radiomics signature was sufficiently robust for clinical practice.ResultsUnivariate analysis showed significant differences between MIA (N = 47) and IAC (N = 53) groups in terms of patient age, lobulation signs, spiculate margins, tumor size, CT values and relative CT values (all P < 0.05). ROC curve analysis showed, when MIA was identified from IAC, that the critical value of tumor length diameter (TLD) was1.39 cm and the area under the ROC curve (AUC) was 0.724 (sensitivity = 0.792, specificity = 0.553). The critical CT value on the largest axial plane (CT-LAP) was − 597.45 HU, and the AUC was 0.666 (sensitivity = 0.698, specificity= 0.638). The radiomics signature consisted of seven features and exhibited a good discriminative performance between IAC and MIA, with an AUC of 0.892 (sensitivity = 0.811, specificity 0.719), and 0.862 (sensitivity = 0.625, specificity = 0.800) in training and testing samples, respectively.ConclusionsOur radiomics signature exhibited good discriminative performance in differentiating IAC from MIA in P-pGGNs, and may offer a crucial reference point for follow-up and selective surgical management.

Prognostic role of positron emission tomography and computed tomography parameters in stage I lung adenocarcinoma.

BackgroundAccording to the current pathological classification, lung adenocarcinoma includes histological subtypes with significantly different prognoses, which may require specific surgical approaches. The aim of the study was to assess the role of CT and PET parameters in stratifying patients with stage I adenocarcinoma according to prognosis.Patients and methodsFifty-eight patients with pathological stage I lung adenocarcinoma who underwent surgical treatment were retrospectively reviewed. Adenocarcinoma in situ and minimally-invasive adenocarcinoma were grouped as non-invasive adenocarcinoma. Other histotypes were referred as invasive adenocarcinoma. CT scan assessed parameters were: ground glass opacity (GGO) ratio, tumour disappearance rate (TDR) and consolidation diameter. The prognostic role of the following PET parameters was also assessed: standardized uptake value (SUV) max, SUVindex (SUVmax to liver SUVratio), metabolic tumour volume (MTV), total lesion glycolysis (TLG).ResultsSeven patients had a non-invasive adenocarcinoma and 51 an invasive adenocarcinoma. Five-year disease-free survival (DFS) and cancer-specific survival (CSS) for non-invasive and invasive adenocarcinoma were 100% and 100%, 70% and 91%, respectively. Univariate analysis showed a significant difference in SUVmax, SUVindex, GGO ratio and TDR ratio values between non-invasive and invasive adenocarcinoma groups. Optimal SUVmax, SUVindex, GGO ratio and TDR cut-off ratios to predict invasive tumours were 2.6, 0.9, 40% and 56%, respectively. TLG, SUVmax, SUVindex significantly correlated with cancer specific survival.ConclusionsCT and PET scan parameters may differentiate between non-invasive and invasive stage I adenocarcinomas. If these data are confirmed in larger series, surgical strategy may be selected on the basis of preoperative imaging.

Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules: A Combined Prediction Model Generated From DECT

To evaluate qualitative and quantitative indicators generated from Dual-energy computed tomography (DECT) for preoperatively differentiating between invasive adenocarcinoma (IAC) and preinvasive or minimally invasive adenocarcinoma (MIA) lesions manifesting as ground-glass opacity-predominant (GGO-predominant) nodules. We retrospectively enrolled 143 cases of completely resected GGO-predominant lung adenocarcinoma with DECT examinations between December 2017 and July 2019. Qualitative and quantitative parameters of GGO-predominant nodules were compared after grouping nodules into IAC and preinvasive-MIA groups. A multivariate logistic regression models were used for analyzing these parameters. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z tests. This study included 137 patients (58 years ± 11; male: female = 52:91) with 143 GGO-predominant nodules. The proportion of margins, internal dilated/distorted/cut-off bronchi, internal thickened/stiff/distorted vasculature, pleural indentation, and vascular convergence were higher in the IAC group than in the preinvasive-MIA group, as were the maximum diameter (Dmax), the diameter of the solid component (Dsolid) and the enhanced monochromatic CT value at 40 keV-190 keV (CT40 keV-190 keV) (p range: 0.001-0.019). Logistic regression analyses revealed that margin, Dmax, and CT60 keV values were independent predictors of the IAC group. The area under the curve (AUC) for the combination of margin, Dmax, and CT60 keV was 0.896 (90.2% sensitivity, 70.7% specificity, 84.6% accuracy), which was significantly higher than that for each two of them (all p < 0.05). The combined prediction model generated from DECT allows for effective preoperative differentiation between IAC and preinvasive-MIA in GGO-predominant lung adenocarcinomas.

Whole-Lesion Computed Tomography-Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules.

The aim of this study was to investigate the differentiation of computed tomography (CT)-based entropy parameters between minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) lesions appearing as pulmonary subsolid nodules (SSNs). This study was approved by the institutional review board in our hospital. From July 2015 to November 2018, 186 consecutive patients with solitary peripheral pulmonary SSNs that were pathologically confirmed as pulmonary adenocarcinomas (74 MIA and 112 IAC lesions) were included and subdivided into the training data set and the validation data set. Chest CT scans without contrast enhancement were performed in all patients preoperatively. The subjective CT features of the SSNs were reviewed and compared between the MIA and IAC groups. Each SSN was semisegmented with our in-house software, and entropy-related parameters were quantitatively extracted using another in-house software developed in the MATLAB platform. Logistic regression analysis and receiver operating characteristic analysis were performed to evaluate the diagnostic performances. Three diagnostic models including subjective model, entropy model, and combined model were built and analyzed using area under the curve (AUC) analysis. There were 119 nonsolid nodules and 67 part-solid nodules. Significant differences were found in the subjective CT features among nodule type, lesion size, lobulated shape, and irregular margin between the MIA and IAC groups. Multivariate analysis revealed that part-solid type and lobulated shape were significant independent factors for IAC (P < 0.0001 and P < 0.0001, respectively). Three entropy parameters including Entropy-0.8, Entropy-2.0-32, and Entropy-2.0-64 were identified as independent risk factors for the differentiation of MIA and IAC lesions. The median entropy model value of the MIA group was 0.266 (range, 0.174-0.590), which was significantly lower than the IAC group with value 0.815 (range, 0.623-0.901) (P < 0.0001). Multivariate analysis revealed that the combined model had an excellent diagnostic performance with sensitivity of 88.2%, specificity of 73.0%, and accuracy of 82.1%. The AUC value of the combined model was significantly higher (AUC, 0.869) than that of the subjective model (AUC, 0.809) or the entropy model alone (AUC, 0.836) (P < 0.0001). The CT-based entropy parameters could help assess the aggressiveness of pulmonary adenocarcinoma via quantitative analysis of intratumoral heterogeneity. The MIA can be differentiated from IAC accurately by using entropy-related parameters in peripheral pulmonary SSNs.

Attenuation and Morphologic Characteristics Distinguishing a Ground-Glass Nodule Measuring 5-10 mm in Diameter as Invasive Lung Adenocarcinoma on Thin-Slice CT.

OBJECTIVE. The purpose of this study is to comprehensively investigate the role of multiple features seen on thin-section CT (TSCT) in the differential diagnosis of ground-glass nodules (GGNs) measuring 5-10 mm in diameter as invasive adenocarcinoma (IAC). MATERIALS AND METHODS. The TSCT features of 313 surgically diagnosed GGNs from 288 patients were retrospectively reviewed. A logistic regression model was applied, and the AUC values for the model and the size and attenuation of the lesions were compared using ROC curve analysis. RESULTS. A total of 247 lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) (hereafter referred to as the AIS-MIA group) and 66 invasive adenocarcinomas (IACs) were identified. Compared with the AIS-MIA group, the IAC groups were significantly larger in size and had higher attenuation values, a higher frequency of mixed GGNs (all p < 0.001), bubblelike appearance, spiculation, pleural indentation, different locations, and a lower frequency of clear tumor-lung interface (all p < 0.05). The logistic model included size and attenuation (both p < 0.001; odds ratio [OR], 1.872 and 1.009, respectively) as well as tumor-lung interface (p = 0.001; OR, 0.242), bubblelike appearance (p < 0.05; OR, 2.205), and type of nodule. The AUC value for the logistic model was 0.847 (sensitivity, 80.3%; specificity, 81.0%) and was significantly higher than that for size or attenuation (both p < 0.01). CONCLUSION. Radiologic features could help in the differential diagnosis of a GGN that was 5-10 mm in diameter as IAC versus AIS or MIA. GGNs larger than 8.12 mm and with attenuation greater than -449.52 HU were more likely to be IAC.

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.

Correction: Solitary ground-glass opacity nodules of stage IA pulmonary adenocarcinoma: combination of 18F-FDG PET/CT and high-resolution computed tomography features to predict invasive adenocarcinoma.

[This corrects the article DOI: 10.18632/oncotarget.15577.].

Changes of lymphatic vessel density in lung adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma and the regulatory factors

To analyze the changes in tumor lymphatic vessel density (LVD) in patients with lung adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) and explore the regulatory factors of LVD. Complete clinicopathological data were collected form a total of 301 patients with lung adenocarcinoma, including 28 (9.3%) with AIS, 86 (28.6%) with MIA, and 187 (62.1%) with IA. The LVD of all the adenocarcinomas were calculated after D2-40 immunohistochemical staining, and MT1-MMP and VEGF-C expression levels were also evaluated. The differences in LVD among the groups and the correlations of tumor LVD with the expressions of MT1-MMP and VEGF-C and the clinicopathological factors were analyzed. The LVD differed significantly among AIS, MIA, and IA groups (P= 0.000). The LVDs was significantly correlated with the level of VEGF-C protein expression (r=0.917, P=0.009), tumor size (r= 0.686, P=0.017), lymph node metastasis (r=0.739, P=0.000), and clinical stage (r=0.874, P=0.012) of the patients. Tumor lymphangiogenesis plays an important role in lung adenocarcinoma progression, and VEGF-C may promote this process.

Contrast analysis of the relationship between the HRCT sign and new pathologic classification in small ground glass nodule-like lung adenocarcinoma

To perform contrast analysis of the relationship between high-resolution computed tomography (HRCT) signs and new pathologic classification of small GGNs-like lung adenocarcinoma. The HRCT data from 145 pathologically confirmed cases of small GGNs of lung adenocarcinoma were analysed retrospectively. The 145 cases of GGNs were divided into pre-invasive (PI) group (n = 46), micro-invasive adenocarcinoma (MIA) group (n = 48), and invasive adenocarcinoma (IAC) group (n = 51). HRCT imaging sign of GGNs in each group was assessed and compared. Significant differences in GGN size were found among the three groups (P < 0.05). The presence of a tumour-lung interface in the MIA and IAC groups was significantly higher than that in the PI group (P < 0.05), but no significant difference was found between the MIA and IAC groups. The presence of a pleural indentation sign in the IAC group was significantly higher than that in the other two groups (P < 0.05), but no significant difference was noted between the latter two groups. Significant differences were found in the lobulated and spicule signs among the three groups (P < 0.05). The presence of a microvascular sign in the MIA and IAC groups was significantly higher than that in the PI group (P < 0.05). No significant difference was found in the GGN density, vacuole sign, air bronchus sign and notch sign among the three groups. The HRCT signs of GGNs could be used to differentiate among pre-invasive lesions, micro-invasive lesions and invasive lung adenocarcinoma.

Solitary ground-glass opacity nodules of stage IA pulmonary adenocarcinoma: combination of 18F-FDG PET/CT and high-resolution computed tomography features to predict invasive adenocarcinoma.

To investigate the performance of combined 18F-FDG Positron Emission Tomography/Computed Tomography with high-resolution CT for differentiating invasive adenocarcinoma from adenocarcinoma in situ (pre-invasive lesion) or minimally invasive adenocarcinoma in stage IA lung cancer patients with solitary ground-glass opacity nodules. This retrospective study enrolled 58 consecutive stage IA pulmonary adenocarcinoma patients with solitary ground-glass opacity nodules. The characteristics and measurements of the ground-glass opacity nodules as pure ground-glass opacity nodules and mixed ground-glass opacity nodules in the pre-invasive or minimally invasive adenocarcinoma and invasive adenocarcinoma groups on Positron Emission Tomography/Computed Tomography and high-resolution CT were compared and analyzed. Ground-glass opacity nodules in the pre-invasive or minimally invasive adenocarcinoma group preferentially manifested as pure ground-glass opacity nodule (p < 0.01) compared to the invasive adenocarcinoma group. While cystic appearance was more common in the invasive adenocarcinoma group (p < 0.05). Significant differences were found in the diameter of the ground-glass opacity nodule itself and its solid component, and consolidation/tumor ratio between the two groups. The sensitivity in predicting invasive adenocarcinoma was higher with a combined consolidation/tumor ratio > 0.38 and SUVmax > 1.46 in mixed ground-glass opacity nodule when compared to those of SUVmax > 0.95 alone or consolidation/tumor ratio> 0.39 alone (both p > 0.05). For a mixed ground-glass opacity nodule combined consolidation/tumor ratio > 0.38 and SUVmax > 1.46 appears to better predict invasive adenocarcinoma in stage IA lung cancer patients with solitary ground-glass opacity nodules.

Incremental value of contrast enhanced computed tomography on diagnostic accuracy in evaluation of small pulmonary ground glass nodules.

To evaluate the information gain by the application of both non-contrast and contrast enhanced computed tomography (CT) with extended mediastinal display window settings in the evaluation of pure ground glass nodules (pGGNs) and or mixed ground glass nodules (mGGNs) in the context of pre-invasive or early stage lung adenocarcinoma. One hundred and fifty patients with ground glass nodules (GGNs) and mGGNs, with contrast enhanced CT scans within 2 weeks of thoracic surgery were included in the study. Quantitative evaluation of all nodules was performed in a conventional mediastinal window (CMW) and an extended mediastinal window (EMW) both on non-contrast images and contrast-enhanced images. Contrast-enhanced images with CMW demonstrated amplification of solid portion in 23 (43%), 41 (77%) with EMW out of 53 minimally invasive adenocarcinoma (MIA) nodules, and in 34 of 37 (91%) of invasive adenocarcinoma (IAC) nodules. Using the increase in size of solid portion of the nodule measured on the enhanced CT images with EMW, area under the receiver operating characteristic (ROC) curve of 0.872 and 0.899 was utilized for differentiating between the pre-invasive nodules and MIA and between MIA and IAC nodules, respectively. Statistically significant differences existed between the pre-invasive and the MIA groups, and MIA and the IAC groups in smaller nodules (P<0.01). Comparative quantitative analysis of the pre and post contrast images can help differentiate between atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), MIAs, and IACs. Extension of the CT mediastinal window setting improves the evaluation of small GGNs, and can augment the diagnostic accuracy when evaluating small pGGNs and mGGNs.

CT diagnosis of different pathological types of ground-glass nodules

To explore the CT features of ground-glass nodules (GGN) including preinvasive lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). Ninety-seven GGN lesions confirmed by operation pathology were included in this study. The lesions were divided into three groups: preinvasive lesion group (24 cases), MIA group (39 cases), IAC group (34 cases). The lesion size, 3-dimensional ratio, 2-dimensional ratio in axial images, lesion density, shape, speculation, lobulation, air-containing space and pleural indentation on the preoperative CT images in the three groups were analyzed and compared with pathological results. The data were statistically analyzed using SPSS 17.0. All preinvasive lesions presented as pure GGN on CT image, most showed round-like shape, clear and smooth border. MIA presented as pure GGN or mixed GGN on CT image, most showed round-like shape, with a clear and smooth border. IAC most presented as mixed GGN on CT image, often showed irregular shape. Speculation, lobulation, air-containing space and pleural indentation displayed gradually increasing from preinvasive lesions to MIA and IAC. There were statistically significant differences in lesion size, CT density, shape, air-containing space, speculation, pleural indentation and long diameter of solid component between the MIA and IAC groups (P < 0.05 for all). There were statistically significant differences in CT density values and long diameters of solid component of the lesions between the preinvasive lesion group and MIA group (P < 0.05). The AUC of solid component of the preinvasive lesion group and MIA group was 0.705, and that of the MIA and IAC groups was 0.814. Comprehensive analysis of the CT image features of GGNs, especially the solid component in the lesions, may help to the preoperative and differential diagnosis of preinvasive lesions, MIA and IAC.