Radiologic implications of the 2011 classification of adenocarcinoma of the lung

A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma

OBJECTIVES

Classification of histologic subgroups has significant prognostic value for lung adenocarcinoma patients who undergo surgical resection. However, clinical histopathology assessment is generally performed on only a small portion of the overall tumor from biopsy or surgery. Our objective is to identify a noninvasive quantitative imaging biomarker (QIB) for the classification of histologic subgroups in lung adenocarcinoma patients.

METHODS

We retrospectively collected and reviewed 1313 CT scans of patients with resected lung adenocarcinomas from two geographically distant institutions who were seen between January 2014 and October 2017. Three study cohorts, the training, internal validation, and external validation cohorts, were created, within which lung adenocarcinomas were divided into two disease-free-survival (DFS)-associated histologic subgroups, the mid/poor and good DFS groups. A comprehensive machine learning- and deep learning-based analytical system was adopted to identify reproducible QIBs and help to understand QIBs' significance.

RESULTS

Intensity-Skewness, a QIB quantifying tumor density distribution, was identified as the optimal biomarker for predicting histologic subgroups. Intensity-Skewness achieved high AUCs (95% CI) of 0.849(0.813,0.881), 0.820(0.781,0.856) and 0.863(0.827,0.895) on the training, internal validation, and external validation cohorts, respectively. A criterion of Intensity-Skewness ≤ 1.5, which indicated high tumor density, showed high specificity of 96% (sensitivity 46%) and 99% (sensitivity 53%) on predicting the mid/poor DFS group in the training and external validation cohorts, respectively.

CONCLUSIONS

A QIB derived from routinely acquired CT was able to predict lung adenocarcinoma histologic subgroups, providing a noninvasive method that could potentially benefit personalized treatment decision-making for lung cancer patients.

KEY POINTS

• A noninvasive imaging biomarker, Intensity-Skewness, which described the distortion of pixel-intensity distribution within lesions on CT images, was identified as a biomarker to predict disease-free-survival-associated histologic subgroups in lung adenocarcinoma. • An Intensity-Skewness of ≤ 1.5 has high specificity in predicting the mid/poor disease-free survival histologic patient group in both the training cohort and the external validation cohort. • The Intensity-Skewness is a feature that can be automatically computed with high reproducibility and robustness.

The Characteristics and Nomogram for Primary Lung Papillary Adenocarcinoma

Background

Primary pulmonary papillary adenocarcinoma (PA) is a specific and rare subtype of invasive pulmonary adenocarcinoma (ADC). The knowledge concerning the clinicopathologic features and prognosis of patients with primary pulmonary PA has not been clarified because of its rarity.

Methods

The clinical data of a total of 3391 patients with primary pulmonary PA were retrospectively analyzed to confirm their clinical characteristics and factors influencing prognosis and were in comparison with 3236 patients with non- PA pulmonary adenocarcinoma. All patients were histologically diagnosed between 1988 and 2015 in The Surveillance Epidemiology and End Results (SEER) database. A nomogram with satisfactory predictive performance was established to visually predict long-term survival of these patients.

Results and conclusion

Collectively, primary pulmonary PA is a rare pathological cancer and its prognosis is analogous to that of non-PA pulmonary adenocarcinoma. Older age, larger lesions, distant metastases, lymph node invasion, and poor pathological differentiation are correlative with unacceptable prognosis. Surgical intervention is conducive to reaping favorable prognosis. Unfortunately, radiotherapy or chemotherapy results of no significant effects on patient survival. In our study, a nomogram with prognostic function is formulated to confer individual prediction of overall survival (OS).

Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma

OBJECTIVES

Lung adenocarcinomas which manifest as ground-glass nodules (GGNs) have different degrees of pathological invasion and differentiating among them is critical for treatment. Our goal was to evaluate the addition of marginal features to a baseline radiomics model on computed tomography (CT) images to predict the degree of pathologic invasiveness.

METHODS

We identified 236 patients from two cohorts (training, n = 189; validation, n = 47) who underwent surgery for GGNs. All GGNs were pathologically confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). The regions of interest were semi-automatically annotated and 40 radiomics features were computed. We selected features using L1-norm regularization to build the baseline radiomics model. Additional marginal features were developed using the cumulative distribution function (CDF) of intratumoral intensities. An improved model was built combining the baseline model with CDF features. Three classifiers were tested for both models.

RESULTS

The baseline radiomics model included five features and resulted in an average area under the curve (AUC) of 0.8419 (training) and 0.9142 (validation) for the three classifiers. The second model, with the additional marginal features, resulted in AUCs of 0.8560 (training) and 0.9581 (validation). All three classifiers performed better with the added features. The support vector machine showed the most performance improvement (AUC improvement = 0.0790) and the best performance was achieved by the logistic classifier (validation AUC = 0.9825).

CONCLUSION

Our novel marginal features, when combined with a baseline radiomics model, can help differentiate IA from AIS and MIA on preoperative CT scans.

KEY POINTS

• Our novel marginal features could improve the existing radiomics model to predict the degree of pathologic invasiveness in lung adenocarcinoma.

Differences in lung cancer characteristics and mortality rate between screened and non-screened cohorts

Screening programs for lung cancer aim to allow diagnosis at the early stage, and therefore the decline in mortality rates. Thus, the aim of this retrospective cohort study was to the comparison of screened and non-screened lung cancer in terms of lung cancer characteristics, overdiagnosis and survival rate. A retrospective study in which 2883 patients with 2883 lung cancer diagnosed according to the hospital-based lung cancer register database between 2007 and 2017. A comparison was performed in term of clinical characteristics and outcomes of lung cancer between the screened and non-screening patient groups. 2883 subjects were identified (93 screened and 2790 non-screened). Screened group patients were younger (59.91 ± 8.14 versus 67.58 ± 12.95; p < 0.0001), and were more likely to be female than non-screened group (61.3% versus 36.8%; p < 0.0001). The screened group showed significantly better outcomes in overall mortality than the non-screened group (10.75% versus 79.06%; <0.0001). In a Cox proportional hazard model, lung cancer in the screened group proved to be an independent prognostic factor in lung cancer subjects. Our findings point to the improved survival outcome in the screened group and might underline the benefit of low-dose computed tomography (LDCT) screening program in Asian populations with the high prevalence of non-smoking-related lung cancer. Further study aimed at the LDCT mass screening program targeting at light smokers and non-smoker outside of existing screening criteria is warranted.

Meta-analysis of segmentectomy versus lobectomy for radiologically pure solid or solid-dominant stage IA non-small cell lung cancer

OBJECTIVE

Whether segmentectomy can be used to treat radiologically determined pure solid or solid-dominant lung cancer remains controversial owing to the invasive pathologic characteristics of these tumors despite their small size. This meta-analysis compared the oncologic outcomes after lobectomy and segmentectomy regarding relapse-free survival (RFS) and overall survival (OS) in patients with radiologically determined pure solid or solid-dominant clinical stage IA non-small cell lung cancer (NSCLC).

METHODS

A literature search was performed in the MEDLINE, EMBASE, and Cochrane Central databases for information from the date of database inception to March 2019. Studies were selected according to predefined eligibility criteria. The hazard ratio (HR) and associated 95% confidence interval (CI) were extracted or calculated as the outcome measure for data combining.

RESULTS

Seven eligible studies published between 2014 and 2018 enrolling 1428 patients were included in the current meta-analysis. Compared with lobectomy, segmentectomy had a significant benefit on the RFS of radiologically determined pure solid or solid-dominant clinical stage IA NSCLC patients (combined HR: 1.46; 95% CI, 1.05-2.03; P = 0.024) and there were no significant differences on the OS of these patients (HR: 1.52; 95% CI, 0.95-2.43; P = 0.08).

CONCLUSIONS

Segmentectomy leads to lower survival than lobectomy for clinical stage IA NSCLC patients with radiologically determined pure solid or solid-dominant tumors. Moreover, applying lobectomy to clinical stage IA NSCLC patients with radiologically determined pure solid or solid-dominant tumors (≤2 cm) could lead to an even bigger survival advantage. However, there are some limitations in the present study, and more evidence is needed to support the conclusion.

Glucose and glutamine metabolism in relation to mutational status in NSCLC histological subtypes

BACKGROUND

Both hypoxia and oncogenic mutations rewire tumor metabolism. In this study, glucose and glutamine metabolism-related markers were examined in stage I - resectable stage IIIA non-small cell lung cancer (NSCLC). Furthermore, expression of metabolism-related markers was correlated with mutational status to examine mutations associated with rewired tumor metabolism.

METHODS

Mutation analysis was performed for 97 tumors. Glucose and glutamine metabolism-related marker expression was measured by immunofluorescent staining (protein) and qPCR (mRNA) (n = 81).

RESULTS

Glutamine metabolism-related markers were significantly higher in adeno- than squamous cell NSCLCs. Glucose transporter 1 (GLUT1) protein expression was higher in solid compared to lepidic adenocarcinomas (P < 0.01). In adenocarcinomas, mRNA expression of glutamine transporter SLC1A5 correlated with tumor size (r(p) = 0.41, P = 0.005). Furthermore, SLC1A5 protein expression was significantly higher in adenocarcinomas with worse pTNM stage (r(s) = 0.39, P = 0.009). EGFR-mutated tumors showed lower GLUT1 protein (P = 0.017), higher glutaminase 2 (GLS2) protein (P = 0.025) and higher GLS2 mRNA expression (P = 0.004), compared to EGFR wild-type tumors. GLS mRNA expression was higher in KRAS-mutated tumors (P = 0.019). TP53-mutated tumors showed higher GLUT1 expression (P = 0.009).

CONCLUSIONS

NSCLC is a heterogeneous disease, with differences in mutational status and metabolism-related marker expression between adeno- and squamous cell NSCLCs, and also within adenocarcinoma subtypes. GLUT1 and SLC1A5 expression correlate with aggressive tumor behavior in adenocarcinomas but not in squamous cell NSCLCs. Therefore, these markers could steer treatment modification for subgroups of adenocarcinoma patients. TP53, EGFR and KRAS mutations are associated with expression of glucose and glutamine metabolism-related markers in NSCLC.

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

OBJECTIVE

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).

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Hook-wire localization versus lipiodol localization for patients with pulmonary lesions having ground-glass opacity

OBJECTIVES

Accurate and safe preoperative localization is useful for video-assisted thoracic surgery of small pulmonary lesions with ground-glass opacity (GGO). However, the optimal localization method is unclear. The aim of this study was to compare the usefulness and safety of the lipiodol and hook-wire localization techniques for video-assisted thoracic surgery of GGO lesions.

METHODS

This prospective, non-randomized comparative study was conducted between April 2014 and December 2016 at 8 qualifying university teaching hospitals. Two-hundred-fifty patients with pulmonary lesions having GGO were included. Patients were assigned in a 1:1 ratio to either the lipiodol (n = 125, 4 hospitals) or hook-wire group (n = 125, 4 hospitals) for preoperative localization procedures. Participants underwent preoperative localization via the lipiodol or hook-wire technique followed by thoracoscopic surgery. The primary endpoint was the procedure success rate.

RESULTS

The procedure success rates (hook-wire vs lipiodol group) were 94.40% versus 99.16% (P = .08). Localization-related complications occurred in 53.60% versus 48.33% of patients (P = .49). Hemorrhage rates were significantly greater in the hook-wire group than in the lipiodol group (21.6% vs 5.83%, P < .001). The lipiodol procedure time was significantly longer than that of the hook-wire technique (20.69 ± 9.34 vs 17.15 ± 7.91 minutes, P = .001). The initially positive surgical resection margin was significantly greater in the hook-wire group than in the lipiodol group (10.89% vs 2.38%, P = .02).

CONCLUSIONS

There was no significant difference in success rate between the hook-wire and lipiodol methods. However, the hemorrhage rate was significantly greater in the hook-wire group, whereas the hook-wire group showed greater initially positive surgical resection margins.

Correlation Study of 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Pathological Subtypes of Invasive Lung Adenocarcinoma and Prognosis

Purpose: To investigate the correlation between ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) metabolic parameters and clinicopathological factors in pathological subtypes of invasive lung adenocarcinoma and prognosis.

Patients and Methods: Metabolic parameters and clinicopathological factors from 176 consecutive patients with invasive lung adenocarcinoma between August 2008 and August 2016 who underwent ¹⁸F-FDG PET/CT examination were retrospectively analyzed. Invasive lung adenocarcinoma was divided into five pathological subtypes:lepidic predominant adenocarcinoma (LPA), acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), solid predominant adenocarcinoma (SPA), and micropapillary predominant adenocarcinoma (MPA). The differences in metabolic parameters [maximal standard uptake value (SUVmax), mean standard uptake value (SUVmean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV)] and tumor diameter for different pathological subtypes were analyzed. Patients were divided into two groups according to their prognosis: good prognosis group (LPA, APA, PPA) and poor prognosis group (SPA, MPA). Logistic regression was used to filter predictors and construct a predictive model, and areas under the receiver operating curve (AUC) were calculated. Cox regression analysis was performed on prognostic factors.

Results: 82 (46.6%) females and 94 (53.4%) males of patients with invasive lung adenocarcinoma were enrolled in this study. Metabolic parameters and tumor diameter of different pathological subtype had statistically significant (P < 0.05). The predictive model constructed using independent predictors (Distant metastasis, Ki-67, and SUVmax) had good classification performance for both groups. The AUC for SUVmax was 0.694 and combined with clinicopathological factors were 0.745. Cox regression analysis revealed that Stage, TTF-1, MTV, and pathological subtype were independent risk factors for patient prognosis. The hazard ratio (HR) of the poor prognosis group was 1.948 (95% CI 1.042–3.641) times the good prognosis group. The mean survival times of good and poor prognosis group were 50.2621 (95% CI 47.818–52.706) and 35.8214 (95% CI 27.483–44.159) months, respectively, while the median survival time was 47.00 (95% CI 45.000–50.000) and 31.50 (95% CI 23.000–49.000) months, respectively.

Conclusion: PET/CT metabolic parameters combined with clinicopathological factors had good classification performance for the different pathological subtypes, which may provide a reference for treatment strategies and prognosis evaluation of patients.

Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma

Purpose

We aimed to assess the classification performance of a computed tomography (CT)-based radiomic signature for discriminating invasive and non-invasive lung adenocarcinoma.

Patients and Methods

A total of 192 patients (training cohort, n=116; validation cohort, n=76) with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in the present study. Radiomic features were extracted from preoperative unenhanced chest CT images to build a radiomic signature. Predictive performance of the radiomic signature were evaluated using an intra-cross validation cohort. Diagnostic performance of the radiomic signature was assessed via receiver operating characteristic (ROC) analysis.

Results

The radiomic signature consisted of 14 selected features and demonstrated good discrimination performance between invasive and non-invasive adenocarcinoma. The area under the ROC curve (AUC) for the training cohort was 0.83 (sensitivity, 0.84 ; specificity, 0.78; accuracy, 0.82), while that for the validation cohort was 0.77 (sensitivity, 0.94; specificity, 0.52 ; accuracy, 0.82).

Conclusion

The CT-based radiomic signature exhibited good classification performance for discriminating invasive and non-invasive lung adenocarcinoma, and may represent a valuable biomarker for determining therapeutic strategies in this patient population.

Correlations Study Between 18F-FDG PET/CT Metabolic Parameters Predicting Epidermal Growth Factor Receptor Mutation Status and Prognosis in Lung Adenocarcinoma

Purpose: This study assessed the ability of metabolic parameters from ¹⁸Fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and clinicopathological data to predict epidermal growth factor receptor (EGFR) expression/mutation status in patients with lung adenocarcinoma and to develop a prognostic model based on differences in EGFR expression status, to enable individualized targeted molecular therapy. Patients and Methods: Metabolic parameters and clinicopathological data from 200 patients diagnosed with lung adenocarcinoma between July 2009 and November 2016, who underwent ¹⁸F-FDG PET/CT and EGFR mutation testing, were retrospectively evaluated. Multivariate logistic regression was applied to significant variables to establish a prediction model for EGFR mutation status. Overall survival for both mutant and wild-type EGFR was analyzed to establish a multifactor Cox regression model. Results: Of the 200 patients, 115 (58%) exhibited EGFR mutations and 85 (42%) were wild-type. Among selected metabolic parameters, metabolic tumor volume (MTV) demonstrated a significant difference between wild-type and mutant EGFR mutation status, with an area under the receiver operating characteristic curve (AUC) of 0.60, which increased to 0.70 after clinical data (smoking status) were combined. Survival analysis of wild-type and mutant EGFR yielded mean survival times of 34.451 (95% CI 28.654-40.249) and 53.714 (95% CI 44.331-63.098) months, respectively. Multivariate Cox regression revealed that mutation type, tumor stage, and thyroid transcription factor-1 (TTF-1) expression status were the main factors influencing patient prognosis. The hazard ratio for mutant EGFR was 0.511 (95% CI 0.303-0.862) times that of wild-type, and the risk of death was lower for mutant EGFR than for wild-type. The risk of death was lower in TTF-1-positive than in TTF-1-negative patients. Conclusion: ¹⁸F-FDG PET/CT metabolic parameters combined with clinicopathological data demonstrated moderate diagnostic efficacy in predicting EGFR mutation status and were associated with prognosis in mutant and wild-type EGFR non-small-cell lung cancer (NSCLC), thus providing a reference for individualized targeted molecular therapy.

Cytological differences between invasive and noninvasive or minimally invasive lung adenocarcinomas diagnosed in Japanese patients using needle biopsy specimens of pulmonary lesions ≤3 cm in diameter

BACKGROUND

According to the WHO classification for lung cancer, adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) have a better prognosis than invasive adenocarcinoma (IAD). However, detecting the foci of invasion in lung adenocarcinomas radiologically remains difficult. The present study examined whether or not differences in the cytological characteristics between IAD and AIS or MIA (noninvasive or minimally invasive adenocarcinomas [NMIAD]) plays a role in the differential diagnosis.

METHODS

Seventy surgical resection specimens of primary lung adenocarcinoma with preoperative cytology, in which several parameters were evaluated and assessed.

RESULTS

The histopathological diagnoses of surgical resection specimens were AIS in 8, MIA in 31, IAD in 31 including lepidic adenocarcinoma in 9, and papillary adenocarcinoma in 22. NMIAD had a 100% 5-year recurrence-free survival (RFS), while IAD had an 82.8% 5-year RFS. The numbers of tumor cells (at ×10 magnification in 10 fields) were 60.3 ± 40.5 in IAD and 39.8 ± 28.7 in NMIAD (P = 0.0017). A univariate analysis of cytological parameters revealed significant differences in large tumor cell clusters, three-dimensional (3D) tumor cell clusters, and irregular nuclear contours between the two groups. The frequency of irregular nuclear contours continued to be significantly different according to a multivariate analysis.

CONCLUSION

Large or 3D tumor cell clusters and irregular nuclear contours may be important cytological factors for distinguishing IAD from NMIAD, with the latter being potentially more important for distinguishing between the two groups.

Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules

109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen’s Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.

The Diagnostic Value of Quantitative CT Analysis of Ground-Glass Volume Percentage in Differentiating Epidermal Growth Factor Receptor Mutation and Subtypes in Lung Adenocarcinoma

Objective

To retrospectively investigate computed tomographic (CT) quantitative analysis of ground-glass opacity (GGO) volume percentage and morphologic features of resected lung adenocarcinomas according to epidermal growth factor receptor (EGFR) mutation status and subtypes.

Methods

Amplification refractory mutation system was used to detect mutations in the EGFR gene. Distribution of demographics and GGO volume percentage were performed according to EGFR mutation status and subtypes.

Results

EGFR mutations were significantly more frequent in women (55.2% vs. 37.0%, p=0.001) and in never-smokers (59.5% vs. 38.4%, p < 0.001) than those without EGFR mutation. GGO volume percentage was significantly higher in tumors with EGFR mutation than in tumors without EGFR mutation (52.8±25.7% vs. 29.0±20.7%, p < 0.001). The GGO volume percentages in tumors with exon 21 mutation and EGFR mutation showed a significant difference compared with those without EGFR mutation (p < 0.001, area under the curve=0.871, sensitivity=94.6%, specificity=73.8%, and p < 0.001, area under the curve=0.783, sensitivity=69.9%, specificity=75.4%, resp.), with cut-off values of 37.7% and 34.3% in receiver operating characteristic curve analysis.

Conclusion

GGO volume percentage in adenocarcinomas with EGFR mutation was significantly higher than that in tumors without EGFR mutation, and adenocarcinomas with exon 21 mutation showed significantly higher GGO volume percentage than in tumors with exon 19 mutation and those without EGFR mutation. Our results indicate that GGO volume percentage cut-off values of more than 37.7% and 34.3% were predictors of positive exon 21 mutation and EGFR mutation, respectively.

Looking for Lepidic Component inside Invasive Adenocarcinomas Appearing as CT Solid Solitary Pulmonary Nodules (SPNs): CT Morpho-Densitometric Features and 18-FDG PET Findings

Objective

To investigate CT morphologic and densitometric features and 18-FDG PET findings of surgically excised lung adenocarcinomas "mixed subtype" with predominant lepidic component, appearing as solid solitary pulmonary nodules (SPNs) on CT scan.

Materials and Methods

Approval for this study was given from each local institutional review board according to its retrospective nature. Nodules pathologically classified as lung adenocarcinoma mixed subtype with bronchioloalveolar otherwise lepidic predominant component, in three different Italian institutions (Napoli; Varese; Parma), were retrospectively selected.

Results

22 patients were identified. The number of SPNs with smooth margins was significantly lower with respect to the number of SPNs with spiculated margins (p: 0.033), radiating spiculations (p: 0.019), and notch sign (p: 0.011). Mean contrast enhancement (CE) was 53.34 HU (min 5.5 HU, max 112 HU); considering 15 HU as cut-off value, CE was positive in 20/22 cases. No significant correlation was found between size and CE. Mean SUVmax was 2.21, ranging from 0.2 up to 7.5 units; considering 2.5 units as cut-off, SUVmax was positive in 7/22 cases. The number of SPNs with positive CE was significantly higher than the number of SPNs with positive SUVmax (p: 0.0005).

Conclusion

CT generally helps in identifying solid SPN suspicious for malignancy but 18-FDG PET may result in false-negative evaluation; when 18-FDG PET findings of a solid SPN are negative even though CT morphology and CE suggest malignancy, radiologist should consider that lepidic component may be present inside the invasive tumor, despite the absence of ground glass.

Chronic Airspace Diseases

Airspace disease can be acute or chronic and commonly present as consolidation or ground-glass opacity on chest imaging. Consolidation or ground-glass opacity occurs when alveolar air is replaced by fluid, pus, blood, cells, or other material. Airspace disease is considered chronic when it persists beyond 4-6 weeks after treatment. These can be secondary to certain infectious, inflammatory, or neoplastic conditions. Computed tomography of the chest is usually performed in this set of patients to identify characteristic imaging findings. Familiarity with the differential diagnosis and characteristic imaging findings for chronic airspace disease is very important for guiding patient's management in a timely fashion.

Qualitative CT Criterion for Subsolid Nodule Subclassification: Improving Interobserver Agreement and Pathologic Correlation in the Adenocarcinoma Spectrum

RATIONALE AND OBJECTIVES

The main aim of this study was to evaluate the clinical validity and correlation with pathologic invasiveness in the pulmonary adenocarcinoma spectrum based on the novel qualitative computed tomography criterion for subsolid nodule (SSN) classification, which classified SSN into pure ground-glass nodule, heterogeneous ground-glass nodule, and part-solid nodule. In addition, we compared the performance of the conventional and novel classifications.

MATERIALS AND METHODS

The computed tomography images of 41 SSN nodules were interpreted by six observers independently, and the SSN characteristics were classified according to both the conventional and the novel classification systems. Each observer assessed 41 nodules in two different classifications separated by a minimum of 8 weeks. The kappa (κ) coefficient test was used to determine the reliability. The correlation between pulmonary adenocarcinoma spectrum and the SSN classification was analyzed with Spearman correlation coefficients.

RESULTS

Interobserver agreement (κ) was 0.702 (range 0.42-0.89) and 0.707 (range 0.58-0.88) for the conventional and the novel classifications for SSN, respectively, and intraobserver agreement (κ) was 0.92 and 0.88 for the conventional and the novel classifications for SSN, respectively. The novel SSN classification (correlation coefficient range 0.622-0.732) is more strongly correlated with the pathologic invasiveness degree of lesions in adenocarcinoma spectrum than the conventional SSN classification (correlation coefficient range 0.458-0.644).

CONCLUSIONS

The agreement between observers on the novel SSN classification system was good and had better correlation with pathologic invasiveness than the conventional SSN classification. Further studies are needed to confirm these results on interobserver agreement.

High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules

Objectives

To evaluate and compare the diagnostic accuracy of high versus low attenuation thresholds for determining the solid component of ground-glass opacity nodules (GGNs) for the differential diagnosis of adenocarcinoma in situ (AIS) from minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA).

Methods

Eighty-six pathologically confirmed GGNs < 3 cm observed in 86 patients (27 male, 59 female; mean age, 59.3 ± 11.0 years) between January 2013 and December 2015 were retrospectively included. The solid component of each GGN was defined using two different attenuation thresholds: high (-160 Hounsfield units [HU]) and low (-400 HU). According to the presence or absence of solid portions, each GGN was categorized as a pure GGN or part-solid GGN. Solid components were regarded as indicators of invasive foci, suggesting MIA or IA.

Results

Among the 86 GGNs, there were 57 cases of IA, 19 of MIA, and 10 of AIS. Using the high attenuation threshold, 44 were categorized as pure GGNs and 42 as part-solid GGNs. Using the low attenuation threshold, 13 were categorized as pure GGNs and 73 as part-solid GGNs. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for the invasive focus were 55.2%, 100%, 100%, 22.7%, and 60.4%, respectively, for the high attenuation threshold, and 93.4%, 80%, 97.2%, 61.5%, and 91.8%, respectively, for the low attenuation threshold.

Conclusion

The low attenuation threshold was better than the conventional high attenuation threshold for determining the solid components of GGNs, which indicate invasive foci.

CT characterization of different pathological types of subcentimeter pulmonary ground-glass nodular lesions

OBJECTIVE

To explore the CT characteristics of small lung nodules and improve the diagnosis of pulmonary ground-glass nodules less than 10  mm in size.

METHODS

We retrospectively analyzed CT images of 161 pulmonary nodules (less than 10  mm in size) with spiculation, lobulation, vacuoles, and pleural indentation and compared these images with pathological results or follow-up CT images. The relationships between the ground-glass nodules (GGNs) and blood vessels were observed. The GGN-vessel relationship was divided into four types, Type I (pass-by), Type II (pass-through), Type III (distorted/dilated), Type IV (complicated). The vessels traveling through a GGN were divided into three categories, category A (arteries), category B (veins), category C (arteries and veins).

RESULTS

161 GGNs were divided into three groups (benign group, pre-invasive group, and adenocarcinoma group) according to their pathological diagnosis. Significant differences in density of nodules were observed among the three different groups (p < 0.05). Significant differences in the shape (round/round-like or not) of the nodules were observed between the benign group and the pre-invasive group and between the pre-invasive group and the adenocarcinoma group (p < 0.05). No significant differences in the presence of vacuoles were observed between the benign group and the pre-invasive group or between the pre-invasive group and the adenocarcinoma group (p ＞0.05), but a significant difference was observed between the benign group and the adenocarcinoma group (p < 0.05). The differences in the vascularization of the lesions among the three groups were statistically significant (p < 0.05). No significant differences or correlations were observed between vascular categories and GGN groups (p > 0.05).

CONCLUSION

For subcentimeter nodules, mixed GGNs with vacuoles, well-defined border, combined with Type III or Type IV GGN-vessel relationship may strongly suggest malignant.

ADVANCES IN KNOWLEDGE

Previous studies mainly focused on CT diagnosis of pulmonary nodules (≤ 3  cm in diameter), but this study focused on ground-glass nodules less than 10  mm in diameter, which had not been fully studied. For subcentimeter nodules, mixed GGNs with vacuoles, well-defined border, especially the GGN-vessel relationship manifest as Type III (distorted/dilated) or Type IV (complicated) may strongly suggest malignant.

Invasive mucinous adenocarcinoma of the lung in a 19-year-old female

Lung cancers commonly occur in the sixth to eighth decades of life. They are extremely uncommon in first two decades of life. We describe the clinical, radiological, and pathological findings in a 19-year-old female diagnosed with an invasive mucinous adenocarcinoma that was initially mistaken and treated as tuberculosis. This case is being presented to emphasize inclusion of this entity in the differential diagnosis of multifocal consolidations and nodules that do not resolve or persist after treatment, and also to create awareness of the occurrence of lung cancers in young patients.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases

BACKGROUND AND OBJECTIVES

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models.

METHODS

local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis.

RESULTS

radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively.

CONCLUSIONS

the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning.

Validation of prediction models for risk stratification of incidentally detected pulmonary subsolid nodules: a retrospective cohort study in a Korean tertiary medical centre

OBJECTIVES

To validate the performances of two prediction models (Brock and Lee models) for the differentiation of minimally invasive adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions among subsolid nodules (SSNs).

DESIGN

A retrospective cohort study.

SETTING

A tertiary university hospital in South Korea.

PARTICIPANTS

410 patients with 410 incidentally detected SSNs who underwent surgical resection for the pulmonary adenocarcinoma spectrum between 2011 and 2015.

PRIMARY AND SECONDARY OUTCOME MEASURES

Using clinical and radiological variables, the predicted probability of MIA/IPA was calculated from pre-existing logistic models (Brock and Lee models). Areas under the receiver operating characteristic curve (AUCs) were calculated and compared between models. Performance metrics including sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were also obtained.

RESULTS

For pure ground-glass nodules (n=101), the AUC of the Brock model in differentiating MIA/IPA (59/101) from preinvasive lesions (42/101) was 0.671. Sensitivity, specificity, accuracy, PPV and NPV based on the optimal cut-off value were 64.4%, 64.3%, 64.4%, 71.7% and 56.3%, respectively. Sensitivity, specificity, accuracy, PPV and NPV according to the Lee criteria were 76.3%, 42.9%, 62.4%, 65.2% and 56.3%, respectively. AUC was not obtained for the Lee model as a single cut-off of nodule size (≥10 mm) was suggested by this model for the assessment of pure ground-glass nodules. For part-solid nodules (n=309; 26 preinvasive lesions and 283 MIA/IPAs), the AUC was 0.746 for the Brock model and 0.771 for the Lee model (p=0.574). Sensitivity, specificity, accuracy, PPV and NPV were 82.3%, 53.8%, 79.9%, 95.1% and 21.9%, respectively, for the Brock model and 77.0%, 69.2%, 76.4%, 96.5% and 21.7%, respectively, for the Lee model.

CONCLUSIONS

The performance of prediction models for the incidentally detected SSNs in differentiating MIA/IPA from preinvasive lesions might be suboptimal. Thus, an alternative risk calculation model is required for the incidentally detected SSNs.

Diameter of the Solid Component in Subsolid Nodules on Low-Dose Unenhanced Chest Computed Tomography: Measurement Accuracy for the Prediction of Invasive Component in Lung Adenocarcinoma

Objective

To determine if measurement of the diameter of the solid component in subsolid nodules (SSNs) on low-dose unenhanced chest computed tomography (CT) is as accurate as on standard-dose enhanced CT in prediction of pathological size of invasive component of lung adenocarcinoma.

Materials and Methods

From February 2012 to October 2015, 114 SSNs were identified in 105 patients that underwent low-dose unenhanced and standard-dose enhanced CT pre-operatively. Three radiologists independently measured the largest diameter of the solid component. Intraclass correlation coefficients (ICCs) were used to assess inter-reader agreement. We estimated measurement differences between the size of solid component and that of invasive component. We measured diagnostic accuracy of the prediction of invasive adenocarcinoma using a size criterion of a solid component ≥ 6 mm, and compared them using a generalized linear mixed model.

Results

Inter-reader agreement was excellent (ICC, 0.84.0.89). The mean ± standard deviation of absolute measurement differences between the solid component and invasive component was 4 ± 4 mm in low-dose unenhanced CT and 5 ± 4 mm in standard-dose enhanced CT. Diagnostic accuracy was 81.3% (95% confidence interval, 76.7.85.3%) in low-dose unenhanced CT and 76.6% (71.8.81.0%) in standard-dose enhanced CT, with no statistically significant difference (p = 0.130).

Conclusion

Measurement of the diameter of the solid component of SSNs on low-dose unenhanced chest CT was as accurate as on standard-dose enhanced CT for predicting the invasive component. Thus, low-dose unenhanced CT may be used safely in the evaluation of patients with SSNs.

HRCT morphological characteristics distinguishing minimally invasive pulmonary adenocarcinoma from invasive pulmonary adenocarcinoma appearing as subsolid nodules with a diameter of ≤3 cm

AIM

To differentiate retrospectively the morphological characteristics at high-resolution computed tomography (CT) between minimally invasive pulmonary adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IAC) appearing as subsolid nodules (SNs) with a diameter of ≤3 cm and to provide information to help operative decision-making.

MATERIALS AND METHODS

The patient notes of 260 patients with SNs of ≤3 cm in diameter (98 with MIA and 162 with IAC) confirmed at surgery and histopathology from September 2008 to June 2012 were reviewed retrospectively at the Department of Radiology, Weifang Respiratory Disease Hospital. Sixty-seven patients had pure ground-glass nodules (PGGNs) and 193 had mixed ground-glass nodules (MGGNs). Patients were grouped according to the final pathology: minimally invasive MIA and IAC. The HRCT characteristics were compared between the two groups.

RESULTS

There were statistically significant differences in the pattern, shape, diameter of solid components, proportion of solid components, CT radiodensity values of the ground-glass and solid components, borders, margins, air bronchograms, microvascular signs, and pleural indentations of the nodules between the two groups (all p<0.05). Multivariate and receiver operating characteristic (ROC) analyses indicated significant predictors of MIAs were as follows: small lesion diameter (≤14.7 mm), solid components ≤7 mm, <50% of solid components, low CT radiodensity values of the solid components (≤-107 HU), air bronchograms in the ground-glass opacity components, and microvascular signs.

CONCLUSION

The morphological characteristics at high-resolution CT can be used to differentiate between MIAs and IACs appearing as SNs with a diameter of ≤3 cm and provide information to help operative decision-making.

Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement

OBJECTIVES

We hypothesized that semi-automatic diameter measurements would improve the accuracy and reproducibility in discriminating preinvasive lesions and minimally invasive adenocarcinomas from invasive pulmonary adenocarcinomas appearing as subsolid nodules (SSNs) and increase the reproducibility in classifying SSNs.

METHODS

Two readers independently performed semi-automatic and manual measurements of the diameters of 102 SSNs and their solid portions. Diagnostic performance in predicting invasive adenocarcinoma based on diameters was tested using logistic regression analysis with subsequent receiver operating characteristic curves. Inter- and intrareader reproducibilities of diagnosis and SSN classification according to Fleischner's guidelines were investigated for each measurement method using Cohen's κ statistics.

RESULTS

Semi-automatic effective diameter measurements were superior to manual average diameters for the diagnosis of invasive adenocarcinoma (AUC, 0.905-0.923 for semi-automatic measurement and 0.833-0.864 for manual measurement; p<0.05). Reproducibility of diagnosis between the readers also improved with semi-automatic measurement (κ=0.924 for semi-automatic measurement and 0.690 for manual measurement, p=0.012). Inter-reader SSN classification reproducibility was significantly higher with semi-automatic measurement (κ=0.861 for semi-automatic measurement and 0.683 for manual measurement, p=0.022).

CONCLUSIONS

Semi-automatic effective diameter measurement offers an opportunity to improve diagnostic accuracy and reproducibility as well as the classification reproducibility of SSNs.

KEY POINTS

• Semi-automatic effective diameter measurement improves the diagnostic accuracy for pulmonary subsolid nodules. • Semi-automatic measurement increases the inter-reader agreement on the diagnosis for subsolid nodules. • Semi-automatic measurement augments the inter-reader reproducibility for the classification of subsolid nodules.

Semiquantative Visual Assessment of Sub-solid Pulmonary Nodules ≦3 cm in Differentiation of Lung Adenocarcinoma Spectrum

We aimed to analyze CT features of persistent subsolid nodules (SSN) ≦3 cm diagnosed pathologically as adenocarcinoma spectrum to investigate whether parameters enable distinction between invasive pulmonary adenocarcinomas (IPAs) and pre-invasive lesions. A total of 129 patients with 141 SSNs confirmed with surgically pathologic proof were retrospectively reviewed. Of 141 SSNs, there were 57 pure ground-glass nodules (GGNs), 22 heterogeneous GGNs, and 62 part-solid nodules. SSN subclassification showed a significant linear trend with invasive degree of the adenocarcinoma spectrum (pure GGNs 7%; heterogeneous GGNs 36.4%; part-solid nodules 85.5%, P for trend <0.0001). For IPA detection in 141 SSNs, a solid part of ≧3 mm was the most specificity (sensitivity, 76.9%; specificity, 94.7%), followed by air-bronchogram sign (sensitivity, 53.8%; specificity, 89.5%), SSN subclassification (sensitivity, 81.5%; specificity, 88.2%), and a lesion size ≧12 mm (sensitivity, 84.6%; specificity, 76.3%). For IPA detection in 79 pure or heterogeneous GGNs, the heterogeneous GGN sign was the most useful finding, with most specificity (sensitivity, 66.7%; specificity, 79.1%), followed by CT attenuation (HU) of ≧-493 (sensitivity, 75%; specificity, 74.6%) and a lesion size ≧10 mm (sensitivity, 83.3%; specificity, 70.1%). In conclusion, this simple combined visual and semiquantitative analysis of CT features helps distinguish IPAs from pre-invasive lesions.

Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network

We present a computer-aided diagnosis system (CADx) for the automatic categorization of solid, part-solid and non-solid nodules in pulmonary computerized tomography images using a Convolutional Neural Network (CNN). Provided with only a two-dimensional region of interest (ROI) surrounding each nodule, our CNN automatically reasons from image context to discover informative computational features. As a result, no image segmentation processing is needed for further analysis of nodule attenuation, allowing our system to avoid potential errors caused by inaccurate image processing. We implemented two computerized texture analysis schemes, classification and regression, to automatically categorize solid, part-solid and non-solid nodules in CT scans, with hierarchical features in each case learned directly by the CNN model. To show the effectiveness of our CNN-based CADx, an established method based on histogram analysis (HIST) was implemented for comparison. The experimental results show significant performance improvement by the CNN model over HIST in both classification and regression tasks, yielding nodule classification and rating performance concordant with those of practicing radiologists. Adoption of CNN-based CADx systems may reduce the inter-observer variation among screening radiologists and provide a quantitative reference for further nodule analysis.

Ominous lung cavity “Tambourine sign”

Mucinous adenocarcinoma represents a rare subtype of adenocarcinoma of the lung, which is frequently invasive and has a poorer prognosis. Of its wide range of imaging appearances, air-space consolidation is the most frequent pattern while cavitary form has only rarely been reported. Despite imaging advancements, the differentiation of benign and malignant cavitary lung lesions sometimes remains imperfect. We propose “Tambourine” sign on computed tomography to raise the suspicion of mucinous adenocarcinoma in a lung cavity, under appropriate clinical settings. The sign indicates an irregular cavity with undistorted prominent thick walled bronchioles within the wall and draping along thereby resembling the musical instrument “tambourine”. Adjacent ground glass and internal septations may also be seen.

Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.

Radiological prediction of tumor invasiveness of lung adenocarcinoma on thin-section CT

To evaluate thin-section computed tomography (CT) (TSCT) features that differentiate adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and to determine the size of solid portion on CT that correlates to pathological invasive components. Forty-eight patients were included. Nodules were classified into ground-glass nodule (GGN), part-solid, solid, and heterogeneous. Visual density of GGNs was subjectively evaluated using reference standard images: faint GGN (Ga), <-700 Hounsfield unit (HU); intermediate GGN (Gb), from -700 to -400 HU; dense GGN (Gc), >-400 HU; and mixed (Ga + Gb, Ga + Gc, and Gb + Gc). The evaluated TSCT findings included margin of nodule, distribution of solid portion, distribution of air bronchiologram, and pleural indentation. The longest diameters of the solid portion and the entire tumor were measured. Invasive diameters were measured in pathological specimens. Twenty-two AISs (16 GGNs [7 Ga, 5 Gb, 2 Gc, 1 Ga + Gc, 1 Gb + Gc], 4 part-solids, and 2 heterogeneous), 6 MIAs (1 GGN [Gb + Gc], 3 part-solids, and 2 solids), and 20 IVAs (1 GGN [Gb], 3 part-solids, and 16 solid) were found. The longest diameter (mean ± standard deviation) of the solid portion and total tumor were 9.7 ± 9.7 and 18.9 ± 5.6 mm, respectively. Significant differences in TSCT findings between AIS and IVA were margin of nodule (Pearson chi-squared test, P = 0.004), distribution of air bronchiologram (P = 0.0148), and pleural indentation (P = 0.0067). A solid portion >5.3 mm on TSCT indicated MIA or IVA, and >7.3 mm indicated IVA (receiver operating characteristic analysis, P < 0.0001). Irregular margin, air bronchiologram with disruption and/or irregular dilatation, and pleural indentation may distinguish IVA from AIS. A 5.3 to 7.3 mm solid portion on TSCT indicates MIA/IVA, and a solid portion >7.3 mm on TSCT indicates IVA.

Improved image quality and diagnostic potential using ultra-high-resolution computed tomography of the lung with small scan FOV: A prospective study

The aim of this study was to assess whether CT imaging using an ultra-high-resolution CT (UHRCT) scan with a small scan field of view (FOV) provides higher image quality and helps to reduce the follow-up period compared with a conventional high-resolution CT (CHRCT) scan. We identified patients with at least one pulmonary nodule at our hospital from July 2015 to November 2015. CHRCT and UHRCT scans were conducted in all enrolled patients. Three experienced radiologists evaluated the image quality using a 5-point score and made diagnoses. The paired images were displayed side by side in a random manner and annotations of scan information were removed. The following parameters including image quality, diagnostic confidence of radiologists, follow-up recommendations and diagnostic accuracy were assessed. A total of 52 patients (62 nodules) were included in this study. UHRCT scan provides a better image quality regarding the margin of nodules and solid internal component compared to that of CHRCT (P < 0.05). Readers have higher diagnostic confidence based on the UHRCT images than of CHRCT images (P<0.05). The follow-up recommendations were significantly different between UHRCT and CHRCT images (P<0.05). Compared with the surgical pathological findings, UHRCT had a relative higher diagnostic accuracy than CHRCT (P > 0.05). These findings suggest that the UHRCT prototype scanner provides a better image quality of subsolid nodules compared to CHRCT and contributes significantly to reduce the patients' follow-up period.

Lung cancer screening with low-dose CT in Europe: strength and weakness of diverse independent screening trials

A North American trial reported a significant reduction of lung cancer mortality and overall mortality as a result of annual screening using low-dose computed tomography (LDCT). European trials prospectively tested a variety of possible screening strategies. The main topics of current discussion regarding the optimal screening strategy are pre-test selection of the high-risk population, interval length of LDCT rounds, definition of positive finding, and post-test apportioning of lung cancer risk based on LDCT findings. Despite the current lack of statistical evidence regarding mortality reduction, the European independent diverse strategies offer a multi-perspective view on screening complexity, with remarkable indications for improvements in cost-effectiveness and harm-benefit balance. The UKLS trial reported the advantage of a comprehensive and simple risk model for selection of patients with 5% risk of lung cancer in 5 years. Subjective risk prediction by biological sampling is under investigation. The MILD trial reported equal efficiency for biennial and annual screening rounds, with a significant reduction in the total number of LDCT examinations. The NELSON trial introduced volumetric quantification of nodules at baseline and volume-doubling time (VDT) for assessment of progression. Post-test risk refinement based on LDCT findings (qualitative or quantitative) is under investigation. Smoking cessation remains the most appropriate strategy for mortality reduction, and it must therefore remain an integral component of any lung cancer screening programme.

INTEGRATIVE ANALYSIS FOR LUNG ADENOCARCINOMA PREDICTS MORPHOLOGICAL FEATURES ASSOCIATED WITH GENETIC VARIATIONS

Lung cancer is one of the most deadly cancers and lung adenocarcinoma (LUAD) is the most common histological type of lung cancer. However, LUAD is highly heterogeneous due to genetic difference as well as phenotypic differences such as cellular and tissue morphology. In this paper, we systematically examine the relationships between histological features and gene transcription. Specifically, we calculated 283 morphological features from histology images for 201 LUAD patients from TCGA project and identified the morphological feature with strong correlation with patient outcome. We then modeled the morphology feature using multiple co-expressed gene clusters using Lasso-regression. Many of the gene clusters are highly associated with genetic variations, specifically DNA copy number variations, implying that genetic variations play important roles in the development cancer morphology. As far as we know, our finding is the first to directly link the genetic variations and functional genomics to LUAD histology. These observations will lead to new insight on lung cancer development and potential new integrative biomarkers for prediction patient prognosis and response to treatments.

Longitudinal evolution of incidentally detected solitary pure ground-glass nodules on CT: relation to clinical metrics

PURPOSE

We aimed to assess the relation between basic clinical parameters and evolution of solitary pure ground-glass nodules (pGGN) in the lungs.

METHODS

Baseline and follow-up computed tomography (CT) of patients with solitary pGGN were selected and two radiologists independently reviewed CTs for nodule characterization. CT features of solitary pGGN were manually measured maximum diameter (D1) and its orthogonal diameter (D2), mean diameter (mD), D1 to D2 ratio as surrogate of roundness, and location according to lobar anatomy. Longitudinal changes were assessed and solitary pGGNs were classified as resolved or persisting. Persisting nodules were further classified as stable or grown according to an increase in mD of ≥2 mm or appearance of solid component. Baseline CT features of solitary pGGNs and clinical metrics of patients were compared between resolved and persisting nodules and, thereafter, between stable and grown lesions.

RESULTS

A total of 95 subjects with solitary pGGN were included. After a median 16-month follow-up, 20 nodules resolved, while 75 persisted. Among persisting nodules, 18 were grown and 57 were stable. Grown nodules showed larger D1 and mD compared with stable pGGNs (P < 0.001). Subjects with grown nodules were older (P = 0.021). Logistic regression analyses showed higher likelihood of growth for nodules ≥10 mm (odds ratio [OR], 8.355; P = 0.001) and subjects older than 67 years (OR, 3.656; P = 0.034).

CONCLUSION

Nodules ≥10 mm in subjects older than 67 years showed higher likelihood of growth. These data could contribute to a more individual approach to the management of solitary pGGN.