Air bronchogram: A potential indicator of epidermal growth factor receptor mutation in pulmonary subsolid nodules

Construction of a predictive model of 2-3 cm ground-glass nodules developing into invasive lung adenocarcinoma using high-resolution CT

Background

The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures.

Methods

We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established.

Results

(1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve Y was close to the X-line, and the decision curve was drawn in the range of 0.0-1.0.

Conclusion

We analyzed the risk factors for the development of 2-3  cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.

The radiological characteristics, tertiary lymphoid structures, and survival status associated with EGFR mutation in patients with subsolid nodules like stage I-II LUAD

BACKGROUND

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) recommended for the patients with subsolid nodule in early lung cancer stage is not routinely. The clinical value and impact in patients with EGFR mutation on survival outcomes is further needed to be elucidated to decide whether the application of EGFR-TKIs was appropriate in early lung adenocarcinoma (LUAD) stage appearing as subsolid nodules.

MATERIALS AND METHODS

The inclusion of patients exhibiting clinical staging of IA-IIB subsolid nodules. Clinical information, computed tomography (CT) features before surgical resection and pathological characteristics including tertiary lymphoid structures of the tumors were recorded for further exploration of correlation with EGFR mutation and prognosis.

RESULTS

Finally, 325 patients were enrolled into this study, with an average age of 56.8 ± 9.8 years. There are 173 patients (53.2%) harboring EGFR mutation. Logistic regression model analysis showed that female (OR = 1.944, p = 0.015), mix ground glass nodule (OR = 2.071, p = 0.003, bubble-like lucency (OR = 1.991, p = 0.003) were significant risk factors of EGFR mutations. Additionally, EGFR mutations were negatively correlated with TLS presence and density. Prognosis analysis showed that the presence of TLS was associated with better recurrence-free survival (RFS)(p = 0.03) while EGFR mutations were associated with worse RFS(p = 0.01). The RFS in patients with TLS was considerably excel those without TLS within EGFR wild type group(p = 0.018). Multivariate analyses confirmed that EGFR mutation was an independent prognostic predictor for RFS (HR = 3.205, p = 0.037).

CONCLUSIONS

In early-phase LUADs, subsolid nodules with EGFR mutation had specific clinical and radiological signatures. EGFR mutation was associated with worse survival outcomes and negatively correlated with TLS, which might weaken the positive impact of TLS on prognosis. Highly attention should be paid to the use of EGFR-TKI for further treatment as agents in early LUAD patients who carrying EGFR mutation.

Distinguishing EGFR mutant subtypes in stage IA non-small cell lung cancer using the presence status of ground glass opacity and final histologic classification: a systematic review and meta-analysis

Background

The progression of early stage non-small cell lung cancer (NSCLC) is closely related to epidermal growth factor receptor (EGFR) mutation status. The purpose of this study was to systematically investigate the relationship between EGFR mutation status and demographic, imaging, and ultimately pathologic features in patients with NSCLC.

Methods

A complete literature search was conducted using the PubMed, Web of Science, EMBASE, and Cochrane Library databases to discover articles published by May 15, 2023 that were eligible. The relationship between EGFR mutation status and specific demographic, imaging, and ultimately pathologic features in patients with NSCLC was evaluated using pooled odds ratios (ORs) and their 95% confidence intervals (CIs). The standardized mean difference (SMD) with 95% CIs was the appropriate statistic to summarize standard deviations (SDs) means for continuous variables.

Results

A total of 9 studies with 1789 patients were included in this analysis. The final findings suggested that patients with a greater age, female gender, and non-smoking status would have a relatively higher incidence of EGFR mutations. Additionally, the risk of EGFR mutations increased with larger tumor diameter, tumor imaging presentation of mixed ground glass opacity (mGGO), and tumor pathological findings of minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC). Significantly, malignancies presenting as MIA are more likely to contain L858R point mutations (OR = 1.80; 95% CI: 1.04-3.13; p = 0.04) rather than exon 19 deletions (OR = 1.81; 95% CI: 0.95-3.44; p = 0.07).

Conclusion

This meta-analysis showed that imaging parameters and histological classifications of pulmonary nodules may be able to predict stage IA NSCLC genetic changes.

Air bronchogram on chest CT in radiological pure-solid appearance lung cancer: Correlation analysis with genetic pathological features and survival outcomes

PURPOSE

To investigate the correlation of air bronchogram sign with clinicopathological characteristics and prognosis in patients with clinical stage (c-stage) I non-small cell lung cancer (NSCLC) with radiological pure-solid appearance.

METHOD

We retrospectively evaluated 276 patients with pure-solid c-stage I NSCLC and assessed the correlation between the air bronchogram and clinicopathological characteristics. A Cox proportional hazards model was performed to identify the effect of air bronchogram and clinicopathological variables on oncological outcomes. Recurrence-free survival (RFS) and overall survival (OS) were calculated by Kaplan-Meier curves and were compared using log-rank tests.

RESULTS

Presence of air bronchogram was associated with a well differentiated degree (P =.026), higher incidence of EGFR mutation (P <.001) and lower recurrence(P =.021). Kaplan-Meier survival curves showed that air bronchogram group was associated with favorable RFS(67.0% vs. 50.2%; P =.015). A multivariable analysis revealed that air bronchogram and EGFR mutation were independent significant prognostic factors associated with RFS (hazard ratio [HR] = 0.495, 95% confidence interval [CI]: 0.322-0.761, P =.001; HR = 1.625, 95% CI: 1.074-2.457, P =.021; respectively), but not with OS. Additionally, we found that pathological lymph node metastasis was identified as an independent prognostic factor associated with poor RFS and OS(HR = 2.808, 95% CI: 1.913-4.123, P <.001 for RFS; HR = 1.983, 95% CI: 1.185-3.318, P =.009 for OS).

CONCLUSIONS

Presence of air bronchogram was associated with well differentiated degree, higher incidence of EGFR mutation and had additional positive prognostic value for RFS in c-stage I NSCLC with a radiological pure-solid appearance.

Clinical and Radiological Characteristics to Differentiate Between EGFR Exon 21 and Exon 19 Mutations in Patients With Lung Adenocarcinoma: A Systematic Literature Review and Meta-Analysis

Epidermal Growth Factor Receptor (EGFR) mutations in lung adenocarcinoma have been previously associated with specific clinical characteristics and Computed Tomography (CT) patterns. However, associations among individual EGFR mutations have not been evaluated. We aim to differentiate if the most common EGFR mutations (exon 21 and 19) are related to specific clinical characteristics or CT patterns. A systematic review and meta-analysis of 5 databases were conducted with literature from January 2002 to July 2021. Eligible studies were of an experimental or observational design that included lung adenocarcinoma patients with confirmed EGFR exon mutations (21 and 19) and associated clinical characteristics and CT imaging patterns. Quality was assessed using the QUADAS-2 tool. The association between clinical and CT patterns and EGFR exon mutations 21 and 19 was evaluated using odds ratios (OR) and then pooled and analyzed with a fixed or random-effects model. This study follows the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines.

A total of 12 retrospective diagnostic accuracy studies were included. Pooled analysis showed that characteristics such as absence of smoking status (OR 1.29 [95% CI 0.97 - 1.70]), and female sex (OR 1.23 [95% CI 0.83 - 1.82]); and CT patterns such as Ground Glass Opacities (GGO) (OR 1.03 [95% CI 0.78 -1.34]), air bronchogram (OR 0.78 [95% CI 0.44 -1.39]), pleural retraction (OR 0.83 [95% CI 0.53 - 1.28]), and spiculation (OR 0.80 [95% CI 0.48 - 1.31]) were not significantly associated to a specific mutation. Specific EGFR exon 21 and 19 mutations cannot be differentiated through characteristics (absence of smoking status and female sex) or radiological patterns (GGO, air bronchogram, pleural retraction, and speculation). There is limited data to assess if early disease stage or vascular convergence aids in differentiating exon 21 from 19 mutations in patients with lung adenocarcinoma.

Artificial Intelligence-Aided Diagnosis Software to Identify Highly Suspicious Pulmonary Nodules

Introduction

To evaluate the value of artificial intelligence (AI)-assisted software in the diagnosis of lung nodules using a combination of low-dose computed tomography (LDCT) and high-resolution computed tomography (HRCT).

Method

A total of 113 patients with pulmonary nodules were screened using LDCT. For nodules with the largest diameters, an HRCT local-target scanning program (combined scanning scheme) and a conventional-dose CT scanning scheme were also performed. Lung nodules were subjectively assessed for image signs and compared by size and malignancy rate measured by AI-assisted software. The nodules were divided into improved visibility and identical visibility groups based on differences in the number of signs identified through the two schemes.

Results

The nodule volume and malignancy probability for subsolid nodules significantly differed between the improved and identical visibility groups. For the combined scanning protocol, we observed significant between-group differences in subsolid nodule malignancy rates.

Conclusion

Under the operation and decision of AI, the combined scanning scheme may be beneficial for screening high-risk populations.

Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis

Purpose

This study aims to determine if the presence of specific clinical and computed tomography (CT) patterns are associated with epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer.

Methods

A systematic literature review and meta-analysis was carried out in 6 databases between January 2002 and July 2021. The relationship between clinical and CT patterns to detect EGFR mutation was measured and pooled using odds ratios (OR). These results were used to build several mathematical models to predict EGFR mutation.

Results

34 retrospective diagnostic accuracy studies met the inclusion and exclusion criteria. The results showed that ground-glass opacities (GGO) have an OR of 1.86 (95%CI 1.34 −2.57), air bronchogram OR 1.60 (95%CI 1.38 – 1.85), vascular convergence OR 1.39 (95%CI 1.12 – 1.74), pleural retraction OR 1.99 (95%CI 1.72 – 2.31), spiculation OR 1.42 (95%CI 1.19 – 1.70), cavitation OR 0.70 (95%CI 0.57 – 0.86), early disease stage OR 1.58 (95%CI 1.14 – 2.18), non-smoker status OR 2.79 (95%CI 2.34 – 3.31), female gender OR 2.33 (95%CI 1.97 – 2.75). A mathematical model was built, including all clinical and CT patterns assessed, showing an area under the curve (AUC) of 0.81.

Conclusions

GGO, air bronchogram, vascular convergence, pleural retraction, spiculated margins, early disease stage, female gender, and non-smoking status are significant risk factors for EGFR mutation. At the same time, cavitation is a protective factor for EGFR mutation. The mathematical model built acts as a good predictor for EGFR mutation in patients with lung adenocarcinoma.

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GGO, air bronchogram, vascular convergence, pleural retraction, and spiculated margins, are risk factors for EGFR mutation.

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Early disease stage, female gender and non-smoking status are risk factors for EGFR mutation.

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Cavitation is a protective factor for EGFR mutation.

AIR-Net: A novel multi-task learning method with auxiliary image reconstruction for predicting EGFR mutation status on CT images of NSCLC patients

Automated and accurate EGFR mutation status prediction using computed tomography (CT) imagery is of great value for tailoring optimal treatments to non-small cell lung cancer (NSCLC) patients. However, existing deep learning based methods usually adopt a single task learning strategy to design and train EGFR mutation status prediction models with limited training data, which may be insufficient to learn distinguishable representations for promoting prediction performance. In this paper, a novel multi-task learning method named AIR-Net is proposed to precisely predict EGFR mutation status on CT images. First, an auxiliary image reconstruction task is effectively integrated with EGFR mutation status prediction, aiming at providing extra supervision at the training phase. Particularly, we adequately employ multi-level information in a shared encoder to generate more comprehensive representations of tumors. Second, a powerful feature consistency loss is further introduced to constrain semantic consistency of original and reconstructed images, which contributes to enhanced image reconstruction and offers more effective regularization to AIR-Net during training. Performance analysis of AIR-Net indicates that auxiliary image reconstruction plays an essential role in identifying EGFR mutation status. Furthermore, extensive experimental results demonstrate that our method achieves favorable performance against other competitive prediction methods. All the results executed in this study suggest that the effectiveness and superiority of AIR-Net in precisely predicting EGFR mutation status of NSCLC.

Molecular Alterations in Lung Adenocarcinoma With Ground-Glass Nodules: A Systematic Review and Meta-Analysis

Background and Aims

Nodular ground-glass lesions have become increasingly common with the increased use of computed tomography (CT), while the genomic features of ground-glass opacities (GGOs) remain unclear. This study aims to comprehensively investigate the molecular alterations of GGOs and their correlation with radiological progression.

Methods

Studies from PubMed, Embase, Cochrane Library, and Web of Science, using PCR, targeted panel sequencing, whole exosome sequencing, and immunohistochemistry, and reporting genomic alterations or PD-L1 expressions in lung nodules presenting as GGOs until January 21, 2021 were included in this study. Chi-square test, random-effects model, and Z-test analysis were adopted to analyze the data.

Results

A total of 22 studies describing mutations in lung adenocarcinoma (LUAD) with GGOs were analyzed. EGFR was the most frequently mutative gene (51%, 95%CI 47%-56%), followed by TP53 (18%, 95%CI 6%-31%), HER2 (10%, 95%CI 0%-21%), ROS1 (6%, 95%CI 0%-18%), and KRAS (6%, 95%CI 3%-9%). The correlation between the frequency of EGFR mutation and radiological was observed and the differences were found to be not statistically significant in the subgroups, which are listed as below: radiological: gGGO 47.40%, 95%CI [38.48%; 56.40%]; sGGO 51.94%, 95%CI [45.15%; 58.69%]. The differences of the frequency of KRAS mutation in the different subgroups were also consistent with this conclusion, which are listed as: radiological gGGO 3.42, 95%CI [1.35%; 6.13%]; sGGO 12.27%, 95%CI [3.89%; 23.96%]. The pooled estimated rate of PD-L1 was 8.82%, 95%CI [5.20%-13.23%]. A total of 11.54% (3/26) of the SMGGNs were confirmed to be intrapulmonary spread by WES.

Conclusions

Somatic genetic alterations are considered in early-stage GGO patients without distinct changes of the frequency following the progress of the tumor. This review sheds insight on molecular alterations in LUAD with GGOs.

Computed Tomography Imaging Characteristics: Potential Indicators of Epidermal Growth Factor Receptor Mutation in Lung Adenocarcinoma

PURPOSE

The purpose of this study was to investigate the correlation between computed tomography imaging characteristics in lung adenocarcinoma and epidermal growth factor receptor (EGFR) mutations.

METHODS

A total of 124 patients with lung adenocarcinoma and known EGFR mutation status were collected in this retrospective study. Computed tomography quantitative parameters of each tumor, including total volume, total surface, surface-to-volume ratio (SVR), average diameter, maximum diameter, and average density, were determined using computer-aided detection software. The correlation between the EGFR mutation status and imaging characteristics was assessed. The predictive value of these imaging characteristics for EGFR mutation was calculated using the area under the receiver operating characteristic curve.

RESULT

Fifty-eight of 124 patients showed EGFR mutations. Patients who are female (P < 0.001) and nonsmokers (P < 0.001) and those with serum carcinoembryonic antigen (CEA) level of ≥5 (P = 0.035) were likely to have EGFR mutation. Computed tomography features including air bronchogram (P = 0.035), absence of cavitation (P = 0.010), and absence of pulmonary emphysema (P = 0.002) and quantitative parameters, such as smaller total surface (P = 0.002), smaller total volume (P = 0.001), higher SVR (P = 0.003), and smaller average diameter (P = 0.001), were associated with EGFR mutation. Logistic regression analysis revealed that the most significant independent prognostic factors of EGFR mutation for the model were nonsmoking (P = 0.035), CEA level of ≥5 (P = 0.004), presence of air bronchogram (P = 0.040), absence of cavitation (P = 0.021), and high SVR (P = 0.014). The area under the receiver operating characteristic curve, sensitivity, and specificity of the model for predicting EGFR mutation were 0.827, 75.8%, and 82.8%, respectively.

CONCLUSIONS

EGFR-mutated adenocarcinoma showed significantly increased CEA level, presence of air bronchogram, absence of cavitation, and higher quantitative parameter SVR than those with wild-type EGFR.

Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.

Biological Significance of 18F-FDG PET/CT Maximum Standard Uptake Value for Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Patients

Purpose

To investigate the potential of maximum standardized uptake value (SUVmax) in predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients.

Methods

Clinical data of 311 NSCLC patients who had undergone both EGFR mutation test and ¹⁸F-FDG PET/CT scans between January 2013 and December 2017 at our hospital were retrospectively analyzed. Patients were sub-grouped by their origin of SUVmax. Univariate and multivariate analyses were performed to investigate the association between clinical factors and EGFR mutations. Receiver operating characteristic curve (ROC) analysis was performed to confirm the predictive value of clinical factors. In vitro experiments were performed to confirm the correlation between EGFR mutations and glycolysis.

Results

EGFR-mutant patients had higher SUVmax than the wild-type patients in both primary tumors and metastases. In the multivariate analysis, SUVmax, gender and histopathologic type were determined as independent predictors of EGFR mutation status for patients whose SUVmax were obtained from the primary tumors; while for patients whose SUVmax were obtained from the metastases, SUVmax, smoking status and histopathologic type were regarded as independent predictors. ROC analysis showed that SUVmax of the primary tumors (cut off >10.92), not of the metastases, has better predictive value than other clinical factors in predicting EGFR mutation status. The predict performance was improved after combined SUVmax with other independent predictors. In addition, our in vitro experiments demonstrated that lung cancer cells with EGFR mutations have higher aerobic glycolysis level than wild-type cells.

Conclusion

SUVmax of the primary tumors has the potential to serve as a biomarker to predict EGFR mutation status in NSCLC patients.

Are there imaging characteristics that can distinguish separate primary lung carcinomas from intrapulmonary metastases using next-generation sequencing as a gold standard?

OBJECTIVES

Distinguishing separate primary lung carcinomas (SPLCs) from intrapulmonary metastases (IPMs) in non-small cell lung cancer (NSCLC) patients is a challenging dilemma in clinical practice. Next-generation sequencing (NGS) was recently shown to represent a robust molecular method for clonal discrimination in this setting. In this study, using clonal relationships established by comprehensive NGS as the ground truth, we investigated whether NSCLC patients with SPLCs versus IPMs exhibit distinct imaging characteristics.

MATERIAL AND METHODS

This retrospective study included patients who underwent pre-treatment computed tomography (CT) and/or positron emission tomography/CT (PET/CT) imaging followed by surgical resection for >1 NSCLC. Nodular, parenchymal, pleural, and ancillary CT features, as well as maximum standardized uptake values (SUVs) on PET/CT were recorded. Rao-Scott chi-square, Wilcoxon rank-sum, and Fisher's exact tests were used in patient- and lesion-level comparisons.

RESULTS

This study included 60 patients (median age = 69 years, 68 % female) with 127 individual tumors comprising 51 SPLC vs 23 IPM tumor pairs based on NGS profiling. SPLCs were associated with subsolid consistency (P = 0.005) and spiculated contours (P <  0.001), while IPMs were associated with greater difference of size between lesions (P = 0.017) or pure solid consistency of the smaller lesion (P = 0.011). Lymph node involvement was more frequent in IPMs than SPLCs (P = 0.036). SUV measurements were not useful for differentiation (P > 0.05).

CONCLUSION

Selected preoperative CT features are distributed differentially in SPLCs and IPMs, suggesting that imaging may have a role in distinguishing clonal relationships of tumors in patients with >1 NSCLC.

Predicting EGFR mutation subtypes in lung adenocarcinoma using 18F-FDG PET/CT radiomic features

Background

Identification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (¹⁸F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.

Methods

We retrospectively studied ¹⁸F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).

Results

Two sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.

Conclusions

In our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.

Imaging Features and Metastatic Patterns of Advanced ALK-Rearranged Non-Small Cell Lung Cancer

OBJECTIVE.ALK rearrangements are an established targetable oncogenic driver in non-small cell lung cancer (NSCLC). The goal of this study was to determine the imaging features of the primary tumor and metastatic patterns in advanced ALK-rearranged (ALK+) NSCLC that may be different from those in EGFR-mutant (EGFR+) or EGFR/ALK wild-type (EGFR-/ALK-) NSCLC. MATERIALS AND METHODS. Patients with advanced ALK+, EGFR+, or EGFR-/ALK- NSCLC were retrospectively identified. Two radiologists concurrently assessed the imaging features of the primary tumor and the distribution of metastases in these patients. RESULTS. We identified a cohort of 333 patients with metastatic NSCLC (119 ALK+ cases, 116 EGFR+ cases, and 98 EGFR-/ALK- cases). Compared with EGFR+ and EGFR-/ALK- NSCLC, the primary tumor in ALK+ NSCLC was more likely to be located in the lower lobes (53% of ALK+, 34% of EGFR+, and 36% of EGFR-/ALK- tumors; p < 0.05), less likely to be subsolid (1% of ALK+, 11% of EGFR+, and 8% of EGFR-/ALK- tumors; p < 0.02), and less likely to have air bronchograms (7% of ALK+, 28% of EGFR+, and 29% of EGFR-/ALK- tumors; p < 0.01). Compared with EGFR+ and EGFR-/ALK- tumors, ALK+ tumors had higher frequencies of distant nodal metastasis (20% of ALK+ tumors vs 2% of EGFR+ and 9% of EGFR-/ALK- tumors; p < 0.05) and lymphangitic carcinomatosis (37% of ALK+ tumors vs 12% of EGFR+ and 12% of EGFR-/ALK- tumors; p < 0.01), but ALK+ tumors had a lower frequency of brain metastasis compared with EGFR+ tumors (24% vs 41%; p = 0.01). Although there was no statistically significant difference in the frequencies of bone metastasis among the three groups, sclerotic bone metastases were more common in the ALK+ tumors (22% vs 7% of EGFR+ tumors and 6% of EGFR-/ALK- tumors; p < 0.01). CONCLUSION. Advanced ALK+ NSCLC has primary tumor imaging features and patterns of metastasis that are different from those of EGFR+ or EGFR-/ALK- wild type NSCLC at the time of initial presentation.

Evaluation of the solitary pulmonary nodule

The solitary pulmonary nodule represents a common diagnostic challenge for clinicians. While most are benign, a significant number represent early, potentially curable lung cancers. With the increased utilisation of chest computed tomography, solitary pulmonary nodules are increasingly being identified and with lung cancer screening programmes now on the horizon globally, it is crucial clinicians are familiar with the evaluation and management of solitary pulmonary nodules. Through the evaluation of patient risk factors combined with computed tomography characteristics of solitary pulmonary nodules, including size, growth rate, margin characteristics, calcification, density and location; a clinician can assess the risk of malignancy. This article provides an up to date review of the imaging features of both benign and malignant solitary pulmonary nodules to assist in the identification of nodules that require histological confirmation or ongoing surveillance. In addition, we summarise the newly updated Fleischner Society Guidelines that provide clinicians with a framework for the evaluation and management of solitary pulmonary nodules.

Computed Tomography Imaging Features and Distribution of Metastases in ROS1-rearranged Non-Small-cell Lung Cancer

BACKGROUND

ROS proto-oncogene 1 (ROS1) rearrangements are a known molecular target in non-small-cell lung cancer (NSCLC). Our goal was to determine whether ROS1-rearranged NSCLC has imaging features and patterns of metastasis, which differ from those of anaplastic lymphoma kinase (ALK)-rearranged or epidermal growth factor receptor (EGFR)-mutant NSCLC.

PATIENTS AND METHODS

We retrospectively identified patients with metastatic ROS1-rearranged, ALK-rearranged, or EGFR-mutant NSCLC from January 2014 to June 2018 and included those with pretreatment imaging studies available. We assessed the imaging features of the primary tumor and the distribution of metastases in these patients. The Wilcoxon rank-sum test and Fisher exact test were used to compare the imaging features.

RESULTS

We identified 257 patients (167 women and 90 men; median age, 56 years; range, 19-90 years) with metastatic NSCLC (ROS1, 53; ALK, 87; EGFR, 117). Compared with ALK-rearranged or EGFR-mutant NSCLC, ROS1-rearranged NSCLC was less likely to present with extrathoracic metastases (ROS1, 49%; ALK, 75%; EGFR, 72%; P < .01), including brain metastases (ROS1, 9%; ALK, 25%; EGFR, 40%; P < .04). Compared with EGFR-mutant NSCLC, ROS1-rearranged tumors were more likely to exhibit imaging features of lymphangitic carcinomatosis (ROS1, 42%; EGFR, 12%; P < .01) and less likely to have air bronchograms in the primary tumor (ROS1, 2%; EGFR, 28%; P < .01). ROS1-rearranged tumors were also more likely to present with distant nodal metastases (ROS1, 15%; EGFR, 2%; P < .01) and sclerotic-type bone metastases (ROS1, 17%; EGFR, 6%; P < .01).

CONCLUSION

Although considerable overlap exists in the imaging features of ROS1-rearranged, ALK-rearranged, and EGFR-mutant NSCLC, we found that ROS1-rearranged NSCLC has certain distinct imaging features and patterns of spread.

Association of radiomic features with epidermal growth factor receptor mutation status in non-small cell lung cancer and survival treated with tyrosine kinase inhibitors

Since the discovery of the fact that tyrosine kinase inhibitors could improve progression-free survival for patients with advanced non-small cell lung cancer compared with traditional chemotherapy, it has been extremely important to identify epidermal growth factor receptor mutation status in treatment stratification. Although lack of sufficient biopsy samples limit the precise detection of epidermal growth factor receptor mutation status in clinical practice, and it is difficult to identify the sensitive patients who confer favorable response to tyrosine kinase inhibitors. An increasing number of scholars tried to deal with these problems using methods based on the non-invasive imaging including computed tomography and PET to find the association with epidermal growth factor receptor mutation status and survival treated with tyrosine kinase inhibitor in non-small cell lung cancer. Although the conclusions have not reached a consensus, quantitative and high-throughput radiomics have brought us a new direction and might successfully help identify patients undergoing tyrosine kinase inhibitors who could get significant benefits.

CT Characteristics of Non-Small Cell Lung Cancer With Anaplastic Lymphoma Kinase Rearrangement: A Systematic Review and Meta-Analysis

OBJECTIVE. The purpose of this study was to perform a systematic review and meta-analysis regarding CT features of non-small cell lung cancer (NSCLC) with anaplastic lymphoma kinase (ALK) rearrangement. MATERIALS AND METHODS. The PubMed and Embase databases were searched up to February 20, 2019. Studies that evaluated CT features of NSCLC with and without ALK rearrangement was included. Methodologic quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2. The association between CT features and ALK rearrangement was pooled in the form of the odds ratio (OR) or the mean difference (MD) using the random-effects model. Heterogeneity was examined using the inconsistency index (I²). Publication bias was examined using funnel plots and Egger tests. RESULTS. Sixteen studies were included, consisting of 3113 patients with NSCLC. The overall prevalence of patients with ALK rearrangement was 17% (528/3113). Compared with NSCLC without ALK rearrangement, on CT images those with ALK rearrangement were more frequently solid (OR = 2.86), central in location (OR = 2.72), and 3 cm or smaller (OR = 0.57); had lower contrast-enhanced CT attenuation (MD = -4.79 HU); more frequently had N2 or N3 disease (OR = 5.63), lymphangitic carcinomatosis (OR = 3.46), pleural effusion (OR = 1.91), or pleural metastasis (OR = 1.81); and less frequently had lung metastasis (OR = 0.66). Heterogeneity varied among CT features (I² = 0-80%). No significant publication bias was seen (p = 0.15). CONCLUSION. NSCLC with ALK rearrangement had several distinctive CT features compared with that without ALK rearrangement. These CT biomarkers may help identify patients likely to have ALK rearrangement.

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.

Lung cancers associated with cystic airspaces: CT features and pathologic correlation

OBJECTIVE

Lung cancer associated with cystic airspaces (LCCA) is a rare entity. The diagnosis and treatment is often delayed due to lack of comprehension of this disease. We aimed to elucidate LCCA's clinicopathological characteristics and investigate imaging features correlated with pathological invasiveness.

METHOD

The preoperative computed tomographic (CT) scans of 10,835 patients diagnosed with NSCLC between January 2015 and December 2016 were reviewed by two thoracic radiologists for association with a cystic airspace. A clinicopathological and radiological feature analysis was done.

RESULT

A total number of 123 LCCA patients were identified and four morphologic patterns were recognized: I, thin-walled type (n = 23, 18.7%); II, thick-walled type (n = 34, 27.6%); III, a cystic airspace with a mural nodule (CWN) type (n = 43, 35.0%); and IV, mixed type (n = 23, 18.7%). A solid component in the cyst wall predicted histological invasiveness in all four types of LCCA. The proportion of moderately/poorly (M/P)-differentiated subtype in type III (85.0%) was higher than in other three patterns (which were 50.0%, 50.0%, and 69.6%, respectively). Multivariate analysis revealed that type III pattern (odds ratio [OR], 6.5; 95% confidence interval [CI], 1.1-36.4; P = 0.035), part-solid/solid component in wall (part-solid: OR, 27.2; 95% CI, 5.6-3131.6; P < 0.001; solid: OR 614.6; 95% CI, 36.4-10,368.6; P < 0.001), and irregular inner surface of cyst (OR 7.0; 95% CI 1.9-26.2; P = 0.004) were independent risk factors for the M/P-differentiated subtype. EGFR mutations were the predominant genetic alterations in each type of LCCAs, but no significant difference was found among them.

CONCLUSIONS

In LCCA, morphological patterns and wall components were two important predictors for determining pathological invasiveness.

Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule

BACKGROUND

Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer sensitive to EGFR-targeted tyrosine kinase inhibitors. We aimed to develop and validate a computed tomography (CT)-based radiomics signature for prediction of EGFR mutation status in LADC appearing as a subsolid nodule.

MATERIALS AND METHODS

A total of 467 eligible patients were divided into training and validation cohorts (n = 306 and 161, respectively). Radiomics features were extracted from unenhanced CT images by using Pyradiomics. A CT-based radiomics signature for distinguishing EGFR mutation status was constructed using the random forest (RF) method in the training cohort and then tested in the validation cohort. A combination of the radiomics signature with a clinical factors model was also constructed using the RF method. The performance of the model was evaluated using the area under the curve (AUC) of a receiver operating characteristic curve.

RESULTS

In this study, 64.2% (300/467) of the patients showed EGFR mutations. L858R mutation of exon 21 was the most common mutation type (185/301). We identified a CT-based radiomics signature that successfully discriminated between EGFR positive and EGFR negative in the training cohort (AUC = 0.831) and the validation cohort (AUC = 0.789). The radiomics signature combined with the clinical factors model was not superior to the simple radiomics signature in the two cohorts (p > .05).

CONCLUSION

As a noninvasive method, the CT-based radiomics signature can be used to predict the EGFR mutation status of LADC appearing as a subsolid nodule.

IMPLICATIONS FOR PRACTICE

Lung adenocarcinoma (LADC) with epidermal growth factor receptor (EGFR) mutation is considered a subgroup of lung cancer that is sensitive to EGFR-targeted tyrosine kinase inhibitors. However, some patients with inoperable subsolid LADC are unable to undergo tissue sampling by biopsy for molecular analysis in clinical practice. A computed tomography-based radiomics signature may serve as a noninvasive biomarker to predict the EGFR mutation status of subsolid LADCs when mutational profiling is not available or possible.

CT and clinical characteristics that predict risk of EGFR mutation in non-small cell lung cancer: a systematic review and meta-analysis

INTRODUCTION

To systematically analyze CT and clinical characteristics to find out the risk factors of epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC). Then the significant characteristics were used to set up a mathematic model to predict EGFR mutation in NSCLC.

MATERIALS AND METHODS

PubMed, Web of Knowledge and EMBASE up to August 17, 2018 were systematically searched for relevant studies that investigated the evidence of association between CT and clinical characteristics and EGFR mutation in NSCLC. After study selection, data extraction, and quality assessment, the pooled odds ratios (ORs) were calculated. Then from May 2017 to August 2018, all NSCLC received EGFR mutation examination and CT examination in our hospital were chosen to test the prediction model by receiver operating characteristic (ROC) curves.

RESULTS

Seventeen original studies met the inclusion criteria. The results showed that the ORs of ground-glass opacity (GGO), air bronchogram, pleural retraction, vascular convergence, smoking history, female gender were, respectively, 1.93 (P = 0.003), 2.09 (P = 0.03), 1.59 (P < 0.01), 1.61 (P = 0.001), 0.28 (P < 0.01), 0.35 (P < 0.01). The result of speculation, cavitation/bubble-like lucency, lesion shape, margin, pathological stage were, respectively, 1.19 (P = 0.32), 0.99 (P = 0.97), 0.82 (P = 0.42), 1.02 (P = 0.90), 0.77 (P = 0.30). 121 NSCLC received EGFR mutation test were included to test the prediction model. The mathematical model based on the results of meta-analysis was: 0.74 × air bronchogram + 0.46 × pleural retraction + 0.48 × vascular convergence - 1.27 × non-smoking history - 1.05 × female. The area under the ROC curve was 0.68.

CONCLUSION

Based on the current evidence, GGO presence, air bronchogram, pleural retraction, vascular convergence were significant risk factors of EGFR mutation in NSCLC. And the prediction model can help to predict EGFR mutation status.

Genomics of non-small cell lung cancer (NSCLC): Association between CT-based imaging features and EGFR and K-RAS mutations in 122 patients-An external validation

OBJECTIVE

To validate previously identified associations between radiological features and clinical features with Epidermal Growth Factor Receptor (EGFR)/ Kirsten RAt Sarcoma (KRAS) alterations in an independent group of patients with Non-Small Cell Lung Cancer (NSCLC).

MATERIAL AND METHODS

A total of 122 patients with NSCLC tested for EGFR/KRAS alterations were included. Clinical and radiological features were recorded. Univariate analysis were performed to look at the associations of the studied features with EGFR/KRAS alterations. Previously calculated composite model parameters for each gene alteration prediction were applied to this validation cohort. ROC (Receiver Operating Characteristic) curves were drawn using the previously validated composite models, and also for each significant individual characteristic of the previous training cohort model. The Area Under the ROC Curve (AUC) with 95% Confidence Intervals (CI) was calculated and compared between the full models.

RESULTS

At univariate analysis, EGFR+ confirmed an association with an internal air bronchogram, pleural retraction, emphysema and lack of smoking; KRAS+ with round shape, emphysema and smoking. The AUC (95%CI) in the new cohort was confirmed to be high for EGFR+ prediction, with a value of: 0.82 (0.69-0.95) vs. 0.82 in the previous cohort, whereas it was smaller for KRAS+ prediction, with a value of 0.60 (0.48-0.72) vs. 0.67 in the previous cohort. Looking at single features in the new cohort, we found that the AUC for the models including only smoking was similar to that of the full model (including radiological and clinical features) for both gene alterations.

CONCLUSIONS

Although this study validated the significant association of clinical and radiological features with EGFR/KRAS alterations, models based on these composite features are not superior to smoking history alone to predict the mutations.

The Association Between Imaging Features of TSCT and the Expression of PD-L1 in Patients With Surgical Resection of Lung Adenocarcinoma

OBJECTIVES

Programmed death-ligand 1 (PD-L1) expression might serve as a predictive biomarker for immune checkpoint inhibitors in lung cancer. However, the relationship between PD-L1 expression and imaging features of lung cancer has not been fully understood.

PATIENTS AND METHODS

A total of 350 patients with pathologically confirmed adenocarcinoma who received surgical treatment and had preoperative thin section computed tomography (CT) examination were included. Quantitative CT features including the mean CT value and tumor mass were measured on multiplanar reconstructed images. PD-L1-positive tumor was defined as the tumor proportion score > 5%.

RESULTS

Seventy-four of 350 (21.1%) specimens were detected as PD-L1-positive tumors. PD-L1 expression was adversely associated with epidermal growth factor receptor mutation status (P < .001) and was significantly associated with invasive adenocarcinomas rather than preinvasive lesions and minimally invasive adenocarcinomas (P < .001). Multivariate analysis identified absence of surrounding ground glass opacity (P = .022), shape (P = .008), pleural indentation (P = .007), tumor mean CT value (P = .004), and the ratio of consolidation mass to tumor mass (P = .003) as being significantly associated with the expression of PD-L1. To improve the diagnostic accuracy, a joint model that combined 5 imaging traits was conducted. The area under the curve of the joint model was 0.783, with a sensitivity of 81.1% and specificity of 64.1%, respectively.

CONCLUSION

PD-L1 expression was associated with pathologic invasiveness of adenocarcinomas and CT features, which suggested the possibility of predicting PD-L1 expression status via imaging features.

Use of a Radiomics Model to Predict Tumor Invasiveness of Pulmonary Adenocarcinomas Appearing as Pulmonary Ground-Glass Nodules

Background

It is important to distinguish the classification of lung adenocarcinoma. A radiomics model was developed to predict tumor invasiveness using quantitative and qualitative features of pulmonary ground-glass nodules (GGNs) on chest CT.

Materials and Methods

A total of 599 GGNs [including 202 preinvasive lesions and 397 minimally invasive and invasive pulmonary adenocarcinomas (IPAs)] were evaluated using univariate, multivariate, and logistic regression analyses to construct a radiomics model that predicted invasiveness of GGNs. In primary cohort (comprised of patients scanned from August 2012 to July 2016), preinvasive lesions were distinguished from IPAs based on pure or mixed density (PM), lesion shape, lobulated border, and pleural retraction and 35 other quantitative parameters (P <0.05) using univariate analysis. Multivariate analysis showed that PM, lobulated border, pleural retraction, age, and fractal dimension (FD) were significantly different between preinvasive lesions and IPAs. After logistic regression analysis, PM and FD were used to develop a prediction nomogram. The validation cohort was comprised of patients scanned after Jan 2016.

Results

The model showed good discrimination between preinvasive lesions and IPAs with an area under curve (AUC) of 0.76 [95% CI: 0.71 to 0.80] in ROC curve for the primary cohort. The nomogram also demonstrated good discrimination in the validation cohort with an AUC of 0.79 [95% CI: 0.71 to 0.88].

Conclusions

For GGNs, PM, lobulated border, pleural retraction, age, and FD were features discriminating preinvasive lesions from IPAs. The radiomics model based upon PM and FD may predict the invasiveness of pulmonary adenocarcinomas appearing as GGNs.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology

Abstract

Subsequent to the widespread use of multidetector computed tomography and growing interest in lung cancer screening, small pulmonary nodules are more frequently detected. The differential diagnosis for a solitary pulmonary nodule is extremely broad and includes both benign and malignant causes. Recognition of early lung cancers is vital, since stage at diagnosis is crucial for prognosis. Estimation of the probability of malignancy is a challenging task, but crucial for follow-up and further work-up. In addition to the clinical setting and metabolic assessment, morphological assessment on thin-section computed tomography is essential. Size and growth are key factors in assessment of the malignant potential of a nodule. The likelihood of malignancy positively correlates with nodule diameter: as the diameter increases, so does the likelihood of malignancy. Although there is a considerable overlap in the features of benign and malignant nodules, the importance of morphology however should not be underestimated. Features that are associated with benignity include a perifissural location and triangular morphology, internal fat and benign calcifications. Malignancy is suspected in nodules presenting with spiculation, lobulation, pleural indentation, vascular convergence sign, associated cystic airspace, bubble-like lucencies, irregular air bronchogram, and subsolid morphology. Nodules often show different features and combination of findings is certainly more powerful.

Teaching points

• Size of a pulmonary nodule is important, but morphological assessment should not be underestimated.

• Lung nodules should be evaluated on thin section CT, in both lung and mediastinal window setting.

• Features associated with benignity include a triangular morphology, internal fat and calcifications.

• Spiculation, pleural retraction and notch sign are highly suggestive of a malignant nature.

• Complex features (e.g. bubble-like lucencies) are highly indicative of a malignant nature.

Computed tomography and clinical features associated with epidermal growth factor receptor mutation status in stage I/II lung adenocarcinoma

Background

The relationship between epidermal growth factor receptor (EGFR) gene mutation status, preoperative computed tomography (CT), and clinical features in patients with small peripheral lung adenocarcinoma (<3 cm) was investigated.

Methods

We included 209 patients who underwent surgical resection for stage I or II lung adenocarcinoma at Nanjing General Hospital between December 2010 and May 2016. 171 cases of patients underwent a pretreatment chest CT. Eleven different CT descriptors were assessed. Multiple logistic regression analyses were performed to identify independent risk factors for the prediction of EGFR mutation. Receiver operating characteristic analysis was used to evaluate the performance of the logistic regression model.

Results

EGFR mutation was determined in 126 patients (60.3%) and appeared more frequently in women (P = 0.025), never‐smokers (P < 0.001), and patients with a carcinoembryonic antigen level <2.6 ng/ml (P = 0.045). Papillary predominant adenocarcinomas (P = 0.014), intermediate/low pathologic grade tumors (P = 0.003), tumors in the upper lobe (P = 0.028), and showing ground‐glass opacity (GGO) or mixed GGO on CT (P = 0.039) also more frequently harbored EGFR mutations. GGO on CT, acinar or papillary predominant adenocarcinoma, and non‐smoker were identified in multivariable analyses as significantly independent risk factors. The multiple logistic regression model showed high predictive power for identifying EGFR mutations. The CT features were similar between the L858R and 19 deletion mutations.

Conclusions

Combined CT and clinical features may be helpful for determining the presence of EGFR mutations in patients with small peripheral lung adenocarcinoma, particularly in patients where mutational profiling is not available or possible.

Correlation in histological subtypes with high resolution computed tomography signatures of early stage lung adenocarcinoma

BACKGROUND

Uncertainty remains on the association between image characteristics of the nodules in computed tomography (CT) scans and lung adenocarcinoma histopathologic subtypes. We aimed to estimate the correlation between preoperative high resolution computed tomography (HRCT) scan and postoperative histopathology of stage IA lung adenocarcinoma in East Asian Chinese population.

METHODS

We retrospectively reviewed the clinical records and HRCT images of 190 patients (106 female and 84 male) with resected, preoperatively untreated stage IA adenocarcinomas. The relationship between image characteristics of nodules at preoperative HRCT and their histological subtypes after resection were analyzed. The one-way ANOVA, chi-square test and logistic regression were used for analysis.

RESULTS

In 190 patients with stage IA lung adenocarcinoma, median tumor diameter was significantly lower in lepidic predominant invasive adenocarcinoma (LPA) (15.96±6.95 mm). Univariate analysis revealed that ground-glass opacity (GGO) proportion (P<0.001), margin (P<0.001), border definition (P=0.015), pleural retraction (P<0.001) and enhancement (P<0.001) had statistically significant differences in four histological subtypes. The multivariate analysis referenced for lepidic group which indicated that GGO proportion and pleural retraction were independent associated with acinar group (RR=4.221, 95% CI: 1.770-10.066, P=0.001; RR=0.380, 95% CI: 0.158-0.916, P=0.031, respectively). Male and whose nodule margin with spiculation or lobulation were prone to papillary predominant invasive adenocarcinoma (PPA) (RR=0.288, 95% CI: 0.090-0.920, P=0.036; RR=0.250, 95% CI: 0.070-0.887, P=0.032, respectively). GGO proportion and nodule margin were independent related factors in solid predominant invasive adenocarcinoma (SPA) (RR=13.338, 95% CI: 2.974-59.811, P=0.001; RR=0.097, 95% CI: 0.016-0.606, P=0.013, respectively).

CONCLUSIONS

Nodules with spiculation or lobulation and less GGO proportion are determinants of histological subtypes with poor prognosis in stage IA lung adenocarcinoma patients according to the 2011 histologic IASLC/ATS/ERS classification.

Predictive radiogenomics modeling of EGFR mutation status in lung cancer

Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient’s tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.