Correlation between EGFR mutation status and computed tomography features in patients with advanced pulmonary adenocarcinoma

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.

CT-based radiomic phenotypes of lung adenocarcinoma: a preliminary comparative analysis with targeted next-generation sequencing

Objectives

This study aimed to explore the relationship between computed tomography (CT)-based radiomic phenotypes and genomic profiles, including expression of programmed cell death-ligand 1 (PD-L1) and the 10 major genes, such as epidermal growth factor receptor (EGFR), tumor protein 53 (TP53), and Kirsten rat sarcoma viral oncogene (KRAS), in patients with lung adenocarcinoma (LUAD).

Methods

In total, 288 consecutive patients with pathologically confirmed LUAD were enrolled in this retrospective study. Radiomic features were extracted from preoperative CT images, and targeted genomic data were profiled through next-generation sequencing. PD-L1 expression was assessed by immunohistochemistry staining (chi-square test or Fisher's exact test for categorical data and the Kruskal-Wallis test for continuous data). A total of 1,013 radiomic features were obtained from each patient's CT images. Consensus clustering was used to cluster patients on the basis of radiomic features.

Results

The 288 patients were classified according to consensus clustering into four radiomic phenotypes: Cluster 1 (n = 11) involving mainly large solid masses with a maximum diameter of 5.1 ± 2.0 cm; Clusters 2 and 3 involving mainly part-solid and solid masses with maximum diameters of 2.1 ± 1.4 cm and 2.1 ± 0.9 cm, respectively; and Cluster 4 involving mostly small ground-glass opacity lesions with a maximum diameter of 1.0 ± 0.9 cm. Differences in maximum diameter, PD-L1 expression, and TP53, EGFR, BRAF, ROS1, and ERBB2 mutations among the four clusters were statistically significant. Regarding targeted therapy and immunotherapy, EGFR mutations were highest in Cluster 2 (73.1%); PD-L1 expression was highest in Cluster 1 (45.5%).

Conclusion

Our findings provide evidence that CT-based radiomic phenotypes could non-invasively identify LUADs with different molecular characteristics, showing the potential to provide personalized treatment decision-making support for LUAD patients.

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

BACKGROUND

This study aims to construct radiomics models based on [¹⁸F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models.

METHODS

A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built.

RESULTS

Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723).

CONCLUSIONS

Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

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.

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.

•

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

•

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

•

Cavitation is a protective factor for EGFR mutation.

Radiomic Signature as a Diagnostic Factor for Classification of Histologic Subtypes of Lung Cancer

<sec> <title>Objectives:</title> To discuss the application of radiomics using Computerized Tomography (CT) analysis, for improving its diagnostic efficacy in lung, specifically in distinguishing Squamous Cell Carcinoma (SCC), lung Adenocarcinoma (ADC), and Small Cell Lung Cancer (SCLC). </sec> <sec> <title>Methods:</title> The pathology of 189 identified cases of lung cancer was analyzed, retrospectively (60 patients with SCC, 69 patients with lung ADC and 60 patients with SCLC). A neural network was used to determine whether the pulmonary or mediastinal window was selected to extract effective radiomic features. The key features of radiomic signature were retrieved by a Least Absolute Shrinkage and Selection Operator (LASSO) multiple logistic regression model. Next, receiver operating characteristic curve and Area Under the Curve (AUC) analysis were used to evaluate the performance of the radiomic signature in both, training(129 patients) and validation cohorts (60 patients). </sec> <sec> <title>Results:</title> About 295 features were extracted from a manually outlined tumor region. Features extracted from mediastinal window CT scans had a better prognostic ability than pulmonary window scans. The average accuracy for mediastinal window scans was 0.933. Our analysis revealed that the radiomic features extracted from mediastinal window scans had the potential to build a prediction model for distinguishing between SCC, lung ADC, and SCLC. The performance of the radiomic signature to diagnose SCC and SCLC in validation cohorts proved effective, with AUC values of 0.869 and 0.859, respectively. </sec> <sec> <title>Conclusions:</title> A unique radiomic signature was constructed as a diagnostic factor for different histologic subtypes of lung cancer. Patients with lung cancer may benefit from this proposed radiomic signature. </sec>

Post-chemotherapy and targeted therapy imaging of the chest in lung cancer

Non-small-cell lung cancer (NSCLC) is frequently diagnosed when it is not amenable to local therapies; therefore, systemic agents are the mainstay of therapy for many patients. In recent years, treatment of advanced NSCLC has evolved from a general approach primarily involving chemotherapy to a more personalised strategy in which biomarkers such as the presence of genomic tumour aberrations and the expression of immune proteins such as programmed death-ligand 1 (PD-L1), in combination with other elements of clinical information such as histology and clinical stage, guide management. For instance, pathways resulting in uncontrolled growth and proliferation of tumour cells due to epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) rearrangements may be targeted by tyrosine kinase inhibitors (TKIs). In this article, we review the current state of medical oncology, imaging characteristics of mutations, pitfalls in response assessments and the imaging of complications.

Radiogenomics in brain, breast, and lung cancer: opportunities and challenges

The field of radiogenomics largely focuses on developing imaging surrogates for genomic signatures and integrating imaging, genomic, and molecular data to develop combined personalized biomarkers for characterizing various diseases. Our study aims to highlight the current state-of-the-art and the role of radiogenomics in cancer research, focusing mainly on solid tumors, and is broadly divided into four sections. The first section reviews representative studies that establish the biologic basis of radiomic signatures using gene expression and molecular profiling information. The second section includes studies that aim to non-invasively predict molecular subtypes of tumors using radiomic signatures. The third section reviews studies that evaluate the potential to augment the performance of established prognostic signatures by combining complementary information encoded by radiomic and genomic signatures derived from cancer tumors. The fourth section includes studies that focus on ascertaining the biological significance of radiomic phenotypes. We conclude by discussing current challenges and opportunities in the field, such as the importance of coordination between imaging device manufacturers, regulatory organizations, health care providers, pharmaceutical companies, academic institutions, and physicians for the effective standardization of the results from radiogenomic signatures and for the potential use of these findings to improve precision care for cancer patients.

A computed tomography (CT)-derived radiomics approach for predicting primary co-mutations involving TP53 and epidermal growth factor receptor (EGFR) in patients with advanced lung adenocarcinomas (LUAD)

Background

Epidermal growth factor receptor (EGFR) co-mutated with TP53 could reduce responsiveness to tyrosine kinase inhibitors (TKIs) and worsen patients' prognosis compared to TP53 wild type patients in.

EGFR

mutated lung adenocarcinomas (LUAD). To identify this genetically unique subset prior to treatment through computed tomography (CT) images had not been reported yet.

Methods

Stage III and IV LUAD with known mutation status of EGFR and TP53 from The First Affiliated Hospital of Sun Yat-sen University (May 1, 2017 to June 1, 2020) were collected. Characteristics of pretreatment enhanced-CT images were analyzed. One-versus-one was used as the multiclass classification strategy to distinguish the three subtypes of co-mutations: EGFR ⁺ & TP53 ⁺, EGFR ⁺ & TP53 ^-, EGFR ^-. The clinical model, semantic model, radiomics model and integrated model were built. Area under the receiver-operating characteristic curves (AUCs) were used to evaluate the prediction efficacy.

Results

A total of 199 patients were enrolled, including 83 (42%) cases of EGFR ^-, 55 (28%) cases of EGFR ⁺ & TP53 ⁺, 61 (31%) cases of EGFR ⁺ & TP53 ^-. Among the four different models, the integrated model displayed the best performance for all the three subtypes of co-mutations: EGFR ^- (AUC, 0.857; accuracy, 0.817; sensitivity, 0.998; specificity, 0.663), EGFR ⁺ & TP53 ⁺ (AUC, 0.791; accuracy, 0.758; sensitivity, 0.762; specificity, 0.783), EGFR ⁺ & TP53 ^- (AUC, 0.761; accuracy, 0.813; sensitivity, 0.594; specificity, 0.977). The radiomics model was slightly inferior to the integrated model. The results for the clinical and the semantic models were dissatisfactory, with AUCs less than 0.700 for all the three subtypes.

Conclusions

CT imaging based artificial intelligence (AI) is expected to distinguish co-mutation status involving TP53 and EGFR. The proposed integrated model may serve as an important alternative marker for preselecting patients who will be adaptable to and sensitive to TKIs.

A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma

OBJECTIVES

This study aims to develop a clinically practical model to predict EGFR mutation in lung adenocarcinoma patients according to radiomics signatures based on PET/CT and clinical risk factors.

METHODS

This retrospective study included 583 lung adenocarcinoma patients, including 295 (50.60%) patients with EGFR mutation and 288 (49.40%) patients without EGFR mutation. The clinical risk factors associated with lung adenocarcinoma were collected at the same time. We developed PET/CT, CT, and PET radiomics models for the prediction of EGFR mutation using multivariate logistic regression analysis, respectively. We also constructed a combined PET/CT radiomics-clinical model by nomogram analysis. The diagnostic performance and clinical net benefit of this risk-scoring model were examined via receiver operating characteristic (ROC) curve analysis while the clinical usefulness of this model was evaluated by decision curve analysis (DCA).

RESULTS

The ROC analysis showed predictive performance for the PET/CT radiomics model (AUC = 0.76), better than the PET model (AUC = 0.71, Delong test: Z = 3.03, p value = 0.002) and the CT model (AUC = 0.74, Delong test: Z = 1.66, p value = 0.098). Also, the PET/CT radiomics-clinical combined model has a better performance (AUC = 0.84) to predict EGFR mutation than the PET/CT radiomics model (AUC = 0.76, Delong test: D = 2.70, df = 790.81, p value < 0.001) or the clinical model (AUC = 0.81, Delong test: Z = 3.46, p value < 0.001).

CONCLUSIONS

We demonstrated that the combined PET/CT radiomics-clinical model has an advantage to predict EGFR mutation in lung adenocarcinoma.

KEY POINTS

• Radiomics from lung tumor increase the efficiency of the prediction for EGFR mutation in clinical lung adenocarcinoma on PET/CT. • A radiomic nomogram was developed to predict EGFR mutation. • Combining PET/CT radiomics-clinical model has an advantage to predict EGFR mutation.

Scoring model of convex probe endobronchial ultrasound multimodal imaging in differentiating benign and malignant lung lesions

Background

Convex probe endobronchial ultrasound images can reflect the morphology, blood flow status and stiffness of the lesions. Endobronchial ultrasound multimodal imaging has great value for the diagnosis of intrathoracic lymph nodes. This study aimed to analyze the application of endobronchial ultrasound multimodal imaging on lung lesions.

Methods

Patients undergoing endobronchial ultrasound-guided transbronchial needle aspiration in Shanghai Chest Hospital from July 2018 to December 2019 were retrospectively enrolled. Nine grayscale features (long and short axes, margin, shape, lobulation sign, echogenicity, necrosis, liquefaction, calcification, and air-bronchogram), blood flow volume and elastography five-score method were analyzed to explore the best diagnostic method. The gold standard for diagnosing lesions depends on the histological and cytopathological findings of endobronchial ultrasound-guided transbronchial needle aspiration, transthoracic biopsy, resected sample of lesions, microbiological examination or clinical follow-up of at least 6 months.

Results

Endobronchial ultrasound multimodal imaging of 97 malignant lung lesions and 19 benign lung lesions from 116 patients were analyzed. There were statistically significant differences in distinct margin, presence of lobulation sign, presence of necrosis, and elastography grading score 4–5 between malignant and benign lung lesions, among which presence of lobulation sign and elastography grading score 4–5 were independent predictors. A diagnostic scoring model was then constructed based on the above four features, and when two or more features were present, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy for malignant lung lesions prediction were 92.78%, 57.89%, 91.84%, 61.11% and 87.07%, respectively.

Conclusions

The combination of endobronchial ultrasound grayscale and elastography has potential value for malignant and benign lung lesions differentiation. The diagnostic scoring model established in this study needs further validation to guide the malignant and benign diagnosis of lung lesions.

CT Radiomics in Predicting EGFR Mutation in Non-small Cell Lung Cancer: A Single Institutional Study

Objective: To evaluate the value of CT radiomics in predicting the epidermal growth factor receptor (EGFR) mutation of patients with non-small cell lung cancer (NSCLC), and combing with the clinical characteristic to construct the prediction model.

Methods: Sixty-seven cases of NSCLC confirmed by pathology were enrolled. The pre-treatment chest CT enhanced images were used in Radiomics analysis. Two experienced radiologists delineated the region of interest (ROI) on open source software 3D-Slicer. The feature of ROI was extracted by Pyradiomics software package and a total of 849 features were extracted. By calculating Pearson correlation coefficient between pair-wise features and LASSO method for feature screening. The prediction model was constructed by logical regression, diagnostic efficacy of the model by the area under the receiver operating characteristic (ROC) curve was calculated.

Results: Based on clinical model and the radiomics model, the AUC under the ROC was 0.8387 and 0.8815, respectively. The model combining clinical and radiomics features perfect best, the AUC under the ROC was 0.9724, the sensitivity and specificity were 85.3 and 90.9%, respectively.

Conclusions: Compared with clinical features or radiomics features alone, the model constructed by combining clinical and pre-treatment chest enhanced CT features may show more utility for improved patient stratification in EGFR mutation and EGFR wild.

A CT-derived deep neural network predicts for programmed death ligand-1 expression status in advanced lung adenocarcinomas

Background

Programmed death ligand-1 (PD-L1) expression remains a crucial predictor in selecting patients for immunotherapy. The current study aimed to non-invasively predict PD-L1 expression based on chest computed tomography (CT) images in advanced lung adenocarcinomas (LUAD), thus help select optimal patients who can potentially benefit from immunotherapy.

Methods

A total of 127 patients with stage III and IV LUAD were enrolled into this study. Pretreatment enhanced thin-section CT images were available for all patients and were analyzed in terms of both morphologic characteristics by radiologists and deep learning (DL), so to further determine the association between CT features and PD-L1 expression status. Univariate analysis and multivariate logical regression analysis were applied to evaluate significant variables. For DL, the 3D DenseNet model was built and validated. The study cohort were grouped by PD-L1 Tumor Proportion Scores (TPS) cutoff value of 1% (positive/negative expression) and 50% respectively.

Results

Among 127 LUAD patients, 46 (36.2%) patients were PD-L1-positive and 38 (29.9%) patients expressed PD-L1-TPS ≥50%. For morphologic characteristics, univariate and multivariate analysis revealed that only lung metastasis was significantly associated with PD-L1 expression status despite of different PD-L1 TPS cutoff values, and its Area under the receiver operating characteristic curve (AUC) for predicting PD-L1 expression were less than 0.700. On the other hand, the predictive value of DL-3D DenseNet model was higher than that of the morphologic characteristics, with AUC more than 0.750.

Conclusions

The traditional morphologic CT characteristics analyzed by radiologists show limited prediction efficacy for PD-L1 expression. By contrast, CT-derived deep neural network improves the prediction efficacy, it may serve as an important alternative marker for clinical PD-L1 detection.

Development and validation of a predictive model for estimating EGFR mutation probabilities in patients with non-squamous non-small cell lung cancer in New Zealand

BACKGROUND

Targeted treatment with Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) is superior to systemic chemotherapy in non-small cell lung cancer (NSCLC) patients with EGFR gene mutations. Detection of EGFR mutations is a challenge in many patients due to the lack of suitable tumour specimens for molecular testing or for other reasons. EGFR mutations are more common in female, Asian and never smoking NSCLC patients.

METHODS

Patients were from a population-based retrospective cohort of 3556 patients diagnosed with non-squamous non-small cell lung cancer in northern New Zealand between 1 Feb 2010 and 31 July 2017. A total of 1694 patients were tested for EGFR mutations, of which information on 1665 patients was available for model development and validation. A multivariable logistic regression model was developed based on 1176 tested patients, and validated in 489 tested patients. Among 1862 patients not tested for EGFR mutations, 129 patients were treated with EGFR-TKIs. Their EGFR mutation probabilities were calculated using the model, and their duration of benefit and overall survival from the start of EGFR-TKI were compared among the three predicted probability groups: < 0.2, 0.2-0.6, and > 0.6.

RESULTS

The model has three predictors: sex, ethnicity and smoking status, and is presented as a nomogram to calculate EGFR mutation probabilities. The model performed well in the validation group (AUC = 0.75). The probability cut-point of 0.2 corresponds 68% sensitivity and 78% specificity. The model predictions were related to outcome in a group of TKI-treated patients with no biopsy testing available (n = 129); in subgroups with predicted probabilities of < 0.2, 0.2-0.6, and > 0.6, median overall survival times from starting EGFR-TKI were 4.0, 5.5 and 18.3 months (p = 0.02); and median times remaining on EGFR-TKI treatment were 2.0, 4.2, and 14.0 months, respectively (p < 0.001).

CONCLUSION

Our model may assist clinical decision making for patients in whom tissue-based mutation testing is difficult or as a supplement to mutation testing.

Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer

BACKGROUND

The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and ¹⁸F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations.

METHODS

Fifty patients diagnosed with NSCLC between 2011 and 2015 and with known EGFR mutation status were retrospectively identified. Texture features extracted from pretreatment CT and FDG PET-CT images by manual contouring of the primary tumor were used to develop multivariate logistic regression (LR) models to predict EGFR mutations in exon 19 and exon 20.

RESULTS

An LR model evaluating FDG PET-texture features was able to differentiate EGFR mutant from wild type with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.87, 0.76, 0.66, and 0.71, respectively. The model derived from CT texture features had an AUC, sensitivity, specificity, and accuracy of 0.83, 0.84, 0.73, and 0.78, respectively. FDG PET-texture features that could discriminate between mutations in EGFR exon 19 and 21 demonstrated AUC, sensitivity, specificity, and accuracy of 0.86, 0.84, 0.73, and 0.78, respectively. Based on CT texture features, the AUC, sensitivity, specificity, and accuracy were 0.75, 0.81, 0.69, and 0.75, respectively.

CONCLUSION

Non-small cell lung cancer texture analysis using FGD-PET and CT images can identify tumors with mutations in EGFR. Imaging signatures could be valuable for pretreatment assessment and prognosis in precision therapy.

Clinicopathological and Prognostic Significance of EML4-ALK Rearrangement in Patients with Surgically Resected Lung Adenocarcinoma: A Propensity Score Matching Study

Objective

The echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) fusion gene is a key oncogenic driver in non-small cell lung cancer (NSCLC). This study analyzed the clinicopathological characteristics and prognostic significance of EML4-ALK fusion gene in patients with surgically resected adenocarcinoma.

Methods

The clinicopathological characteristics of 1056 consecutive patients with surgically resected stage I-IIIA adenocarcinoma were collected from February 2014 to October 2014, and EML4-ALK rearrangement was detected using real-time polymerase chain reaction (RT-PCR) technology. To compare the imaging and pathological features, a propensity score matching (PSM) method was performed. The follow-up information was collected to evaluate the long-term outcomes of patients with EML4-ALK rearrangement.

Results

The prevalence of EML4-ALK rearrangement was 6.6% in 1056 consecutive patients. A total of 70 EML4-ALK-positive and 210 EML4-ALK-negative patients were identified after PSM. Imaging and pathological analyses showed that EML4-ALK rearrangement was significantly associated with less ground-glass opacity (GGO) (adjusted OR=1.38, 95% CI=1.03-1.85, P_trend =0.029) and higher prevalence of non-invasive mucinous adenocarcinoma mucin-laden adenocarcinomas (non-IMA MLA, adjusted OR=6.79, 95% CI=2.69-17.17, P<0.001). EML4-ALK rearrangement was found to be an unfavorable prognostic factor for disease-free survival (DFS) in female patients (HR=2.26, 95% CI=1.13-4.53, P=0.021).

Conclusion

Our results suggest that adenocarcinomas harboring EML4-ALK fusion gene exhibit specific radiological and pathological characteristics compared with EML4-ALK-negative adenocarcinomas. In female patients, EML4-ALK rearrangement was associated with shorter DFS.

Is there any correlation between spectral CT imaging parameters and PD-L1 expression of lung adenocarcinoma?

Background

The aim of this study was to explore whether spectral computed tomography (CT) imaging parameters are associated with PD‐L1 expression of lung adenocarcinoma.

Methods

Spectral CT imaging parameters (iodine concentrations [IC] of lesion in arterial phase [ICLa] and venous phase [ICLv], normalized IC [NICa/NICv]‐normalized to the IC in the aorta, slope of the spectral HU curve [λHUa/λHUv] and enhanced monochromatic CT number [CT40keVa/v, CT70keVa/v] on 40 and 70 keV images) were analyzed in 34 prospectively enrolled lung adenocarcinoma patients with common molecular pathological markers including PD‐L1 expression detected with immunohistochemistry. Patients were divided into two groups: positive PD‐L1 expression and negative PD‐L1 expression groups. Two‐sample Mann‐Whitney U test was used to test the difference of spectral CT imaging parameters between the two groups.

Results

The CT40keVa (127.03 ± 37.92 vs. −54.69 ± 262.04), CT40keVv (124.39 ± 34.71 vs. −45.73 ± 238.97), CT70keVa (49.56 ± 11.76 vs. −136.51 ± 237.08) and CT70keVv (46.13 ± 15.81 vs. −133.10 ± 230.72) parameters in the positive PD‐L1 expression group of lung adenocarcinoma were significantly higher than the negative PD‐L1 expression group (all P < 0.05). There was no difference detected in IC, NIC and λHU of the arterial and venous phases between both groups (all P > 0.05).

Conclusion

CT40keVa, CT40keVv, CT70keVa and CT70keVv were increased in positive PD‐L1 expression. These parameters may be used to distinguish the PD‐L1 expression state of lung adenocarcinoma.

Cancer genome landscape: a radiologist's guide to cancer genome medicine with imaging correlates

The introduction of high throughput sequence analysis in the past decade and the decrease in sequencing costs has made available an enormous amount of genomic data. These data have shaped the landscape of cancer genome, which encompasses mutations determining tumorigenesis, the signaling pathways involved in cancer growth, the tumor heterogeneity, and its role in development of metastases. Tumors develop acquiring a series of driver mutations over time. Of the many mutated genes present in cancer, only few specific mutations are responsible for invasiveness and metastatic potential, which, in many cases, have characteristic imaging appearance. Ten signaling pathways, each with targetable components, have been identified as responsible for cancer growth. Blockage of any of these pathways form the basis for molecular targeted therapies, which are associated with specific pattern of response and toxicities. Tumor heterogeneity, responsible for the different mutation pattern of metastases and primary tumor, has been classified in intratumoral, intermetastatic, intrametastatic, and interpatient heterogeneity, each with specific imaging correlates. The purpose of this article is to introduce the key components of the landscapes of cancer genome and their imaging counterparts, describing the types of mutations associated with tumorigenesis, the pathways of cancer growth, the genetic heterogeneity involved in metastatic disease, as well as the current challenges and opportunities for cancer genomics research.

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.

A Radiologist's Guide to the Changing Treatment Paradigm of Advanced Non-Small Cell Lung Cancer: The ASCO 2018 Molecular Testing Guidelines and Targeted Therapies

OBJECTIVE. The purpose of this article is to provide an imaging-based guide of the modern genomic classifications and targeted therapies for advanced non-small cell lung cancer (NSCLC) with an emphasis on the relevance of the 2018 American Society of Clinical Oncology molecular testing guidelines for radiologists. CONCLUSION. Knowledge of the radiologic relevance of lung cancer driver mutations and modern targeted agents is essential for imaging interpretation of advanced NSCLC in the modern age of precision medicine.

NCTN Assessment on Current Applications of Radiomics in Oncology

Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.

Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology

OBJECTIVES

To compare the predictive performance of radiomics signature and CT morphological features for epidermal growth factor receptor (EGFR) mutation status; then further to develop and compare the different predictive models for EGFR mutation in non-small cell lung cancer (NSCLC) patients.

MATERIALS AND METHODS

This retrospective study involved 404 patients with NSCLC (243 cases in the training cohort and 161 cases in the validation cohort). Radiomics features were extracted from preoperative non-contrast CT images of the entire tumor. Correlations between the EGFR mutation status and candidate predictors were assessed using Mann-Whitney U test or Chi-square test. Unsupervised consensus clustering was used to analyze the representativeness and reduce the redundancy of radiomics features. Multivariable logistic regression analysis was performed to build radiomics signature and develop predictive models of EGFR mutation. ROC curve analysis and Delong test were used to compare the predictive performance among individual features and models.

RESULTS

Of the 234 radiomics features, 93 radiomics features with high repeatability and high predictive significance were selected. The radiomics signature, which was built with one histogram and two textural features, showed the best predictive performance (AUC = 0.762 and 0.775 in the training and validation cohort) in comparison with all the clinical characteristics and conventional CT morphological features to differentiate EGFR mutation status (P < 0.05). The integrated model was developed with maximum diameter, location, sex and radiomics signature. In the training and validation cohort, the integrated model showed the most optimal predictive performance (AUC = 0.798, 0.818 in the training and validation cohort) compared with the clinical models.

CONCLUSION

The radiomics signature showed better performance for predicting EGFR mutant than all the clinical and morphological features. Moreover, the integrated model built with radiomics signature, clinical and morphological features outperformed the clinical models, which is helpful for physicians to determine the targeted therapy.

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.

Correlation between high-resolution computed tomography lung nodule characteristics and EGFR mutation in lung adenocarcinomas

Background

The aim of this study was to investigate the correlation of EGFR mutation on the high-resolution computed tomography (HRCT) features in lung adenocarcinoma.

Patients and methods

A total of 121 patients were diagnosed with lung adenocarcinoma from January 2014 to December 2016. The correlation of indexes (gender, age, tumor diameter, and EGFR mutation) was analyzed based on the HRCT characteristics of lung adenocarcinoma.

Results

There were 73 cases of EGFR mutation and 48 cases of wild-type EGFR. One hundred and three cases had pleural indentation that was significant in patients with EGFR mutation than those with wild-type EGFR (P=0.038). Forty-two out of 121 cases exhibited the bronchus cutoff sign. Patients with EGFR mutation were likely to develop the bronchus cutoff sign (P=0.017). Sixty-one out of 121 cases exhibited the lobulation sign, which was significant in patients with EGFR mutation than those with wild-type EGFR (P<0.001). A significant correlation was found between lobulation sign and tumor diameter (P=0.024). Forty-eight out of 121 and 23 out of 121 cases showed the vessel and vacuole signs, respectively. However, patients with EGFR mutation did not exert a significant correlation on either of these signs (P=0.555 and P=0.372, respectively). A statistical significance was not observed in indexes such as age, gender, and tumor diameter on pleural indentation, bronchus cutoff sign, vessel sign, and vacuole sign (P>0.05). Age and gender did not vary significantly in the lobulation sign (P>0.05).

Conclusion

HRCT characteristics such as pleural indentation, bronchus cutoff sign, and lobulation sign in lung adenocarcinoma with EGFR mutation were significantly greater than those with wild-type EGFR; however, further study is essential in determining the predictive ability of computed tomography (CT) for EGFR mutations in lung adenocarcinoma.

Prediction of EGFR mutations by conventional CT-features in advanced pulmonary adenocarcinoma

OBJECTIVE

This study assessed the ability of conventional computed tomography (CT) features (including primary tumors, metastatic lesions, lymph nodes, and emphysema) to predict epidermal growth factor receptor (EGFR) mutations in advanced pulmonary adenocarcinoma.

METHODS

Patients who were diagnosed with advanced pulmonary adenocarcinoma between January 2017 and August 2017 and had undergone a chest CT and EGFR mutation testing were enrolled in this retrospective study. Qualitative and quantitative CT-features and clinical characteristics evaluated in this study included: primary tumor location, size, and morphology (including degree of lobulation, density, calcification, cavitation, vacuole sign, and air bronchogram), size and distribution of lung and pleural metastatic nodules, size and status of hilar and mediastinal lymph nodes, emphysema, organs with distant metastasis, and patient age, sex, and smoking history.

RESULTS

Of 201 patients, 107 (53.23%) were EGFR-mutation positive. The multivariate logistic regression indicated that EGFR mutations were significantly associated with smaller lymph nodes, a lower percentage of deep lobulation of the primary tumor and partial fusion of lymph nodes, and absence of emphysema. The area under the curve of logistic regression model for predicting EGFR mutations was 0.898.

CONCLUSIONS

Conventional CT-features, including emphysema, degree of primary tumor lobulation, and lymph node size and status, help to predict the presence or absence of EGFR mutations in advanced pulmonary adenocarcinoma. Additionally, these same CT-features demonstrated that the CT manifestations of the EGFR mutant group were of relatively lower malignancy and less invasive as compared to the wild-type EGFR group.

Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer

OBJECTIVE

Radiogenomics investigates radiographic imaging phenotypes associated with gene expression patterns. This study aims to explore relationships between CT imaging radiomics features and gene expression data in non-small cell lung cancer (NSCLC).

METHODS

Eighty-nine NSCLC patients are included in the study. Radiomics features are extracted and selected to quantify the phenotype of tumors on CT-scans. Co-expressed genes are also clustered and the first principal component of the cluster is represented, which is defined as a metagene. Then, statistical analysis was performed to assess association of CT radiomics features with metagenes. In addition, predictive models are built and metagene enrichment are conducted to further evaluate performance of NSCLC radiogenomics statistically and biologically.

RESULTS

There are 187 significant pairwise correlations between a CT radiomics feature and a metagene of NSCLC, where eighteen metagenes are annotated with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Metagenes are predicted in terms of radiomics features with an accuracy of 41.89% -89.93%.

CONCLUSIONS

This study reveals the associations between CT imaging radiomics features and NSCLC co-expressed gene sets. The findings suggest that CT radiomics features can reflect important biological information of NSCLC patients, which may have a significant clinical impact as CT is routinely used in clinical practice, assisting in improving medical decision-support at low cost.

CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study

We evaluated whether the optimal selection of CT reconstruction settings enables the construction of a radiomics model to predict epidermal growth factor receptor (EGFR) mutation status in primary lung adenocarcinoma (LAC) using standard of care CT images. Fifty-one patients (EGFR:wildtype = 23:28) with LACs of clinical stage I/II/IIIA were included in the analysis. The LACs were segmented in four conditions, two slice thicknesses (Thin: 1 mm; Thick: 5 mm) and two convolution kernels (Sharp: B70f/B70s; Smooth: B30f/B31f/B31s), which constituted four groups: (1) Thin-Sharp, (2) Thin-Smooth, (3) Thick-Sharp, and (4) Thick-Smooth. Machine learning algorithms selected and combined 1,695 quantitative image features to build prediction models. The performance of prediction models was assessed by calculating the area under the curve (AUC). The best prediction model yielded AUC (95%CI) = 0.83 (0.68, 0.92) using the Thin-Smooth reconstruction setting. The AUC of models using thick slices was significantly lower than that of thin slices (P < 10-3), whereas the impact of reconstruction kernel was not significant. Our study showed that the optimal prediction of EGFR mutational status in early stage LACs was achieved by using thin CT-scan slices, independently of convolution kernels. Results from the prediction model suggest that tumor heterogeneity is associated with EGFR mutation.

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.

Better Progression-Free Survival in Elderly Patients with Stage IV Lung Adenocarcinoma Harboring Uncommon Epidermal Growth Factor Receptor Mutations Treated with the First-line Tyrosine Kinase Inhibitors

Patients with lung adenocarcinoma harboring common epidermal growth factor receptor (EGFR) mutations usually have a good response rate (RR) and longer progression-free survival (PFS) to EGFR tyrosine kinase inhibitors (TKIs). However, the treatment efficacy to uncommon EGFR mutations remains controversial. We, therefore, performed a retrospective study, screening 2958 patients. A total of 67 patients with lung adenocarcinoma harboring uncommon EGFR mutations were enrolled and 57 patients with stage IV diseases receiving a first-line EGFR TKI were included for further analyses. The patients were classified into 27 (47%) “a single sensitizing uncommon mutation”, 7 (12%) “multiple sensitizing mutations”, 5 (9%) “a sensitizing mutation and a resistant uncommon mutation”, and 18 (32%) “other resistant uncommon mutations”. No significant difference was noted in PFS or overall survival (OS) between groups. Patients receiving different first-line EGFR TKIs had similar PFS and OS. The elder patients had a significantly poorer performance status than the younger patients but a significantly longer PFS than the younger patients (median PFS: 10.5 vs. 5.5 months, p = 0.0320). In conclusion, this is the first study to identify that elderly patients with stage IV lung adenocarcinoma harboring uncommon EGFR mutation might have a longer PFS. Large-scale prospective studies are mandatory to prove our findings.

Clinicoradiological features associated with epidermal growth factor receptor exon 19 and 21 mutation in lung adenocarcinoma

AIM

To retrospectively identify clinicopathological and radiological characteristics that could be independent predictors of epidermal growth factor receptor (EGFR) exon 19 and 21 mutation in surgically resected lung adenocarcinomas in a cohort of Asian patients.

MATERIALS AND METHODS

Demographics, histopathology data, and preoperative chest computed tomography (CT) images were evaluated retrospectively in 471 surgically resected lung adenocarcinomas. A total of 24 CT descriptors were assessed. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predicted factors of harbouring EGFR mutations.

RESULTS

EGFR mutations were existed in 252 (53.5%) of 471 patients, and associated with 11 clinicoradiological features. For the model with both clinical and radiological features, the independent predictors of harbouring EGFR mutation were small maximum diameter (≤3.9 cm), non-smokers, micropapillary pattern, pleural retraction, vascular convergence, and absence of solid pattern. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.784. Multivariable logistic regression analysis indicated that non-smokers, vascular convergence, and absence of solid pattern were important independent predictors of EGFR exon 19 mutation, while non-smokers and vascular convergence were independent predictors of EGFR exon 21 mutation. The AUCs were 0.807 and 0.794, respectively. A lepidic growth pattern appeared more frequently in exon 21 mutant tumours than in exon 19 mutant group (p<0.001).

CONCLUSION

CT imaging features of lung adenocarcinomas in combination with clinical variables could be used to prognosticate EGFR mutation status. The separate analysis of EGFR exon 19 or 21 mutation could further improve diagnostic performance.

EGFR-TKI-sensitive mutations in lung carcinomas: are they related to clinical features and CT findings?

Background

Epidermal growth factor receptor (EGFR) mutation testing is restricted to several limitations. In this study, we examined the relationship between EGFR mutation status and clinicoradiological characteristics in a Chinese cohort of patients.

Materials and methods

The data of patients who were diagnosed with lung carcinoma and underwent both EGFR testing and chest computed tomography (CT) at our hospital between January 1, 2011, and November 31, 2015, were retrospectively analyzed. The age, sex, and smoking index of the patients, the size, margin, and density of the tumor, and the presence of specific signs visible on the CT images were assessed.

Results

The results showed a higher rate of EGFR-tyrosine kinase inhibitor (TKI)-sensitive group than nonsensitive group in female patients and patients with a low smoking index (P<0.001, both). In logistic regression analyses, tumor size (P<0.001), smooth margins (P=0.015), and angular margins (P<0.001) were independent negative predictors of EGFR-TKI-sensitive group. Pleural indentation (P<0.001) and air bronchogram (P=0.025) were independent positive predictors of EGFR-TKI-sensitive group. Patients with squamous cell carcinoma had fewer sensitive mutations than those with either adenocarcinoma (P<0.001) or adenosquamous carcinoma (P<0.001).

Conclusion

Clinical and CT characteristics differed significantly between EGFR-TKI-sensitive and nonsensitive groups. Our findings may be useful in deciding therapeutic strategies for patients in whom EGFR testing is not possible.

Score for lung adenocarcinoma in China with EGFR mutation of exon 19: Combination of clinical and radiological characteristics analysis

BACKGROUD

The biopsy samples might be the only tumor material available for testing the EGFR mutation status in some cases, but these samples are often composed of variable ratios of tumor to normal cells. In this study, we sought to build a scoring system to predict Epidermal growth factor receptor (EGFR) exon 19 mutation in lung adenocarcinoma by clinical and radiological features.

METHODS

Enrolled in this study were 601 patients with lung adenocarcinoma. Qualitative evaluation of the clinical and radiological features included 25 aspects. Statistical analysis was used to assess the association of these features between the EGFR wild type and exon 19 mutation, based on a clinical scoring system built by the statistical model and the experience of the radiologists.

RESULTS

EGRF-exon-19-mutation was associated with the female gender [odds ratios (OR), 2.573; 95% confidence intervals (CI), 1.689-3.920], tumor maximum diameter (OR, 0.357; 95% CI, 0.235-0.542), the absence of emphysema (OR, 0.202; 95% CI, 0.110-0.368), the absence of fibrosis (OR, 0.168; 95% CI, 0.083-0.339), and pleural retraction (OR, 2.170; 95% CI, 1.434-3.285). The clinical scoring model assigned 3 points to the female gender, 2 points to small tumor maximum diameter (≤34.5 mm), 2 to the absence of emphysema, 2 to the absence of fibrosis, and 1 to the presence of pleural retraction.

CONCLUSIONS

The scoring system based on the statistical analysis of clinical and radiological features may be a new alternative to the prediction of EGFR mutation subtypes.

Computed tomography characteristics of lung adenocarcinomas with epidermal growth factor receptor mutation: A propensity score matching study

OBJECTIVES

We investigated the relationship between computed tomography (CT) characteristics and epidermal growth factor receptor (EGFR) mutations in a large Asian cohort who received surgical resection of invasive lung adenocarcinoma.

MATERIALS AND METHODS

We retrospectively included 864 patients (524 with EGFR mutation and 340 with EGFR wild-type) who received surgical resections for invasive lung adenocarcinomas. After applying propensity score matching, 312 patients with mutated EGFR were matched with 312 patients with wild-type EGFR. CT characteristics, predominant histologic subtype, and CT measurement parameters (volume and estimated diameter of the total tumor and inner solid portion and ground-glass opacity [GGO] proportion) were compared within matched pairs.

RESULTS

Tumors in the EGFR mutation group showed higher proportions of pure ground-glass nodules (4.1% vs 1.3%), GGO-predominant (23.7% vs 14.7%), and solid-predominant part-solid nodules (37.2% vs 31.7%) CT characteristics, whereas EGFR wild-type tumors predominantly presented as pure solid nodules (34.6% vs 52.2%, P < 0.0001). EGFR mutation tumors more frequently had a lepidic-predominant subtype than did EGFR wild-type tumors (20.2% and 11.9%; P < 0.0001), and showed a smaller whole tumor size and solid portion (P < 0.0001) with a higher GGO proportion (P < 0.0001). Tumors with exon 21 missense mutations showed the highest GGO proportion and the smallest inner solid portion size, followed by tumors harboring an exon 19 deletion, compared with EGFR wild-type tumors (posthoc P < 0.01).

CONCLUSION

Adenocarcinomas with EGFR mutations had a higher GGO proportion than those with wild-type EGFR after matching of clinical variables. Lesions with an exon 21 mutation had a higher GGO proportion than lesions with other mutations.

Imaging on Lung Cancer and Treatment with Targeted Therapy

The identification of genetic mutations known as oncogenic driver mutations that lead to the growth and survival of cancer cells has been an important advance in the field of oncology. Treatment in advanced non-small-cell lung cancer (NSCLC) has transitioned from a more general approach to a more personalized approach based on genetic mutations of the cancer itself. Common mutations detected in patients with advanced NSCLC include mutations of epidermal growth factor receptor and anaplastic lymphoma kinase (ALK). Targeted therapies are aimed at the products of these gene mutations and include erlotinib (used in epidermal growth factor receptor mutant NSCLC) and crizotinib (used in anaplastic lymphoma kinase positive NSCLC). In this review, we discuss common genetic mutations in advanced NSCLC, the role of targeted therapies, and imaging findings that can be associated with various genetic mutations.

Targeted Therapy and Imaging Findings

Non-small cell lung cancer (NSCLC) is usually diagnosed when it is not amenable to curative surgery or radiation. Many of these patients are candidates for systemic therapy. Median survival is only approximately 10 months, and, accordingly, treatment in advanced NSCLC is evolving toward a more personalized approach with the identification of genetic abnormalities based on biomarkers. For example, gene mutations in EGFR (epidermal growth factor receptor) and ALK (anaplastic lymphoma kinase) lead to a cascade of pathways resulting in uncontrolled growth, proliferation, and survival of tumor cells. Targeted therapies are aimed at the products of these mutated genes and include agents such as erlotinib and gefitinib (in EGFR-mutant NSCLC) or crizotinib (in ALK-positive NSCLC). Antiangiogenesis agents such as bevacizumab are another category of targeted therapy that inhibits vascular endothelial growth factors. The imaging characteristics of advanced NSCLC with genetic abnormalities, the evolution of targeted therapies and their imaging manifestations will be discussed.

Characterization of Computed Tomography Imaging of Rearranged During Transfection-rearranged Lung Cancer

BACKGROUND

Rearranged during transfection (RET)-rearranged non-small-cell lung cancer (NSCLC) is relatively rare and the clinical and computed tomography (CT) image characteristics of patients with an advanced disease stage have not been well documented.

PATIENTS AND METHODS

We identified patients with advanced-stage RET-rearranged NSCLC treated in the Cancer Institute Hospital, Japanese Foundation for Cancer Research, and analyzed the clinical and CT imaging characteristics.

RESULTS

In 21 patients with advanced RET-rearranged NSCLC, RET rearrangements were identified using fluorescence in situ hybridization and/or reverse transcriptase-polymerase chain reaction. The fusion partner genes were identified as KIF5B (57%), CCDC6 (19%), and unknown (24%). CT imaging showed that 12 primary lesions (92%) were peripherally located and all were solid tumors without ground-glass, air bronchograms, or cavitation. The median size of the primary lesions was 30 mm (range, 12-63 mm). Of the 18 patients with CT images before initial chemotherapy, 12 (67%) showed an absence of lymphadenopathy. Distant metastasis included 13 with pleural dissemination (72%), 10 with lung metastasis (56%), 8 with bone metastasis (44%), and 2 with brain metastasis (11%).

CONCLUSION

Advanced RET-rearranged NSCLC manifested as a relatively small and peripherally located solid primary lesion with or without small solitary lymphadenopathy. Pleural dissemination was frequently observed.

A new predictive scoring system based on clinical data and computed tomography features for diagnosing EGFR-mutated lung adenocarcinoma

Background

We aimed to develop a new EGFR mutation-predictive scoring system to use in screening for EGFR-mutated lung adenocarcinomas (lacs).

Methods

The study enrolled 279 patients with lac, including 121 patients with EGFR wild-type tumours and 158 with EGFR-mutated tumours. The Student t-test, chi-square test, or Fisher exact test was applied to discriminate clinical and computed tomography (ct) features between the two groups. Using a principal component analysis (pca) model, we derived predictive coefficients for the presence of EGFR mutation in lac.

Results

The EGFR mutation-predictive score includes sex, smoking history, homogeneity, ground-glass opacity (ggo) on imaging, and the presence of pericardial effusion. The pca predictive model took this form: [Formula: see text]Model scores ranged from 79 to 147. The area under the receiver operating characteristic curve was 0.752 [95% confidence interval (ci): 0.697 to 0.801] in the lac population at the optimal cut-off value of 109, and the sensitivity and specificity were 68.4% (95% ci: 60.5% to 75.5%) and 74.4% (95% ci: 65.6% to 81.9%) respectively.

Conclusions

The EGFR mutation risk scoring system based on clinical data and ct features is noninvasive and user-friendly. The model appears to frame a positive predictive value and was able to determine the value of repeating a biopsy if tissue is limited.

Targeted Therapy and Immunotherapy in the Treatment of Non-Small Cell Lung Cancer

The treatment strategy in advanced non-small cell lung cancer (NSCLC) has evolved from empirical chemotherapy to a personalized approach based on histology and molecular markers of primary tumors. Targeted therapies are directed at the products of oncogenic driver mutations. Immunotherapy facilitates the recognition of cancer as foreign by the host immune system, stimulates the immune system, and alleviates the inhibition that allows the growth and spread of cancer cells. The authors describes the role of targeted therapy and immunotherapy in the treatment of NSCLC, patterns of disease present on imaging studies, and immune-related adverse events encountered with immunotherapy.

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.

Influence of EGFR mutational status on metastatic behavior in non squamous non small cell lung cancer

Epidermal Growth Factor Receptor (EGFR) mutated Non Small Cell Lung Cancers (NSCLCs) are a molecularly subgroup of patients with peculiar clinic-pathological characteristics. Previous studies have suggested a possible interaction between oncogene status and metastatic behavior in non squamous NSCLCs with conflicting results. The aim of this study was to compare the different metastatic patterns, at baseline and during the course of the disease, in a cohort of 137 Caucasian patients with non-squamous NSCLC according to the EGFR mutational status and survival differences according to the different metastatic behavior. We observed unique metastatic distributions between EGFR-mutated and EGFR wild type non-squamous NSCLCs. These data support the hypothesis that tumor bio-molecular characteristics and genotype may influence the metastatic process in NSCLC and might help the development of enrichment strategies for tumor genotyping in these tumors, especially in the presence of limited tissue availability.

Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer

OBJECTIVES

To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables.

METHODS

The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model.

RESULTS

Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone.

CONCLUSION

Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.

Associations between clinical data and computed tomography features in patients with epidermal growth factor receptor mutations in lung adenocarcinoma

BACKGROUND

To analyse the differences in computed tomography (CT) features between patients with lung adenocarcinoma who have epidermal growth factor receptor (EGFR) mutations and those who have wild-type EGFR.

METHODS

Patients with lung adenocarcinoma (n = 156) were enrolled from October 2013 to March 2016, including 56 patients with wild-type EGFR and 100 patients with EGFR mutations. Two independent radiologists evaluated patient characteristics and imaging features. Chi-squared test, Fisher's exact test or ANOVA was applied to discriminate clinical and CT characteristics between the genotypes. A prediction tool for EGFR mutation was devised from principal component analysis.

RESULTS

The proportion of females and non-smokers in the exon 19 deletion and exon 21 missense groups was higher than in the wild-type group (P < 0.01). Severe emphysema was higher in the wild-type group than in the exon 19 deletion group (P < 0.01). The maximum diameter in the mediastinal window (MaxD_mediastinal) in the wild-type group was longer than in the exon 19 deletion and exon 21 missense groups. The minimum diameter in the mediastinal window (MinD_mediastinal) in the wild-type group was also longer than in the exon 21 missense group, with a significant difference (P < 0.05). The tumor shadow disappearance rate (TDR) in the exon 19 deletion group was higher than in the wild-type group. Ground glass opacity (GGO) appeared to be more common in the exon 19 deletion group (P = 0.010). The prediction score for exon 19 deletion mutation was: 0.305 × gender + 0.254 × smoking history + 0.198 × MaxD_mediastinal + TDR × 0.254 + 0.280 × GGO + 0.095 × emphysema. The sensitivity and specificity for predicting exon 19 deletion were 59.09 and 76.79%, respectively. The prediction score for the exon 21 missense mutation was: 0.354 × gender + 0.291 × smoking history + 0.410 × MaxD_mediastinal + 0.408 × MinD_mediastinal. The sensitivity and specificity for predicting exon 21 missense mutation were 72.34 and 78.57%, respectively.

CONCLUSION

As well as gender, smoking history and GGO, adenocarcinomas with EGFR mutation were significantly associated with emphysema, TDR, and the diameter in the mediastinal window. As exon 19 deletion and 21 missense mutations might be predicted by those features, the scoring system might be valuable for clinical diagnosis.

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.

CT characteristics of non-small cell lung cancer with epidermal growth factor receptor mutation: a systematic review and meta-analysis

Background

To systematically investigate the relationship between CT morphological features and the presence of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC).

Methods

All studies about the CT morphological features of NSCLC with EGFR mutations published between January 1, 2000 and March 15, 2015 were searched in the PubMed and EMBASE databases. Qualified studies were selected according to inclusion criteria. The frequency of EGFR mutations and CT features of ground-glass opacity (GGO) content, tumor size, cavitation, air-bronchogram, lobulation, and spiculation were extracted. The relationship between EGFR mutations and each of these CT features was tested based upon the weighted mean difference or inverse variance in the form of an odds ratio at a 95% confidence interval using Forest Plots. The publication bias was examined using Egger’s test.

Results

A total of 13 studies, consisting of 2146 NSCLC patients, were included, and 51.12% (1097/2146) of patients had EGFR mutations. The EGFR mutations were present in NSCLC with part-solid GGO in contrast to nonsolid GGO (OR = 0.49, 95% CI = 0.25–0.96, P = 0.04). Other CT features such as tumor size, cavitation, air-bronchogram, lobulation and spiculation did not demonstrate statistically significant correlation with EGFR mutations individually (P = 0.91; 0.67; 0.12; 0.45; and 0.36, respectively). No publication bias among the selected studies was noted in this meta-analysis (Egger’s tests, P > 0.05 for all).

Conclusion

This meta-analysis demonstrated that NSCLC with CT morphological features of part-solid GGO tended to be EGFR mutated, which might provide an important clue for the correct selection of patients treated with molecular targeted therapies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12880-016-0175-3) contains supplementary material, which is available to authorized users.

Radiogenomic evaluation of lung cancer - Are there imaging characteristics associated with lung adenocarcinomas harboring BRAF mutations?

INTRODUCTION

We studied computed tomography (CT) features associated with BRAF mutated lung cancer.

MATERIALS AND METHODS

CT features of BRAF mutated lung cancers were compared to stage matched lesions without BRAF mutation.

RESULTS

47 (25%) patients with BRAF mutation and 141 (75%) without BRAF mutation were included. BRAF lesions were most frequently solid 37 (84%), spiculated 22 (50%), and peripheral 37 (84%). No feature of the primary tumor was significantly different between BRAF and non-BRAF groups. BRAF patients were more likely than KRAS patients to have pleural metastases [5 (11%) vs 0 (0%), p=0.045].

CONCLUSION

No feature of the primary tumor differentiates BRAF lesions from non-BRAF lesions.