Comparative analysis of clinicoradiologic characteristics of lung adenocarcinomas with ALK rearrangements or EGFR mutations

Cytomorphological and histomorphological features of lung adenocarcinoma with epidermal growth factor receptor mutation and anaplastic lymphoma kinase gene rearrangement

Lung cancer is among the lethal and most prevalent oncological diseases globally. It is known that two types of mutations, namely anaplastic lymphoma kinase (ALK) gene rearrangement and epidermal growth factor receptor (EGFR) gene mutation, are responsible for the development of lung adenocarcinoma. The present study aimed to investigate the differences in the frequency of clinical, cytomorphological and histomorphological features of ALK and EGFR-positive lung adenocarcinomas. The present retrospective study comprised 101 patients diagnosed with lung adenocarcinoma. Based on the molecular findings, the patients were categorized into three groups as follows: The ALK-rearranged group (n=28), the EGFR group (n=42) and the negative group (n=31). The clinical features analyzed included sex, age, smoking status and disease stage. The cytomorphological and histomorphological features examined encompassed the following: Cell cluster size, the arrangement of tumor cells, the size of nuclei, nuclear atypia, the visibility of nucleoli, the presence of necrosis, intracytoplasmic vacuoles, signet ring cells, stromal characteristics and the presence of inflammatory infiltrate presence. The results indicated that the female sex was more prevalent in the EGFR group, but statistically significant differences (P<0.05) were observed between the EGFR and negative group. A significantly greater percentage of non-smokers was identified in the EGFR group compared with the ALK group (P<0.01). The majority of patients with confirmed ALK or EGFR mutations received onco-specific treatment. Focal and abundant necrosis was significantly less common in cytological samples in the EGFR group than in the other groups (21.43 vs. 57.14 and 51.61%, combined, P<0.01). No significant differences were observed in other cytomorphological features between the groups. Intracytoplasmic vacuoles, signet ring cells and cells with visible nucleoli were significantly more frequent in histological specimens of the ALK group (P<0.01). The predictive model composed of these features or combined with sex and smoking habits exhibited statistically significant differences for mutation status as a criterion (P<0.01). Collectively, the findings of the present study confirmed that, in addition to clinical characteristics, certain cytological and histological features of lung adenocarcinoma are associated with the mutational status of the tumor.

Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks

Non-small cell lung carcinoma (NSCLC) is the most common type of pulmonary cancer, one of the deadliest malignant tumors worldwide. Given the increased emphasis on the precise management of lung cancer, identifying various subtypes of NSCLC has become pivotal for enhancing diagnostic standards and patient prognosis. In response to the challenges presented by traditional clinical diagnostic methods for NSCLC pathology subtypes, which are invasive, rely on physician experience, and consume medical resources, we explore the potential of radiomics and deep learning to automatically and non-invasively identify NSCLC subtypes from computed tomography (CT) images. An integrated model is proposed that investigates both radiomic features and deep learning features and makes comprehensive decisions based on the combination of these two features. To extract deep features, a three-dimensional convolutional neural network (3D CNN) is proposed to fully utilize the 3D nature of CT images while radiomic features are extracted by radiomics. These two types of features are combined and classified with multi-head attention (MHA) in our proposed model. To our knowledge, this is the first work that integrates different learning methods and features from varied sources in histological subtype classification of lung cancer. Experiments are organized on a mixed dataset comprising NSCLC Radiomics and Radiogenomics. The results show that our proposed model achieves 0.88 in accuracy and 0.89 in the area under the receiver operating characteristic curve (AUC) when distinguishing lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SqCC), indicating the potential of being a non-invasive way for predicting histological subtypes of lung cancer.

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice.

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.

Residual neural network with mixed loss based on batch training technique for identification of EGFR mutation status in lung cancer

Epidermal growth factor receptor (EGFR) is the key to targeted therapy with tyrosine kinase inhibitors in lung cancer. Traditional identification of EGFR mutation status requires biopsy and sequence testing, which may not be suitable for certain groups who cannot perform biopsy. In this paper, using easily accessible and non-invasive CT images, the residual neural network (ResNet) with mixed loss based on batch training technique is proposed for identification of EGFR mutation status in lung cancer. In this model, the ResNet is regarded as the baseline for feature extraction to avoid the gradient disappearance. Besides, a new mixed loss based on the batch similarity and the cross entropy is proposed to guide the network to better learn the model parameters. The proposed mixed loss utilizes the similarity among batch samples to evaluate the distribution of training data, which can reduce the similarity of different classes and the difference of the same classes. In the experiments, VGG16Net, DenseNet, ResNet18, ResNet34 and ResNet50 models with the mixed loss are trained on the public CT dataset with 155 patients including EGFR mutation status from TCIA. The trained networks are employed to the collected preoperative CT dataset with 56 patients from the cooperative hospital for validating the efficiency of the proposed models. Experimental results show that the proposed models are more appropriate and effective on the lung cancer dataset for identifying the EGFR mutation status. In these models, the ResNet34 with mixed loss is optimal (accuracy = 81.58%, AUC = 0.8861, sensitivity = 80.02%, specificity = 82.90%).

Clinicopathological and computed tomography features of patients with early-stage non-small-cell lung cancer harboring ALK rearrangement

BACKGROUND

Although some studies have assessed the correlation between computed tomography (CT) features and anaplastic lymphoma kinase (ALK) rearrangement in patients with non-small-cell lung cancer (NSCLC), few have focused on early-stage patients. The results of some previous studies are inconsistent and contradictory. Therefore, this study aimed to analyze the clinicopathological and CT features of patients with early-stage NSCLC harboring ALK rearrangement.

METHODS

This retrospective analysis included 65 patients with ALK rearrangement and 629 ALK-negative patients. All patients had surgically resected NSCLC and were diagnosed with stage IA or stage IIB NSCLC. Clinicopathological features and CT signs, including tumor size and density, consolidation tumor ratio (CTR), lesion location, round or irregular shape, lobulated or spiculated margins, air bronchograms, bubble-like lucency or cavities, and pleural retraction, were investigated according to different genotypes.

RESULTS

The prevalence of ALK rearrangement in patients with early-stage NSCLC was 9.3% (65/694). Patients with ALK rearrangement were significantly younger than those without ALK rearrangement (P = 0.033). The frequency of moderate cell differentiation was significantly lower in tumors with ALK rearrangement than in those without ALK rearrangement (46.2% vs. 59.8%, P = 0.034). The frequency of the mucinous subtype was significantly higher in the ALK-positive group than in the ALK-negative group (13.8% vs. 5.4%, P = 0.007). No significant differences were found in any CT signs between the ALK-positive and ALK-negative groups.

CONCLUSIONS

Patients with ALK-positive lung cancer may have specific clinicopathological features, including younger age, lower frequency of moderate cell differentiation, and higher frequency of the mucinous type. CT features may not correlate with ALK rearrangement in early-stage lung cancer. Immunohistochemistry or next-generation sequencing is needed to further clarify the genomic mutation status.

Predicting anaplastic lymphoma kinase rearrangement status in patients with non-small cell lung cancer using a machine learning algorithm that combines clinical features and CT images

PURPOSE

To develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features.

METHOD AND MATERIALS

This study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves.

RESULTS

The support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731-0.877) and an AUC=0.735 (95% CI 0.566-0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913-1.0) in the training cohort and an AUC=0.890 (95% CI 0.778-0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826-0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804-0.893) in the validation cohort.

CONCLUSION

Our findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.

Computed tomography-based radiomics quantification predicts epidermal growth factor receptor mutation status and efficacy of first-line targeted therapy in lung adenocarcinoma

Background

Biomarkers that predict the efficacy of first-line tyrosine kinase inhibitors (TKIs) are pivotal in epidermal growth factor receptor (EGFR) mutant advanced lung adenocarcinoma. Imaging-based biomarkers have attracted much attention in anticancer therapy. This study aims to use the machine learning method to distinguish EGFR mutation status and further explores the predictive role of EGFR mutation-related radiomics features in response to first-line TKIs.

Methods

We retrospectively analyzed pretreatment CT images and clinical information from a cohort of lung adenocarcinomas. We entered the top-ranked features into a support vector machine (SVM) classifier to establish a radiomics signature that predicted EGFR mutation status. Furthermore, we identified the best response-related features based on EGFR mutant-related features in first-line TKI therapy patients. Then we test and validate the predictive effect of the best response-related features for progression-free survival (PFS).

Results

Six hundred ninety-two patients were enrolled in building radiomics signatures. The 13 top-ranked features were input into an SVM classifier to establish the radiomics signature of the training cohort (n = 514), and the predictive score of the radiomics signature was assessed on an independent validation group with 178 patients and obtained an area under the curve (AUC) of 74.13%, an F1 score of 68.29%, a specificity of 79.55%, an accuracy of 70.79%, and a sensitivity of 62.22%. More importantly, the skewness-Low (≤0.882) or 10th percentile-Low group (≤21.132) had a superior partial response (PR) rate than the skewness-High or 10th percentile-High group (p < 0.01). Higher skewness (hazard ratio (HR) = 1.722, p = 0.001) was also found to be significantly associated with worse PFS.

Conclusions

The radiomics signature can be used to predict EGFR mutation status. Skewness may contribute to the stratification of disease progression in lung cancer patients treated with first-line TKIs.

Importance of tumor subtypes in cancer imaging

Cancer therapy has evolved from being broadly directed towards tumor types, to highly specific treatment protocols that target individual molecular subtypes of tumors. With the ever-increasing data on imaging characteristics of tumor subtypes and advancements in imaging techniques, it is now often possible for radiologists to differentiate tumor subtypes on imaging. Armed with this knowledge, radiologists may be able to provide specific information that can obviate the need for invasive methods to identify tumor subtypes. Different tumor subtypes also differ in their patterns of metastatic spread. Awareness of these differences can direct radiologists to relevant anatomical sites to screen for early metastases that may otherwise be difficult to detect during cursory inspection. Likewise, this knowledge will help radiologists to interpret indeterminate findings in a more specific manner.

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Tumor subtypes can be identified based on their different imaging characteristics.

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Awareness of tumor subtype can help radiologists chose the appropriate modality for additional imaging workup.

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Awareness of differences in metastatic pattern between tumor subtypes can be helpful to identify early metastases.

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma

Objectives

Spread through air spaces (STAS), a new invasive pattern in lung adenocarcinoma (LUAD), is a risk factor for poor outcome in early-stage LUAD. This study aimed to develop and validate a CT-based radiomics model for predicting STAS in stage IA LUAD.

Methods

A total of 395 patients (169 STAS positive and 226 STAS negative cases, including 316 and 79 patients in the training and test sets, respectively) with stage IA LUAD before surgery were retrospectively included. On all CT images, tumor size, types of nodules (solid, mix ground-glass opacities [mGGO] and pure GGO [pGGO]), and GGO percentage were recorded. Region of interest (ROI) segmentation was performed semi-automatically, and 1,037 radiomics features were extracted from every segmented lesion. Intraclass correlation coefficients (ICCs), Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were used to filter unstable (ICC &lt; 0.75) and redundant features (r &gt; 0.8). A temporary model was established by multivariable logistic regression (LR) analysis based on selected radiomics features. Then, seven radiomics features contributing the most were selected for establishing the radiomics model. We then built two predictive models (clinical-CT model and MixModel) based on clinical and CT features only, and the combination of clinical-CT and Rad-score, respectively. The performances of these three models were assessed.

Results

The radiomics model achieved good performance with an area under of curve (AUC) of 0.812 in the training set, versus 0.850 in the test set. Furthermore, compared with the clinical-CT model, both radiomics model and MixModel showed higher AUC and better net benefit to patients in the training and test cohorts.

Conclusion

The CT-based radiomics model showed satisfying diagnostic performance in early-stage LUAD for preoperatively predicting STAS, with superiority over the clinical-CT model.

EGFR Mutation Status and Subtypes Predicted by CT-Based 3D Radiomic Features in Lung Adenocarcinoma

Objective

In this study, we aim to establish a non-invasive tool to predict epidermal growth factor receptor (EGFR) mutation status and subtypes based on radiomic features of computed tomography (CT).

Methods

A total of 233 lung adenocarcinoma patients were investigated and randomly divided into the training and test cohorts. In this study, 2300 radiomic features were extracted from original and filtered (Exponential, Laplacian of Gaussian, Logarithm, Gabor, Wavelet) CT images. The radiomic features were divided into four categories, including histogram, volumetric, morphologic, and texture features. An RF-BFE algorithm was developed to select the features for building the prediction models. Clinicopathological features (including age, gender, smoking status, TNM staging, maximum diameter, location, and growth pattern) were combined to establish an integrated model with radiomic features. ROC curve and AUC quantified the effectiveness of the predictor of EGFR mutation status and subtypes.

Results

A set of 10 features were selected to predict EGFR mutation status between EGFR mutant and wild type, while 9 selected features were used to predict mutation subtypes between exon 19 deletion and exon 21 L858R mutation. To predict the EGFR mutation status, the AUC of the training cohort was 0.778 and the AUC of the test cohort was 0.765. To predict the EGFR mutation subtypes, the AUC of training cohort was 0.725 and the AUC of test cohort was 0.657. The integrated model showed the most optimal predictive performance with EGFR mutation status (AUC = 0.870 and 0.759) and subtypes (AUC = 0.797 and 0.554) in the training and test cohorts.

Conclusion

CT-based radiomic features can extract information on tumor heterogeneity in lung adenocarcinoma. In addition, we have established a radiomic model and an integrated model to non-invasively predict the EGFR mutation status and subtypes of lung adenocarcinoma, which is conducive to saving clinical costs and guiding targeted therapy.

Development of a Nomogram Based on 3D CT Radiomics Signature to Predict the Mutation Status of EGFR Molecular Subtypes in Lung Adenocarcinoma: A Multicenter Study

Background

This study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features.

Methods

In total, 728 patients with lung adenocarcinoma were included, and divided into three groups according to EGFR mutation subtypes. 1727 radiomics features were extracted from the three-dimensional images of each patient. Wilcoxon test, least absolute shrinkage and selection operator regression, and multiple logistic regression were used for feature selection. ROC curve was used to evaluate the predictive performance of the model. Nomogram was constructed by combining radiomics features and clinical risk factors. Calibration curve was used to evaluate the goodness of fit of the model. Decision curve analysis was used to evaluate the clinical applicability of the model.

Results

There were three, two, and one clinical factor and fourteen, thirteen, and four radiomics features, respectively, which were significantly related to each EGFR molecular subtype. Compared with the clinical and radiomics models, the combined model had the highest predictive performance in predicting EGFR molecular subtypes [Del-19 mutation vs. wild-type, AUC=0.838 (95% CI, 0.799-0.877); L858R mutation vs. wild-type, AUC=0.855 (95% CI, 0.817-0.894); and Del-19 mutation vs. L858R mutation, AUC=0.906 (95% CI, 0.869-0.943), respectively], and it has a stable performance in the validation set [AUC was 0.813 (95% CI, 0.740-0.886), 0.852 (95% CI, 0.790-0.913), and 0.875 (95% CI, 0.781-0.929), respectively].

Conclusion

Our combined model showed good performance in predicting EGFR molecular subtypes in patients with lung adenocarcinoma. This model can be applied to patients with lung adenocarcinoma.

Establishment and Evaluation of EGFR Mutation Prediction Model Based on Tumor Markers and CT Features in NSCLC

Background

Lung cancer has become one of the leading causes of cancer deaths worldwide. EGFR gene mutation has been reported in up to 60% of Asian populations and is currently one of the main targets for genotype-targeted therapy for NSCLC.

Objective

The objective is to determine if a complex model combining serum tumor makers and computed tomographic (CT) features can predict epidermal growth factor receptor (EGFR) mutation with higher accuracy. Material and Methods. Retrospective analysis of the data of patients diagnosed with in nonsmall cell lung cancer (NSCLC) by EGFR gene testing was carried out in the Department of Thoracic Surgery, Jinan Central Hospital. Multivariate logistic regression analysis was used to determine the independent predictors of EGFR mutations, and logistic regression prediction models were developed. The subject operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was calculated to assess the accuracy and clinical application of the EGFR mutation prediction model.

Results

Logistic regression analysis identified the predictive factors of EGFR mutation including nonsmoking, high expression level of Carcinoembryonic Antigen (CEA), low expression level of cytokeratin 19 fragments (CYFRA21-1), and subsolid density containing ground-glass opacity (GGO) component. Using the results of multivariate logistic regression analysis, we built a statistically determined clinical prediction model. The AUC of the complex prediction model increased significantly from 0.735 to 0.813 (p = 0.014) when CT features are added and from 0.612 to 0.813 (p < 0.001) when serum variables are added. When P was 0.441, the sensitivity was 86.7% and the specificity was 65.8%.

Conclusion

A complex model combining serum tumor makers and CT features is more accurate in predicting EGFR mutation status in NSCLC patients than using either serum variables or imaging features alone. Our finding for EGFR mutation is urgently needed and helpful in clinical practice.

Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects

Objective

The purpose of this paper was to perform a narrative review of current research evidence on conventional computed tomography (CT) imaging features and CT image-based radiomic features for predicting gene mutations in lung adenocarcinoma and discuss how to translate the research findings to guide future practice.

Background

Lung cancer, especially lung adenocarcinoma, is the leading cause of cancer-related deaths. With advances in the diagnosis and treatment of lung adenocarcinoma with the emergence of molecular testing, the prediction of oncogenes and even drug resistance gene mutations have become key to individualized and precise clinical treatment in order to prolong survival and improve quality of life. The progress of imageological examination includes the development of CT and radiomics are promising quantitative methods for predicting different gene mutations in lung adenocarcinoma, especially common mutations, such as epidermal growth factor receptor (EGFR) mutation, anaplastic lymphoma kinase (ALK) mutation and Kirsten rat sarcoma viral oncogene (KRAS) mutation.

Methods

The PubMed electronic database was searched along with a set of terms specific to lung adenocarcinoma, radiomics (including texture analysis), CT, computed tomography, EGFR, ALK, KRAS, rearranging transfection (RET) rearrangement and c-ros oncogene 1 (ROS-1), v-raf murine sarcoma viral oncogene homolog B1 (BRAF), and human epidermal growth factor receptor 2 (HER2) mutations et al. This review has been reported in compliance with the Narrative Review checklist guidelines. From each full-text article, information was extracted regarding a set of terms above.

Conclusions

Research on the application of conventional CT features and CT image-based radiomic features for predicting the gene mutation status of lung adenocarcinoma is still in a preliminary stage. Noninvasively determination of mutation status in lung adenocarcinoma before targeted therapy with conventional CT features and CT image-based radiomic features remains both hopes and challenges. Before radiomics could be applied in clinical practice, more work needs to be done.

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.

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>

Radiomic Feature-Based Nomogram: A Novel Technique to Predict EGFR-Activating Mutations for EGFR Tyrosin Kinase Inhibitor Therapy

Objectives

To develop and validate a radiomic feature-based nomogram for preoperative discriminating the epidermal growth factor receptor (EGFR) activating mutation from wild-type EGFR in non-small cell lung cancer (NSCLC) patients.

Material

A group of 301 NSCLC patients were retrospectively reviewed. The EGFR mutation status was determined by ARMS PCR analysis. All patients underwent nonenhanced CT before surgery. Radiomic features were extracted (GE healthcare). The maximum relevance minimum redundancy (mRMR) and LASSO, were used to select features. We incorporated the independent clinical features into the radiomic feature model and formed a joint model (i.e., the radiomic feature-based nomogram). The performance of the joint model was compared with that of the other two models.

Results

In total, 396 radiomic features were extracted. A radiomic signature model comprising 9 selected features was established for discriminating patients with EGFR-activating mutations from wild-type EGFR. The radiomic score (Radscore) in the two groups was significantly different between patients with wild-type EGFR and EGFR-activating mutations (training cohort: P<0.0001; validation cohort: P=0.0061). Five clinical features were retained and contributed as the clinical feature model. Compared to the radiomic feature model alone, the nomogram incorporating the clinical features and Radscore exhibited improved sensitivity and discrimination for predicting EGFR-activating mutations (sensitivity: training cohort: 0.84, validation cohort: 0.76; AUC: training cohort: 0.81, validation cohort: 0.75). Decision curve analysis demonstrated that the nomogram was clinically useful and surpassed traditional clinical and radiomic features.

Conclusions

The joint model showed favorable performance in the individualized, noninvasive prediction of EGFR-activating mutations in NSCLC 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.

Relationship between epidermal growth factor receptor mutations and CT features in patients with lung adenocarcinoma

AIM

The purpose of this study was to investigate the relationship between epidermal growth factor receptor (EGFR) mutation status and computed tomography (CT) features in patients with lung adenocarcinoma.

MATERIALS AND METHODS

A total of 483 patients with lung adenocarcinoma diagnosed between January 2015 and April 2020 were included in this study. All patients underwent a preoperative chest CT, and a total of 31 detailed CT features were quantified. The mutation status of EGFR exon 18-21 was detected by a polymerase chain reaction (PCR)-based amplified refractory mutation system. Student's t and Fisher's exact or chi-square tests were used to compare continuous and categorical variables, respectively. Least absolute shrinkage and selection operator (LASSO) regularisation was used to determine the optimal combination of CT features and clinical characteristics to predict the EGFR mutation status. The model was tested using a validation set consisting of 120 patients.

RESULTS

EGFR mutations were found in 249 (51.6%) of 483 patients with lung adenocarcinoma. Univariate analysis showed that 14 CT features and two clinical characteristics correlated significantly with the EGFR mutation status. Smoking history, long-axis diameter, bubble-like lucency, pleural retraction, thickened bronchovascular bundles, and peripheral emphysema were independent predictors of the EGFR mutation status, according to LASSO regularisation. In the training and verification cohorts, the areas under the curve of the prediction model were 0.766 and 0.745, respectively.

CONCLUSIONS

CT features of patients with lung adenocarcinoma can help predict the EGFR mutation status.

Value of CT features for predicting EGFR mutations and ALK positivity in patients with lung adenocarcinoma

The aim of this study was to identify the relationships of epidermal growth factor receptor (EGFR) mutations and anaplastic large-cell lymphoma kinase (ALK) status with CT characteristics in adenocarcinoma using the largest patient cohort to date. In this study, preoperative chest CT findings prior to treatment were retrospectively evaluated in 827 surgically resected lung adenocarcinomas. All patients were tested for EGFR mutations and ALK status. EGFR mutations were found in 489 (59.1%) patients, and ALK positivity was found in 57 (7.0%). By logistic regression, the most significant independent prognostic factors of EGFR effective mutations were female sex, nonsmoker status, GGO air bronchograms and pleural retraction. For EGFR mutation prediction, receiver operating characteristic (ROC) curves yielded areas under the curve (AUCs) of 0.682 and 0.758 for clinical only or combined CT features, respectively, with a significant difference (p < 0.001). Furthermore, the exon 21 mutation rate in GGO was significantly higher than the exon 19 mutation rate(p = 0.029). The most significant independent prognostic factors of ALK positivity were age, solid-predominant-subtype tumours, mucinous lung adenocarcinoma, solid tumours and no air bronchograms on CT. ROC curve analysis showed that for predicting ALK positivity, the use of clinical variables combined with CT features (AUC = 0.739) was superior to the use of clinical variables alone (AUC = 0.657), with a significant difference (p = 0.0082). The use of CT features for patients may allow analyses of tumours and more accurately predict patient populations who will benefit from therapies targeting treatment.

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.

Radiologic Features of Resected Lung Adenocarcinoma With Epithelial-Mesenchymal Transition

BACKGROUND

Epithelial-mesenchymal transition plays a crucial role in cancer progression and is a significant prognosticator for postoperative survival in patients with lung cancer. Predicting epithelial-mesenchymal transition preoperatively using computed tomography may help to determine the optimal surgical strategy.

METHODS

We performed an immunohistochemical analysis of E-cadherin and vimentin expressions using tumor specimens from resected primary lung adenocarcinoma and classified the results into 3 subgroups according to the expressions: epithelial, intermediate, and mesenchymal. The intermediate and mesenchymal groups were classified as the epithelial-mesenchymal transition conversion group. We analyzed the association between epithelial-mesenchymal transition and radiologic characteristics, especially computed tomographic features.

RESULTS

The epithelial-mesenchymal transition conversion group comprised 162 patients (49.1%). Computed tomography revealed that tumors with epithelial-mesenchymal transition conversion showed a high consolidation/tumor ratio compared with those without conversion. Univariate analysis demonstrated that tumors with epithelial-mesenchymal transition were significantly associated with bronchial and/or vascular convergence (P < .001) and notching (P = .028). When the cutoff value for the consolidation/tumor ratio was set by the receiver operating characteristic curve, independent predictive factors for epithelial-mesenchymal transition by multivariate analysis were high ratio (>0.7946; P < .001) and the presence of convergence (P = .05). Tumors with a high consolidation/tumor ratio and convergence had a 4-fold higher odds ratio for epithelial-mesenchymal transition, and patients had significantly poorer survival.

CONCLUSIONS

Convergence and a high consolidation/tumor ratio were independently associated with epithelial-mesenchymal transition conversion. These preoperative radiologic results will help to predict epithelial-mesenchymal transition conversion in lung adenocarcinoma.

Individualized nomogram for predicting ALK rearrangement status in lung adenocarcinoma patients

OBJECTIVES

To develop a nomogram to identify anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients using clinical, CT, PET/CT, and histopathological features.

METHODS

This retrospective study included 399 lung adenocarcinoma patients (129 ALK-rearranged patients and 270 ALK-negative patients) that were randomly divided into a training cohort and an internal validation cohort (4:1 ratio). Clinical factors, radiologist-defined CT features, maximum standard uptake values (SUVmax), and histopathological features were used to construct predictive models with stepwise backward-selection multivariate logistic regression (MLR). The models were then evaluated using the AUC. The integrated model was compared to the clinico-radiological model using the DeLong test to evaluate the role of histopathological features. An associated individualized nomogram was established.

RESULTS

The integrated model reached an AUC of 0.918 (95% CI, 0.886-0.950), sensitivity of 0.774, and specificity of 0.934 in the training cohort and an AUC of 0.857 (95% CI, 0.777-0.937), sensitivity of 0.739, and specificity of 0.810 in the validation cohort. The MLR analysis showed that younger age, never smoker, lymph node enlargement, the presence of cavity, high SUVmax, solid or micropapillary predominant histology subtype, and local invasiveness were strong and independent predictors of ALK rearrangements. The nomogram calculated the risk of harboring ALK mutation for lung adenocarcinoma patients and exhibited a good generalization ability.

CONCLUSION

Our study demonstrates that histopathological features added value to the imaging characteristics-based model. The nomogram with clinical, imaging, and histopathological features can serve as a supplementary non-invasive tool to evaluate the probability of ALK rearrangement in lung adenocarcinoma.

KEY POINTS

• The developed nomogram can accurately predict the probability of lung adenocarcinoma harboring ALK-fused gene. • Pathological analysis is important to predict ALK rearrangement in lung adenocarcinoma. • Lung adenocarcinoma with lepidic predominant growth pattern and TTF-1 negativity is unlikely to have ALK rearrangement.

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.

3D radiomics predicts EGFR mutation, exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma

BACKGROUND

To establish a radiomic approach to identify epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients based on CT images, and to distinguish exon-19 deletion and exon-21 L858R mutation.

METHODS

Two hundred sixty-three patients who underwent pre-surgical contrast-enhanced CT and molecular testing were included, and randomly divided into the training (80%) and test (20%) cohort. Tumor images were three-dimensionally segmented to extract 1,672 radiomic features. Clinical features (age, gender, and smoking history) were added to build classification models together with radiomic features. Subsequently, the top-10 most relevant features were used to establish classifiers. For the classifying tasks including EGFR mutation, exon-19 deletion, and exon-21 L858R mutation, four logistic regression models were established for each task.

RESULTS

The training and test cohort consisted of 210 and 53 patients, respectively. Among the established models, the highest accuracy and sensitivity among the four models were 75.5% (61.7-86.2%) and 92.9% (76.5-99.1%) to classify EGFR mutation, respectively. The highest specificity values were 86.7% (69.3-96.2%) and 70.4% (49.8-86.3%) to classify exon-19 deletion and exon-21 L858R mutation, respectively.

CONCLUSIONS

CT radiomics can sensitively identify the presence of EGFR mutation, and increase the certainty of distinguishing exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma patients. CT radiomics may become a helpful non-invasive biomarker to select EGFR mutation patients for invasive sampling.

CT features associated with EGFR mutations and ALK positivity in patients with multiple primary lung adenocarcinomas

Background

In multiple primary lung adenocarcinomas (MPLAs), the relationship between imaging and gene mutations remains unclear. This retrospective study aimed to identify the correlation of epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) status with CT characteristics in MPLA patients.

Methods

Sixty-seven patients (135 lesions) with MPLAs confirmed by pathology were selected from our institution. All subjects were tested for EGFR mutations and ALK status and underwent chest CT prior to any treatment. The criteria for MPLA definitions closely adhered to the comprehensive histologic assessment (CHA).

Results

Among MPLA patients, EGFR mutations were more common in females (p = 0.002), in those who had never smoked (p = 0.010), and in those with less lymph node metastasis (p < 0.001), and the tumours typically presented with ground-glass opacity (GGO) (p = 0.003), especially mixed GGO (p < 0.001), and with air bronchograms (p = 0.012). Logistics regression analysis showed that GGO (OR = 6.550, p = 0.010) was correlated with EGFR mutation, while air bronchograms were not correlated with EGFR mutation (OR = 3.527, p = 0.060). A receiver operating characteristic (ROC) curve yielded area under the curve (AUC) values of 0.647 and 0.712 for clinical-only or combined CT features, respectively, for prediction of EGFR mutations, and a significant difference was found between them (p = 0.0344). ALK-positive status was found most frequently in MPLA patients who were younger (p = 0.002) and had never smoked (p = 0.010). ALK positivity was associated with solid nodules or masses in MPLAs (p < 0.004) on CT scans. Logistics regression analysis showed that solid nodules (OR = 6.550, p = 0.010) were an independent factor predicting ALK positivity in MPLAs. For prediction of ALK positivity, the ROC curve yielded AUC values of 0.767 and 0.804 for clinical-only or combined CT features, respectively, but no significant difference was found between them (p = 0.2267).

Conclusion

Among MPLA patients, nonsmoking women with less lymph node metastasis and patients with lesions presenting GGO or mixed GGO and air bronchograms on CT were more likely to exhibit EGFR mutations. In nonsmoking patients, young patients with solid lesions on CT are recommended to undergo an ALK status test.

Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers

Purpose

To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively.

Materials and Methods

This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 pre-contrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPV of pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538–0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630–0.9567), 76.92%, 83.33%, 71.43%, and 86.96%.

Conclusion

Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard post-contrast CT radiomics biomarkers produced superior performance compared with that of pre-contrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.

Quantitative volumetric assessment of the solid portion percentage on CT images to predict ROS1/ALK rearrangements in lung adenocarcinomas

In the present study, the predictive role of the percentage of the solid portion volume (PSV) in patients with lung adenocarcinoma was investigated. The PSV was obtained through quantitative volumetric assessments based on reconstructed CT images of lung adenocarcinoma by comparing the index among tumors with c-ros oncogene 1 (ROS1) rearrangement, epidermal growth factor receptor (EGFR) mutations, echinoderm anaplastic lymphoma kinase (ALK) rearrangements or wild-type (WT) status for the three genes. Among 1,120 patients with lung adenocarcinoma, 28 patients with ROS1 rearrangement lung adenocarcinoma, 71 with ALK rearrangement and 578 with EGFR mutations were diagnosed. PSV was quantitatively measured by semi-automated nodule assessment software and compared in patients with different mutation statuses. The PSV (presented as the median with interquartile range) in the ROS1 rearrangement group [87.9 (82.7-92.3)%] was higher than that in the EGFR mutation group [70.4 (51.4-83.4%)] and the WT group [63.0 (50.9-83.2)%; P<0.001], but was similar to that in the ALK rearrangement group [84.0 (70.3-90.0)%; P=0.251]. The area under the receiver operating characteristic curve (AUC) for the PSV to predict ROS1 or ALK rearrangement combined was 0.702 (95% CI: 0.631-0.773; P<0.001); at a cut-off value of 0.805 (when the Youden index was maximal), the predictive sensitivity was 0.697 and the specificity was 0.702. Younger age and higher PSV values were independent predictors of ROS1/ALK rearrangements. The AUC for the predictive model combined with age and PSV was 0.785. In conclusion, the PSV in the lung adenocarcinomas with ROS1 rearrangement was significantly higher compared with that in the EGFR-mutated and WT lung adenocarcinoma, but was similar to that in lung adenocarcinoma with ALK rearrangement. Younger age and higher PSV values on CT in patients with lung adenocarcinomas were predictive factors for ROS1/ALK rearrangement.

Management of Ground-Glass Opacities in the Lung Cancer Spectrum

BACKGROUND

Along with the popularity of low-dose computed tomography lung cancer screening, an increasing number of lung ground-glass opacity (GGO) lesions are detected. This review focuses on lung adenocarcinoma manifesting as GGO.

METHODS

We performed a literature search of the PubMed/MEDLINE database to identify articles reporting GGO. The following terms were used: GGO, ground-glass opacity, GGN, ground-glass nodule, part-solid nodule, and subsolid nodule.

RESULTS

GGO is a nonspecific radiologic finding showing a hazy opacity without blocking underlying pulmonary vessels or bronchial structures. The pathology of GGO can be benign, preinvasive, or invasive adenocarcinoma. Although radiographic features may indicate malignancy, a short period of follow-up is the optimal method to distinguish between benign and malignant GGO lesions. Pathologically, not only lepidic, but also nonlepidic growth patterns can present as GGO. Lung adenocarcinoma with a GGO component is associated with excellent survival compared with solid lesions. Moreover, there are distinct prognostic factors in patients with lung adenocarcinoma manifesting as GGO or solid lesions. For selected GGO-featured lung adenocarcinoma, sublobar resection with selective or no mediastinal lymph node dissection may be sufficient. Intraoperative frozen section is an effective method to guide resection strategy. A less intensive postoperative surveillance strategy may be more appropriate given the excellent survival. Management of multiple GGO lesions requires comprehensive considerations of GGO characteristics and patient conditions.

CONCLUSIONS

Lung adenocarcinoma manifesting as GGO defines a special clinical subtype with excellent prognosis. The management of GGO-featured lung adenocarcinoma should be distinct from that of solid lesions.

18F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma

Background

To investigate whether radiomic features from (¹⁸F)-fluorodeoxyglucose positron emission tomography/computed tomography [(¹⁸F)-FDG PET/CT] can predict epidermal growth factor receptor (EGFR) mutation status and prognosis in patients with lung adenocarcinoma.

Methods

One hundred and seventy-four consecutive patients with lung adenocarcinoma underwent (¹⁸F)-FDG PET/CT and EGFR gene testing were retrospectively analyzed. Radiomic features combined with clinicopathological factors to construct a random forest (RF) model to identify EGFR mutation status. The mutant/wild-type model was trained on a training group (n=139) and validated in an independent validation group (n=35). The second RF classifier predicting the 19/21 mutation site was also built and evaluated in an EGFR mutation subset (training group, n=80; validation group, n=25). Radiomic score and 5 clinicopathological factors were integrated into a multivariate Cox proportional hazard (CPH) model for predicting overall survival (OS). AUC (the area under the receiver characteristic curve) and C-index were calculated to evaluate the model’s performance.

Results

Of 174 patients, 109 (62.6%) harbored EGFR mutations, 21L858R was the most common mutation type [55.9% (61/109)]. The mutant/wild-type model was identified in the training (AUC, 0.77) and validation (AUC, 0.71) groups. The 19/21 mutation site model had an AUC of 0.82 and 0.73 in the training and validation groups, respectively. The C-index of the CPH model was 0.757. The survival time between targeted therapy and chemotherapy for patients with EGFR mutations was significantly different (P=0.03).

Conclusions

Radiomic features based on (¹⁸F)-FDG PET/CT combined with clinicopathological factors could reflect genetic differences and predict EGFR mutation type and prognosis.

Preoperative CT features for prediction of ALK gene rearrangement in lung adenocarcinomas

AIM

To identify preoperative features on computed tomography (CT) associated with ALK rearrangement in lung adenocarcinomas presenting as a nodule.

MATERIALS AND METHODS

This retrospective analysis included 56 patients with ALK rearrangement and 57 that were ALK-negative. All patients had surgically resected lung adenocarcinomas <3 cm. Univariate and multivariate analyses were conducted to analyse clinicopathological and CT features associated with ALK rearrangement. Receiver operating characteristic (ROC) analyses were performed to quantify the performance status of the model.

RESULTS

ALK rearrangement was associated with lymph node metastases (p=0.004), later pathological stage (p=0.005), lower lobe (p=0.019), lobulation (p=0.006), thickened adjacent bronchovascular bundles (p=0.006), homogeneous tumour (p=0.008), absence of ground-glass opacity (GGO; p<0.001), absence of air bronchogram (p=0.010), smaller relative enhancement (p=0.019), and larger short axis of the largest lymph node (p=0.012). Cavity larger than 1 cm was found in 3 ALK-positive tumours while not in ALK-negative tumours. Multivariate analysis revealed a single predictive model with an AUC of 0.794 that lobulation (OR=4.50, p=0.026), GGO (OR=0.19, p=0.003), and short axis of the largest lymph node (OR=12.49, p=0.047) were independent predictors of ALK rearrangement status.

CONCLUSIONS

This study identified a modestly predictive radiological model to identify ALK rearrangement in small lung adenocarcinomas.

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.

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features.

Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test.

Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83–0.88, P = 0.01), but not in the test cohort (AUC = 0.80–0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma.

Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.

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.

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.

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.

Imaging phenotype using radiomics to predict dry pleural dissemination in non-small cell lung cancer

Background

Dry pleural dissemination (DPD) in non-small cell lung cancer (NSCLC) is defined as having solid pleural metastases without malignant pleural effusion. We aim to identify DPD by applying radiomics, a novel approach to decode the tumor phenotype.

Methods

Preoperative chest computed tomographic images and basic clinical feature were retrospectively evaluated in patients with surgically resected NSCLC between January 1, 2015 and December 31, 2016. Propensity score was applied to match the DPD and non-DPD groups. One thousand and eighty radiomics features were quantitatively extracted by the 3D slicer software and "pyradiomics" package. Least absolute shrinkage and selection operator (LASSO) binary model was applied for feature selection and developing the radiomics signature. The discrimination was evaluated using area under the curve (AUC) and Youden index.

Results

Sixty-four DPD patients and paired 192 non-DPD patients were enrolled. Using the LASSO model, this study developed a radiomics signature including 10 radiomic features. The mean ± standard deviation values of the radiomics signature with DPD status (-2.129±1.444) was significantly higher compared to those with non-DPD disease (0.071±0.829, P<0.001). The ten-feature based signature showed good discrimination between DPD and non-DPD, with an AUC of 0.93 (95% confidence-interval, 0.891-0.958) respectively. The sensitivity and specificity of the radiomics signature was 85.94% and 85.94%, with the optimal cut-off value of -0.696 and Youden index of 0.71.

Conclusions

The signature based on radiomics features can provide potential predictive value to identify DPD in patients with NSCLC.

Computed Tomography Imaging Characteristics of Non-Small-Cell Lung Cancer With Anaplastic Lymphoma Kinase Rearrangements: A Systematic Review and Meta-Analysis

INTRODUCTION

Several studies have suggested that non-small-cell lung cancer (NSCLC) patients who harbor anaplastic lymphoma kinase (ALK) rearrangement might have different imaging features compared with those without the rearrangement. The goal of this work was to systematically investigate the computed tomography (CT) imaging features of ALK-rearranged NSCLC.

MATERIALS AND METHODS

We searched published studies that investigated CT imaging features of ALK-rearranged NSCLC compared with ALK-negative, including epidermal growth factor receptor (EGFR)-mutant and ALK/EGFR-negative, NSCLC. We extracted clinicopathologic characteristics and CT imaging features of patients in the included studies. Features were compared and tested in the form of odds ratios (ORs) or weighted mean differences at a 95% confidence interval.

RESULTS

Twelve studies with 2210 patients with NSCLC were included. Compared with ALK-negative NSCLC, ALK-rearranged NSCLC was more likely to be solid (OR, 2.37; P < .001) and less likely to have cavitation (OR, 0.45; P = .002). In advanced stages, patients with ALK-rearranged NSCLC, compared with EGFR-mutant NSCLC, were more likely to have lymphadenopathy (OR, 3.47; P < .001), pericardial metastasis (OR, 2.18; P = .04), pleural metastasis (OR, 2.07; P = .004), and lymphangitic carcinomatosis (OR, 3.41; P = .02), but less likely to have lung metastasis (OR, 0.52; P = .003). Compared with ALK/EGFR-negative NSCLC, ALK-rearranged NSCLC was more likely to have lymphangitic carcinomatosis (OR, 3.88; P = .03), pleural metastasis (OR, 1.89; P = .02), and pleural effusion (OR, 2.94; P = .003).

CONCLUSION

ALK-rearranged NSCLC has imaging features that are different compared with EGFR-mutant and ALK/EGFR-negative NSCLC. These imaging features might provide clues as to the presence of ALK rearrangement and help in the selection of patients who might benefit from expedited molecular testing or repeat testing after a negative assay.

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning

Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is invasive and may suffer from the difficulty of accessing tissue samples. Here, we propose a deep learning model to predict EGFR mutation status in lung adenocarcinoma using non-invasive computed tomography (CT).

We retrospectively collected data from 844 lung adenocarcinoma patients with pre-operative CT images, EGFR mutation and clinical information from two hospitals. An end-to-end deep learning model was proposed to predict the EGFR mutation status by CT scanning.

By training in 14 926 CT images, the deep learning model achieved encouraging predictive performance in both the primary cohort (n=603; AUC 0.85, 95% CI 0.83–0.88) and the independent validation cohort (n=241; AUC 0.81, 95% CI 0.79–0.83), which showed significant improvement over previous studies using hand-crafted CT features or clinical characteristics (p<0.001). The deep learning score demonstrated significant differences in EGFR-mutant and EGFR-wild type tumours (p<0.001).

Since CT is routinely used in lung cancer diagnosis, the deep learning model provides a non-invasive and easy-to-use method for EGFR mutation status prediction.

Deep learning provides a noninvasive method for EGFR mutation prediction (AUC 0.81) in lung adenocarcinoma, which shows significant improvement over using hand-crafted CT features or clinical characteristicshttp://ow.ly/LtDJ30nhc5Q

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.

Clinical and computed tomography characteristics of non-small cell lung cancer with ALK gene rearrangement: Comparison with EGFR mutation and ALK/EGFR-negative lung cancer

Background

The study was conducted to evaluate the clinical and computed tomography (CT) findings of non‐small cell lung cancer (NSCLC) patients to distinguish between ALK gene rearrangement, EGFR mutation, and non‐ALK/EGFR (no genetic abnormalities).

Methods

We enrolled 201 patients with primary NSCLC who had undergone molecular testing for both ALK gene rearrangement and EGFR mutation. The clinical features and CT findings of the main lesion and associated pulmonary abnormalities were investigated.

Results

Female gender (P = 0.0043 vs. non‐ALK/EGFR), young age (P = 0.0156 vs. EGFR), and a light or never smoking history (P = 0.0039 vs. non‐ALK/EGFR) were significant clinical characteristics of NSCLC with ALK gene rearrangement. The significant CT characteristics compared to NSCLC with EGFR mutation were a large mass (P = 0.0155), solid mass (P = 0.0048), and no air bronchogram (P = 0.0148). A central location (P = 0.0322) and lymphadenopathy (P = 0.0353) were also more frequently observed. Coexisting emphysema was significantly less frequent in NSCLC patients with ALK gene rearrangement (P = 0.0135) than non‐ALK/EGFR.

Conclusions

NSCLC with ALK gene rearrangement was more likely to develop in younger women with a light or never smoking history. The characteristic CT findings of NSCLC with ALK gene rearrangement were a large solid mass, less air bronchogram, a central location, and lymphadenopathy.

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.

Epidermal growth factor receptor mutation accelerates radiographic progression in lung adenocarcinoma presented as a solitary ground-glass opacity

Background

We aimed to investigate the impact of epidermal growth factor receptor (EGFR) mutation in the progression of lung adenocarcinoma presented as a solitary ground-glass opacity (GGO) by retrospectively evaluating the correlation between EGFR mutation status and the radiographic features.

Methods

One hundred fifty-six cases of lung adenocarcinoma presented as a solitary GGO were enrolled between 2013 and 2015. Chest CT scans were performed 3 times (1st ≥3 months, 2nd ≤1 week preoperatively and 3rd ≥3 months postoperatively) in each patient. The diameter and volume of every lesion was measured by semiautomated algorithm. EGFR mutation hotspots from exons 18, 19 and 21 were detected by real-time PCR.

Results

In the 156 patients who were enrolled in our study, tumors in 75 patients (48.1%) were pathologically diagnosed with EGFR-mutant, with 1, 29 and 45 cases harboring tumors with mutation in exon 18, 19 and 21, respectively. EGFR mutation occurred more frequently in women (P=0.005) and non-smokers (P=0.019). Comparison between the 1st and 2nd preoperative CT scans showed that 28 (37.3%) of 75 patients with EGFR mutations had an over 50% increment of tumor size and 38 (52.0%) displayed a growth of solid component. On the other hand, we found only 9 (11.1%) and 14 (17.3%) in 81 lesions without EGFR mutation had a distinct volume growth and component solidification, respectively, which is significantly less than that in EGFR mutation lesions (P<0.001). Further, in the postoperative CT scan, recurrent GGOs or nodes were identified in 6 (8%) EGFR-mutant patients and 6 (7.4%) in wild-type patients (P=0.89), which indicates no overt statistically difference. At last, we found that EGFR amplification is more frequent as GGO volume percentage decreases and diameter increases.

Conclusions

We found GGOs with EGFR mutation grew faster in volume and solidified more quickly in component than wild-type GGOs. Moreover, in the follow-up after surgery, patients in the EGFR mutation group and EGFR wild-type group showed no significant difference in the imaging evolvement.

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.

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.

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.