Are there imaging characteristics associated with lung adenocarcinomas harboring ALK rearrangements?

Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study

BACKGROUND

To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma.

METHODS

We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1-3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV3, GPTV6, GPTV9, GPTV12, and GPTV15), and screened the most relevant features to construct radiomics models to predict ALK (+). The combined model incorporated radiomics scores (Rad-scores) of the best radiomics model and clinical predictors was constructed. Performance was evaluated using receiver operating characteristic (ROC) analysis. Progression-free survival (PFS) outcomes were examined using the Cox proportional hazards model.

RESULTS

In the four sets, 21.19% (107/505) patients were ALK (+). The GPTV3 radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. Clinical TNM stage and pleural indentation were independent predictors. The combined model incorporating the GPTV3-Rad-score and clinical predictors achieved higher performance than the clinical model alone in predicting ALK (+) in three validation sets [AUC: 0.855 (95% CI: 0.766-0.919) vs. 0.648 (95% CI: 0.543-0.745), P = 0.001; 0.882 (95% CI: 0.801-0.962) vs. 0.634 (95% CI: 0.548-0.714), P < 0.001; 0.810 (95% CI: 0.727-0.877) vs. 0.663 (95% CI: 0.570-0.748), P = 0.006]. The prediction score of the combined model could stratify PFS outcomes in patients receiving ALK-TKI therapy (HR: 0.37; 95% CI: 0.15-0.89; P = 0.026) and immunotherapy (HR: 2.49; 95% CI: 1.22-5.08; P = 0.012).

CONCLUSION

The presented combined model based on GPTV3 effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma.

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

Unusual presentation of ROS1 rearranged metastatic non-small cell lung cancer

The spectrum of clinical and radiographic presentations of lung adenocarcinoma is increasingly broad, including in the metastatic setting. Here, we report on a patient who initially presented with a mild chronic cough that remained stable over a decade, with serial CT scans showing gradual worsening of multifocal areas of consolidation and ground-glass opacities of the bilateral lungs. The patient was ultimately diagnosed with ROS1 rearranged lung adenocarcinoma and achieved a dramatic response with entrectinib. This case highlights the variable presentation of non-small cell lung cancer (NSCLC) and the importance of comprehensive molecular testing for newly diagnosed metastatic NSCLC.

A multicenter, retrospective archive study of radiological and clinical features of ALK-positive non-small cell lung cancer patients and crizotinib efficacy

To evaluate radiological and clinical features in metastatic anaplastic lymphoma kinase+ non-small cell lung cancer patients and crizotinib efficacy in different lines. This national, non-interventional, multicenter, retrospective archive screening study evaluated demographic, clinical, and radiological imaging features, and treatment approaches in patients treated between 2013-2017. Totally 367 patients (54.8% males, median age at diagnosis 54 years) were included. Of them, 45.4% were smokers, and 8.7% had a family history of lung cancer. On radiological findings, 55.9% of the tumors were located peripherally, 7.7% of the patients had cavitary lesions, and 42.9% presented with pleural effusion. Pleural effusion was higher in nonsmokers than in smokers (37.3% vs. 25.3%, P = .018). About 47.4% of cases developed distant metastases during treatment, most frequently to the brain (26.2%). Chemotherapy was the first line treatment in 55.0%. Objective response rate was 61.9% (complete response: 7.6%; partial response: 54.2%). The highest complete and partial response rates were observed in patients who received crizotinib as the 2nd line treatment. The median progression-free survival was 14 months (standard error: 1.4, 95% confidence interval: 11.2-16.8 months). Crizotinib treatment lines yielded similar progression-free survival (P = .078). The most frequent treatment-related adverse event was fatigue (14.7%). Adrenal gland metastasis was significantly higher in males and smokers, and pleural involvement and effusion were significantly higher in nonsmokers-a novel finding that has not been reported previously. The radiological and histological characteristics were consistent with the literature data, but several differences in clinical characteristics might be related to population characteristics.

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.

Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results

Simple Summary

Radiogenomics is a promising new approach in cancer assessment, providing an evaluation of the molecular basis of imaging phenotypes after establishing associations between radiological features and molecular features at the genomic–transcriptomic–proteomic level. This review focuses on describing key aspects of radiogenomics while discussing limitations of translatability to the clinic and possible solutions to these challenges, providing the clinician with an up-to-date handbook on how to use this new tool.

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide, and elucidation of its complicated pathobiology has been traditionally targeted by studies incorporating genomic as well other high-throughput approaches. Recently, a collection of methods used for cancer imaging, supplemented by quantitative aspects leading towards imaging biomarker assessment termed “radiomics”, has introduced a novel dimension in cancer research. Integration of genomics and radiomics approaches, where identifying the biological basis of imaging phenotypes is feasible due to the establishment of associations between molecular features at the genomic–transcriptomic–proteomic level and radiological features, has recently emerged termed radiogenomics. This review article aims to briefly describe the main aspects of radiogenomics, while discussing its basic limitations related to lung cancer clinical applications for clinicians, researchers and patients.

Driver and novel genes correlated with metastasis of non-small cell lung cancer: A comprehensive analysis

Although mutations of genes are crucial events in tumorigenesis and development, the association between gene mutations and lung cancer metastasis is still largely unknown. The goal of this study is to identify driver and novel genes associated with non-small cell lung cancer (NSCLC) metastasis. Candidate genes were identified using a novel comprehensive analysis, which was based on bioinformatics technology and meta-analysis. Firstly, EGFR, KRAS, ALK, TP53, BRAF and PIK3CA were identified as candidate driver genes. Further meta-analysis identified that EGFR (Pooled OR 1.33, 95% CI 1.19, 1.50; P < .001) and ALK (Pooled OR 1.52, 95% CI 1.22, 1.89; P < .001) mutations were associated with distant metastasis of NSCLC. Besides, ALK (Pooled OR 2.40, 95% CI 1.71, 3.38; P < .001) mutation was associated with lymph node metastasis of NSCLC. In addition, thirteen novel gene mutations were identified to be correlated with NSCLC metastasis, including SMARCA1, GGCX, KIF24, LRRK1, LILRA4, OR2T10, EDNRB, NR1H4, ARID4A, PRKCI, PABPC5, ACAN and TLN1. Furthermore, elevated mRNA expression level of SMARCA1 and EDNRB was associated with poor overall survival in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively. Additionally, pathway and protein-protein interactions network analyses found the two genes were correlated with epithelial-mesenchymal transition process. In conclusion, mutations of EGFR and ALK were significantly correlated with NSCLC metastasis. In addition, thirteen novel genes were identified to be associated with NSCLC metastasis, especially SMARCA1 in LUAD and EDNRB in LUSC.

Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives

Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.

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.

Radiomics in immuno-oncology

With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

Radiogenomics in Interventional Oncology

PURPOSE OF REVIEW

Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential value in improving patient outcomes. Current applications have only begun to delve into the potential of radiogenomics, and particularly in interventional oncology, there is room for development and increased value of these applications.

RECENT FINDINGS

The field of interventional oncology (IO) has seen valuable radiogenomic applications, from prediction of response to locoregional therapies in hepatocellular carcinoma to identification of genetic mutations in non-small cell lung cancer. Future directions that can increase the value of radiogenomics include applications that address tumor heterogeneity, predict immune responsiveness of tumors, and differentiate between oligoprogression and early widespread progression, among others. Radiogenomics, whether in terms of methodologies or applications, is still in the early stages of development and far from maturation. Future applications, particularly in the field of interventional oncology, will allow realization of its full potential.

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

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.

Differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis by CT features, EGFR mutations and ALK rearrangement status

Background

Differentiating synchronous double primary lung adenocarcinoma (SDPLA) from interpulmonary metastasis (IPM) has significant therapeutic and prognostic implications. This retrospective study aimed to investigate the potential of computed tomography (CT) features and two known oncogenic driver mutations [epidermal growth factor receptor (EGFR) and anaplastic large-cell lymphoma kinase (ALK)] to discriminate synchronous double primary lung adenocarcinoma from one primary pulmonary adenocarcinoma with intrapulmonary metastasis.

Methods

Patients with SDPLA were selected at our hospital, and those with IPM served as the control group. All 60 patients (40 with SDPLA and 20 with IPM) were tested for EGFR mutations and ALK status, and they underwent chest CT prior to any treatment. Independent-sample Student's t-test was used for comparisons between two groups of normally distributed variables, and the Chi-square test was used to compare categorical variables.

Results

The discordance rate of EGFR mutations was significantly higher in patients with SDPLA than in patients with IPM (40% vs. 5%, P<0.001). The incidence of ALK-positive status was 15%, and patients with IPM were more likely to be ALK-positive than patients with SDPLA (35% vs. 5%, P<0.001). Compared to IPM, SDPLA more frequently occurred in different lobes (P=0.024), presented with less lymphadenopathy (P=0.014), showed a smaller difference in diameter (Äd) between tumors (P=0.001) and more commonly presented as lobulated tumors (P<0.001), spiculated masses (P<0.001), ground-glass opacities (GGOs) (P=0.001) and air bronchograms (P=0.020).

Conclusion

Patients with SDPLA showed higher discordance with EGFR mutations and were less frequently ALK-positive than those with IPM. Thus, the CT characteristics are significantly different between SDPLA and IPM.

Role of imaging biomarkers in mutation-driven non-small cell lung cancer

Lung cancer remains the leading cause of cancer-related deaths worldwide. The treatment of non-small cell lung cancer (NSCLC), which accounts for a vast majority of lung cancers, has shifted to personalized, targeted therapy following discoveries of several targetable oncogenic mutations. Targeting of specific mutations has improved outcomes in many patients. This success has led to several target-specific agents replacing chemotherapy as first-line treatment in certain mutated NSCLC. Several researchers have reported that there may be imaging biomarkers that may be predictive of the presence of these mutations. These features, when present, have the potential in triaging patients into the most appropriate diagnostic and treatment algorithms. Distinct imaging features and patterns of metastases that have been associated with NSCLC with various targetable oncogenic mutations are presented in this review.

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.

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.

Imaging Features and Patterns of Metastasis in Non-Small Cell Lung Cancer with RET Rearrangements

Rearranged during transfection proto-oncogene (RET) fusions represent a potentially targetable oncogenic driver in non-small cell lung cancer (NSCLC). Imaging features and metastatic patterns of advanced RET fusion-positive (RET+) NSCLC are not well established. Our goal was to compare the imaging features and patterns of metastases in RET+, ALK+ and ROS1+ NSCLC. Patients with RET+, ALK+, or ROS1+ NSCLC seen at our institution between January 2014 and December 2018 with available pre-treatment imaging were identified. The clinicopathologic features, imaging characteristics, and the distribution of metastases were reviewed and compared. We identified 215 patients with NSCLC harboring RET, ALK, or ROS1 gene fusion (RET = 32; ALK = 116; ROS1 = 67). Patients with RET+ NSCLC were older at presentation compared to ALK+ and ROS1+ patients (median age: RET = 64 years; ALK = 51 years, p < 0.001; ROS = 54 years, p = 0.042) and had a higher frequency of neuroendocrine histology (RET = 12%; ALK = 2%, p = 0.025; ROS1 = 0%, p = 0.010). Primary tumors in RET+ patients were more likely to be peripheral (RET = 69%; ALK = 47%, p = 0.029; ROS1 = 36%, p = 0.003), whereas lobar location, size, and density were comparable across the three groups. RET+ NSCLC was associated with a higher frequency of brain metastases at diagnosis compared to ROS1+ NSCLC (RET = 32%, ROS1 = 10%; p = 0.039. Metastatic patterns were otherwise similar across the three molecular subgroups, with high incidences of lymphangitic carcinomatosis, pleural metastases, and sclerotic bone metastases. RET+ NSCLC shares several distinct radiologic features and metastatic spread with ALK+ and ROS1+ NSCLC. These features may suggest the presence of RET fusions and help identify patients who may benefit from further molecular genotyping.

A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma

To predict the epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using quantitative radiomic biomarkers and semantic features. We analyzed the computed tomography (CT) images and medical record data of 104 patients with lung adenocarcinoma who underwent surgical excision and EGFR mutation detection from 2016 to 2018 at our center. CT radiomic and semantic features that reflect the tumors' heterogeneity and phenotype were extracted from preoperative non-enhanced CT scans. The least absolute shrinkage and selection operator method was applied to select the most distinguishable features. Three logistic regression models were built to predict the EGFR mutation status by combining the CT semantic with clinicopathological characteristics, using the radiomic features alone, and by combining the radiomic and clinicopathological features. Receiver operating characteristic (ROC) curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for predicting EGFR mutation. Furthermore, radiomic nomograms were constructed to demonstrate the performance of the model. In total, 1025 radiomic features were extracted and reduced to 13 features as the most important predictors to build the radiomic signature. The combined radiomic and clinicopathological features model was developed based on the radiomic signature, sex, smoking, vascular infiltration, and pathohistological type. The AUC was 0.90  ±  0.02 for the training, 0.88  ±  0.11 for the verification, and 0.894 for the test dataset. This model was superior to the other prediction models that used the combined CT semantic and clinicopathological features (AUC for the test dataset: 0.768) and radiomic features alone (AUC for the test dataset: 0.837). The prediction model built by radiomic biomarkers and clinicopathological features, including the radiomic signature, sex, smoking, vascular infiltration, and pathological type, outperformed the other two models and could effectively predict the EGFR mutation status in patients with peripheral lung adenocarcinoma. The radiomic nomogram of this model is expected to become an effective biomarker for patients with lung adenocarcinoma requiring adjuvant targeted treatment.

Combining molecular and imaging metrics in cancer: radiogenomics

Background

Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing.

Main body

In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis.

Conclusion

Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

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

BACKGROUND

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

Diffuse Lung Metastases in EGFR-Mutant Non-Small Cell Lung Cancer

Diffuse lung metastases have been reported in non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) mutations. The purpose of our study was to compare the incidence of diffuse lung metastases in EGFR-mutant NSCLC and EGFR-wild type NSCLC and to assess other imaging features that may be associated with diffuse lung metastases in EGFR-mutant NSCLC. Two radiologists retrospectively reviewed pre-treatment imaging of metastatic NSCLC cases with known EGFR mutation status. We assessed the imaging features of the primary tumor and patterns of metastases. The cohort consisted of 217 patients (117 EGFR-mutant, 100 EGFR wild-type). Diffuse lung metastasis was significantly more common in EGFR-mutant NSCLC compared with wild-type (18% vs. 3%, p < 0.01). Among the EGFR-mutant group, diffuse lung metastases were inversely correlated with the presence of a nodule greater than 6 mm other than the primary lung lesion (OR: 0.13, 95% CI: 0.04–0.41, p < 0.01). EGFR mutations in NSCLC are associated with increased frequency of diffuse lung metastases. The presence of diffuse lung metastases in EGFR-mutant NSCLC is also associated with a decreased presence of other larger discrete lung metastases. EGFR mutations in NSCLC should be suspected in the setting of a dominant primary lung mass associated with diffuse lung metastases.

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.

Quantitative features of dual-energy spectral computed tomography for solid lung adenocarcinoma with EGFR and KRAS mutations, and ALK rearrangement: a preliminary study

Background

The present work aimed to evaluate radio-genomic associations of quantitative parameters obtained by dual-energy spectral computed tomography (DESCT) for solid lung adenocarcinoma with epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations, as well as anaplastic lymphoma kinase (ALK) rearrangement.

Methods

Ninety-six cases of solid lung cancer were selected and assessed for EGFR and KRAS mutations, and ALK rearrangement. Then, they underwent chest DESCT, and quantitative parameters, including water concentration (WC), iodine concentration (IC), CT value at 70 keV, effective atomic number (Effective-Z) and spectral Hounsfield unit curve slope (λHU slope) were measured. Finally, the associations of quantitative radiological features with various gene alterations were evaluated.

Results

The positive rates were 51.0% (49/96) for EGFR, 13.5% (13/96) for KRAS and 16.7% (16/96) for ALK. In univariate analysis, EGFR mutation was associated with smoking status, CT value at 70 keV, IC, Effective-Z, and λHU slope; KRAS mutation was associated with CT value at 70 keV, IC, Effective-Z, and λHU slope, and ALK rearrangement was correlated with age and WC. In multivariate analysis, smoking status (OR =2.924, P=0.019) and CT value at 70 keV (OR =1.036, P=0.006) were significantly associated with EGFR mutation; Effective-Z and age were significantly associated with KRAS mutation (OR =0.047, P=0.032) and ALK rearrangement (OR =0.933, P=0.008), respectively.

Conclusions

Quantitative analysis of DESCT could help detect solid lung adenocarcinoma harboring EGFR or KRAS mutation, or ALK rearrangement.

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

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

Radiogenomics: bridging imaging and genomics

From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

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.

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.

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.

CT features of HER2-mutant lung adenocarcinomas

OBJECTIVES

To describe the radiological phenotype of HER2-mutant lung cancers on CT at presentation.

METHODS

Eligible patients with lung adenocarcinomas with HER2 mutations were stage-matched with two control groups (EGFR- and KRAS-mutant groups). Evaluated CT features of the primary tumor included size, location, consistency, contour, presence of pleural tags and pleural retractions. Presence of pleural effusions, lung metastases, adenopathy, chest wall invasion, and were also recorded. Wilcoxon rank-sum and Fisher's exact tests were used to compare continuous and categorical features, respectively.

RESULTS

One hundred and fifty-four patients were identified: 50 (33%) harbored HER2 mutations, 56 (36%) harbored KRAS mutations, and 48 (31%) harbored EGFR mutations. Compared with KRAS, HER2 tumors presented as smaller lesions (2.3 cm versus 2.9 cm, p = 0.005 for length; 1.6 cm versus 2.1 cm, p = 0.002 for width) with the presence of pleural tags (74% vs. 52%, p = 0.03), pleural retractions (58% vs. 39%, p = 0.006), ipsilateral hilar (36% vs. 16%, p = 0.03) and scalene/supraclavicular N3 adenopathy (24% vs. 7%, p = 0.03). Compared with EGFR, pleural retractions were more prevalent among the HER2 tumors (58% vs. 37%, p = 0.05).

CONCLUSIONS

Lung adenocarcinomas with HER2 gene mutation exhibit an aggressive behavior manifesting by higher incidence of local invasion, compared to KRAS and EGFR mutant controls, and a nodal metastatic spread compared to KRAS-mutant control. This is the first radiogenomics study of HER2 mutations in lung cancer.

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

BACKGROUD

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Higher frequency of occult lymph node metastasis in clinical N0 pulmonary adenocarcinoma with ALK rearrangement

Objectives

There have been few studies that have fully elucidated the relationship between genomic mutations in pulmonary adenocarcinomas and occult lymph node (LN) metastases (pN1-2) despite a preoperative clinical N0 stage (cN0). It is well known that anaplastic lymphoma kinase (ALK) rearrangements are more likely to occur in younger patients with high grade tumors. The aim of this study was to investigate the genomic status, examine the clinicopathologic features, and evaluate whether ALK mutations are associated with occult LN metastases.

Materials and methods

We retrospectively evaluated 459 Japanese patients who underwent pulmonary resection of cN0 adenocarcinomas between January 2012 and December 2015. The clinicopathologic characteristics, including age, sex, smoking index, tumor maximum diameter and consolidation/tumor ratio on computed tomography (CT), maximum standardized uptake value on positron emission tomography (PET) and gene mutations (epidermal growth factor receptor [EGFR], ALK, and kirsten ras genes (KRAS), were evaluated.

Results

ALK and EGFR and KRAS mutations were all mutually exclusive. Among 324 patients found to have mutations, ALK was involved in 19 (5.9%), EGFR in 266 (82.1%), and KRAS in 39 (12.0%). The incidence of occult LN metastases did not differ significantly between those with or without mutations (p=0.27). On univariate and multivariate analyses, tumors with ALK were more likely to have occult LN metastases (p=0.03). In cN0 tumors with ALK, pN1 was diagnosed in 26.3% and pN2 in 10.5%, whereas pN1 or pN2 stage was found in <10.0% in those with EGFR or KRAS mutations or with no mutations at all. No significant difference was found in the 2-year disease-free survival among those with gene mutations (p=0.08).

Conclusion

This study highlights the frequency of PET- and CT-negative occult LN metastases in resected adenocarcinomas with ALK rearrangement. Our multivariate analysis showed that ALK rearrangements were associated with a significantly higher incidence of occult LN metastasis compared with ALK-negative adenocarcinomas.

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

BACKGROUND

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Comparison of clinical and radiological characteristics between anaplastic lymphoma kinase rearrangement and epidermal growth factor receptor mutation in treatment naïve advanced lung adenocarcinoma

Background

Gene analysis could not be performed in all patients, especially in advanced non-small cell lung cancer (NSCLC). We aimed to find some clinical futures and CT or FDG-PET characteristics, which could be combined to help distinguish anaplastic lymphoma kinase (ALK) rearrangement form epidermal growth factor receptor (EGFR) mutations in treatment naïve advanced lung adenocarcinoma of Chinese patients.

Methods

We retrospectively reviewed clinical and radiological characteristics of 145 patients with treatment naïve advanced lung adenocarcinoma. The one-way ANOVA, the Mann-Whitney test, chi-square test and logistic regression were used for comparison between patients with ALK rearrangement and those with EGFR mutation.

Results

Among 145 patients with advanced lung adenocarcinoma, only six patients had both ALK rearrangement and EGFR mutation, the sample size was too small to analysis. Univariate analysis revealed that patients with ALK rearrangement were younger (P=0.001) and with lower serum carcinoembryonic antigen (CEA) level (P=0.008) than those with EGFR mutation. More of tumors with ALK rearrangement were well defined (P=0.023) and have bubble lucency (P=0.026) compared with those with EGFR mutation (P=0.026). Lymphadenopathy was seen more frequently in patients with ALK rearrangement (P=0.167). Twenty-six patients received FDG-PET/CT, among this population, lesion standardized uptake values (SUV) >6.95 and lymph nodes SUV_max >6.25 were more often seen in ALK rearrangement group (P=0.011, both). In multivariate analysis, patients younger than 50 years (RR =9.878, 95% CI: 2.318-42.090, P=0.002), with lower CEA level than 4.95 µg/L (RR =8.166, 95% CI: 1.085-31.983, P=0.003) and without brain metastasis (RR =7.304, 95% CI: 1.099-48.558, P=0.040) were more likely to be ALK rearrangement than EGFR mutation. Tumor diameter less than 36 mm were prone to be EGFR mutation (RR =0.078, 95% CI: 0.017-0.356, P=0.001).

Conclusions

Treatment naïve advanced lung adenocarcinomas with ALK rearrangement were more likely to have younger age, lower serum CEA level, larger tumor volume, well defined tumor border, and non-brain metastasis than those with EGFR mutation. Bubble lucency and higher FDG uptake of lesion and lymph nodes may help distinguish ALK rearrangement from EGFR mutation in the absence of genetic analysis.

Diagnosis and Treatment of Anaplastic Lymphoma Kinase-Positive Non-Small Cell Lung Cancer

Anaplastic lymphoma kinase (ALK) gene rearrangements occur in a small portion of patients with non-small cell lung cancer (NSCLC). These gene rearrangements lead to constitutive activation of the ALK kinase and subsequent ALK-driven tumor formation. Patients with tumors harboring such rearrangements are highly sensitive to ALK inhibitors, such as crizotinib, ceritinib, and alectinib. Resistance to these kinase inhibitors occurs through several mechanisms, resulting in ongoing clinical challenges. This review summarizes the biology of ALK-positive lung cancer, methods for diagnosing ALK-positive NSCLC, current FDA-approved ALK inhibitors, mechanisms of resistance to ALK inhibition, and potential strategies to combat resistance.

Can CT measures of tumour heterogeneity stratify risk for nodal metastasis in patients with non-small cell lung cancer?

AIM

To undertake a preliminary assessment of the potential for computed tomography (CT) measurement of tumour heterogeneity to stratify risk of nodal metastasis in patients with non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

Tumour heterogeneity in CT images from combined positron-emission tomography (PET)/CT examinations in 150 consecutive patients with NSCLC was assessed using CT texture analysis (CTTA). The short axis diameter of the largest mediastinal node was also measured. Forty-two patients without distant metastases subsequently had tumour nodal status confirmed at surgery (n=26) or endobronchial ultrasound (EBUS; n=16). CTTA parameters and largest nodal diameter were related to nodal status using the rank correlation and the risk ratio for each nodal stage (>N0, >N1, >N2) was compared between patients categorised as high and low risk by CTTA or nodal size. The most significant predictor of nodal status was related to overall survival using Kaplan-Meier analysis.

RESULTS

N-stage was more significantly correlated with CTTA than nodal diameter (Rs = -0.39, p=0.011, Rs = -0.45, p=0.0025, Rs = -0.40, p=0.0091 for normalised standard deviation (SD), normalised entropy and kurtosis respectively; Rs = -0.39, p=0.042 for nodal diameter). The presence of two or more high-risk CTTA values was the greatest risk factor for mediastinal metastasis (risk ratio: 11.0, 95% confidence interval: 1.56-77.8, p=0.0014) and was associated with significantly poorer overall survival (p=0.016).

CONCLUSION

CTTA in NSCLC is related to nodal status in patients without distant metastases and has the potential to inform selection of investigative strategies for the assessment of mediastinal malignancy.

Radiological and Clinical Features associated with Epidermal Growth Factor Receptor Mutation Status of Exon 19 and 21 in Lung Adenocarcinoma

The exon 19 and 21 in Epidermal Growth Factor Receptor (EGFR) mutation are the most common subtype of lung adenocarcinoma, and the strongest predictive biomarker for progression-free survival and tumor response. Although some studies have shown differences in radiological features between cases with and without EFGR mutations, they lacked necessary stratification. This article is to evaluate the association of CT features between the wild type and the subtype (exon 19 and 21) of EGFR mutations in patients with lung adenocarcinoma. Of the 721 finally included patients, 132 were positive for EGFR mutation in exon 19, 140 were positive for EGFR mutation in exon 21, and 449 were EGFR wild type. EGFR mutation in exon 19 was associated with a small-maximum diameter (28.51 ± 14.07) (p < 0.0001); sex (p < 0.0001); pleural retraction (p = 0.0034); and the absence of fibrosis (p < 0.0001), while spiculated margins (p = 0.0095), subsolid density (p < 0.0001) and no smoking (p < 0.0001) were associated with EGFR mutation in exon 21. Receiver Operating Characteristic (ROC) curves suggested that the maximum Area Under the Curve (AUC) was related to the female gender (AUC = 0.636) and the absence of smoking (AUC = 0.681). This study demonstrated the radiological and clinical features could be used to prognosticate EGFR mutation subtypes in exon 19 and 21.

Uncommon features of surgically resected ALK-positive cavitary lung adenocarcinoma: a case report

Some features found on chest computed tomography (CT), such as central tumor location, large pleural effusion, and the absence of a pleural tail, and a patient age of less than 60 years, have been suggested to be useful in predicting anaplastic lymphoma kinase (ALK) rearrangement in patients with non-small cell lung cancer (NSCLC).

A 68-year-old female patient with a history of gynecological treatment was found to have a cavitary mass in the right lower lobe on an annual chest roentgenogram. The tumor was located in the peripheral area with a pleural tail showing no pleural effusion. In addition, two pure ground-glass-opacity nodules (p-GGNs) in the right upper lobe of the lung were detected on consecutive chest CT scans. The patient underwent right lower lobectomy, partial resection of the right upper lobe, and hilar mediastinal lymph node dissection for complete resection of each tumor. The pathological diagnosis was invasive mucinous adenocarcinoma with signet-ring cells for the cavitary mass in the right lower lobe and invasive adenocarcinoma for the rest of the p-GGNs; subcarinal lymph node metastasis was also detected. The ALK rearrangement was detected by fluorescence in situ hybridization from the cavitary mass. The patient underwent four cycles of cisplatin and vinorelbine chemotherapy as standard adjuvant chemotherapy for pStage III NSCLC. The ALK fusion gene status of NSCLC with atypical CT features should also be investigated.

Investigation of patterns of nodal metastases in BRAF mutant lung cancer

Axillary lymph nodes (axLN) are a rare site of nodal metastases in patients with lung cancer. BRAF mutated lung cancer is a genetically distinct subtype that occurs in 2-5% of non-small cell lung carcinomas (NSCLC). A recent study identified a highly unusual pattern of metastatic spread to axLN in patients with BRAF mutated colorectal cancer (CRC). The purpose of the study is to assess the incidence of axLN metastases in BRAF mutated NSCLC. Baseline computed tomography (CT) imaging at diagnosis and all follow up CTs of patients with BRAF mutated NSCLC treated at our institution were retrospectively reviewed by two radiologists for evidence of axLN metastases. Positron emission tomography (PET)/CT was reviewed when available. A control group of patients with non-BRAF mutated NSCLC was assessed. Three criteria were used for the diagnosis of a metastatic node; pathologic confirmation, radiologic size greater ≥1.5cm in short axis diameter or fluorodeoxyglucose avidity on PET/CT and radiologic size ≥1.0cm in short axis diameter. Forty-six patients with BRAF mutated NSCLC and CT images on the institutional PACS were identified. 7 (15%) patients with BRAF mutated NSCLC had axLN metastases using the proposed diagnostic criteria. One patient had a pathologic proven axLN metastasis, 3 had axLNs measuring ≥1.5cm in short axis, and 3 had nodes which were FDG avid on PET/CT and measured ≥1.0cm in short axis. By comparison, 1 of 46 (2%) control patients with non-BRAF mutated NSCLC had axLN metastases. Previous series have reported the prevalence of axLN metastases in patients with NSCLC as 0.61-0.75%. We have found a higher incidence of axLN metastases in BRAF mutated NSCLC patients than described in non-BRAF mutated NSCLC patients. Examination of the axilla should be a routine part of physical examination in this genetically distinct subgroup of lung cancer patients.

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

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Clinical and CT characteristics of surgically resected lung adenocarcinomas harboring ALK rearrangements or EGFR mutations

PURPOSE

To determine if clinical and CT characteristics of surgically resected lung adenocarcinomas can distinguish those harboring ALK rearrangements from EGFR mutations.

MATERIALS AND METHODS

Patients who had surgical resection and histologically confirmed lung adenocarcinoma were enrolled, including 41 patients with ALK rearrangements and 66 patients with EGFR mutations. Eighteen categorical and six quantitative CT characteristics were used to evaluate the tumors. Differences in clinical and CT characteristics between the two groups were investigated.

RESULTS

Age (P=0.003), histological subtypes (P<0.001), pathological stage (P=0.007), and five CT characteristics, including size (P<0.001), GGO (P=0.001), bubble-like lucency (P=0.048), lymphadenopathy (P=0.001), and tumor shadow disappearance rate (P=0.005) were significantly different between patients harboring ALK rearrangements compared to patients with EGFR mutations. When we compared histologic components, a solid pattern was more common (P=0.009) in tumors with ALK rearrangements, and lepidic and acinar patterns were more common (P<0.001 and P=0.040, respectively) in those with EGFR mutations. Backward elimination analyses revealed that age (OR=0.93; 95% CI 0.89-0.98), GGO (OR=0.14; 95% CI 0.03-0.67), and lymphadenopathy (OR=4.15; 95% CI 1.49-11.60) were significantly associated with ALK rearrangement status.

CONCLUSION

Our analyses revealed that clinical and CT characteristics of lung adenocarcinomas harboring ALK rearrangements were significantly different, compared with those with EGFR mutations. These differences may be related to the molecular pathology of these diseases.

Imaging Characteristics of Driver Mutations in EGFR, KRAS, and ALK among Treatment-Naïve Patients with Advanced Lung Adenocarcinoma

This study aimed to identify the computed tomography characteristics of treatment-naïve patients with lung adenocarcinoma and known driver mutations in EGFR, KRAS, or ALK. Patients with advanced lung adenocarcinoma (stage IIIB-IV) and known mutations in EGFR, KRAS, or ALK were assessed. The radiological findings for the main tumor and intra-thoracic status were retrospectively analyzed in each group, and the groups' characteristics were compared. We identified 265 treatment-naïve patients with non-small-cell carcinoma, who had EGFR mutations (n = 159), KRAS mutations (n = 55), or ALK rearrangements (n = 51). Among the three groups, we evaluated only patients with stage IIIB-IV lung adenocarcinoma who had EGFR mutations (n = 126), KRAS mutations (n = 35), or ALK rearrangements (n = 47). We found that ground-glass opacity at the main tumor was significantly more common among EGFR-positive patients, compared to ALK-positive patients (p = 0.009). Lymphadenopathy was significantly more common among ALK-positive patients, compared to EGFR-positive patients (p = 0.003). Extranodal invasion was significantly more common among ALK-positive patients, compared to EGFR-positive patients and KRAS-positive patients (p = 0.001 and p = 0.049, respectively). Lymphangitis was significantly more common among ALK-positive patients, compared to EGFR-positive patients (p = 0.049). Pleural effusion was significantly less common among KRAS-positive patients, compared to EGFR-positive patients and ALK-positive patients (p = 0.046 and p = 0.026, respectively). Lung metastases were significantly more common among EGFR-positive patients, compared to KRAS-positive patients and ALK-positive patients (p = 0.007 and p = 0.04, respectively). In conclusion, EGFR mutations were associated with ground-glass opacity, KRAS-positive tumors were generally solid and less likely to metastasize to the lung and pleura, and ALK-positive tumors tended to present with lymphadenopathy, extranodal invasion, and lymphangitis. These mutation-specific imaging characteristics may be related to the biological differences between these cancers.

Reproducibility of radiomics for deciphering tumor phenotype with imaging

Radiomics (radiogenomics) characterizes tumor phenotypes based on quantitative image features derived from routine radiologic imaging to improve cancer diagnosis, prognosis, prediction and response to therapy. Although radiomic features must be reproducible to qualify as biomarkers for clinical care, little is known about how routine imaging acquisition techniques/parameters affect reproducibility. To begin to fill this knowledge gap, we assessed the reproducibility of a comprehensive, commonly-used set of radiomic features using a unique, same-day repeat computed tomography data set from lung cancer patients. Each scan was reconstructed at 6 imaging settings, varying slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and reconstruction algorithms (sharp, smooth). Reproducibility was assessed using the repeat scans reconstructed at identical imaging setting (6 settings in total). In separate analyses, we explored differences in radiomic features due to different imaging parameters by assessing the agreement of these radiomic features extracted from the repeat scans reconstructed at the same slice thickness but different algorithms (3 settings in total). Our data suggest that radiomic features are reproducible over a wide range of imaging settings. However, smooth and sharp reconstruction algorithms should not be used interchangeably. These findings will raise awareness of the importance of properly setting imaging acquisition parameters in radiomics/radiogenomics research.

Clinical characteristics associated with non-small-cell lung cancer harboring ALK rearrangements in Chinese patients

AIM

The ALK inhibitor, crizotinib, has demonstrated effectiveness in patients with non-small-cell lung cancer harboring ALK rearrangements. As few studies of the clinical characteristics of Chinese patients with ALK rearrangements have been reported, we conduct this study to gain more understanding in such area among Chinese patients.

PATIENTS & METHODS

We undertook a retrospective study of 288 non-small-cell lung cancer patients admitted to our institution over a period of 4.5 years.

RESULTS

Following testing, 14.9% of the patients (43/288) were found to be ALK fusion gene positive. Patient data including gender, age, smoking status, EGFR mutation status and medical imaging data were collected and analyzed.

CONCLUSION

The findings suggested that patients with ALK rearrangements are more likely to be young, have EGFR wild-type, and more likely to exhibit mucus secretion, solid tumor growth, lymph node metastasis and pleural metastasis.