CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma

CT-guided Hookwire localization before video-assisted thoracoscopic surgery for solitary ground-glass opacity dominant pulmonary nodules: radiologic-pathologic analysis

The optimal screening or treatment strategies of solitary pulmonary nodules especially ground-glass opacities (GGOs) remain controversial. With CT-guided Hookwire localization, it is accurate to find the small lesions during video-assisted thoracoscopic surgery (VATS). In this study, we evaluate the efficiency and safety of CT-guided Hookwire localization of GGO-dominant (GGO component > 50%) pulmonary nodules before VATS and investigate the correlation between the radiologic features and pathology. From April 2008 to April 2014, a total of 273 patients with solitary GGO-dominant pulmonary nodules were included. Tumor size was 12.4 ± 5.7 mm in diameter, including 208 pulmonary adenocarcinomas and 65 benign nodules. Dislodgement occurred in six patients (2.20%) during surgery. Postoperative complications included asymptomatic needle track hemorrhage (27.1%), minimal pneumothorax (5.9%) and hemoptysis (0.4%). In 208 (76.2%) pulmonary adenocarcinomas, 82 nodules showed ≥90% GGO and 126 showed 50%≤GGO<90%, while 84 nodules staged as T_1aN0M0, 96 staged as T_1bN₀M_0, and 28 staged as T_1cN₀M₀. The multivariable analysis demonstrated that 50%≤GGO<90% (HR=2.459, 95% CI: 1.246-4.853, P=0.010), speculation (HR=3.911, 95% CI: 1.966-7.663, P<0.001), lobulation (HR=4.582, 95% CI: 2.149-9.767, P<0.001) and vascular convergence (HR=4.096, 95% CI: 1.132-14.824, P=0.032) were the independent risk factors to identification of the malignant GGO-dominant pulmonary nodules. In conclusions,CT-guided Hookwire localizati for GGO-dominant pulmonary nodules before VATS is a safe and effective procedure for accurate diagnosis and resection of indeterminate solitary pulmonary nodules.

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

Abstract

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

Teaching points

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

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

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

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

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

Volume doubling time of lung adenocarcinomas considering epidermal growth factor receptor mutation status of exon 19 and 21: three-dimensional volumetric evaluation

Background

The current study was to investigate the volume doubling time (VDT) of lung adenocarcinomas considering epidermal growth factor receptor (EGFR) mutation status of exon 19 and 21, when compared with EGFR wide type.

Methods

Eighty-eight patients with pathologically proven adenocarcinomas, which underwent two or more computed tomography (CT) scans spared by 25 or more days, were included. EGFR mutations at exons 19 and 21 were determined using amplification refractory mutation system and all patients were divided into three groups-EGFR wide type group, EGFR mutation in exon 19 and 21 groups. Three-dimensional manual segmentations for all tumors were performed on first and latest follow-up CT scans; subsequently, VDTs were calculated and compared among three groups. Clinicopathoradiologic characters were also collected for subgroup analysis.

Results

EGFR mutations occurred in 49 (55.7%) patients, 19 in exon 19 and 30 in exon 21, respectively. The median VDT of all patients (33 men, 55 women; median age, 62 years) was 214 days (range, -4,092 to 10,920 days). Highly differentiated adenocarcinomas (median, 408 days) demonstrated longer VDT than those moderately (median, 172 days) or poorly (median, 144 days) differentiated (P=0.04). The VDT distribution was similar among EGFR wide type group (median, 207 days), EGFR mutation in exon 19 group (median, 288 days) and exon 21 group (median, 144 days) (P=0.21). In subgroup analysis, the median VDT of adenocarcinomas with EGFR mutation in exon 19 was longer than that of EGFR wide type for males (P=0.03) or patients without spiculation sign in chest CT (P=0.04). Totally 24 adenocarcinomas presented negative VDTs, most of which tended to be stable. Positive VDT values were used for all median description.

Conclusions

Overall VDT of lung adenocarcinomas seems not affected by EGFR mutation status. Researches with large population are warranted for further study.

Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial

RATIONALE

Lung computed tomography (CT) Screening Reporting and Data System (lung-RADS) has standardized follow-up and management decisions in lung cancer screening. To date, little is known how lung-RADS classification compares with radiological semantic features in risk prediction and diagnostic discrimination.

OBJECTIVES

To compare the performance of radiological semantic features and lung-RADS in predicting nodule malignancy in lung cancer screening.

METHODS

We used data and low-dose CT (LDCT) images from the National Lung Screening Trial (NLST). The training cohort contained 60 patients with screen-detected incident lung cancers who had a positive baseline screen (T0) that was not diagnosed and then was diagnosed at second follow-up (T2), and 139 nodule-positive controls who had 3 consecutive positive screens (T0 to T2) that were not diagnosed as lung cancer. The testing cohort included 40 patients with incident lung cancers that were diagnosed at first follow-up (T1) and 40 nodule-positive controls. Twenty-four semantic features were scored on a point scale from the LDCT images. Multivariable linear predictor model was built on the semantic features and the performances were compared with lung-RADS in 3 screening rounds. We also combined non-size-based semantic features with lung-RADS to improve malignancy detection.

RESULTS

At T0, the average area under the receiver operating characteristic curve (AUROC) for border definition in risk prediction was 0.72. The average AUROC for contour at T1 in risk prediction and T2 in diagnostic discrimination was 0.82 and 0.88, respectively. By comparison, the average AUROC of lung-RADS at T0, T1 and T2 were 0.60, 0.76 and 0.87, respectively. The combined model of the semantic features and lung-RADS shows improvement with AUROCs of 0.74, 0.88 and 0.96 at T0, T1, and T2, respectively, achieved by adding border definition (at T0) or contour (at T1 and T2).

CONCLUSION

We find semantic features defined by border definition and contour performed similar to lung-RADS at follow-up time point and outperformed lung-RADS at baseline. These semantics alongside of lung-RADS shows improved performance to detect malignancy.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy

BACKGROUND

Predicting recurrence after stereotactic body radiotherapy (SBRT) in non-small cell lung cancer (NSCLC) patients is problematic, but critical for the decision of following treatment. This study aims to investigate the association of imaging features derived from the first follow-up computed tomography (CT) on lung cancer patient outcomes following SBRT, and identify patients at high risk of recurrence.

METHODS

Fifty nine biopsy-proven non-small cell lung cancer patients were qualified for this study. The first follow-up CTs were performed about 3 months after SBRT (median time: 91 days). Imaging features included 34 manually scored radiological features (semantics) describing the lesion, lung and thorax and 219 quantitative imaging features (radiomics) extracted automatically after delineation of the lesion. Cox proportional hazard models and Harrel's C-index were used to explore predictors of overall survival (OS), recurrence-free survival (RFS), and loco-regional recurrence-free survival (LR-RFS). Five-fold cross validation was performed on the final prognostic model.

RESULTS

The median follow-up time was 42 months. The model for OS contained Eastern Cooperative Oncology Group (ECOG) performance status (HR = 3.13, 95% CI: 1.17-8.41), vascular involvement (HR = 3.21, 95% CI: 1.29-8.03), lymphadenopathy (HR = 3.59, 95% CI: 1.58-8.16) and the 1st principle component of radiomic features (HR = 1.24, 95% CI: 1.02-1.51). The model for RFS contained vascular involvement (HR = 3.06, 95% CI: 1.40-6.70), vessel attachment (HR = 3.46, 95% CI: 1.65-7.25), pleural retraction (HR = 3.24, 95% CI: 1.41-7.42), lymphadenopathy (HR = 6.41, 95% CI: 2.58-15.90) and relative enhancement (HR = 1.40, 95% CI: 1.00-1.96). The model for LR-RFS contained vascular involvement (HR = 4.96, 95% CI: 2.23-11.03), lymphadenopathy (HR = 2.64, 95% CI: 1.19-5.82), circularity (F13, HR = 1.60, 95% CI: 1.10-2.32) and 3D Laws feature (F92, HR = 1.96, 95% CI: 1.35-2.83). Five-fold cross-validated the areas under the receiver operating characteristic curves (AUC) of these three models were all above 0.8.

CONCLUSIONS

Our analysis reveals disease progression could be prognosticated as early as 3 months after SBRT using CT imaging features, and these features would be helpful in clinical decision-making.

Prognostic value and molecular correlates of a CT image-based quantitative pleural contact index in early stage NSCLC

PURPOSE

To evaluate the prognostic value and molecular basis of a CT-derived pleural contact index (PCI) in early stage non-small cell lung cancer (NSCLC).

EXPERIMENTAL DESIGN

We retrospectively analysed seven NSCLC cohorts. A quantitative PCI was defined on CT as the length of tumour-pleura interface normalised by tumour diameter. We evaluated the prognostic value of PCI in a discovery cohort (n = 117) and tested in an external cohort (n = 88) of stage I NSCLC. Additionally, we identified the molecular correlates and built a gene expression-based surrogate of PCI using another cohort of 89 patients. To further evaluate the prognostic relevance, we used four datasets totalling 775 stage I patients with publically available gene expression data and linked survival information.

RESULTS

At a cutoff of 0.8, PCI stratified patients for overall survival in both imaging cohorts (log-rank p = 0.0076, 0.0304). Extracellular matrix (ECM) remodelling was enriched among genes associated with PCI (p = 0.0003). The genomic surrogate of PCI remained an independent predictor of overall survival in the gene expression cohorts (hazard ratio: 1.46, p = 0.0007) adjusting for age, gender, and tumour stage.

CONCLUSIONS

CT-derived pleural contact index is associated with ECM remodelling and may serve as a noninvasive prognostic marker in early stage NSCLC.

KEY POINTS

• A quantitative pleural contact index (PCI) predicts survival in early stage NSCLC. • PCI is associated with extracellular matrix organisation and collagen catabolic process. • A multi-gene surrogate of PCI is an independent predictor of survival. • PCI can be used to noninvasively identify patients with poor prognosis.

Survival and risk factors for progression after resection of the dominant tumor in multifocal, lepidic-type pulmonary adenocarcinoma

BACKGROUND

It remains unclear whether a dominant lung adenocarcinoma that presents with multifocal ground glass opacities (GGOs) should be treated by local therapy. We sought to address survival in this setting and to identify risk factors for progression of unresected GGOs.

METHODS

Retrospective review of 70 patients who underwent resection of a pN0, lepidic adenocarcinoma, who harbored at least 1 additional GGO. Features associated with GGO progression were determined using logistic regression and survival was evaluated using the Kaplan-Meier method.

RESULTS

Subjects harbored 1 to 7 GGOs beyond their dominant tumor (DT). Mean follow-up was 4.1 ± 2.8 years. At least 1 GGO progressed after DT resection in 21 patients (30%). In 11 patients (15.7%), this progression prompted resection (n = 5) or stereotactic radiotherapy (n = 6) at mean 2.8 ± 2.3 years. Several measures of the overall tumor burden were associated with GGO progression (all P values < .03) and with progression prompting intervention (all P values < .01). In logistic regression, greater DT size (odds ratio, 1.07; 95% confidence interval, 1.01-1.14) and an initial GGO > 1 cm (odds ratio, 4.98; 95% confidence interval, 1.15-21.28) were the only factors independently associated with GGO progression. Survival was not negatively influenced by GGO progression (100% with vs 80.7% without; P = .1) or by progression-prompting intervention (P = .4).

CONCLUSIONS

At 4.1-year mean follow-up, 15.7% of patients with unresected GGOs after resection of a pN0 DT underwent subsequent intervention for a progressing GGO. Some features correlated with GGO growth, but neither growth, nor need for an intervention, negatively influenced survival. Thus, even those at highest risk for GGO progression should not be denied resection of a DT.

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined "semantic" and computer-derived "radiomic" features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32-41 radiomic features were associated with the binary semantic features (AUC = 0.56-0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen's correlation| = 0.002-0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa.

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n = 353) and verified them in an independent validation cohort (n = 352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter). We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR⁺ and EGFR^- cases (AUC = 0.69). Combining this signature with a clinical model of EGFR status (AUC = 0.70) significantly improved prediction accuracy (AUC = 0.75). The highest performing signature was capable of distinguishing between EGFR⁺ and KRAS⁺ tumors (AUC = 0.80) and, when combined with a clinical model (AUC = 0.81), substantially improved its performance (AUC = 0.86). A KRAS⁺/KRAS^- radiomic signature also showed significant albeit lower performance (AUC = 0.63) and did not improve the accuracy of a clinical predictor of KRAS status. Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost. Cancer Res; 77(14); 3922-30. ©2017 AACR.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

Pulmonary ground-glass opacity: computed tomography features, histopathology and molecular pathology

The incidence of pulmonary ground-glass opacity (GGO) lesions is increasing as a result of the widespread use of multislice spiral computed tomography (CT) and the low-dose CT screening for lung cancer detection. Besides benign lesions, GGOs can be a specific type of lung adenocarcinomas or their preinvasive lesions. Evaluation of pulmonary GGO and investigation of the correlation between CT imaging features and lung adenocarcinoma subtypes or driver genes can be helpful in confirming the diagnosis and in guiding the clinical management. Our review focuses on the pathologic characteristics of GGO detected at CT, involving histopathology and molecular pathology.

Predictive radiogenomics modeling of EGFR mutation status in lung cancer

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

Evolving Cancer Classification in the Era of Personalized Medicine: A Primer for Radiologists

Traditionally tumors were classified based on anatomic location but now specific genetic mutations in cancers are leading to treatment of tumors with molecular targeted therapies. This has led to a paradigm shift in the classification and treatment of cancer. Tumors treated with molecular targeted therapies often show morphological changes rather than change in size and are associated with class specific and drug specific toxicities, different from those encountered with conventional chemotherapeutic agents. It is important for the radiologists to be familiar with the new cancer classification and the various treatment strategies employed, in order to effectively communicate and participate in the multi-disciplinary care. In this paper we will focus on lung cancer as a prototype of the new molecular classification.

Lung adenocarcinoma: Assessment of epidermal growth factor receptor mutation status based on extended models of diffusion-weighted image

PURPOSE

To evaluate the diagnostic performance of extended models of diffusion-weighted (DW) imaging to help differentiate the epidermal growth factor receptor (EGFR) mutation status in stage IIIA-IV lung adenocarcinoma.

MATERIALS AND METHODS

This retrospective study had institutional research board approval and was HIPAA compliant. Preoperative extended DW imaging including intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) 3 Tesla MRI were retrospectively evaluated in 53 patients with pathologically confirmed non-early stage (IIIA-IV) lung adenocarcinoma. EGFR mutationsat exons 18-21 were determined by using polymerase chain reaction-based ARMS. Quantitative parameters (mean, kurtosis, skewness, 10^th and 90^th percentiles) of IVIM (true-diffusion coefficient D, pseudo-diffusion coefficient D*, and perfusion fraction f) and DKI (kurtosis value Kapp, kurtosis corrected diffusion coefficient Dapp) were calculated by outlining entire-volume histogram analysis. Receiver operating characteristic analysis was constructed to determine the diagnostic performance of each parameter. Multivariate logistic regression was used to differentiate the probability of EGFR mutation status.

RESULTS

Twenty-four of 53 patients with lung adenocarcinoma were EGFR mutations, which occurred most often in acinar (10 of 13 [76.9%]) and papillary predominant tumors (9 of 13 [69.2%]). Patients with EGFR mutation showed significant higher 10^th percentile of D, lower D* value in terms of kurtosis, and lower K_app value in terms of mean, skewness, 10^th and 90^th percentiles (all P values < 0.05). The 90^th K_app showed significantly higher sensitivity (97%; P < 0.05) and Az (0.817; P < 0.05) value. Multivariate logistic regression showed 90^th K_app was a independent factor for determining EGFR mutation with odds ratio -1.657.

CONCLUSION

Multiple IVIM and DKI parameters, especially the histogram 90^th K_app value, helped differentiate EGFR mutation status in stage IIIA-IV lung adenocarcinoma.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:281-289.

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.