Dataset Information

Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.

ABSTRACT: BACKGROUND:In this study we retrospectively evaluated the capability of computed tomography (CT)-based radiomic features to predict epidermal growth factor receptor (EGFR) mutation status in surgically-resected peripheral lung adenocarcinomas in an Asian cohort of patients. PATIENTS AND METHODS:Two hundred ninety-eight patients with surgically resected peripheral lung adenocarcinomas were investigated in this institutional review board-approved retrospective study with requirement waived to obtain informed consent. Two hundred nineteen quantitative 3-D features were extracted from segmented volumes of each tumor, and 59 of these, which were considered independent features, were included in the analysis. Clinical and pathological information was obtained from the institutional database. RESULTS:Mutant EGFR was significantly associated with female sex (P = .0005); never smoker status (P < .0001), lepidic predominant adenocarcinomas (P = .017), and low or intermediate pathologic grade (P = .0002). Statistically significant differences were found in 11 radiomic features between EGFR mutant and wild type groups in univariate analysis. Mutant EGFR status could be predicted by a set of 5 radiomic features that fell into 3 broad groups: CT attenuation energy, tumor main direction, and texture defined according to wavelets and Laws (area under the curve [AUC], 0.647). A multiple logistic regression model showed that adding radiomic features to a clinical model resulted in a significant improvement of predicting power, because the AUC increased from 0.667 to 0.709 (P < .0001). CONCLUSION:Computed tomography-based radiomic features of peripheral lung adenocarcinomas can capture useful information regarding tumor phenotype, and the model we built can be useful to predict the presence of EGFR mutations in peripheral lung adenocarcinoma in Asian patients when mutational profiling is not available or possible.

SUBMITTER: Liu Y

PROVIDER: S-EPMC5548419 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.

Liu Ying Y Kim Jongphil J Balagurunathan Yoganand Y Li Qian Q Garcia Alberto L AL Stringfield Olya O Ye Zhaoxiang Z Gillies Robert J RJ

Clinical lung cancer 20160216 5

<h4>Background</h4>In this study we retrospectively evaluated the capability of computed tomography (CT)-based radiomic features to predict epidermal growth factor receptor (EGFR) mutation status in surgically-resected peripheral lung adenocarcinomas in an Asian cohort of patients.<h4>Patients and methods</h4>Two hundred ninety-eight patients with surgically resected peripheral lung adenocarcinomas were investigated in this institutional review board-approved retrospective study with requirement ...[more]

PMID: 27017476

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Project description:To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686-0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646-0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656-0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.

Project description:BackgroundIdentification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.MethodsWe retrospectively studied 18F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).ResultsTwo sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.ConclusionsIn our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.

Project description:BackgroundsEpidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non-small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline 18F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.MethodsA sum of 313 NSCLC patients with pre-treatment 18F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.ResultsThree hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.ConclusionsThe PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients' survival outcome of targeted therapy in clinical practice.

Project description:BackgroundThe rapid aging of the population in Japan has been accompanied by an increased rate of surgery for lung cancer among elderly patients. It is thus an urgent priority to map out a treatment strategy for elderly patients with primary lung cancer. Although surgical resection remains standard treatment for early stage non-small-cell lung cancer (NSCLC), it is now essential to confirm the status of epidermal growth factor receptor (EGFR) gene mutations when planning treatment strategies. Furthermore, several studies have reported that EGFR mutations are an independent prognostic marker in NSCLC. However, the relations between age group and the molecular and pathological characteristics of NSCLC remain unclear. We studied the status of EGFR mutations in elderly patients with NSCLC and examined the relations of EGFR mutations to clinicopathological factors and outcomes according to age group.MethodsA total of 388 consecutive patients with NSCLC who underwent complete tumor resection in our hospital from 2006 through 2008 were studied retrospectively. Formalin-fixed, paraffin-embedded tissue sections were used to isolate DNA from carcinoma lesions. Mutational analyses of EGFR gene exons 19, 20, and 21 and KRAS gene exons 12 and 13 were performed by loop-hybrid mobility shift assay, a highly sensitive polymerase chain reaction-based method.ResultsEGFR mutations were detected in 185 (47.7%) and KRAS mutations were detected in 33 (8.5%) of the 388 patients. EGFR mutations were found in a significantly higher proportion of patients younger than 80 years (younger group; 178/359, 49.6%) than in patients 80 years or older (older group; 7/29, 24.1%) (P = 0.008). In contrast, KRAS mutations were more common in the older group (6/29, 20.7%) than in the younger group (27/359, 7.5%) (P = 0.014). The older group showed a trend toward a higher rate of 5-year overall survival among elderly patients with EGFR mutations (100%) than among those with wild-type EGFR (66.2%), but the difference was not significant.ConclusionsOur results suggest that the EGFR status of patients with NSCLC differs between patients 80 years or older and those younger than 80 years. EGFR mutation status might be a prognostic marker in elderly patients with completely resected NSCLC.

Project description:How to improve the efficacy and reverse the resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), such as erlotinib, remains a major challenge in the targeted therapy of lung adenocarcinoma with EGFR-activating mutation. Phosphoglycerate dehydrogenase (PHGDH) is the key enzyme of de novo serine biosynthesis over-expressed in various types of cancer including lung cancer. Elevated PHGDH expression is correlated with a worse overall survival in clinical lung adenocarcinoma patients. Here we investigated the role of PHGDH in lung adenocarcinoma with the acquisition of resistance to erlotinib. Methods: The necessary genes required for the acquired erlotinib resistance in lung adenocarcinoma cells were screened out by RNA-Seq analysis. Then the protein and mRNA levels of PHGDH were confirmed by immunoblotting and qRT-PCR in the erlotinib resistant cells. The effects of PHGDH inhibition or overexpression on erlotinib resistance were examined using cell culture and tumor xenograft mouse models respectively. To explore mechanism, the ROS level and DNA damage marker, ?H2AX, were tested by DCFH-DA staining and immunofluorescence after PHGDH inhibition. Results: We found that PHGDH level was significantly increased in the lung adenocarcinoma PC9ER4 and HCC827ER9 cells that acquired resistance to erlotinib. Perturbation of PHGDH by siPHGDH transfection or NCT-503, a small molecular PHGDH inhibitor, synergistically augmented the tumoricidal effect and restored sensitivity to erlotinib in cell lines and xenografts. Over-expression of PHGDH caused xenografts resistant to erlotinib. Furthermore, multiple DNA damage repair pathways related genes were changed by PHGDH depletion specifically in erlotinib resistant cells. ROS stress and DNA damage marker ?H2AX were enhanced by siPHGDH and NCT-503, which was reversed by NAC. Conclusion: Our study indicated that PHGDH inhibition has potential therapeutic value in lung adenocarcinoma with the acquired resistance to EGFR-TKIs.

Dataset Information

Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.

Publications

Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.

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