Nomogram based on preoperative CT imaging predicts the EGFR mutation status in lung adenocarcinoma.
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ABSTRACT:
SUBMITTER: Zhang G
PROVIDER: S-EPMC7691609 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
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Nomogram based on preoperative CT imaging predicts the EGFR mutation status in lung adenocarcinoma.
Zhang Guojin G Zhang Jing J Cao Yuntai Y Zhao Zhiyong Z Li Shenglin S Deng Liangna L Zhou Junlin J
Translational oncology 20201121 1
Tyrosine kinase inhibitors (TKIs) provide clinical benefits to the lung cancer patients with epidermal growth factor receptor (EGFR) mutations. However, non-invasively determine EGFR mutation status in patients before targeted therapy remains a challenge. This study aimed to develop and validate a nomogram for preoperative prediction of EGFR mutation status in patients with lung adenocarcinoma. The medical records of 403 patients with lung adenocarcinoma confirmed by histology from January 2016 ...[more]
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Project description:ObjectiveTo identify imaging markers that reflect the epidermal growth factor receptor (EGFR) mutation status by comparing computed tomography (CT) imaging-based histogram features between bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma.Materials and methodsThis retrospective study included 57 patients, with pathologically confirmed bone metastasis of primary lung adenocarcinoma. EGFR mutation status of bone metastases was confirmed by gene detection. The CT imaging of the metastatic bone lesions which were obtained between June 2014 and December 2017 were collected and analyzed. A total of 42 CT imaging-based histogram features were automatically extracted. Feature selection was conducted using Student's t-test, Mann-Whitney U test, single-factor logistic regression analysis and Spearman correlation analysis. A receiver operating characteristic (ROC) curve was plotted to compare the effectiveness of features in distinguishing between EGFR(+) and EGFR(-) groups. DeLong's test was used to analyze the differences between the area under the curve (AUC) values.ResultsThree histogram features, namely range, skewness, and quantile 0.975 were significantly associated with EGFR mutation status. After combining these three features and combining range and skewness, we obtained the same AUC values, sensitivity and specificity. Meanwhile, the highest AUC value was achieved (AUC 0.783), which also had a higher sensitivity (0.708) and specificity (0.788). The differences between AUC values of the three features and their various combinations were statistically insignificant.ConclusionCT imaging-based histogram features of bone metastases with and without EGFR mutation in patients with primary lung adenocarcinoma were identified, and they may contribute to diagnosis and prediction of EGFR mutation status.
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Project description:ObjectiveThis study aimed to develop a model to predict KRAS mutations in colorectal cancer according to radiomic signatures based on CT and clinical risk factors.MethodsThis retrospective study included 172 patients with colorectal cancer. All patients were randomized at a 7:3 ratio into a training cohort (n = 121, 38.8% positive for KRAS mutation) and a validation cohort (n = 51, 39.2% positive for KRAS mutation). Radiomics features were extracted from single-slice and full-volume regions of interest on the portal-venous CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was adopted to construct a radiomics signature, and logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA).Results1018 radiomics features were extracted from single-slice and full-volume ROIs. Eight features were retained to construct 2D (two-dimensional, 2D) radiomics model. Similarly, eight features were retained to construct 3D (three-dimensional, 3D) radiomics model. The area under the curve (AUC) values of the test cohort were 0.75 and 0.84, respectively. Delong test showed that the integrated nomogram (AUC = 0.92 in the test cohort) had better clinical predictive efficiency than 2D radiomics (p-value < 0.05) model and 3D radiomics model (p-value < 0.05).ConclusionThe 2D and 3D radiomics models can both predict KRAS mutations. And, the integrated nomogram can be better applied to predict KRAS mutation status in colorectal cancer.Advances in knowledgeCT-based radiomics showed satisfactory diagnostic significance for the KRAS status in colorectal cancer, the clinical-combined model may be applied in the individual pre-operative prediction of KRAS mutation.
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Project description:BackgroundTo establish a radiomic approach to identify epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients based on CT images, and to distinguish exon-19 deletion and exon-21 L858R mutation.MethodsTwo hundred sixty-three patients who underwent pre-surgical contrast-enhanced CT and molecular testing were included, and randomly divided into the training (80%) and test (20%) cohort. Tumor images were three-dimensionally segmented to extract 1,672 radiomic features. Clinical features (age, gender, and smoking history) were added to build classification models together with radiomic features. Subsequently, the top-10 most relevant features were used to establish classifiers. For the classifying tasks including EGFR mutation, exon-19 deletion, and exon-21 L858R mutation, four logistic regression models were established for each task.ResultsThe training and test cohort consisted of 210 and 53 patients, respectively. Among the established models, the highest accuracy and sensitivity among the four models were 75.5% (61.7-86.2%) and 92.9% (76.5-99.1%) to classify EGFR mutation, respectively. The highest specificity values were 86.7% (69.3-96.2%) and 70.4% (49.8-86.3%) to classify exon-19 deletion and exon-21 L858R mutation, respectively.ConclusionsCT radiomics can sensitively identify the presence of EGFR mutation, and increase the certainty of distinguishing exon-19 deletion and exon-21 L858R mutation in lung adenocarcinoma patients. CT radiomics may become a helpful non-invasive biomarker to select EGFR mutation patients for invasive sampling.
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Project description:Background Identifying epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LADC) is vital for treatment decision-making. This study aimed to establish a convenient and noninvasive nomogram prediction model based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging and clinical features to predict EGFR mutation status in patients with LADC. Methods A total of 274 patients (male 130, female 144, median age 65 years) were enrolled in this retrospective study. Imaging data from 18F-FDG PET/CT and clinical information were analyzed, with the Mann-Whitney U test, Student’s t-test, and chi-square test used to compare categorical or continuous covariates as appropriate. Logistic regression analyses were performed to identify independent variables associated with EGFR mutation status, from which the nomogram prediction model was constructed. Leave-one-out cross-validation was performed, and the discrimination ability and calibration of the nomogram were assessed by calculating the area under the curve of the receiver operating characteristic curve and the calibration curve. The clinical net benefit of the nomogram was evaluated. Results Of the 274 patients, 143 (52.2%) had EGFR mutations. Female sex [odds ratios (OR): 2.64, 95% confidence interval (CI): 1.29–5.45, P=0.008], non-smoking status (OR: 2.78, 95% CI: 1.30–5.88, P=0.008), mean standardized uptake value ≤9.23 (OR: 2.44, 95% CI: 1.35–4.55, P=0.004), metabolic tumor volume ≤17.72 cm3 (OR: 5.00, 95% CI: 2.38–12.50, P<0.001) and the presence of pleural retraction (OR: 1.88, 95% CI: 1.05–3.40, P=0.034) were independent predictors for EGFR mutations in LADCs. The nomogram based on these risk factors showed good predictive efficacy, with an area under the curve of 0.805 (95% CI: 0.753–0.857), a sensitivity of 90.2%, a specificity of 59.5% and an accuracy of 73.0%. Conclusions The nomogram prediction model incorporating sex, smoking status, mean standardized uptake value, metabolic tumor volume, and the presence of pleural retraction could effectively discriminate EGFR-mutant from wild-type LADCs.
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Project description:IntroductionIn lung adenocarcinoma, the mutational spectrum is dominated by EGFR and KRAS mutations. Improved knowledge about genomic and transcriptional alterations in and between mutation-defined subgroups may identify genes involved in disease development or progression.MethodsGenomic profiles from 457 adenocarcinomas, including 113 EGFR-mutated, 134 KRAS-mutated and 210 EGFR and KRAS-wild type tumors (EGFRwt/KRASwt), and gene expression profiles from 914 adenocarcinomas, including 309 EGFR-mutated, 192 KRAS-mutated, and 413 EGFRwt/KRASwt tumors, were assembled from different repositories. Genomic and transcriptional differences between the three mutational groups were analyzed by both supervised and unsupervised methods.ResultsEGFR-mutated adenocarcinomas displayed a larger number of copy number alterations and recurrent amplifications, a higher fraction of total loss-of-heterozygosity, higher genomic complexity, and a more distinct expression pattern than EGFR-wild type adenocarcinomas. Several of these differences were also consistent when the three mutational groups were stratified by stage, gender and smoking status. Specific copy number alterations were associated with mutation status, predominantly including regions of gain with the highest frequency in EGFR-mutated tumors. Differential regions included both large and small regions of gain on 1p, 5q34-q35.3, 7p, 7q11.21, 12p12.1, 16p, and 21q, and losses on 6q16.3-q21, 8p, and 9p, with 20-40% frequency differences between the mutational groups. Supervised gene expression analyses identified 96 consistently differentially expressed genes between the mutational groups, and together with unsupervised analyses these analyses highlighted the difficulty in broadly resolving the three mutational groups into distinct transcriptional entities.ConclusionsWe provide a comprehensive overview of the genomic and transcriptional landscape in lung adenocarcinoma stratified by EGFR and KRAS mutations. Our analyses suggest that the overall genomic and transcriptional landscape of lung adenocarcinoma is affected, but only to a minor extent, by EGFR and KRAS mutation status.
| S-EPMC3812039 | biostudies-literature
Project description:Purpose To retrospectively identify the relationship between epidermal growth factor receptor (EGFR) mutation status, predominant histologic subtype, and computed tomographic (CT) characteristics in surgically resected lung adenocarcinomas in a cohort of Asian patients. materials and Methods This study was approved by the institutional review board, with waiver of informed consent. Preoperative chest CT findings were retrospectively evaluated in 385 surgically resected lung adenocarcinomas. A total of 30 CT descriptors were assessed. EGFR mutations at exons 18-21 were determined by using the amplification refractory mutation system. Multiple logistic regression analyses were performed to identify independent factors of harboring EGFR mutation status. The final model was selected by using the backward elimination method, and two areas under the receiver operating characteristic curve (ROC) were compared with the nonparametric approach of DeLong, DeLong, and Clarke-Pearson. Results EGFR mutations were found in 168 (43.6%) of 385 patients. Mutations were found more frequently in (a) female patients (P < .001); (b)those who had never smoked (P < .001); (c)those with lepidic predominant adenocarcinomas (P = .001) or intermediate pathologic grade (P < .001); (e) smaller tumors (P < .001); (f)tumors with spiculation (P = .019), ground-glass opacity (GGO) or mixed GGO (P < .001), air bronchogram (P = .006), bubblelike lucency (P < .001), vascular convergence (P = .024), thickened adjacent bronchovascular bundles (P = .027), or pleural retraction (P < .001); and (g) tumors without pleural attachment (P = .004), a well-defined margin (P = .010), marked heterogeneous enhancement (P = .001), severe peripheral emphysema (P = .002), severe peripheral fibrosis (P = .013), or lymphadenopathy (P = .028). The most important and significantly independent prognostic factors of harboring EGFR-activating mutation for the model with both clinical variables and CT features were those who had never smoked and those with smaller tumors, bubblelike lucency, homogeneous enhancement, or pleural retraction when adjusting for histologic subtype, pathologic grade, or thickened adjacent bronchovascular bundles. ROC curve analysis showed that use of clinical variables combined with CT features (area under the ROC curve = 0.778) was superior to use of clinical variables alone (area under the ROC curve = 0.690). Conclusion CT imaging features of lung adenocarcinomas in combination with clinical variables can be used to prognosticate EGFR mutation status better than use of clinical variables alone. (©) RSNA, 2016 Online supplemental material is available for this article.
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