Stefania Rizzo1, Sara Raimondi2, Evelyn E C de Jong3, Wouter van Elmpt4, Francesca De Piano5, Francesco Petrella6, Vincenzo Bagnardi7, Arthur Jochems8, Massimo Bellomi9, Anne Marie Dingemans8, Philippe Lambin3. 1. Department of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy. Electronic address: stefania.rizzo@ieo.it. 2. Department of Epidemiology and Biostatistics, IEO, European Institute of Oncology IRCCS, Milan, Italy. 3. Department of Radiation Oncology (The D-lab), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands. 4. Department of Radiation Oncology (MAASTRO Clinic), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands. 5. Department of Health Sciences, University of Milan, via A. di Rudinì 8, 20142 Milan, Italy. 6. Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy. 7. Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy. 8. Department of Pneumonology, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands. 9. Department of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
Abstract
OBJECTIVE: To validate previously identified associations between radiological features and clinical features with Epidermal Growth Factor Receptor (EGFR)/ Kirsten RAt Sarcoma (KRAS) alterations in an independent group of patients with Non-Small Cell Lung Cancer (NSCLC). MATERIAL AND METHODS: A total of 122 patients with NSCLC tested for EGFR/KRAS alterations were included. Clinical and radiological features were recorded. Univariate analysis were performed to look at the associations of the studied features with EGFR/KRAS alterations. Previously calculated composite model parameters for each gene alteration prediction were applied to this validation cohort. ROC (Receiver Operating Characteristic) curves were drawn using the previously validated composite models, and also for each significant individual characteristic of the previous training cohort model. The Area Under the ROC Curve (AUC) with 95% Confidence Intervals (CI) was calculated and compared between the full models. RESULTS: At univariate analysis, EGFR+ confirmed an association with an internal air bronchogram, pleural retraction, emphysema and lack of smoking; KRAS+ with round shape, emphysema and smoking. The AUC (95%CI) in the new cohort was confirmed to be high for EGFR+ prediction, with a value of: 0.82 (0.69-0.95) vs. 0.82 in the previous cohort, whereas it was smaller for KRAS+ prediction, with a value of 0.60 (0.48-0.72) vs. 0.67 in the previous cohort. Looking at single features in the new cohort, we found that the AUC for the models including only smoking was similar to that of the full model (including radiological and clinical features) for both gene alterations. CONCLUSIONS: Although this study validated the significant association of clinical and radiological features with EGFR/KRAS alterations, models based on these composite features are not superior to smoking history alone to predict the mutations.
OBJECTIVE: To validate previously identified associations between radiological features and clinical features with Epidermal Growth Factor Receptor (EGFR)/ Kirsten RAt Sarcoma (KRAS) alterations in an independent group of patients with Non-Small Cell Lung Cancer (NSCLC). MATERIAL AND METHODS: A total of 122 patients with NSCLC tested for EGFR/KRAS alterations were included. Clinical and radiological features were recorded. Univariate analysis were performed to look at the associations of the studied features with EGFR/KRAS alterations. Previously calculated composite model parameters for each gene alteration prediction were applied to this validation cohort. ROC (Receiver Operating Characteristic) curves were drawn using the previously validated composite models, and also for each significant individual characteristic of the previous training cohort model. The Area Under the ROC Curve (AUC) with 95% Confidence Intervals (CI) was calculated and compared between the full models. RESULTS: At univariate analysis, EGFR+ confirmed an association with an internal air bronchogram, pleural retraction, emphysema and lack of smoking; KRAS+ with round shape, emphysema and smoking. The AUC (95%CI) in the new cohort was confirmed to be high for EGFR+ prediction, with a value of: 0.82 (0.69-0.95) vs. 0.82 in the previous cohort, whereas it was smaller for KRAS+ prediction, with a value of 0.60 (0.48-0.72) vs. 0.67 in the previous cohort. Looking at single features in the new cohort, we found that the AUC for the models including only smoking was similar to that of the full model (including radiological and clinical features) for both gene alterations. CONCLUSIONS: Although this study validated the significant association of clinical and radiological features with EGFR/KRAS alterations, models based on these composite features are not superior to smoking history alone to predict the mutations.