Jay Kumar Raghavan Nair1,2,3, Umar Abid Saeed1,3, Connor C McDougall4, Ali Sabri5,6, Bojan Kovacina6, B V S Raidu7, Riaz Ahmed Khokhar1,8, Stephan Probst9, Vera Hirsh10, Jeffrey Chankowsky1, Léon C Van Kempen11,12, Jana Taylor1. 1. Department of Radiology, 54473McGill University Health Centre, Montreal, Québec, Canada. 2. Department of Radiology, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada. 3. Department of Radiology, 2129University of Calgary, Calgary, Alberta, Canada. 4. Department of Mechanical Engineering, 2129University of Calgary, Calgary, Alberta, Canada. 5. Department of Radiology, McMaster University, Hamilton, Ontario, Canada. 6. Department of Radiology, Jewish General Hospital, Montreal, Québec, Canada. 7. Raidu Analysts and Associates, Mumbai, India. 8. Department of Surgery, Khokhar Medical Centre, Rawalpindi, Pakistan. 9. Department of Nuclear Medicine, Jewish General Hospital, Québec, Montreal, Canada. 10. Department of Oncology, 5620McGill University Health Centre, Montreal, Québec, Canada. 11. Department of Pathology, 10173University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. 12. Department of Pathology, Jewish General Hospital, Montreal, Québec, Canada.
Abstract
BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations. METHODS: Fifty patients diagnosed with NSCLC between 2011 and 2015 and with known EGFR mutation status were retrospectively identified. Texture features extracted from pretreatment CT and FDG PET-CT images by manual contouring of the primary tumor were used to develop multivariate logistic regression (LR) models to predict EGFR mutations in exon 19 and exon 20. RESULTS: An LR model evaluating FDG PET-texture features was able to differentiate EGFR mutant from wild type with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.87, 0.76, 0.66, and 0.71, respectively. The model derived from CT texture features had an AUC, sensitivity, specificity, and accuracy of 0.83, 0.84, 0.73, and 0.78, respectively. FDG PET-texture features that could discriminate between mutations in EGFR exon 19 and 21 demonstrated AUC, sensitivity, specificity, and accuracy of 0.86, 0.84, 0.73, and 0.78, respectively. Based on CT texture features, the AUC, sensitivity, specificity, and accuracy were 0.75, 0.81, 0.69, and 0.75, respectively. CONCLUSION: Non-small cell lung cancer texture analysis using FGD-PET and CT images can identify tumors with mutations in EGFR. Imaging signatures could be valuable for pretreatment assessment and prognosis in precision therapy.
BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations. METHODS: Fifty patients diagnosed with NSCLC between 2011 and 2015 and with known EGFR mutation status were retrospectively identified. Texture features extracted from pretreatment CT and FDG PET-CT images by manual contouring of the primary tumor were used to develop multivariate logistic regression (LR) models to predict EGFR mutations in exon 19 and exon 20. RESULTS: An LR model evaluating FDG PET-texture features was able to differentiate EGFR mutant from wild type with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.87, 0.76, 0.66, and 0.71, respectively. The model derived from CT texture features had an AUC, sensitivity, specificity, and accuracy of 0.83, 0.84, 0.73, and 0.78, respectively. FDG PET-texture features that could discriminate between mutations in EGFR exon 19 and 21 demonstrated AUC, sensitivity, specificity, and accuracy of 0.86, 0.84, 0.73, and 0.78, respectively. Based on CT texture features, the AUC, sensitivity, specificity, and accuracy were 0.75, 0.81, 0.69, and 0.75, respectively. CONCLUSION: Non-small cell lung cancer texture analysis using FGD-PET and CT images can identify tumors with mutations in EGFR. Imaging signatures could be valuable for pretreatment assessment and prognosis in precision therapy.
Authors: Andrew Hope; Maikel Verduin; Thomas J Dilling; Ananya Choudhury; Rianne Fijten; Leonard Wee; Hugo Jwl Aerts; Issam El Naqa; Ross Mitchell; Marc Vooijs; Andre Dekker; Dirk de Ruysscher; Alberto Traverso Journal: Cancers (Basel) Date: 2021-05-14 Impact factor: 6.639