Gurvan Dissaux1,2, Dimitris Visvikis2, Ronrick Da-Ano2, Olivier Pradier3,2, Enrique Chajon4, Isabelle Barillot5, Loig Duvergé4, Ingrid Masson6, Ronan Abgral7, Maria-Joao Santiago Ribeiro8, Anne Devillers9, Amandine Pallardy10, Vincent Fleury11, Marc-André Mahé6, Renaud De Crevoisier4, Mathieu Hatt2, Ulrike Schick3,2. 1. Radiation Oncology Department, University Hospital, Brest, France gurvan.dissaux@chu-brest.fr. 2. LaTIM, INSERM, UMR 1101, University of Brest, Brest, France. 3. Radiation Oncology Department, University Hospital, Brest, France. 4. Radiotherapy Department, Centre Eugene Marquis, Rennes, France. 5. Department of Radiation Oncology, University Hospital, Tours, France. 6. Department of Radiation Oncology, ICO, Saint-Herblain, France. 7. Nuclear Medicine Department, University Hospital, Brest, France. 8. Nuclear Medicine Department, University Hospital, Tours, France. 9. Nuclear Medicine Department, Centre Eugene Marquis, Rennes, France. 10. Nuclear Medicine Department, University Hospital, Nantes, France; and. 11. Nuclear Medicine Department, ICO, Saint-Herblain, France.
Abstract
The aim of this retrospective multicentric study was to develop and evaluate a prognostic 18F-FDG PET/CT radiomic signature in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy (SBRT). Methods: Patients from 3 different centers (n = 27, 29, and 8) were pooled to constitute the training set, whereas the patients from a fourth center (n = 23) were used as the testing set. The primary endpoint was local control. The primary tumor was semiautomatically delineated in the PET images using the fuzzy locally adaptive Bayesian algorithm, and manually in the low-dose CT images. In total, 184 Image Biomarkers Standardization Initiative-compliant radiomic features were extracted. Seven clinical and treatment parameters were included. We used ComBat to harmonize radiomic features extracted from the 4 institutions relying on different PET/CT scanners. In the training set, variables found significant in the univariate analysis were fed into a multivariate regression model, and models were built by combining independent prognostic factors. Results: Median follow-up was 21.1 mo (range, 1.7-63.4 mo) and 25.5 mo (range, 7.7-57.8 mo) in training and testing sets, respectively. In univariate analysis, none of the clinical variables, 2 PET features, and 2 CT features were significantly predictive of local control. The best predictive models in the training set were obtained by combining one feature from PET (Information Correlation 2) and one feature from CT (flatness), reaching a sensitivity of 100% and a specificity of 96%. Another model combining 2 PET features (Information Correlation 2 and strength) reached sensitivity of 100% and specificity of 88%, both with an undefined hazard ratio (P < 0.001). The latter model obtained an accuracy of 0.91 (sensitivity, 100%; specificity, 81%), with a hazard ratio undefined (P = 0.023) in the testing set; however, other models relying on CT radiomic features only or the combination of PET and CT features failed to validate in the testing set. Conclusion: We showed that 2 radiomic features derived from 18F-FDG PET were independently associated with local control in patients with non-small cell lung cancer undergoing SBRT and could be combined in an accurate predictive model. This model could provide local relapse-related information and could be helpful in clinical decision making.
The aim of this retrospective multicentric study was to develop and evaluate a prognostic 18F-FDG PET/CT radiomic signature in early-stage non-small cell lung cancerpatients treated with stereotactic body radiotherapy (SBRT). Methods:Patients from 3 different centers (n = 27, 29, and 8) were pooled to constitute the training set, whereas the patients from a fourth center (n = 23) were used as the testing set. The primary endpoint was local control. The primary tumor was semiautomatically delineated in the PET images using the fuzzy locally adaptive Bayesian algorithm, and manually in the low-dose CT images. In total, 184 Image Biomarkers Standardization Initiative-compliant radiomic features were extracted. Seven clinical and treatment parameters were included. We used ComBat to harmonize radiomic features extracted from the 4 institutions relying on different PET/CT scanners. In the training set, variables found significant in the univariate analysis were fed into a multivariate regression model, and models were built by combining independent prognostic factors. Results: Median follow-up was 21.1 mo (range, 1.7-63.4 mo) and 25.5 mo (range, 7.7-57.8 mo) in training and testing sets, respectively. In univariate analysis, none of the clinical variables, 2 PET features, and 2 CT features were significantly predictive of local control. The best predictive models in the training set were obtained by combining one feature from PET (Information Correlation 2) and one feature from CT (flatness), reaching a sensitivity of 100% and a specificity of 96%. Another model combining 2 PET features (Information Correlation 2 and strength) reached sensitivity of 100% and specificity of 88%, both with an undefined hazard ratio (P < 0.001). The latter model obtained an accuracy of 0.91 (sensitivity, 100%; specificity, 81%), with a hazard ratio undefined (P = 0.023) in the testing set; however, other models relying on CT radiomic features only or the combination of PET and CT features failed to validate in the testing set. Conclusion: We showed that 2 radiomic features derived from 18F-FDG PET were independently associated with local control in patients with non-small cell lung cancer undergoing SBRT and could be combined in an accurate predictive model. This model could provide local relapse-related information and could be helpful in clinical decision making.
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