Luca Cozzi1,2, Ciro Franzese3, Antonella Fogliata3, Davide Franceschini3, Pierina Navarria3, Stefano Tomatis3, Marta Scorsetti4,3. 1. Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy. luca.cozzi@humanitas.it. 2. Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy. luca.cozzi@humanitas.it. 3. Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy. 4. Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy.
Abstract
PURPOSE: To appraise the ability of a radiomics signature to predict clinical outcome after definitive radiochemotherapy (RCT) of stage III-IV head and neck cancer. METHODS: A cohort of 110 patients was included in a retrospective analysis. Radiomics texture features were extracted from the gross tumor volumes contoured on planning computed tomography (CT) images. The cohort of patients was randomly divided into a training (70 patients) and a validation (40 patients) cohorts. Textural features were correlated to survival and control data to build predictive models. All the significant predictors of the univariate analysis were included in a multivariate model. The quality of the models was appraised by means of the concordance index (CI). RESULTS: A signature with 3 features was identified as predictive of overall survival (OS) with CI = 0.88 and 0.90 for the training and validation cohorts, respectively. A signature with 2 features was identified for progression-free survival (PFS; CI = 0.72 and 0.80); 2 features also characterized the signature for local control (LC; CI = 0.72 and 0.82). In all cases, the stratification in high- and low-risk groups for the training and validation cohorts led to significant differences in the actuarial curves. In the validation cohort the mean OS times (in months) were 78.9 ± 2.1 vs 67.4 ± 6.0 in the low- and high-risk groups, respectively, the PFS was 73.1 ± 3.7 and 50.7 ± 7.2, while the LC was 78.7 ± 2.1 and 63.9 ± 6.5. CONCLUSION: CT-based radiomic signatures that correlate with survival and control after RCT were identified and allow low- and high-risk groups of patients to be identified.
PURPOSE: To appraise the ability of a radiomics signature to predict clinical outcome after definitive radiochemotherapy (RCT) of stage III-IV head and neck cancer. METHODS: A cohort of 110 patients was included in a retrospective analysis. Radiomics texture features were extracted from the gross tumor volumes contoured on planning computed tomography (CT) images. The cohort of patients was randomly divided into a training (70 patients) and a validation (40 patients) cohorts. Textural features were correlated to survival and control data to build predictive models. All the significant predictors of the univariate analysis were included in a multivariate model. The quality of the models was appraised by means of the concordance index (CI). RESULTS: A signature with 3 features was identified as predictive of overall survival (OS) with CI = 0.88 and 0.90 for the training and validation cohorts, respectively. A signature with 2 features was identified for progression-free survival (PFS; CI = 0.72 and 0.80); 2 features also characterized the signature for local control (LC; CI = 0.72 and 0.82). In all cases, the stratification in high- and low-risk groups for the training and validation cohorts led to significant differences in the actuarial curves. In the validation cohort the mean OS times (in months) were 78.9 ± 2.1 vs 67.4 ± 6.0 in the low- and high-risk groups, respectively, the PFS was 73.1 ± 3.7 and 50.7 ± 7.2, while the LC was 78.7 ± 2.1 and 63.9 ± 6.5. CONCLUSION: CT-based radiomic signatures that correlate with survival and control after RCT were identified and allow low- and high-risk groups of patients to be identified.
Entities:
Keywords:
Diagnostic imaging; Local control; Phenotype; Textural analysis; Textural signature
Authors: Jairo A Socarrás Fernández; David Mönnich; Sara Leibfarth; Stefan Welz; Alex Zwanenburg; Stefan Leger; Steffen Löck; Christina Pfannenberg; Christian La Fougère; Gerald Reischl; Michael Baumann; Daniel Zips; Daniela Thorwarth Journal: Phys Imaging Radiat Oncol Date: 2020-07