Emerging data suggest that radiomics can be used to predict outcomes in SCCHN. At present, only few data are available for pre-treatment MRI.
Study population was retrieved from an ongoing multicenter, randomized, prospective trial (NCT02262221, HETeCo) evaluating health and economic outcomes of two different follow-up (FUP) strategies (intensive vs non-intensive) in effectively cured stage III-IV (VIII TNM ed.) SCCHN. We selected only patients with both pre- and post-contrast enhancement T1 and T2-weighted baseline MRI (b-MRI) and at least 2 years (2y) of FUP. A radiomic model was developed to identify high risk (HR) and low risk (LR) of disease recurrence. Radiomic features (RF) were extracted from the primary tumor in the b-MRI. The best RF combination was selected by Least Absolute Shrinkage and Selection Operator (LASSO). Ten-fold cross-validation was used to compute sensitivity, specificity and area under the curve (AUC) of the classifier. Kaplan-Meier (KM) curves were estimated for HR and LR, for both overall survival (OS) and disease-free survival (DFS) and log rank test was performed. Three years (3y)-DFS and OS were also estimated for the two groups. The radiomic risk class was used as a new variable in a multivariate Cox model including well established prognostic factors in SCCHN (TNM stage, subsite and HPV).
Out of 155 enrolled HETeCO patients, 98 baseline imaging were retrieved of which 57 b-MRI. Of these, 51 met the eligibility criteria (25 in intensive and 26 in non-intensive arm). Baseline patients’ characteristics were: median age 66 yr (38-86); sex (M 42; F 9); median smoking history: 30 packs/y (1-100); 25 oral cavity (49%), 18 oropharynx (35%, 14 HPV+), 6 larynx (12%), 2 hypopharynx (4%). At a median FUP of 42 months (25-64), 45 (88%) patients are still alive. The recurrence rate was 20% (10/51, of which 2 distant). In total, 1608 RF were extracted. The sensitivity, specificity and AUC of the classifier were 90%, 76%, and 80%, respectively. The radiomic risk class was found to be an independent prognostic factor for both DFS and OS (p=0.01 and p=0.046, respectively). KM curves for DFS and OS were significantly different between HR and LR groups (p=0.002 and p=0.04, respectively). In HR vs LR, 3-y DFS and OS were: 78% [61-100%] vs 97% [90-100%], and 88% [75-100%] vs 96% [88-100%], respectively.
Radiomics of pre-treatment MRI can predict outcomes in SCCHN. External validation of this preliminary radiomics-based model is currently ongoing.
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