MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy

Abstract

A reliable prognostic stratification of patients with locally advanced hypopharyngeal cancer who had been treated with concurrent chemoradiotherapy (CCRT) is crucial for informing tailored management strategies. The purpose of this retrospective study was to develop robust and objective magnetic resonance imaging (MRI) radiomics-based models for predicting overall survival (OS) and progression-free survival (PFS) in this patient population. The study participants included 198 patients (median age: 52.25 years (interquartile range = 46.88–59.53 years); 95.96% men) who were randomly divided into a training cohort (n = 132) and a testing cohort (n = 66). Radiomic parameters were extracted from post-contrast T1-weighted MR images. Radiomic features for model construction were selected from the training cohort using least absolute shrinkage and selection operator–Cox regression models. Prognostic performances were assessed by calculating the integrated area under the receiver operating characteristic curve (iAUC). The ability of radiomic models to predict OS (iAUC = 0.580, 95% confidence interval (CI): 0.558–0.591) and PFS (iAUC = 0.625, 95% CI = 0.600–0.633) was validated in the testing cohort. The combination of radiomic signatures with traditional clinical parameters outperformed clinical variables alone in the prediction of survival outcomes (observed iAUC increments = 0.279 [95% CI = 0.225–0.334] and 0.293 [95% CI = 0.232–0.351] for OS and PFS, respectively). In summary, MRI radiomics has value for predicting survival outcomes in patients with hypopharyngeal cancer treated with CCRT, especially when combined with clinical prognostic variables.

MRI radiomics in head and neck cancer from reproducibility to combined approaches

“… Ref. Image pre-processing Features Feature normalization Signature threshold R1 Bos 2021 29 (iii); (iv); (v) 10 (T1wCont) Z-score (no details) NA R2 Chen 2022 30 NA 6 (T1wCont) NA NA R3 Alfieri 2022 31 (i); (ii); (iii); (iv); (v) 3 (T1w, T1wCont, T2w) Z-score (µ and σ provided) NA R4 Siow 2022 32 (ii); (iii); (iv); (v) 4 (T1wCont) NA 0.5 R5 Mossinelli 2023 33 NA 2 (T2w) Unspecified standardization NA R6 Bologna 2023 19 (i); (ii); (iii); (iv); (v) 4 (T1w, T2w) Z-score (µ and σ provided) NA R7 Corti 2023 18 (iii); (iv); (v) 5 (T2w) Z-score (µ and σ provided) 0.082 (i) denoising, through a 3D Gaussian filter with a 3 × 3x3 voxel kernel and σ = 0.5; (ii) intensity non-uniformities correction, through the N4ITK algorithm; (iii) intensity standardization, using Z-score; (iv) voxel size resampling to a specific isotropic resolution, through B-spline interpolation, and (v) fixed-bin histogram discretization, with a specific number of bins. µ: media value for Z-score standardization σ: standard deviation for Z-score sta...…”