Dosiomic-based prediction of dysgeusia in head & neck cancer patients treated with radiotherapy

Fabio Busato; Davide Fiorentin; Andrea Bettinelli; Giuseppe Anile; Maria Grazia Ghi; Alessandro Scaggion; Francesca Dusi; Marta Paiusco; Marco Ferrari; Piero Nicolai; Francesca Marturano

doi:10.1016/j.radonc.2023.109896

Dosiomic-based prediction of dysgeusia in head & neck cancer patients treated with radiotherapy

Radiother Oncol. 2023 Nov:188:109896. doi: 10.1016/j.radonc.2023.109896. Epub 2023 Sep 1.

Affiliations

¹ Radiotherapy Unit, Veneto Institute of Oncology - IOV IRCCS, Padova, Italy; Department of Radiation Oncology, Abano Terme Hospital, Padova, Italy.
² Medical Physics Department, Veneto Institute of Oncology - IOV IRCCS, Padova, Italy.
³ Medical Oncology 2 Unit, Veneto Institute of Oncology - IOV IRCCS, Padova, Italy.
⁴ Section of Otorhinolaryngology-Head and Neck Surgery, Azienda Ospedaliera di Padova, University of Padova, Padova, Italy.
⁵ Medical Physics Department, Veneto Institute of Oncology - IOV IRCCS, Padova, Italy; A. A. Martinos Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. Electronic address: framarturan@gmail.com.

PMID: 37660751
DOI: 10.1016/j.radonc.2023.109896

Abstract

Purpose: To investigate the potential of dosiomics in predicting radiotherapy-induced taste distortion (dysgeusia) in head & neck (H&N) cancer.

Methods: A cohort of 80 H&N cancer patients treated with radical or adjuvant radiotherapy and with a follow-up of at least 24 months was enrolled. Treatment information, as well as tobacco and alcohol consumption were also collected. The whole tongue was manually delineated on the planning CT and mapped to the dose map retrieved from the treatment planning system. For every patient, 6 regions of the tongue were examined; for each of them, 145 dosiomic features were extracted from the dose map and fed to a logistic regression model to predict the grade of dysgeusia at follow-up, with and without including clinical features. A mean dose-based model was considered for reference.

Results: Both dosiomics and mean dose models achieved good prediction performance for acute dysgeusia with AUC up to 0.88. For the dosiomic model, the central and anterior ⅔ regions of the tongue were the most predictive. For all models, a gradual reduction in the performance was observed at later times for chronic dysgeusia prediction, with higher values for dosiomics. The inclusion of smoke and alcohol habits did not improve model performances.

Conclusion: The dosiomic analysis of the dose to the tongue identified features able to predict acute dysgeusia. Dosiomics resulted superior to the conventional mean dose-based model for chronic dysgeusia prediction. Larger, prospective studies are needed to support these results before integrating dosiomics in radiotherapy planning.

Keywords: Artificial intelligence; Cancer of head and neck; Dysgeusia; Radiotherapy.