Predicting radiotherapy-induced xerostomia in head and neck cancer patients using day-to-day kinetics of radiomics features. (October 2022)

• Record Type: Journal Article
• Authors: Berger, Thomas; Noble, David J.; Shelley, Leila E.A.; McMullan, Thomas; Bates, Amy; Thomas, Simon; Carruthers, Linda J.; Beckett, George; Duffton, Aileen; Paterson, Claire; Jena, Raj; McLaren, Duncan B.; Burnet, Neil G.; Nailon, William H.
• Is Part Of: Physics and imaging in radiation oncology. Volume 24(2022)
• Page Start: 95
• Page End: 101
• Publication Date: 2022-10
• Subjects: Head and neck cancer -- Radiomics -- Xerostomia -- Parotid gland -- Mega-voltage CT; Radiotherapy -- Periodicals; Radiation dosimetry -- Periodicals; Cancer -- Imaging -- Periodicals; Oncology -- Periodicals; 615.842
• DOI: 10.1016/j.phro.2022.10.004
• Languages: English
• ISSNs: 2405-6316

Abstract

Highlights: Textural features on daily images outperformed dose/volume parameters. The best models could be used before or at mid-treatment for personalisation. Area under the curve of the best models at 6, 12 and 24 months was 0.69, 0.74 and 0.86. Abstract: Background and purpose: The images acquired during radiotherapy for image-guidance purposes could be used to monitor patient-specific response to irradiation and improve treatment personalisation. We investigated whether the kinetics of radiomics features from daily mega-voltage CT image-guidance scans (MVCT) improve prediction of moderate-to-severe xerostomia compared to dose/volume parameters in radiotherapy of head-and-neck cancer (HNC). Materials and Methods: All included HNC patients (N = 117) received 30 or more fractions of radiotherapy with daily MVCTs. Radiomics features were calculated on the contra-lateral parotid glands of daily MVCTs. Their variations over time after each complete week of treatment were used to predict moderate-to-severe xerostomia (CTCAEv4.03 grade ≥ 2) at 6, 12 and 24 months post-radiotherapy. After dimensionality reduction, backward/forward selection was used to generate combinations of predictors. Three types of logistic regression model were generated for each follow-up time: 1) a pre-treatment reference model using dose/volume parameters, 2) a combination of dose/volume and radiomics-based predictors, and 3) radiomics-based predictors. The models were internally validated by cross-validation and bootstrapping and their performance evaluated using Area Under the Curve (AUC) on separate training and testing sets. Results: Moderate-to-severe xerostomia was reported by 46 %, 33 % and 26 % of the patients at 6, 12 and 24 months respectively. The selected models using radiomics-based features extracted at or before mid-treatment outperformed the dose-based models with an AUCtrain /AUCtest of 0.70/0.69, 0.76/0.74, 0.86/0.86 at 6, 12 and 24 months, respectively. Conclusion: Our results suggest that radiomics features calculated on MVCTs from the first half of the radiotherapy course improve prediction of moderate-to-severe xerostomia in HNC patients compared to a dose-based pre-treatment model.