Radiomics and dosiomics-based prediction of radiotherapy-induced xerostomia in head and neck cancer patients

Abstract

Background and aim

Dose-response modeling for radiotherapy-induced xerostomia in head and neck cancer (HN) patients is a promising frontier for personalized therapy. Feature extraction from diagnostic and therapeutic images (radiomics and dosiomics features) can be used for data-driven response modeling. The aim of this study is to develop xerostomia predictive models based on radiomics-dosiomics features.

Methods

Data from the cancer imaging archive (TCIA) for 31 HN cancer patients were employed. For all patients, parotid CT radiomics features were extracted, utilizing Lasso regression for feature selection and multivariate modeling. The models were developed by selected features from pretreatment (CT₁), mid-treatment (CT₂), post-treatment (CT₃), and delta features (ΔCT_2-1, ΔCT_3-1, ΔCT_3-2). We also considered dosiomics features extracted from the parotid dose distribution images (Dose model). Thus, combination models of radio-dosiomics (CT + dose & ΔCT + dose) were developed. Moreover, clinical, and dose-volume histogram (DVH) models were built. Nested 10-fold cross-validation was used to assess the predictive classification of patients into those with and without xerostomia, and the area under the receiver operative characteristic curve (AUC) was used to compare the predictive power of the models. The sensitivity and accuracy of models also were obtained.

Results

In total, 59 parotids were assessed, and 13 models were developed. Our results showed three models with AUC of 0.89 as most predictive, namely ΔCT_2-1 + Dose (Sensitivity 0.99, Accuracy 0.94 & Specificity 0.86), CT₃ model (Sensitivity 0.96, Accuracy 0.94 & Specificity 0.86) and DVH (Sensitivity 0.93, Accuracy 0.89 & Specificity 0.84). These models were followed by Clinical (AUC 0.89, Sensitivity 0.81, Accuracy 0.97 & Specificity 0.89) and CT₂ & Dose (AUC 0.86, Sensitivity 0.97, Accuracy 0.87 & Specificity 0.82). The Dose model (developed by dosiomics features only) had AUC, Sensitivity, Specificity, and Accuracy of 0.72, 0.98, 0.33, and 0.79 respectively.

Conclusion

Quantitative features extracted from diagnostic imaging during and after radiotherapy alone or in combination with dosiomics markers obtained from dose distribution images can be used for radiotherapy response modeling, opening up prospects for personalization of therapies toward improved therapeutic outcomes.

Keywords:

• Head and neck cancer
• radiotherapy
• radiomics
• dosiomics
• predictive modeling

Disclosure statement

The authors declare no competing interests.

Additional information

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Notes on contributors

Hamid Abdollahi

Hamid Abdollahi earned his PhD in Medical Physics from Iran University of Medical Sciences, Tehran, Iran. He now serves as a postdoctoral researcher at BC Cancer Research Institute in Vancouver, BC, Canada, specializing in radiobiology, radiomics, and personalized cancer treatment.

Tania Dehesh

Tania Dehesh specializes in Biostatistics and serves as a respected faculty member at Kerman University of Medical Sciences, Kerman, Iran. Her expertise lies in applying statistical methods to analyze complex health data, design clinical trials, and conduct epidemiological studies.

Neda Abdalvand

Neda Abdalvand holds a PhD in Medical Physics from Iran University of Medical Sciences in Tehran, Iran. She has extensive experience as a senior Medical Physicist at Golestan Hospital, Jundishapoor University of Medical Sciences in Ahvaz, Iran.

Arman Rahmim

Arman Rahmim is Professor of Radiology, Physics and Biomedical Engineering at the University of British Columbia (UBC), as well as Distinguished Scientist and Provincial Medical Imaging Physicist at BC Cancer, Vancouver, Canada. He leads the Quantitative Radiomolecular Imaging and Therapy (Qurit) lab.