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 (ΔCT2-1, ΔCT3-1, ΔCT3-2). 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 ΔCT2-1 + 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.
Disclosure statement
The authors declare no competing interests.
Additional information
Funding
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.