Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy. (July 2020)

• Record Type: Journal Article
• Authors: Wentzel, Andrew; Hanula, Peter; van Dijk, Lisanne V.; Elgohari, Baher; Mohamed, Abdallah S.R.; Cardenas, Carlos E.; Fuller, Clifton D.; Vock, David M.; Canahuate, Guadalupe; Marai, G.E.
• Is Part Of: Radiotherapy and oncology. Volume 148(2020)
• Page Start: 245
• Page End: 251
• Publication Date: 2020-07
• Subjects: Radiation therapy -- Head and Neck cancer -- Intensity-modulated radiotherapy -- Data Interpretation -- Statistical -- Medical Informatics -- Dysphagia; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.05.023
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: 200 H&N cancer patients under radiation treatment were analyzed. We aim to predict post-treatment dysphagia. Patient similarity was defined in terms of tumor location with respect to organs at risk. Tumor location and predicted doses were used to stratify the cohort into 4 groups. Patient groups were significantly correlated with dysphagia 6 months post treatment. Abstract: Purpose: Using a 200 Head and Neck cancer (HNC) patient cohort, we employ patient similarity based on tumor location, volume, and proximity to organs at risk to predict radiation-associated dysphagia (RAD) in a new patient receiving intensity modulated radiation therapy (IMRT). Material and methods: All patients were treated using curative-intent IMRT. Anatomical features were extracted from contrast-enhanced tomography scans acquired pre-treatment. Patient similarity was computed using a topological similarity measure, which allowed for the prediction of normal tissues' mean doses. We performed feature selection and clustering, and used the resulting groups of patients to forecast RAD. We used Logistic Regression (LG) cross-validation to assess the potential toxicity risk of these groupings. Results: Out of 200 patients, 34 patients were recorded as having RAD. Patient clusters were significantly correlated with RAD ( p < .0001). The area under the receiver-operator curve (AUC) using pre-established, baseline features gave a predictive accuracy of 0.79, while the addition of our cluster labels improved accuracy to 0.84. Conclusion: Our results show that spatial information available pre-treatment can be used to robustly identify groups of RAD high-risk patients. We identify feature sets that considerably improve toxicity risk prediction beyond what is possible using baseline features. Our results also suggest that similarity-based predicted mean doses to organs can be used as valid predictors of risk to organs.