Room: Exhibit Hall | Forum 2
Purpose: To investigate radiation-induced changes of CT radiomics in parotid glands (PG) and salivary amount (SA), in predicting acute xerostomia during radiotherapy (RT) for Nasopharyngeal Cancer (NPC).
Methods: CT and outcome data for 35 patients with stage Iâ€“IVB, randomly from the NPC clinical trial registered on the clinicaltrials.gov (ID: NCT01762514), were involved prospectively. All patients received radical treatment based on IMRT with a prescription dose of 68.1 Gy in 30 fractions. The patients ages ranged 24â€“72y and each patient had five CT sets acquired at treatment position at 0áµ—Ê°, 10áµ—Ê°, 20áµ—Ê°, 30áµ—Ê° fractions during RT, and at 3-month after RT. The PGs were delineated on each CT set by a radiation oncologist and verified independently by another one. Patients salivary were also collected every other 10-days during RT. Acute xerostomia was evaluated based on RTOG acute toxicity scoring and SA. 1703 radiomics features were calculated for PG from each CT set, including feature value at 0áµ—Ê° fraction (FVâ‚€), FVâ‚?â‚€ and delta FV (Î”FVâ‚?â‚€â‚‹â‚€) respectively. Extensive experiments were conducted and finally RidgeCV and Recursive Feature Elimination (RFE) were used for feature selection, while Linear Regression was used for SAâ‚ƒâ‚€ prediction.
Results: Substantial changes in various radiomics metrics of PG during RT were observed. 14 normalized FV (NFV), selected from â–³NFVâ‚?â‚€â‚‹â‚€, NFVâ‚€ and NFVâ‚?â‚€, could have the best predictive ability with a Mean Squared Error (MSE) of 0.0569. The model predicts the level of acute xerostomia with a precision of 92.2% and a sensitivity of 100%, as compared to the clinical observed SA.
Conclusion: This study demonstrates that radiation-induced acute xerostomia can be early predicted based on the radiomics changes of the PG during RT. SA, NFVâ‚€, NFVâ‚?â‚€ and especially â–³NFVâ‚?â‚€â‚‹â‚€ could have the best predictive ability of acute xerostomia level for individual patient based on the delta radiomics.
Funding Support, Disclosures, and Conflict of Interest: Student's Platform for Innovation and Entrepreneurship Training Program - 201813902075; 201813902071; 201713902050; Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis and Treatment - PJS140011504; Natural Science Foundation of Guangdong Province - 2017A030310217; Pearl River S&T Nova Program of Guangzhou - 201710010162
IM/TH- Image Analysis (Single modality or Multi-modality): Computer-aided decision support systems (detection, diagnosis, risk prediction, staging, treatment response assessment/monitoring, prognosis prediction)