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Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images

	author = {Yanxia Liu and Hongyu Shi and Sijuan Huang and Xiaochuan Chen and Huimin Zhou and Hui Chang and Yunfei Xia and Guohua Wang and Xin Yang},
	title = {Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {9},
	number = {7},
	year = {2019},
	keywords = {},
	abstract = {Background: Acute xerostomia is the most common side effect of radiation therapy (RT) for head and neck (H&N) malignancies. Investigating radiation-induced changes of computed tomography (CT) radiomics in parotid glands (PGs) and saliva amount (SA) can predict acute xerostomia during the RT for nasopharyngeal cancer (NPC).
Methods: CT and SA data from 35 patients with stages I–IVB were randomly collected from an NPC clinical trial registered on the (ID: NCT01762514). All patients received radical treatment based on intensity-modulated RT (IMRT) with a prescription dose of 68.1 Gy in 30 fractions. The patients’ ages ranged 24–72 years, and each patient had five CT sets acquired at treatment position: at the 0th, 10th, 20th, 30th fractions during the RT, and at 3-month later after the RT. The PGs for each CT set were delineated by a radiation oncologist and verified independently by another. Patients’ saliva was collected every other 10 days during the RT. Acute xerostomia was evaluated based on the RTOG acute toxicity scoring and the SA. In total, 1,703 radiomics features were calculated for PGs from each CT set, including feature value at 0th fraction (FV0F), FV10F, and delta FV (ΔFV10F-0F), respectively. Extensive experiments were conducted to achieve the optimal results. RidgeCV and Recursive Feature Elimination (RFE) were used for feature selection, while linear regression was used for predicting SA30F. Four more patients were added for independent testing.
Results: Substantial changes in various radiomics metrics of PGs were observed during the RT. Eight normalized feature value (NFV), selected from NFV0F, predicted SA10F with a mean square error (MSE) of 0.9042 and a R2 score of 0.7406. Fourteen NFV, selected from ΔNFV10F-0F, NFV0F, and NFV10F to predict SA30F, showed the best predictive ability with an MSE of 0.0569. The model predicted the level of acute xerostomia with a precision of 0.9220 and a sensitivity of 100%, compared to the clinical observed SA. For the independent test, the MSE of PSA30F was 0.0233.
Conclusions: This study demonstrated that radiation-induced acute xerostomia level could be early predicted based on the SA and radiomics changes of the PGs during IMRT delivery. SA, NFV0F, NFV10F, and especially ΔNFV10F-0F provided the best performance on acute xerostomia prediction for individual patient based on RidgeCV_RFE_LinearRegression method of delta radiomics.},
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