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Prediction of Acute Xerostomia in Nasopharyngeal Cancer for Radiotherapy Using 3D Convolutional Neural Network

Y LIU1*, X CHEN2 , s Huang3 , H SHI4 , H ZHOU5 , H CHANG6 , Y XIA7 , X Yang8 , (1) School of Software Engineering, South China University of Technology, Guangzhou, ,(2) School of Software Engineering, South China University of Technology, Guangzhou,(3) State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, (4) School of Software Engineering, South China University of Technology, Guangzhou, ,(5) the 74th Group Army Hospital of the Chinese People's Liberation Army, Guangzhou, ,(6) SYSUCC, Guangzhou, ,(7) SYSUCC, Guangzhou, ,(8) Sun Yat-Sen University Cancer Center (SYSUCC), Guangzhou City


(Sunday, 7/14/2019) 3:00 PM - 3:30 PM

Room: Exhibit Hall | Forum 4

Purpose: We evaluated the usefulness of 3D Convolutional Neural Network (3D-CNN) for acute xerostomia prediction during the radiotherapy (RT) for Nasopharyngeal Cancer (NPC).

Methods: 3D-CNN is an end-to-end framework, reducing the difficulty of multi-stage processing of the prevalent radiomics methods. We proposal a 3D-CNN adapted from ResNet which can automatically extract efficient deep-level non-linear features. 3D-ResNet reduces reliance on the manual operation that requires professional domain knowledge.89 NPC patients were selected from a clinical trial which was registered on the (ID: NCT01762514). All patients received radical treatment based on IMRT with a prescription dose of 68.1 Gy in 30 fractions. Acute xerostomia was evaluated based on RTOG acute toxicity scoring (TS). The patients ages ranged 24–72y and each patient had five CT sets acquired in treatment position at 0ᵗʰ, 10ᵗʰ, 20ᵗʰ, 30ᵗʰ fractions during RT, and at 3-month after RT. Each CT volume contained 90~130 slices with 512x512 pixels. Pre-processing includes redundant slices reduction, position calibration, pixels HU value clip according soft-tissue threshold and non-relevant area filter. The 3D-ResNet model is implemented via Pytorch and all experiments are conducted on a server with 4 GeForce_GTX_Titan_Xp_GPU. Three experiments sets are conducted: TS0F to predict TS10F (TS0F->10F), TS0F to predict TS30F (TS0F->30F) and TS10F to predict TS30F (TS10F->30F). The fine-grained classification accuracy (FA) of four xerostomia levels were used to evaluate and predict the acute xerostomia level at 10F and 30F.

Results: Similar accuracy in three sets of experiments is observed. Each set of the experiment are conducted under a 5 fold cross-validation setting and the average accuracy are taken as the final judging metric. The model predicts the level of acute xerostomia with a FA of 98.82%, 97.50%, 98.75% on TS0F->10F,TS0F->30F and TS10F->30F, respectively.

Conclusion: This study demonstrates that radiation-induced acute xerostomia level can be predicted using 3D-CNN.

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


CT, Image Analysis, Computer Vision


TH- response assessment : Machine learning

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