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Abstract #2381

Detailed MRI Report Findings Play Important Role in Establishing Predictive Machine Learning Models For Recurrence in Nasopharyngeal Carcinoma

Weijing Zhang1, Chunyan Cui1, Huali Ma1, Li Tian1, Annan Dong1, Zhiqiang Tian2, Xinlei Deng3, Xucheng Zhang3, Nian Lu1, Haojiang Li1, and Lizhi Liu1

1Sun Yat-sen University Cancer Center, Guangzhou, China, 2Xi’an Jiaotong University, Xi an, China, 3Sun Yat-sen University, Guangzhou, China

To compare different machine-learning approaches, develop the best predictive model for recurrence, and explore interactions between different types of data in non-metastatic nasopharyngeal carcinoma (NPC). Auto Machine Learning (AutoML) classifier plus the minimum redundancy and maximum correlation (mRMR) method achieved the best predictive accuracy to build prediction model for recurrence in NPC. The model incorporating databases including T/N stage data, clinical data, or detailed MRI report findings showed the best performance. Detailed MRI report findings have potential as useful biomarkers in predicting NPC recurrence, which may help develop more individualized multidisciplinary treatment and follow-up strategies.

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