Abstract #1803

Using Radiomics Analysis derived from Multiple MR Series to Differentiate Adenocarcinoma and Squamous cell carcinoma of Cervix

wei wang^1,2, Yining Jiao³, LiChi Zhang³, Jianhui Ding^1,2, Weijun Peng^1,2, and Qian Wang³

¹Department of Radiology, Fudan University Shanghai Cancer Center (FUSCC), Shanghai, China, ²Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China, ³Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

In this study, we investigated the feasibility of differentiating AC from SCC using radiomics features extracted from multiple MR series (T2TRA, T2SAG, ADC, CETRA and CESAG). The results indicated that radiomics features identified by careful feature selection and machine learning can have good performance for distinguishing AC from SCC. In particular, T2SAG sequences had the best ability, followed by ADC and T2TRA sequences, as demonstrated by both unsupervised clustering and supervised classification. In general, we conclude that ACs have greater textural heterogeneity than SCCs, which was revealed through radiomics.

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