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Xu, Zhan; Rauch, David E; Mohamed, Rania M; Pashapoor, Sanaz; Zhou, Zijian; Panthi, Bikash; Son, Jong Bum; Hwang, Ken-Pin; Musall, Benjamin C; Adrada, Beatriz E; Candelaria, Rosalind P; Leung, Jessica W T; Le-Petross, Huong T C; Lane, Deanna L; Perez, Frances; White, Jason; Clayborn, Alyson; Reed, Brandy; Chen, Huiqin; Sun, Jia; Wei, Peng; Thompson, Alastair; Korkut, Anil; Huo, Lei; Hunt, Kelly K; Litton, Jennifer K; Valero, Vicente; Tripathy, Debu; Yang, Wei; Yam, Clinton; Ma, Jingfei
Cancers, 10/2023, Letnik: 15, Številka: 19Journal Article
Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.
