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Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer.
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.
Afiliação
  • Xu Z; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Rauch DE; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Mohamed RM; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Pashapoor S; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Zhou Z; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Panthi B; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Son JB; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Hwang KP; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Musall BC; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Adrada BE; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Candelaria RP; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Leung JWT; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Le-Petross HTC; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Lane DL; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Perez F; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • White J; Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Clayborn A; Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Reed B; Department of Clinical Research Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Chen H; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Sun J; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Wei P; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Thompson A; Section of Breast Surgery, Baylor College of Medicine, Houston, TX 77030, USA.
  • Korkut A; Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Huo L; Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Hunt KK; Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Litton JK; Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Valero V; Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Tripathy D; Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Yang W; Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Yam C; Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Ma J; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers (Basel) ; 15(19)2023 Oct 02.
Article em En | MEDLINE | ID: mdl-37835523
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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article