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Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies.
Akin, Oguz; Lema-Dopico, Alfonso; Paudyal, Ramesh; Konar, Amaresha Shridhar; Chenevert, Thomas L; Malyarenko, Dariya; Hadjiiski, Lubomir; Al-Ahmadie, Hikmat; Goh, Alvin C; Bochner, Bernard; Rosenberg, Jonathan; Schwartz, Lawrence H; Shukla-Dave, Amita.
Afiliação
  • Akin O; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
  • Lema-Dopico A; Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA.
  • Paudyal R; Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA.
  • Konar AS; Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA.
  • Chenevert TL; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Malyarenko D; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Hadjiiski L; Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
  • Al-Ahmadie H; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
  • Goh AC; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
  • Bochner B; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
  • Rosenberg J; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
  • Schwartz LH; Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA.
  • Shukla-Dave A; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Cancers (Basel) ; 15(22)2023 Nov 18.
Article em En | MEDLINE | ID: mdl-38001728
ABSTRACT
This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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