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Grahovac, M., Spielvogel, C.P., Krajnc, D., Ecsedi, B., Traub-Weidinger, T., Rasul, S., et al. (2023) Machine Learning Predictive Performance Evaluation of Conventional and Fuzzy Radiomics in Clinical Cancer Imaging Cohorts. European Journal of Nuclear Medicine and Molecular Imaging, 50, 1607-1620.
https://doi.org/10.1007/s00259-023-06127-1

has been cited by the following article:

• TITLE: Recent Research Advances in Imaging of Prostate Cancer

  AUTHORS: Quansen Hu, Shaoping Cheng, Changsheng Yuan, Chenghua Jin

  KEYWORDS: Prostate Cancer, Novel Ultrasound, Positron Emission Tomography/Computed Tomography, Multiparametric Magnetic Resonance Imaging, Imaging Histology

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.12 No.6, June 19, 2024

  ABSTRACT: Imaging study plays a crucial role in the diagnosis of prostate cancer. As early screening and management of prostate cancer has evolved over the past decade, research is now focusing on how to detect clinically significant prostate cancer and avoid overdiagnosis accurately. This article provides an overview of recent advances in imaging in prostate cancer diagnosis, including new ultrasound imaging techniques, positron emission computed tomography, multiparametric magnetic resonance imaging, and emerging areas such as imaging histology, by systematically reviewing and summarizing the existing literature.