Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

European Radiology Experimental

Martin W. Huellner¹  Irene A. Burger¹  Riccardo Laudicella¹  Tommaso Vincenzo Bartolotta²  Leda Lorenzon³  Désirée Deandreis⁴  Serena Grimaldi⁴  Virginia Liberini⁴  Daniele G. Nicolotti⁵  Ambra Buschiazzo⁵  Simona Peano⁵  Alberto Papaleo⁵  Michele Balma⁵  Andrea Bianchi⁵  Sergio Baldari⁶  Mohsen Farsad⁷ 

[1]Department of Nuclear Medicine, University Hospital Zurich, University of Zurich

[2]Department of Radiology, Fondazione Istituto G. Giglio, Ct.da Pietrapollastra

[3]Medical Physics Department, Central Bolzano Hospital

[4]Medical Physiopathology — A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino

[5]Nuclear Medicine Department, S. Croce e Carle Hospital

[6]Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, University of Messina

[7]Nuclear Medicine, Central Hospital Bolzano

关键词: Prostate cancer;    Positron emission tomography;    Artificial intelligence;    Radiomics;    Theragnostics;   

DOI  :  10.1186/s41747-022-00282-0

来源: DOAJ

【 摘 要 】

Abstract In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

【 授权许可】

Unknown