LEADER 04587nam 22008535 450 001 9910841856303321 005 20260119154013.0 010 $a9783031484469 010 $a3031484460 024 7 $a10.1007/978-3-031-48446-9 035 $a(CKB)30597117400041 035 $a(MiAaPQ)EBC31201033 035 $a(Au-PeEL)EBL31201033 035 $a(DE-He213)978-3-031-48446-9 035 $a(OCoLC)1427063498 035 $a(EXLCZ)9930597117400041 100 $a20240224d2023 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBasics of Image Processing $eThe Facts and Challenges of Data Harmonization to Improve Radiomics Reproducibility /$fedited by Ángel Alberich-Bayarri, Fuensanta Bellvís-Bataller 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (0 pages) 225 1 $aImaging Informatics for Healthcare Professionals,$x2662-155X 311 08$a9783031484452 311 08$a3031484452 320 $aIncludes bibliographical references. 327 $aEra of AI quantitative imaging -- Principles of image formation in the different modalities -- How to extract radiomic features from the image? -- Facts and needs to improve Radiomics reproductibility -- What is harmonization and how does it differ from standardization? -- Harmonization in the image domain -- Harmonization across MRI -- Harmonization in the features domain -- Selection of the optimal harmonization method(s) for the problem under study -- Conclusions. 330 $aThis book, endorsed by EuSoMii, provides clinicians, researchers and scientists a useful handbook to navigate the intricate landscape of data harmonization, as we embark on a journey to improve the reproducibility, robustness and generalizability of multi-centric real-world data radiomic studies. In these pages, the authors delve into the foundational principles of radiomics and its far-reaching implications for precision medicine. They describe the different methodologies used in extracting quantitative features from medical images, the building blocks that enable the transformation of images into actionable predictions. This book sweeps from understanding the basis of harmonization to the implementation of all the knowledge acquired to date, with the aim of conveying the importance of harmonizing medical data and providing a useful guidance to enable its applicability and the future use of advanced radiomics-based models in routine clinical practice. As authors embark on this exploration of data harmonization in radiomics, they hope to ignite discussions, foster new ideas, and inspire researchers, clinicians, and scientists alike to embrace the challenges and opportunities that lie ahead. Together, they elevate radiomics as a reproducible technology and establish it as an indispensable and actionable tool in the quest for improved cancer diagnosis and treatment. 410 0$aImaging Informatics for Healthcare Professionals,$x2662-155X 606 $aRadiology 606 $aNuclear medicine 606 $aBioinformatics 606 $aBiomedical engineering 606 $aData mining 606 $aRadiology 606 $aNuclear Medicine 606 $aBioinformatics 606 $aBiomedical Engineering and Bioengineering 606 $aData Mining and Knowledge Discovery 606 $aDiagnòstic per la imatge$2thub 606 $aRadiografia mèdica$2thub 606 $aRadiologia mèdica$2thub 606 $aProcessament d'imatges$2thub 606 $aBioinformàtica$2thub 608 $aLlibres electrònics$2thub 615 0$aRadiology. 615 0$aNuclear medicine. 615 0$aBioinformatics. 615 0$aBiomedical engineering. 615 0$aData mining. 615 14$aRadiology. 615 24$aNuclear Medicine. 615 24$aBioinformatics. 615 24$aBiomedical Engineering and Bioengineering. 615 24$aData Mining and Knowledge Discovery. 615 7$aDiagnòstic per la imatge 615 7$aRadiografia mèdica 615 7$aRadiologia mèdica 615 7$aProcessament d'imatges 615 7$aBioinformàtica 676 $a616.0754 702 $aAlberich-Bayarri$b Angel 702 $aBellvi?s-Bataller$b Fuensanta 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910841856303321 996 $aBasics of Image Processing$94142062 997 $aUNINA