Literature DB >> 31690962

Quantitative imaging biomarkers in nuclear medicine: from SUV to image mining studies. Highlights from annals of nuclear medicine 2018.

Martina Sollini1,2, Francesco Bandera3,4, Margarita Kirienko5.   

Abstract

OBJECTIVE: Quantification in medical imaging is one of the main goals in research and clinical practice since it allows immediate understanding, objective communication, and comparison. Our aim was to summarize relevant investigations on quantification in nuclear medicine studies published in the volume 32 of Annals of Nuclear Medicine.
METHODS: In this article, we summarized the data of 14 selected papers from international research groups that were published between January and December 2018. This is a descriptive review with an inherently subjective selection of articles.
RESULTS: We discussed the role of parameters ranging from standardized uptake value to ratios, to flow within a region of interest, to volumetric parameters and to texture indices in different clinical scenarios in oncology, cardiology, and neurology.
CONCLUSIONS: In all the medical disciplines in which nuclear medicine examinations play a role, quantification is essential both in research and in clinical practice. Standardization and high-quality protocols are crucial for the success and reliability of imaging biomarkers.

Keywords:  Artificial intelligence; Quantification; Radiomics; Standardized uptake value; Volumetric parameters

Mesh:

Substances:

Year:  2019        PMID: 31690962     DOI: 10.1007/s00259-019-04531-0

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN: 1619-7070            Impact factor:   9.236


  44 in total

Review 1.  An introduction to PET and SPECT neuroreceptor quantification models.

Authors:  M Ichise; J H Meyer; Y Yonekura
Journal:  J Nucl Med       Date:  2001-05       Impact factor: 10.057

2.  Commentary: The rhetoric of evidence-based medicine.

Authors:  Andrew H Van de Ven; Margaret S Schomaker
Journal:  Health Care Manage Rev       Date:  2002

3.  Preliminary feasibility study on differential diagnosis between radiation-induced cerebral necrosis and recurrent brain tumor by means of [18F]fluoro-borono-phenylalanine PET/CT.

Authors:  Rouaa Beshr; Kayako Isohashi; Tadashi Watabe; Sadahiro Naka; Genki Horitsugi; Victor Romanov; Hiroki Kato; Shin-Ichi Miyatake; Eku Shimosegawa; Jun Hatazawa
Journal:  Ann Nucl Med       Date:  2018-09-07       Impact factor: 2.668

4.  Predictive value of primary tumor parameters using 18F-FDG PET/CT for occult lymph node metastasis in breast cancer with clinically negative axillary lymph node.

Authors:  Jang Yoo; Bom Sahn Kim; Hai-Jeon Yoon
Journal:  Ann Nucl Med       Date:  2018-08-09       Impact factor: 2.668

Review 5.  The advancing clinical impact of molecular imaging in CVD.

Authors:  Eric A Osborn; Farouc A Jaffer
Journal:  JACC Cardiovasc Imaging       Date:  2013-12

6.  Intratumoral heterogeneity in 18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate.

Authors:  David Molina-García; Ana María García-Vicente; Julián Pérez-Beteta; Mariano Amo-Salas; Alicia Martínez-González; María Jesús Tello-Galán; Ángel Soriano-Castrejón; Víctor M Pérez-García
Journal:  Ann Nucl Med       Date:  2018-06-05       Impact factor: 2.668

Review 7.  The biology and treatment of oligometastatic cancer.

Authors:  Diane K Reyes; Kenneth J Pienta
Journal:  Oncotarget       Date:  2015-04-20

Review 8.  PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology.

Authors:  M Sollini; L Cozzi; L Antunovic; A Chiti; M Kirienko
Journal:  Sci Rep       Date:  2017-03-23       Impact factor: 4.379

Review 9.  The reproducibility crisis in the age of digital medicine.

Authors:  Aaron Stupple; David Singerman; Leo Anthony Celi
Journal:  NPJ Digit Med       Date:  2019-01-29

10.  Correction of scan time dependence of standard uptake values in oncological PET.

Authors:  Jörg van den Hoff; Alexandr Lougovski; Georg Schramm; Jens Maus; Liane Oehme; Jan Petr; Bettina Beuthien-Baumann; Jörg Kotzerke; Frank Hofheinz
Journal:  EJNMMI Res       Date:  2014-04-03       Impact factor: 3.138

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  5 in total

1.  Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images.

Authors:  Panwen Tian; Bingxi He; Wei Mu; Kunqin Liu; Li Liu; Hao Zeng; Yujie Liu; Lili Jiang; Ping Zhou; Zhipei Huang; Di Dong; Weimin Li
Journal:  Theranostics       Date:  2021-01-01       Impact factor: 11.556

2.  Deep learning for the fully automated segmentation of the inner ear on MRI.

Authors:  Raymond van de Berg; Philippe Lambin; Akshayaa Vaidyanathan; Marly F J A van der Lubbe; Ralph T H Leijenaar; Marc van Hoof; Fadila Zerka; Benjamin Miraglio; Sergey Primakov; Alida A Postma; Tjasse D Bruintjes; Monique A L Bilderbeek; Hammer Sebastiaan; Patrick F M Dammeijer; Vincent van Rompaey; Henry C Woodruff; Wim Vos; Seán Walsh
Journal:  Sci Rep       Date:  2021-02-03       Impact factor: 4.379

Review 3.  Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology.

Authors:  Martina Sollini; Francesco Bartoli; Andrea Marciano; Roberta Zanca; Riemer H J A Slart; Paola A Erba
Journal:  Eur J Hybrid Imaging       Date:  2020-12-09

Review 4.  Metabolic Volume Measurements in Multiple Myeloma.

Authors:  Maria Emilia Seren Takahashi; Irene Lorand-Metze; Carmino Antonio de Souza; Claudio Tinoco Mesquita; Fernando Amorim Fernandes; José Barreto Campello Carvalheira; Celso Dario Ramos
Journal:  Metabolites       Date:  2021-12-16

5.  Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer.

Authors:  Margarita Kirienko; Martina Sollini; Marinella Corbetta; Emanuele Voulaz; Noemi Gozzi; Matteo Interlenghi; Francesca Gallivanone; Isabella Castiglioni; Rosanna Asselta; Stefano Duga; Giulia Soldà; Arturo Chiti
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-07       Impact factor: 9.236

  5 in total

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