Literature DB >> 32060219

Introduction to Radiomics.

Marius E Mayerhoefer1,2, Andrzej Materka3, Georg Langs2, Ida Häggström4, Piotr Szczypiński3, Peter Gibbs5, Gary Cook6,7.   

Abstract

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.
© 2020 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  PET; artificial intelligence; machine learning; radiomics; single-photon emission tomography

Mesh:

Year:  2020        PMID: 32060219      PMCID: PMC9374044          DOI: 10.2967/jnumed.118.222893

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   11.082


  47 in total

1.  External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy.

Authors:  François Lucia; Dimitris Visvikis; Martin Vallières; Marie-Charlotte Desseroit; Omar Miranda; Philippe Robin; Pietro Andrea Bonaffini; Joanne Alfieri; Ingrid Masson; Augustin Mervoyer; Caroline Reinhold; Olivier Pradier; Mathieu Hatt; Ulrike Schick
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-12-07       Impact factor: 9.236

2.  Intratumor heterogeneity predicts metastasis of triple-negative breast cancer.

Authors:  Fang Yang; Yucai Wang; Quan Li; Lulu Cao; Zijia Sun; Juan Jin; Hehui Fang; Aiyu Zhu; Yan Li; Wenwen Zhang; Yanru Wang; Hui Xie; Jan-Åke Gustafsson; Shui Wang; Xiaoxiang Guan
Journal:  Carcinogenesis       Date:  2017-09-01       Impact factor: 4.944

3.  Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.

Authors:  Paulina E Galavis; Christian Hollensen; Ngoneh Jallow; Bhudatt Paliwal; Robert Jeraj
Journal:  Acta Oncol       Date:  2010-10       Impact factor: 4.089

Review 4.  Radiomics: the process and the challenges.

Authors:  Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies
Journal:  Magn Reson Imaging       Date:  2012-08-13       Impact factor: 2.546

5.  DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

Authors:  Ida Häggström; C Ross Schmidtlein; Gabriele Campanella; Thomas J Fuchs
Journal:  Med Image Anal       Date:  2019-03-30       Impact factor: 8.545

6.  Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer.

Authors:  Seung Hwan Moon; Jinho Kim; Je-Gun Joung; Hongui Cha; Woong-Yang Park; Jin Seok Ahn; Myung-Ju Ahn; Keunchil Park; Joon Young Choi; Kyung-Han Lee; Byung-Tae Kim; Se-Hoon Lee
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-08-25       Impact factor: 9.236

7.  Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas.

Authors:  Thomas Pyka; Jens Gempt; Daniela Hiob; Florian Ringel; Jürgen Schlegel; Stefanie Bette; Hans-Jürgen Wester; Bernhard Meyer; Stefan Förster
Journal:  Eur J Nucl Med Mol Imaging       Date:  2015-07-29       Impact factor: 9.236

8.  Iterative PET Image Reconstruction Using Convolutional Neural Network Representation.

Authors:  Georges El Fakhri
Journal:  IEEE Trans Med Imaging       Date:  2018-09-12       Impact factor: 10.048

Review 9.  The causes and consequences of genetic heterogeneity in cancer evolution.

Authors:  Rebecca A Burrell; Nicholas McGranahan; Jiri Bartek; Charles Swanton
Journal:  Nature       Date:  2013-09-19       Impact factor: 49.962

10.  The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.

Authors:  Ralph T H Leijenaar; Georgi Nalbantov; Sara Carvalho; Wouter J C van Elmpt; Esther G C Troost; Ronald Boellaard; Hugo J W L Aerts; Robert J Gillies; Philippe Lambin
Journal:  Sci Rep       Date:  2015-08-05       Impact factor: 4.379

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  127 in total

1.  Will Radiomics Replace Sentinel Lymph Node Biopsy?

Authors:  Abdullah S Eldaly; Ayman R Fath; Sarah M Mashaly
Journal:  Eur J Breast Health       Date:  2022-04-01

2.  Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study.

Authors:  Zhiying He; Yitao Mao; Shanhong Lu; Lei Tan; Juxiong Xiao; Pingqing Tan; Hailin Zhang; Guo Li; Helei Yan; Jiaqi Tan; Donghai Huang; Yuanzheng Qiu; Xin Zhang; Xingwei Wang; Yong Liu
Journal:  Eur Radiol       Date:  2022-06-24       Impact factor: 5.315

Review 3.  Artificial intelligence for nuclear medicine in oncology.

Authors:  Kenji Hirata; Hiroyuki Sugimori; Noriyuki Fujima; Takuya Toyonaga; Kohsuke Kudo
Journal:  Ann Nucl Med       Date:  2022-01-14       Impact factor: 2.668

4.  Initial CT-based radiomics nomogram for predicting in-hospital mortality in patients with traumatic brain injury: a multicenter development and validation study.

Authors:  Rui-Zhe Zheng; Zhi-Jie Zhao; Xi-Tao Yang; Shao-Wei Jiang; Yong-de Li; Wen-Jie Li; Xiu-Hui Li; Yue Zhou; Cheng-Jin Gao; Yan-Bin Ma; Shu-Ming Pan; Yang Wang
Journal:  Neurol Sci       Date:  2022-02-24       Impact factor: 3.307

Review 5.  Functional imaging using radiomic features in assessment of lymphoma.

Authors:  Marius E Mayerhoefer; Lale Umutlu; Heiko Schöder
Journal:  Methods       Date:  2020-07-04       Impact factor: 3.608

6.  Radiomics for intracerebral hemorrhage: are all small hematomas benign?

Authors:  Chenyi Zhan; Qian Chen; Mingyue Zhang; Yilan Xiang; Jie Chen; Dongqin Zhu; Chao Chen; Tianyi Xia; Yunjun Yang
Journal:  Br J Radiol       Date:  2020-12-17       Impact factor: 3.039

7.  Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study.

Authors:  Jialin Ding; Rubin Zhao; Qingtao Qiu; Jinhu Chen; Jinghao Duan; Xiujuan Cao; Yong Yin
Journal:  Quant Imaging Med Surg       Date:  2022-02

Review 8.  Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature.

Authors:  Chen-Yi Xie; Chun-Lap Pang; Benjamin Chan; Emily Yuen-Yuen Wong; Qi Dou; Varut Vardhanabhuti
Journal:  Cancers (Basel)       Date:  2021-05-19       Impact factor: 6.639

Review 9.  Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations.

Authors:  Yuan Cao; Xiao Zhong; Wei Diao; Jingshi Mu; Yue Cheng; Zhiyun Jia
Journal:  Cancers (Basel)       Date:  2021-05-18       Impact factor: 6.639

10.  Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status.

Authors:  Lukas Lenga; Simon Bernatz; Simon S Martin; Christian Booz; Christine Solbach; Rotraud Mulert-Ernst; Thomas J Vogl; Doris Leithner
Journal:  Cancers (Basel)       Date:  2021-05-18       Impact factor: 6.639

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