Literature DB >> 29662558

Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies.

Ji Eun Park1, Ho Sung Kim1.   

Abstract

Radiomics utilizes high-dimensional imaging data to discover the association with diagnostic, prognostic, predictive endpoint or radiogenomics. It is an emerging field of study that potentially depicts the intratumoral heterogeneity from quantitative and classified high-throughput data. The radiomics approach has an analytic pipeline where the imaging features are extracted, processed and analyzed. At this point, special data handling is essential because it faces issues of a high-dimensional biomarker compared to a single biomarker approach. This article describes the potential role of radiomics in oncologic studies, the basic analytic pipeline and special data handling with high-dimensional data to facilitate the radiomics approach as a tool for personalized medicine in oncology.

Keywords:  High-dimensional; Imaging; Magnetic resonance; Modeling; Neuro-oncology; Radiomics

Year:  2018        PMID: 29662558      PMCID: PMC5897262          DOI: 10.1007/s13139-017-0512-7

Source DB:  PubMed          Journal:  Nucl Med Mol Imaging        ISSN: 1869-3474


  44 in total

1.  Model-based boosting in high dimensions.

Authors:  Torsten Hothorn; Peter Bühlmann
Journal:  Bioinformatics       Date:  2006-08-29       Impact factor: 6.937

2.  Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features.

Authors:  Philipp Kickingereder; David Bonekamp; Martha Nowosielski; Annekathrin Kratz; Martin Sill; Sina Burth; Antje Wick; Oliver Eidel; Heinz-Peter Schlemmer; Alexander Radbruch; Jürgen Debus; Christel Herold-Mende; Andreas Unterberg; David Jones; Stefan Pfister; Wolfgang Wick; Andreas von Deimling; Martin Bendszus; David Capper
Journal:  Radiology       Date:  2016-09-16       Impact factor: 11.105

3.  Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

Authors:  Luke Macyszyn; Hamed Akbari; Jared M Pisapia; Xiao Da; Mark Attiah; Vadim Pigrish; Yingtao Bi; Sharmistha Pal; Ramana V Davuluri; Laura Roccograndi; Nadia Dahmane; Maria Martinez-Lage; George Biros; Ronald L Wolf; Michel Bilello; Donald M O'Rourke; Christos Davatzikos
Journal:  Neuro Oncol       Date:  2015-07-16       Impact factor: 12.300

4.  Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models.

Authors:  Philipp Kickingereder; Sina Burth; Antje Wick; Michael Götz; Oliver Eidel; Heinz-Peter Schlemmer; Klaus H Maier-Hein; Wolfgang Wick; Martin Bendszus; Alexander Radbruch; David Bonekamp
Journal:  Radiology       Date:  2016-06-20       Impact factor: 11.105

5.  Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response.

Authors:  Philipp Kickingereder; Michael Götz; John Muschelli; Antje Wick; Ulf Neuberger; Russell T Shinohara; Martin Sill; Martha Nowosielski; Heinz-Peter Schlemmer; Alexander Radbruch; Wolfgang Wick; Martin Bendszus; Klaus H Maier-Hein; David Bonekamp
Journal:  Clin Cancer Res       Date:  2016-10-10       Impact factor: 12.531

6.  Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.

Authors:  Leland S Hu; Shuluo Ning; Jennifer M Eschbacher; Leslie C Baxter; Nathan Gaw; Sara Ranjbar; Jonathan Plasencia; Amylou C Dueck; Sen Peng; Kris A Smith; Peter Nakaji; John P Karis; C Chad Quarles; Teresa Wu; Joseph C Loftus; Robert B Jenkins; Hugues Sicotte; Thomas M Kollmeyer; Brian P O'Neill; William Elmquist; Joseph M Hoxworth; David Frakes; Jann Sarkaria; Kristin R Swanson; Nhan L Tran; Jing Li; J Ross Mitchell
Journal:  Neuro Oncol       Date:  2016-08-08       Impact factor: 12.300

7.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.

Authors:  David A Gutman; Lee A D Cooper; Scott N Hwang; Chad A Holder; Jingjing Gao; Tarun D Aurora; William D Dunn; Lisa Scarpace; Tom Mikkelsen; Rajan Jain; Max Wintermark; Manal Jilwan; Prashant Raghavan; Erich Huang; Robert J Clifford; Pattanasak Mongkolwat; Vladimir Kleper; John Freymann; Justin Kirby; Pascal O Zinn; Carlos S Moreno; Carl Jaffe; Rivka Colen; Daniel L Rubin; Joel Saltz; Adam Flanders; Daniel J Brat
Journal:  Radiology       Date:  2013-02-07       Impact factor: 11.105

8.  Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings.

Authors:  Lin Lu; Ross C Ehmke; Lawrence H Schwartz; Binsheng Zhao
Journal:  PLoS One       Date:  2016-12-29       Impact factor: 3.240

9.  Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.

Authors:  Hui Li; Yitan Zhu; Elizabeth S Burnside; Erich Huang; Karen Drukker; Katherine A Hoadley; Cheng Fan; Suzanne D Conzen; Margarita Zuley; Jose M Net; Elizabeth Sutton; Gary J Whitman; Elizabeth Morris; Charles M Perou; Yuan Ji; Maryellen L Giger
Journal:  NPJ Breast Cancer       Date:  2016-05-11

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  22 in total

1.  Heterogeneity Does Matter for Tumor Characterization.

Authors:  Won Woo Lee
Journal:  Nucl Med Mol Imaging       Date:  2018-05-18

2.  Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients.

Authors:  Margherita Mottola; Alessandro Bevilacqua; Stephan Ursprung; Leonardo Rundo; Lorena Escudero Sanchez; Tobias Klatte; Iosif Mendichovszky; Grant D Stewart; Evis Sala
Journal:  Sci Rep       Date:  2021-06-02       Impact factor: 4.379

3.  Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters.

Authors:  Bo Li; Yong-Kang Xin; Gang Xiao; Gang-Feng Li; Shi-Jun Duan; Yu Han; Xiu-Long Feng; Wei-Qiang Yan; Wei-Cheng Rong; Shu-Mei Wang; Yu-Chuan Hu; Guang-Bin Cui
Journal:  Eur Radiol       Date:  2019-03-15       Impact factor: 5.315

4.  Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas.

Authors:  Ching-Chung Ko; Yang Zhang; Jeon-Hor Chen; Kai-Ting Chang; Tai-Yuan Chen; Sher-Wei Lim; Te-Chang Wu; Min-Ying Su
Journal:  Front Neurol       Date:  2021-05-14       Impact factor: 4.003

Review 5.  A Spotlight on the Role of Radiomics and Machine-Learning Applications in the Management of Intracranial Meningiomas: A New Perspective in Neuro-Oncology: A Review.

Authors:  Lara Brunasso; Gianluca Ferini; Lapo Bonosi; Roberta Costanzo; Sofia Musso; Umberto E Benigno; Rosa M Gerardi; Giuseppe R Giammalva; Federica Paolini; Giuseppe E Umana; Francesca Graziano; Gianluca Scalia; Carmelo L Sturiale; Rina Di Bonaventura; Domenico G Iacopino; Rosario Maugeri
Journal:  Life (Basel)       Date:  2022-04-14

Review 6.  Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging.

Authors:  Ji Eun Park; Philipp Kickingereder; Ho Sung Kim
Journal:  Korean J Radiol       Date:  2020-07-27       Impact factor: 3.500

Review 7.  Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives.

Authors:  Ji Eun Park; Seo Young Park; Hwa Jung Kim; Ho Sung Kim
Journal:  Korean J Radiol       Date:  2019-07       Impact factor: 3.500

8.  Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma.

Authors:  Bing Xiao; Yanghua Fan; Zhe Zhang; Zilong Tan; Huan Yang; Wei Tu; Lei Wu; Xiaoli Shen; Hua Guo; Zhen Wu; Xingen Zhu
Journal:  Front Oncol       Date:  2021-04-15       Impact factor: 6.244

9.  Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging.

Authors:  Simon Bernatz; Yauheniya Zhdanovich; Jörg Ackermann; Ina Koch; Peter J Wild; Daniel Pinto Dos Santos; Thomas J Vogl; Benjamin Kaltenbach; Nicolas Rosbach
Journal:  Sci Rep       Date:  2021-07-09       Impact factor: 4.379

10.  Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly.

Authors:  Yanghua Fan; Shenzhong Jiang; Min Hua; Shanshan Feng; Ming Feng; Renzhi Wang
Journal:  Front Endocrinol (Lausanne)       Date:  2019-08-27       Impact factor: 5.555

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