Literature DB >> 29688159

Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters.

Roberto Berenguer1, María Del Rosario Pastor-Juan1, Jesús Canales-Vázquez1, Miguel Castro-García1, María Victoria Villas1, Francisco Mansilla Legorburo1, Sebastià Sabater1.   

Abstract

Purpose To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy. © RSNA, 2018 Online supplemental material is available for this article.

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Year:  2018        PMID: 29688159     DOI: 10.1148/radiol.2018172361

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  135 in total

1.  Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer.

Authors:  Jia Wu; Michael F Gensheimer; Nasha Zhang; Meiying Guo; Rachel Liang; Carrie Zhang; Nancy Fischbein; Erqi L Pollom; Beth Beadle; Quynh-Thu Le; Ruijiang Li
Journal:  J Nucl Med       Date:  2019-08-16       Impact factor: 10.057

2.  Matching and Homogenizing Convolution Kernels for Quantitative Studies in Computed Tomography.

Authors:  Dennis Mackin; Rachel Ger; Skylar Gay; Cristina Dodge; Lifei Zhang; Jinzhong Yang; Aaron Kyle Jones; Laurence Court
Journal:  Invest Radiol       Date:  2019-05       Impact factor: 6.016

3.  Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.

Authors:  Burak Kocak; Ece Ates; Emine Sebnem Durmaz; Melis Baykara Ulusan; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2019-02-12       Impact factor: 5.315

4.  Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging.

Authors:  Hubert Beaumont; Antoine Iannessi; Anne-Sophie Bertrand; Jean Michel Cucchi; Olivier Lucidarme
Journal:  Eur Radiol       Date:  2021-01-18       Impact factor: 5.315

5.  STAN-CT: Standardizing CT Image using Generative Adversarial Networks.

Authors:  Md Selim; Jie Zhang; Baowei Fei; Guo-Qiang Zhang; Jin Chen
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

Review 6.  Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis.

Authors:  Hidetaka Arimura; Mazen Soufi; Kenta Ninomiya; Hidemi Kamezawa; Masahiro Yamada
Journal:  Radiol Phys Technol       Date:  2018-10-29

7.  Integrating Tumor and Nodal Imaging Characteristics at Baseline and Mid-Treatment Computed Tomography Scans to Predict Distant Metastasis in Oropharyngeal Cancer Treated With Concurrent Chemoradiotherapy.

Authors:  Jia Wu; Micheal F Gensheimer; Nasha Zhang; Fei Han; Rachel Liang; Yushen Qian; Carrie Zhang; Nancy Fischbein; Erqi L Pollom; Beth Beadle; Quynh-Thu Le; Ruijiang Li
Journal:  Int J Radiat Oncol Biol Phys       Date:  2019-03-30       Impact factor: 7.038

Review 8.  How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts.

Authors:  Burak Kocak; Ece Ates Kus; Ozgur Kilickesmez
Journal:  Eur Radiol       Date:  2020-10-01       Impact factor: 5.315

9.  Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.

Authors:  Evangelos K Oikonomou; Musib Siddique; Charalambos Antoniades
Journal:  Cardiovasc Res       Date:  2020-11-01       Impact factor: 10.787

Review 10.  Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review.

Authors:  Natally Horvat; David D B Bates; Iva Petkovska
Journal:  Abdom Radiol (NY)       Date:  2019-11
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