OBJECTIVE: The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity. MATERIALS AND METHODS: A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. RESULTS: A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. CONCLUSION: The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.
OBJECTIVE: The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity. MATERIALS AND METHODS: A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. RESULTS: A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. CONCLUSION: The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.
Authors: Roberto Berenguer; María Del Rosario Pastor-Juan; Jesús Canales-Vázquez; Miguel Castro-García; María Victoria Villas; Francisco Mansilla Legorburo; Sebastià Sabater Journal: Radiology Date: 2018-04-24 Impact factor: 11.105
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Authors: Alex Zwanenburg; Martin Vallières; Mahmoud A Abdalah; Hugo J W L Aerts; Vincent Andrearczyk; Aditya Apte; Saeed Ashrafinia; Spyridon Bakas; Roelof J Beukinga; Ronald Boellaard; Marta Bogowicz; Luca Boldrini; Irène Buvat; Gary J R Cook; Christos Davatzikos; Adrien Depeursinge; Marie-Charlotte Desseroit; Nicola Dinapoli; Cuong Viet Dinh; Sebastian Echegaray; Issam El Naqa; Andriy Y Fedorov; Roberto Gatta; Robert J Gillies; Vicky Goh; Michael Götz; Matthias Guckenberger; Sung Min Ha; Mathieu Hatt; Fabian Isensee; Philippe Lambin; Stefan Leger; Ralph T H Leijenaar; Jacopo Lenkowicz; Fiona Lippert; Are Losnegård; Klaus H Maier-Hein; Olivier Morin; Henning Müller; Sandy Napel; Christophe Nioche; Fanny Orlhac; Sarthak Pati; Elisabeth A G Pfaehler; Arman Rahmim; Arvind U K Rao; Jonas Scherer; Muhammad Musib Siddique; Nanna M Sijtsema; Jairo Socarras Fernandez; Emiliano Spezi; Roel J H M Steenbakkers; Stephanie Tanadini-Lang; Daniela Thorwarth; Esther G C Troost; Taman Upadhaya; Vincenzo Valentini; Lisanne V van Dijk; Joost van Griethuysen; Floris H P van Velden; Philip Whybra; Christian Richter; Steffen Löck Journal: Radiology Date: 2020-03-10 Impact factor: 29.146
Authors: Janna E van Timmeren; Ralph T H Leijenaar; Wouter van Elmpt; Jiazhou Wang; Zhen Zhang; André Dekker; Philippe Lambin Journal: Tomography Date: 2016-12
Authors: Natalie L Demirjian; Bino A Varghese; Steven Y Cen; Darryl H Hwang; Manju Aron; Imran Siddiqui; Brandon K K Fields; Xiaomeng Lei; Felix Y Yap; Marielena Rivas; Sharath S Reddy; Haris Zahoor; Derek H Liu; Mihir Desai; Suhn K Rhie; Inderbir S Gill; Vinay Duddalwar Journal: Eur Radiol Date: 2021-11-10 Impact factor: 5.315