OBJECTIVES: Radiomics refers to the extraction and analysis of advanced quantitative imaging from medical images to diagnose and/or predict diseases. In the dentistry field, the bone data from mandibular condyles could be computationally analyzed using the voxel information provided by high-resolution CBCT scans to increase the diagnostic power of temporomandibular joint (TMJ) conditions. However, such quantitative information demands innovative computational software, algorithm implementation, and validation. Our study's aim was to compare a newly developed BoneTexture application to two-consolidated software with previous applications in the medical field, Ibex and BoneJ, to extract bone morphometric and textural features from mandibular condyles. METHODS: We used an imaging database of HR-CBCT TMJs scans with an isotropic voxel size of 0.08 mm3 . A single group with 66 distinct mandibular condyles composed the final sample. We calculated 18 variables for bone textural features and 5 for bone morphometric measurements using the Ibex, BoneJ and BoneTexture applications. Spearman correlation and Bland-Altman plot analyses were done to compare the agreement among software. RESULTS: The results showed a high Spearman correlation among the software applications ( r = 0.7-1), with statistical significance for all variables, except Grey Level Non-Uniformity and Short Run Emphasis. The Bland-Altman vertical axis showed, in general, good agreement between the software applications and the horizontal axis showed a narrow average distribution for Correlation, Long Run Emphasis and Long Run High Grey Level Emphasis. CONCLUSIONS: Our data showed consistency among the three applications to analyze bone radiomics in high-resolution CBCT. Further studies are necessary to evaluate the applicability of those variables as new bone imaging biomarkers to diagnose bone diseases affecting TMJs.
OBJECTIVES: Radiomics refers to the extraction and analysis of advanced quantitative imaging from medical images to diagnose and/or predict diseases. In the dentistry field, the bone data from mandibular condyles could be computationally analyzed using the voxel information provided by high-resolution CBCT scans to increase the diagnostic power of temporomandibular joint (TMJ) conditions. However, such quantitative information demands innovative computational software, algorithm implementation, and validation. Our study's aim was to compare a newly developed BoneTexture application to two-consolidated software with previous applications in the medical field, Ibex and BoneJ, to extract bone morphometric and textural features from mandibular condyles. METHODS: We used an imaging database of HR-CBCT TMJs scans with an isotropic voxel size of 0.08 mm3 . A single group with 66 distinct mandibular condyles composed the final sample. We calculated 18 variables for bone textural features and 5 for bone morphometric measurements using the Ibex, BoneJ and BoneTexture applications. Spearman correlation and Bland-Altman plot analyses were done to compare the agreement among software. RESULTS: The results showed a high Spearman correlation among the software applications ( r = 0.7-1), with statistical significance for all variables, except Grey Level Non-Uniformity and Short Run Emphasis. The Bland-Altman vertical axis showed, in general, good agreement between the software applications and the horizontal axis showed a narrow average distribution for Correlation, Long Run Emphasis and Long Run High Grey Level Emphasis. CONCLUSIONS: Our data showed consistency among the three applications to analyze bone radiomics in high-resolution CBCT. Further studies are necessary to evaluate the applicability of those variables as new bone imaging biomarkers to diagnose bone diseases affecting TMJs.
Authors: Mary L Bouxsein; Stephen K Boyd; Blaine A Christiansen; Robert E Guldberg; Karl J Jepsen; Ralph Müller Journal: J Bone Miner Res Date: 2010-07 Impact factor: 6.741
Authors: Paul A Yushkevich; Joseph Piven; Heather Cody Hazlett; Rachel Gimpel Smith; Sean Ho; James C Gee; Guido Gerig Journal: Neuroimage Date: 2006-03-20 Impact factor: 6.556
Authors: Michael Doube; Michał M Kłosowski; Ignacio Arganda-Carreras; Fabrice P Cordelières; Robert P Dougherty; Jonathan S Jackson; Benjamin Schmid; John R Hutchinson; Sandra J Shefelbine Journal: Bone Date: 2010-09-15 Impact factor: 4.398
Authors: David W Dempster; Juliet E Compston; Marc K Drezner; Francis H Glorieux; John A Kanis; Hartmut Malluche; Pierre J Meunier; Susan M Ott; Robert R Recker; A Michael Parfitt Journal: J Bone Miner Res Date: 2013-01 Impact factor: 6.741
Authors: William Rogers; Sithin Thulasi Seetha; Turkey A G Refaee; Relinde I Y Lieverse; Renée W Y Granzier; Abdalla Ibrahim; Simon A Keek; Sebastian Sanduleanu; Sergey P Primakov; Manon P L Beuque; Damiënne Marcus; Alexander M A van der Wiel; Fadila Zerka; Cary J G Oberije; Janita E van Timmeren; Henry C Woodruff; Philippe Lambin Journal: Br J Radiol Date: 2020-02-26 Impact factor: 3.039
Authors: Amanda Drumstas Nussi; Sérgio Lucio Pereira de Castro Lopes; Catharina Simioni De Rosa; João Pedro Perez Gomes; Celso Massahiro Ogawa; Paulo Henrique Braz-Silva; Andre Luiz Ferreira Costa Journal: Oral Radiol Date: 2022-05-18 Impact factor: 1.852
Authors: Winston Zhang; Jonas Bianchi; Najla Al Turkestani; Celia Le; Romain Deleat-Besson; Antonio Ruellas; Lucia Cevidanes; Marilia Yatabe; Joao Goncalves; Erika Benavides; Fabiana Soki; Juan Prieto; Beatriz Paniagua; Kayvan Najarian; Jonathan Gryak; Reza Soroushmehr Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2021-11