Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg
Introduction<p>The intravoxel incoherent motion (IVIM) model of diffusion weighted imaging (DWI) provides imaging biomarkers for breast tumor characterization. It has been extensively applied for both diagnostic and prognostic goals in breast cancer, with increasing evidence supporting its cli...
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2025
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| _version_ | 1852022520342904832 |
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| author | Dibash Basukala (20772110) |
| author2 | Artem Mikheev (20772113) Xiaochun Li (143491) Judith D. Goldberg (7928777) Nima Gilani (20772116) Linda Moy (20772119) Katja Pinker (2742532) Savannah C. Partridge (9986472) Debosmita Biswas (18481035) Masako Kataoka (818662) Maya Honda (9469770) Mami Iima (774464) Sunitha B. Thakur (14983581) Eric E. Sigmund (20772122) |
| author2_role | author author author author author author author author author author author author author |
| author_facet | Dibash Basukala (20772110) Artem Mikheev (20772113) Xiaochun Li (143491) Judith D. Goldberg (7928777) Nima Gilani (20772116) Linda Moy (20772119) Katja Pinker (2742532) Savannah C. Partridge (9986472) Debosmita Biswas (18481035) Masako Kataoka (818662) Maya Honda (9469770) Mami Iima (774464) Sunitha B. Thakur (14983581) Eric E. Sigmund (20772122) |
| author_role | author |
| dc.creator.none.fl_str_mv | Dibash Basukala (20772110) Artem Mikheev (20772113) Xiaochun Li (143491) Judith D. Goldberg (7928777) Nima Gilani (20772116) Linda Moy (20772119) Katja Pinker (2742532) Savannah C. Partridge (9986472) Debosmita Biswas (18481035) Masako Kataoka (818662) Maya Honda (9469770) Mami Iima (774464) Sunitha B. Thakur (14983581) Eric E. Sigmund (20772122) |
| dc.date.none.fl_str_mv | 2025-02-24T05:07:35Z |
| dc.identifier.none.fl_str_mv | 10.3389/fonc.2025.1524634.s006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Image_5_Retrospective_BReast_Intravoxel_Incoherent_Motion_Multisite_BRIMM_multisoftware_study_jpeg/28467365 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Oncology and Carcinogenesis not elsewhere classified IVIM DWI breast cancer diagnosis multisite multisoftware radiomics robust |
| dc.title.none.fl_str_mv | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | Introduction<p>The intravoxel incoherent motion (IVIM) model of diffusion weighted imaging (DWI) provides imaging biomarkers for breast tumor characterization. It has been extensively applied for both diagnostic and prognostic goals in breast cancer, with increasing evidence supporting its clinical relevance. However, variable performance exists in literature owing to the heterogeneity in datasets and quantification methods.</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1<sub>st</sub> order radiomics of IVIM parameters perfusion fraction (f<sub>p</sub>), pseudo-diffusion (D<sub>p</sub>) and tissue diffusivity (D<sub>t</sub>). Pearson correlation (r) coefficients between software pairs were computed while logistic regression model was implemented to test malignancy detection and assess robustness of the IVIM metrics.</p>Results<p>D<sub>t</sub> and f<sub>p</sub> maps generated from different software showed consistency across platforms while D<sub>p</sub> maps were variable. The average correlation between the three software pairs at three different sites for 1<sub>st</sub> order radiomics of IVIM parameters were D<sub>t</sub>min/D<sub>t</sub>max/D<sub>t</sub>mean/D<sub>t</sub>variance/D<sub>t</sub>skew/D<sub>t</sub>kurt: 0.791/0.891/0.98/0.815/0.697/0.584; f<sub>p</sub>max/f<sub>p</sub>mean/f<sub>p</sub>variance/f<sub>p</sub>skew/f<sub>p</sub>kurt: 0.615/0.871/0.679/0.541/0.433; D<sub>p</sub>max/D<sub>p</sub>mean/D<sub>p</sub>variance/D<sub>p</sub>skew/D<sub>p</sub>kurt: 0.616/0.56/0.587/0.454/0.51. Correlation between least-squares algorithms were the highest. D<sub>t</sub>mean showed highest area under the ROC curve (AUC) with 0.85 and lowest coefficient of variation (CV) with 0.18% for benign and malignant differentiation using logistic regression. D<sub>t</sub> metrics were highly diagnostic as well as consistent along with f<sub>p</sub> metrics.</p>Discussion<p>Multiple 1<sub>st</sub> order radiomic features of D<sub>t</sub> and f<sub>p</sub> obtained from a heterogeneous multi-site breast lesion dataset showed strong software robustness and/or diagnostic utility, supporting their potential consideration in controlled prospective clinical trials.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_c01d7d2ac7e2cbdd5e8545667d913ef6 |
| identifier_str_mv | 10.3389/fonc.2025.1524634.s006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28467365 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpegDibash Basukala (20772110)Artem Mikheev (20772113)Xiaochun Li (143491)Judith D. Goldberg (7928777)Nima Gilani (20772116)Linda Moy (20772119)Katja Pinker (2742532)Savannah C. Partridge (9986472)Debosmita Biswas (18481035)Masako Kataoka (818662)Maya Honda (9469770)Mami Iima (774464)Sunitha B. Thakur (14983581)Eric E. Sigmund (20772122)Oncology and Carcinogenesis not elsewhere classifiedIVIMDWIbreast cancerdiagnosismultisitemultisoftwareradiomicsrobustIntroduction<p>The intravoxel incoherent motion (IVIM) model of diffusion weighted imaging (DWI) provides imaging biomarkers for breast tumor characterization. It has been extensively applied for both diagnostic and prognostic goals in breast cancer, with increasing evidence supporting its clinical relevance. However, variable performance exists in literature owing to the heterogeneity in datasets and quantification methods.</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1<sub>st</sub> order radiomics of IVIM parameters perfusion fraction (f<sub>p</sub>), pseudo-diffusion (D<sub>p</sub>) and tissue diffusivity (D<sub>t</sub>). Pearson correlation (r) coefficients between software pairs were computed while logistic regression model was implemented to test malignancy detection and assess robustness of the IVIM metrics.</p>Results<p>D<sub>t</sub> and f<sub>p</sub> maps generated from different software showed consistency across platforms while D<sub>p</sub> maps were variable. The average correlation between the three software pairs at three different sites for 1<sub>st</sub> order radiomics of IVIM parameters were D<sub>t</sub>min/D<sub>t</sub>max/D<sub>t</sub>mean/D<sub>t</sub>variance/D<sub>t</sub>skew/D<sub>t</sub>kurt: 0.791/0.891/0.98/0.815/0.697/0.584; f<sub>p</sub>max/f<sub>p</sub>mean/f<sub>p</sub>variance/f<sub>p</sub>skew/f<sub>p</sub>kurt: 0.615/0.871/0.679/0.541/0.433; D<sub>p</sub>max/D<sub>p</sub>mean/D<sub>p</sub>variance/D<sub>p</sub>skew/D<sub>p</sub>kurt: 0.616/0.56/0.587/0.454/0.51. Correlation between least-squares algorithms were the highest. D<sub>t</sub>mean showed highest area under the ROC curve (AUC) with 0.85 and lowest coefficient of variation (CV) with 0.18% for benign and malignant differentiation using logistic regression. D<sub>t</sub> metrics were highly diagnostic as well as consistent along with f<sub>p</sub> metrics.</p>Discussion<p>Multiple 1<sub>st</sub> order radiomic features of D<sub>t</sub> and f<sub>p</sub> obtained from a heterogeneous multi-site breast lesion dataset showed strong software robustness and/or diagnostic utility, supporting their potential consideration in controlled prospective clinical trials.</p>2025-02-24T05:07:35ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.3389/fonc.2025.1524634.s006https://figshare.com/articles/figure/Image_5_Retrospective_BReast_Intravoxel_Incoherent_Motion_Multisite_BRIMM_multisoftware_study_jpeg/28467365CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284673652025-02-24T05:07:35Z |
| spellingShingle | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg Dibash Basukala (20772110) Oncology and Carcinogenesis not elsewhere classified IVIM DWI breast cancer diagnosis multisite multisoftware radiomics robust |
| status_str | publishedVersion |
| title | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg |
| title_full | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg |
| title_fullStr | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg |
| title_full_unstemmed | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg |
| title_short | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg |
| title_sort | Image 5_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg |
| topic | Oncology and Carcinogenesis not elsewhere classified IVIM DWI breast cancer diagnosis multisite multisoftware radiomics robust |