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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1524634
This article is part of the Research Topic Quantitative Imaging: Revolutionizing Cancer Management with biological sensitivity, specificity, and AI integration View all 23 articles

Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) Multisoftware Study

Provisionally accepted
Dibash Basukala Dibash Basukala 1,2*Artem Mikheev Artem Mikheev 1Xiaochun Li Xiaochun Li 3Judith Goldberg Judith Goldberg 3Nima Gilani Nima Gilani 1Linda Moy Linda Moy 1Katja Pinker Katja Pinker 4,5Savannah C Partridge Savannah C Partridge 6,7Debosmita Biswas Debosmita Biswas 6,7Masako Kataoka Masako Kataoka 8Maya Honda Maya Honda 8,9Mami Iima Mami Iima 10,8Sunitha B Thakur Sunitha B Thakur 2Eric E Sigmund Eric E Sigmund 1,11
  • 1 Department of Radiology, Grossman School of Medicine, New York University, New York, New York, United States
  • 2 Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
  • 3 Department of Population Health, Grossman School of Medicine, New York University, New York, United States
  • 4 Department of Radiology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York City, New York, United States
  • 5 Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
  • 6 Department of Bioengineering, University of Washington, Seattle, California, United States
  • 7 Department of Radiology, School of Medicine, University of Washington, Seattle, Washington, United States
  • 8 Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
  • 9 Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan
  • 10 Department of Fundamental Development for Advanced Low Invasive Diagnostic Imaging, Graduate School of Medicine, Nagoya University, Nagoya, Aichi, Japan
  • 11 Center for Advanced Imaging Innovation and Research, New York University, New York, New York, United States

The final, formatted version of the article will be published soon.

    IntroductionThe 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.Methods 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 1st order radiomics of IVIM parameters perfusion fraction (fp), pseudo-diffusion (Dp) and tissue diffusivity (Dt). 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.ResultsDt and fp maps generated from different software showed consistency across platforms while Dp maps were variable. The average correlation between the three software pairs at three different sites for 1st order radiomics of IVIM parameters were Dtmin/Dtmax/Dtmean/Dtvariance/Dtskew/Dtkurt: 0.791/0.891/0.98/0.815/0.697/0.584; fpmax/fpmean/fpvariance/fpskew/fpkurt: 0.615/0.871/0.679/0.541/0.433; Dpmax/Dpmean/Dpvariance/Dpskew/Dpkurt: 0.616/0.56/0.587/0.454/0.51. Correlation between least-squares algorithms were the highest. Dtmean 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. Dt metrics were highly diagnostic as well as consistent along with fp metrics.DiscussionMultiple 1st order radiomic features of Dt and fp 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.

    Keywords: IVIM, DWI, breast cancer, diagnosis, multisite, multisoftware, Radiomics, Robust

    Received: 07 Nov 2024; Accepted: 28 Jan 2025.

    Copyright: © 2025 Basukala, Mikheev, Li, Goldberg, Gilani, Moy, Pinker, Partridge, Biswas, Kataoka, Honda, Iima, Thakur and Sigmund. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dibash Basukala, Department of Radiology, Grossman School of Medicine, New York University, New York, 10016, New York, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.