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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Breast Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1452128

Combined morphology and radiomics of intravoxel incoherent movement as a predictive model for the pathologic complete response before neoadjuvant chemotherapy in patients with breast cancer

Provisionally accepted
Yunyan Zheng Yunyan Zheng 1Hui Zhang Hui Zhang 2*Huijian Chen Huijian Chen 1*Yang Song Yang Song 3Ping Lu Ping Lu 4*Mingping Ma Mingping Ma 1,5Mengbo Lin Mengbo Lin 6*Muzhen He Muzhen He 1*
  • 1 Shengli Clinical College of Fujian Medical University & Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China
  • 2 Shengli Clinical College of Fujian Medical University & Department of Surgical Oncology, Fujian provincial hospital, Fuzhou 350001, China
  • 3 MR Scientific Markerting, Siemens Healthineers Ltd, Shanghai, China
  • 4 Fujian Medical University, Fuzhou, Fujian Province, China
  • 5 Department of Radiology, Fujian Provincial Hospital, Fuzhou, China
  • 6 Shengli Clinical College of Fujian Medical University & Department of Surgical Oncology, Fujian provincial hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China

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

    Background: To develop a predictive model using baseline imaging of morphology and radiomics derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) to determine the pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) in breast cancer patients.Methods: A total of 265 patients who underwent 3.0 T MRI scans before NACT were examined. Among them, 113 female patients with stage II-III breast cancer were included. The training data set consisted of 79 patients (31/48=pCR/Non-PCR, npCR), while the remaining 34 cases formed the validation cohort (13/21=pCR/npCR).Radiomics and conventional magnetic resonance imaging features analysis were performed. To build a nomogram model that integrates the radiomics signature and conventional imaging, a logistic regression method was employed. The performance evaluation of the nomogram involved the area under the receiver operating characteristic curve (AUC), a decision curve analysis, and the calibration slope. Results: In an assessment for predicting pCR, the radiomics model displayed an AUC of 0.778 and 0.703 for the training and testing cohorts, respectively. Conversely, the morphology model exhibited an AUC of 0.721 and 0.795 for the training and testing cohorts, respectively. The nomogram displayed superior predictive discrimination with an AUC of 0.862 for the training cohort and 0.861 for the testing cohort. Decision curve analyses indicated that the nomogram provided the highest clinical net benefit. Conclusion: Performing a nomogram consisting of integrated morphology and radiomics assessment using IVIM-DWI before NACT enables effective prediction of pCR in breast cancer. This predictive model therefore can facilitate medical professionals in making individualized treatment decisions.

    Keywords: intravoxel incoherent motion (IVIM), breast cancer, Neoadjuvant chemotherapy (NACT), Radiomics, Pathologic complete response (pCR)

    Received: 20 Jun 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Zheng, Zhang, Chen, Song, Lu, Ma, Lin and He. 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:
    Hui Zhang, Shengli Clinical College of Fujian Medical University & Department of Surgical Oncology, Fujian provincial hospital, Fuzhou 350001, China
    Huijian Chen, Shengli Clinical College of Fujian Medical University & Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China
    Ping Lu, Fujian Medical University, Fuzhou, 350108, Fujian Province, China
    Mengbo Lin, Shengli Clinical College of Fujian Medical University & Department of Surgical Oncology, Fujian provincial hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China
    Muzhen He, Shengli Clinical College of Fujian Medical University & Department of Radiology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China

    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.