Work

Multiparametric MRI-based longitudinal-radiomics analysis for early prediction of treatment response of breast cancers to neoadjuvant chemotherapy

Year: 2025

Type: article

Abstract: PURPOSE: Our study examined the capability of longitudinal radiomics, based on multi-parametric MRI and validated with a radiomics strategy, to predict how breast cancer responds to neoadjuvant chemotherapy. METHODS AND MATERIALS: Longitudinal radiomics analysis was conducted using 3D Slicer software on dynamic contrast-enhanced, T2-weighted, and diffusion-weighted magnetic resonance imaging (MRI) before and during neoadjuvant therapy (after three to four courses). PyRadiomics (V3.0.1) was used to extract radiomics features from both unaltered and filtered images. The filters applied were Gaussian Laplacian and wavelet. A stacking strategy was used, which included random forest (RF), multilayer perceptron (MLP), logistic regression (LR), linear discriminant analysis (LDA), extreme gradient boosting (XGB) and support vector machine (SVM, radial basis function), to integrate the results from the base models and offer secondary prediction outputs. RESULTS: In distinguishing between Residual Cancer Burden (RCB) 0 and RCB 1-3, the longitudinal radiomics model achieved an area under the receiver operating characteristic curve (AUC) of 0.909 in the training set and 0.893 in the test set. When identifying RCB 3 and RCB 0-2, the maximum AUC of the longitudinal radiomics model in the training and test sets was 0.981 and 0.916, respectively. CONCLUSION: In summary, our research results exhibit good predictive power for RCB assessment. In the future, they will assist clinicians in guiding individualized treatment plans for breast cancer patients undergoing NAC.

Source: Applied Radiation and Isotopes

DOI: 10.1016/j.apradiso.2025.112278

Authors: Wei Li, Yuhong Huang, Teng Zhu, Guolin Ye, Kun Wang

Institutions: First People's Hospital of Foshan, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Southern Medical University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,220