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| Prediction of Multi-Ethnic Pathological Information Based on Multi-Parameter MRI |
| Liu Jiabao, Fan Ming, Li Lihua* |
| (Institute of Biomedical Engineering and Instrument, Hangzhou Dianzi University, Hangzhou 310018, China) |
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Abstract Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis and treatment of breast cancer. Among its modalities, dynamic contrast-enhanced MRI (DCE-MRI), apparent diffusion coefficient (ADC) imaging, and T2-weighted imaging (T2WI) provide comprehensive imaging information that offers valuable insights for predicting the pathological characteristics of breast cancer. In this study, we integrated multi-parametric imaging features derived from DCE-MRI, ADC, T2WI, delayed-phase imaging, DCE-MRI-based tumor subregions, tumor pharmacokinetics, and quantitative background parenchymal enhancement (BPE) features to conduct predictive analyses on both multi-ethnic classification and Ki-67 expression levels. A total of 781 breast cancer cases were collected. Following preprocessing, tumor regions and fibroglandular tissue (FGT) on multi-parametric MR images were delineated. Tumor subregions were segmented using pixel-based enhancement clustering into three enhancement patterns. Radiomics features were extracted from both tumors and FGT. In addition, pharmacokinetic features of the tumors on DCE-MRI, the percentage of FGT volume within the entire breast (%BPEF/B) and the proportion of enhancing FGT volume exceeding different signal intensity thresholds within the FGT (%BPEF/F) were calculated. An unsupervised feature selection strategy was employed to eliminate irrelevant features. A genetic algorithm combined with cross-validation was used to identify the optimal feature subset. A random forest classifier was constructed for prediction, and model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and ROC curve plotting. Furthermore, a feature-level fusion approach was adopted to integrate radiomic, kinetic, and BPE features, thereby constructing a multi-parametric imaging fusion model. In single-modality models for the multi-ethnic classification task (691 Han, 70 Tibetan, and 20 Yi patients), ADC images achieved the best AUCs of 0.841±0.024, 0.819±0.031, and 0.775±0.092 for Han, Tibetan, and Yi ethnic groups, respectively. For the Ki-67 expression prediction task (103 low expression, 498 high expression cases), the Fast Flow subregion yielded the best AUC of 0.756, with a sensitivity of 0.845 and specificity of 0.684. Upon fusion of multi-parametric imaging and BPE features, the AUCs for the multi-ethnic prediction task improved to 0.909±0.027 (Han), 0.890±0.031 (Tibetan), and 0.863±0.070 (Yi), which improved by 8.1%, 8.7%, and 11.3%, respectively compared to the best single-parameter model. For Ki-67 prediction, the fusion model incorporating ADC, DCE-MRI, and BPE features yielded an improved AUC of 0.770, with sensitivity and specificity increasing to 0.866 and 0.728, respectively. In summary, the integration of multi-parametric MRI features, quantitative BPE metrics, and tumor subregion enhancement patterns provides a more comprehensive characterization of breast cancer imaging and underlying biology. This approach enhances diagnostic accuracy and offers valuable guidance for personalized treatment and prognostic assessment in breast cancer patients across different ethnic groups.
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Received: 10 January 2025
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Corresponding Authors:
*E-mail: lilh@hdu.edu.cn
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