Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, # 1473, Houston, TX, 77030, USA.
Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Sci Rep. 2024 Jul 12;14(1):16073. doi: 10.1038/s41598-024-66220-9.
Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.
三阴性乳腺癌（TNBC）常采用新辅助全身治疗（NAST）。我们研究了基于 NAST 早期获得的多参数磁共振成像（MRI）的放射组学模型是否可以预测病理完全缓解（pCR）。共纳入 163 例 I-III 期 TNBC 患者，基线及 NAST 第 2 周期（C2）和第 4 周期后均行多参数 MRI 检查。78 例（48%）患者达到 pCR，85 例（52%）未达到 pCR。结合动态对比增强 MRI 和弥散加权成像的放射组学特征的 36 个多变量模型的受试者工作特征曲线下面积（AUC）＞0.7。表现最好的模型结合了 C2 与基线之间的 35 个相对差异的放射组学特征，在训练集中 AUC 值为 0.905，在测试集中 AUC 值为 0.802。两位读者对 pCR 预测模型的一致性高，AUC 值也非常相似。我们的数据支持基于多参数 MRI 的放射组学模型用于早期预测 TNBC 的 NAST 反应。
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