MRI特征可提升乳腺癌新辅助化疗疗效预测准确率
Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy
- 用标准化MRI指标分析肿瘤形态，筛选与治疗响应相关的影像特征
- 非刺状边缘和单发病灶是独立预测完全缓解的关键影像标志
- 结合临床生物标志物能显著提升机器学习模型的预测性能
目的：评估治疗前磁共振成像（MRI）描述符与乳腺癌（BC）新辅助化疗（NAC）后病理完全缓解（pCR）的关系。方法：纳入2016至2020年间接受NAC并完成乳腺MRI检查的患者，进行回顾性单中心研究。使用标准化BI-RADS及T2加权成像上的乳腺水肿评分描述MRI表现。通过单变量与多变量逻辑回归分析评估各变量与残余癌负荷相关性。采用随机森林分类器在70%数据上训练，并在剩余30%验证。结果：共129例患者中，59例（46%）达到pCR（管腔型：7/37, 19%；三阴性：30/55, 55%；HER2阳性：22/37, 59%）。与pCR相关的临床生物学因素包括乳腺癌亚型（p<0.001）、T分期0/I/II（p=0.008）、较高Ki67水平（p=0.005）和较高肿瘤浸润淋巴细胞（TILs）水平（p=0.016）。单变量分析显示，卵圆形或圆形形状（p=0.047）、单发病灶（p=0.026）、非刺状边缘（p=0.018）、无非肿块强化（NME）（p=0.024）以及较小的MRI肿瘤大小（p=0.031）均与pCR显著相关。多变量分析中，单发病灶和非刺状边缘仍为独立相关因素。将显著的MRI特征加入临床生物标志物构建随机森林模型后，敏感度（0.67对0.62）、特异度（0.69对0.67）和精确度（0.71对0.67）均显著提高。结论：非刺状边缘和单发病灶是独立于其他因素的pCR预测指标，能有效提升模型性能。临床意义：整合治疗前MRI特征与临床生物标志物（如TILs）的多模态方法可用于构建机器学习模型，识别非响应高风险患者，从而优化治疗策略。
原文摘要 · Abstract (English)
Objectives: To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Materials \& Methods: Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases. Results: Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n=7/37, 19%), triple negative (TN) (n=30/55, 55%), HER2+ (n=22/37, 59%). Clinical and biological items associated with pCR were BC subtype (p<0.001), T stage 0/I/II (p=0.008), higher Ki67 (p=0.005) and higher tumor-infiltrating lymphocytes levels (p=0.016). Univariate analysis showed that the following MRI features, oval or round shape (p=0.047), unifocality (p=0.026), non-spiculated margins (p=0.018), no associated non-mass enhancement (NME) (p = 0.024) and a lower MRI size (p = 0.031) were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67) and precision (0.71 versus 0.67) for pCR prediction. Conclusion: Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC. Clinical Relevance Statement: A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including TILs, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes
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