Ramtohul Toulsie, Lollivier Derek, Spriet Justine, Jin Maxime, Djerroudi Lounes, Gaillard Thomas, Bonneau Claire, Loirat Delphine, Bello-Roufai Diana, Kirova Youlia, Loap Pierre, Malhaire Caroline, Vincent Salomon Anne, Bidard François-Clément, Tardivon Anne, Maillez Audrey, Wallaert Lauren, Ceugnart Luc, Nhy Caroline, Cabel Luc
Department of Radiology, Institut Curie, PSL Research University, 26 rue d'Ulm, Paris 75005, France.
Department of Radiology, Institut Oscar Lambret, Lille, France.
Radiology. 2025 Jul;316(1):e243824. doi: 10.1148/radiol.243824.
Background Neoadjuvant chemoimmunotherapy (NACI) has substantially improved pathologic complete response (pCR) rates in early triple-negative breast cancer (TNBC). However, the predictive accuracy of posttreatment MRI remains unexplored. Purpose To assess the performance of posttreatment MRI in the prediction of pCR in participants with TNBC treated with NACI. Materials and Methods In this prospective multicenter study (August 2021-June 2024), women with early TNBC were recruited from three centers (training set: Institut Curie; test set: Institut Godinot and Institut Oscar Lambret). Post-NACI dynamic contrast-enhanced MRI scans from multiple vendors were analyzed. Radiologic complete response (rCR)-defined as no enhancement in the tumor bed-was evaluated for predicting pCR. A multivariable logistic regression model incorporating rCR, nodal involvement, and Ki-67 index was developed and externally validated. In cases with residual enhancement (non-rCR), a radiomic score using shape and first-order features was tested. Results A total of 175 women were included in the training set (mean age, 49 years ± 11 [SD]) and 84 women in the external test set (mean age, 52 years ± 12). The rCR at MRI was predictive of pCR, with an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI: 0.75, 0.92). The combined model (rCR + nodal status + Ki-67) yielded an AUC of 0.88 (95% CI: 0.81, 0.96) in the test set. In node-negative patients with Ki-67 greater than 30%, the rCR false-discovery rate (ie, the proportion of rCR cases that were actually non-pCR or residual disease missed at breast MRI) was 3.6% (two of 56) in the training set and 3.5% (one of 29) in the test set; all cancers were limited to residual cancer burden I. In non-rCR cases, a model incorporating the radiomics score and lesion count achieved an AUC of 0.80 (95% CI: 0.69, 0.90). Conclusion Posttreatment rCR at MRI demonstrated strong predictive value for pCR in early TNBC following NACI. © RSNA, 2025 See also the editorial by Onishi in this issue.
背景 新辅助化疗免疫疗法(NACI)已显著提高了早期三阴性乳腺癌(TNBC)的病理完全缓解(pCR)率。然而,治疗后MRI的预测准确性仍未得到探索。目的 评估治疗后MRI在预测接受NACI治疗的TNBC患者pCR方面的性能。材料与方法 在这项前瞻性多中心研究(2021年8月至2024年6月)中,从三个中心招募了早期TNBC女性患者(训练集:居里研究所;测试集:戈迪诺研究所和奥斯卡·兰布雷特研究所)。分析了来自多个供应商的NACI治疗后的动态对比增强MRI扫描图像。评估将肿瘤床无强化定义为放射学完全缓解(rCR)以预测pCR。构建并外部验证了一个纳入rCR、淋巴结受累情况和Ki-67指数的多变量逻辑回归模型。在有残余强化(非rCR)的病例中,测试了使用形状和一阶特征的放射组学评分。结果 训练集共纳入175名女性(平均年龄49岁±11[标准差]),外部测试集纳入84名女性(平均年龄52岁±12)。MRI上的rCR可预测pCR,受试者操作特征曲线(AUC)下面积为0.83(95%CI:0.75,0.92)。联合模型(rCR+淋巴结状态+Ki-67)在测试集中的AUC为0.88(95%CI:0.81,0.96)。在Ki-67大于30%的淋巴结阴性患者中,训练集中rCR的假发现率(即乳腺MRI上实际为非pCR或遗漏的残余疾病的rCR病例比例)为3.6%(56例中的2例),测试集中为3.5%(29例中的1例);所有癌症均局限于残余癌负荷I级。在非rCR病例中,一个纳入放射组学评分和病灶数的模型的AUC为0.80(95%CI:0.69,0.90)。结论 MRI治疗后的rCR对NACI治疗后的早期TNBC患者的pCR具有很强的预测价值。©RSNA,2025 另见本期大西的社论。