Zhu Xuelin, Shen Jing, Zhang Huanlei, Wang Xiulin, Zhang Huihui, Yu Jing, Zhang Qing, Song Dongdong, Guo Liping, Zhang Dianlong, Zhu Ruiping, Wu Jianlin
Graduate School, Tianjin Medical University, Tianjin, China.
Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Front Oncol. 2022 Jun 6;12:916526. doi: 10.3389/fonc.2022.916526. eCollection 2022.
To explore the value of a predictive model combining the multiparametric magnetic resonance imaging (mpMRI) radiomics score (RAD-score), clinicopathologic features, and morphologic features for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in invasive breast carcinoma of no specific type (IBC-NST).
We enrolled, retrospectively and consecutively, 206 women with IBC-NST who underwent surgery after NAC and obtained pathological results from August 2018 to October 2021. Four RAD-scores were constructed for predicting the pCR based on fat-suppression T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI+C) and their combination, which was called mpMRI. The best RAD-score was combined with clinicopathologic and morphologic features to establish a nomogram model through binary logistic regression. The predictive performance of the nomogram was evaluated using the area under receiver operator characteristic (ROC) curve (AUC) and calibration curve. The clinical net benefit of the model was evaluated using decision curve analysis (DCA).
The mpMRI RAD-score had the highest diagnostic performance, with AUC of 0.848 among the four RAD-scores. T stage, human epidermal growth factor receptor-2 (HER2) status, RAD-score, and roundness were independent factors for predicting the pCR ( < 0.05 for all). The combined nomogram model based on these factors achieved AUCs of 0.930 and 0.895 in the training cohort and validation cohort, respectively, higher than other models ( < 0.05 for all). The calibration curve showed that the predicted probabilities of the nomogram were in good agreement with the actual probabilities, and DCA indicated that it provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and validation datasets.
The combined nomogram model based on the mpMRI RAD-score combined with clinicopathologic and morphologic features may improve the predictive performance for the pCR of NAC in patients with IBC-NST.
探讨将多参数磁共振成像（mpMRI）影像组学评分（RAD评分）、临床病理特征及形态学特征相结合的预测模型对非特殊类型浸润性乳腺癌（IBC-NST）新辅助化疗（NAC）后病理完全缓解（pCR）的预测价值。
回顾性连续纳入206例IBC-NST患者，这些患者于2018年8月至2021年10月接受NAC后手术并获得病理结果。基于脂肪抑制T2加权成像（FS-T2WI）、扩散加权成像（DWI）、对比增强T1加权成像（T1WI+C）及其组合（即mpMRI）构建了4个用于预测pCR的RAD评分。将最佳RAD评分与临床病理及形态学特征相结合，通过二元逻辑回归建立列线图模型。使用受试者操作特征（ROC）曲线下面积（AUC）和校准曲线评估列线图的预测性能。使用决策曲线分析（DCA）评估模型的临床净效益。
mpMRI RAD评分具有最高的诊断性能，在4个RAD评分中AUC为0.848。T分期、人表皮生长因子受体2（HER2）状态、RAD评分及圆度是预测pCR的独立因素（均P<0.05）。基于这些因素的联合列线图模型在训练队列和验证队列中的AUC分别为0.930和0.895，高于其他模型（均P<0.05）。校准曲线显示列线图的预测概率与实际概率吻合良好，DCA表明在训练和验证数据集中，通过决策曲线分析，其比不治疗或全部治疗方案提供了更多的净效益。
基于mpMRI RAD评分联合临床病理及形态学特征的联合列线图模型可能会提高IBC-NST患者NAC后pCR的预测性能。
