From the Department of Radiology (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.) and Medical Research Collaborating Center (Y.C.), Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.Y.K., N.C., S.H.L., S.M.H., E.S.K., J.M.C., W.K.M.).
Radiology. 2021 May;299(2):290-300. doi: 10.1148/radiol.2021203871. Epub 2021 Mar 23.
Background There is an increasing need to develop a more accurate prediction model for pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. Purpose To develop a nomogram based on MRI and clinical-pathologic variables to predict pCR. Materials and Methods In this single-center retrospective study, consecutive women with stage II-III breast cancer who underwent NAC followed by surgery between January 2011 and December 2017 were considered for inclusion. The women were divided into a development cohort between January 2011 and September 2015 and a validation cohort between October 2015 and December 2017. Clinical-pathologic data were collected, and mammograms and MRI scans obtained before and after NAC were analyzed. Logistic regression analyses were performed to identify independent variables associated with pCR in the development cohort from which the nomogram was created. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope. Results A total of 359 women (mean age, 49 years ± 10 [standard deviation]) were in the development cohort and 351 (49 years ± 10) in the validation cohort. Hormone receptor negativity (odds ratio [OR], 3.1; 95% CI: 1.4, 7.1; = .006), high Ki-67 index (OR, 1.05; 95% CI: 1.03, 1.07; < .001), and post-NAC MRI variables, including small tumor size (OR, 0.6; 95% CI: 0.4, 0.9; = .03), low lesion-to-background parenchymal signal enhancement ratio (OR, 0.2; 95% CI: 0.1, 0.6; = .004), and absence of enhancement in the tumor bed (OR, 3.8; 95% CI: 1.4, 10.5; = .009) were independently associated with pCR. The nomogram incorporating these variables showed good discrimination (AUC, 0.90; 95% CI: 0.86, 0.94) and calibration abilities (calibration slope, 0.91; 95% CI: 0.69, 1.13) in the independent validation cohort. Conclusion A nomogram incorporating hormone receptor status, Ki-67 index, and MRI variables showed good discrimination and calibration abilities in predicting pathologic complete response. © RSNA, 2021 See also the editorial by Imbriaco and Ponsiglione in this issue.
背景：目前，人们越来越需要开发一种更准确的预测模型，以预测新辅助化疗（NAC）后乳腺癌的病理完全缓解（pCR）。目的：基于 MRI 和临床病理变量开发一个列线图，以预测 pCR。材料与方法：本单中心回顾性研究纳入了 2011 年 1 月至 2017 年 12 月期间接受 NAC 后手术治疗的 II 期-III 期乳腺癌连续女性患者。这些女性被分为 2011 年 1 月至 2015 年 9 月的发展队列和 2015 年 10 月至 2017 年 12 月的验证队列。收集临床病理数据，并分析 NAC 前后的乳腺 X 线摄影和 MRI 扫描。对发展队列中的独立变量进行 logistic 回归分析，以确定与 pCR 相关的独立变量，并据此创建列线图。通过受试者工作特征曲线（ROC）下面积（AUC）和校准斜率评估列线图的性能。结果：共有 359 名女性（平均年龄 49 岁±10[标准差]）纳入发展队列，351 名女性（49 岁±10）纳入验证队列。激素受体阴性（比值比[OR]，3.1；95%CI：1.4，7.1； =.006）、高 Ki-67 指数（OR，1.05；95%CI：1.03，1.07； <.001）和 NAC 后的 MRI 变量，包括肿瘤体积小（OR，0.6；95%CI：0.4，0.9； =.03）、病变与背景实质信号增强比低（OR，0.2；95%CI：0.1，0.6； =.004）和肿瘤床无强化（OR，3.8；95%CI：1.4，10.5； =.009）与 pCR 独立相关。纳入这些变量的列线图在独立验证队列中显示出良好的区分能力（AUC，0.90；95%CI：0.86，0.94）和校准能力（校准斜率，0.91；95%CI：0.69，1.13）。结论：纳入激素受体状态、Ki-67 指数和 MRI 变量的列线图在预测病理完全缓解方面具有良好的区分能力和校准能力。©RSNA，2021 参见本期 Imbriaco 和 Ponsiglione 的社论。
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