The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No.106 Zhongshan Er Road, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No.106 Zhongshan Er Road, Guangzhou, 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Acad Radiol. 2023 Sep;30 Suppl 2:S62-S70. doi: 10.1016/j.acra.2023.02.024. Epub 2023 Apr 4.
To develop an easy-to-use model by combining pretreatment MRI and clinicopathologic features for early prediction of tumor regression pattern to neoadjuvant chemotherapy (NAC) in breast cancer.
We retrospectively analyzed 420 patients who received NAC and underwent definitive surgery in our hospital from February 2012 to August 2020. Pathologic findings of surgical specimens were used as the gold standard to classify tumor regression patterns into concentric and non-concentric shrinkage. Morphologic and kinetic MRI features were both analyzed. Univariable and multivariable analyses were performed to select the key clinicopathologic and MRI features for pretreatment prediction of regression pattern. Logistic regression and six machine learning methods were used to construct prediction models, and their performance were evaluated with receiver operating characteristic curve.
Two clinicopathologic variables and three MRI features were selected as independent predictors to construct prediction models. The apparent area under the curve (AUC) of seven prediction models were in the range of 0.669-0.740. The logistic regression model yielded an AUC of 0.708 (95% confidence interval [CI]: 0.658-0.759), and the decision tree model achieved the highest AUC of 0.740 (95% CI: 0.691-0.787). For internal validation, the optimism-corrected AUCs of seven models were in the range of 0.592-0.684. There was no significant difference between the AUCs of the logistic regression model and that of each machine learning model.
Prediction models combining pretreatment MRI and clinicopathologic features are useful for predicting tumor regression pattern in breast cancer, which can assist to select patients who can benefit from NAC for de-escalation of breast surgery and modify treatment strategy.
为了建立一个简单易用的模型，我们结合预处理 MRI 与临床病理特征，以预测乳腺癌新辅助化疗（NAC）后的肿瘤退缩模式。
我们回顾性分析了 2012 年 2 月至 2020 年 8 月在我院接受 NAC 并接受根治性手术的 420 例患者。以手术标本的病理发现为金标准，将肿瘤退缩模式分为同心性和非同心性退缩。分析形态学和动力学 MRI 特征。进行单变量和多变量分析，以选择用于预测肿瘤退缩模式的关键临床病理和 MRI 特征。使用逻辑回归和 6 种机器学习方法构建预测模型，并通过接受者操作特征曲线评估其性能。
两个临床病理变量和三个 MRI 特征被选为独立预测因素来构建预测模型。七个预测模型的曲线下面积（AUC）范围为 0.669-0.740。逻辑回归模型的 AUC 为 0.708（95%置信区间[CI]：0.658-0.759），决策树模型的 AUC 最高，为 0.740（95%CI：0.691-0.787）。内部验证时，七个模型的校正后 AUC 范围为 0.592-0.684。逻辑回归模型与每个机器学习模型的 AUC 之间没有显著差异。
结合预处理 MRI 和临床病理特征的预测模型可用于预测乳腺癌的肿瘤退缩模式，有助于选择可从 NAC 中获益的患者，以减少乳腺癌手术的范围，并修改治疗策略。
