联合ADC组学标签和激素受体预测乳腺癌新辅助化疗病理完全化解

Combined Apparent Diffusion Coefficient Maps Radiomics Signature and Hormone Receptors Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer

目的探讨使用联合ADC图组学标签和激素受体模型来预测乳腺癌新辅助化疗(NC)病理完全缓解的价值。方法共收集了165例NC前行DWI检查女性乳腺癌患者资料,年龄在28-70岁之间。基于ADC共提取了396个组学特征并进行特征筛选。比较病理完全缓解和病理部分缓解组间临床特征的差异。将有差异临床特征和最优影像组学特征纳入logistic回归以建立模型。使用ROC曲线和决策曲线来评估模型的效能。结果在训练集和测试集中,激素受体表达状态在病理完全缓解和病理部分缓解组间的差异均具有统计学意义(P<0.05)。基于ADC图组学标签模型在训练集和测试集中预测乳腺癌NC病理完全缓解的曲线下面积(AUC)分别为0.785和0.639。基于联合ADC图组学标签和激素受体建立的联合模型在训练集和测试集中预测乳腺癌NC病理完全缓解的AUC分别为0.904和0.789。联合模型的临床获益高于基于ADC图组学标签模型。结论联合ADC图学标签和激素受体建立的联合模型对预测乳腺癌NC病理完全缓解具有较好的价值。

Objective To investigate the value of a combined apparent diffusion coefficient(ADC)mapsbased radiomics signature and hormone receptors model in predicting pathological complete response to neoadjuvant chemotherapy(NC)in breast cancer.Methods Data was collected from 165 female breast cancer patients,aged 28 to 70 years,who underwent NC prior to diffusion-weighted imaging examination.A total of 396 radiomics features were extracted from the ADC maps followed byfeature selection.The clinical features of the pathological complete response(PCR)group and pathological partial response(PPR)group were compared.Significant features and optimal radiomics features were then included in the logistic regression to establish the models.The performance of the model was evaluated using the ROC curve and decision analysis curve.Results There was significant difference in hormone receptors between the two groups both in the training and testing set.The performance of ADC maps maps-based radiomics model yield an AUC of 0.785 in the training set and an AUC of 0.639 in the testing set.The proposed model that combined(ADC)maps-based radiomics signature and hormone receptors yielded a maximum AUC of 0.904 in the training set and an AUC of 0.789 in the testing set.The decision curve showed that the benefit of the combined model was higher than that of the(ADC)maps-based radiomics model.Conclusion The combined model,which integrated with(ADC)maps-based radiomics signature and hormone receptors,may serve as a potential markers to successfully predict complete pathological response to NC.