PURPOSE Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is commonly accepted as the gold standard to assess outcome after NAC in breast cancer patients. 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) has unique value in tumor staging, predicting prognosis, and evaluating treatment response. Our aim was to determine if we could identify radiomic predictors from PET/CT in breast cancer patient therapeutic efficacy prior to NAC. METHODS This retrospective study included 100 breast cancer patients who received NAC; there were 2210 PET/CT radiomic features extracted. Unsupervised and supervised machine learning models were used to identify the prognostic radiomic predictors through the following: (1) selection of the significant (p < 0.05) imaging features from consensus clustering and the Wilcoxon signed-rank test; (2) selection of the most discriminative features via univariate random forest (Uni-RF) and the Pearson correlation matrix (PCM); and (3) determination of the most predictive features from a traversal feature selection (TFS) based on a multivariate random forest (RF). The prediction model was constructed with RF and then validated with 10-fold cross-validation for 30 times and then independently validated. The performance of the radiomic predictors was measured in terms of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS The PET/CT radiomic predictors achieved a prediction accuracy of 0.857 (AUC = 0.844) on the training split set and 0.767 (AUC = 0.722) on the independent validation set. When age was incorporated, the accuracy for the split set increased to 0.857 (AUC = 0.958) and 0.8 (AUC = 0.73) for the independent validation set and both outperformed the clinical prediction model. We also found a close association between the radiomic features, receptor expression, and tumor T stage. CONCLUSION Radiomic predictors from pre-treatment PET/CT scans when combined with patient age were able to predict pCR after NAC. We suggest that these data will be valuable for patient management.

中文翻译：

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乳腺癌患者对新辅助化疗的病理完全反应（pCR）的18F-FDG PET / CT放射性预测因子。

目的对乳腺癌新辅助化疗（NAC）的病理完全反应（pCR）通常被认为是评估NAC疗效的金标准。18F-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（PET / CT）在肿瘤分期，预测预后和评估治疗反应方面具有独特的价值。我们的目标是确定在NAC之前是否可以从PET / CT中确定放射线预测因子对乳腺癌患者的治疗效果。方法这项回顾性研究包括100名接受NAC的乳腺癌患者。提取了2210个PET / CT放射特征。无监督和有监督的机器学习模型用于通过以下方式识别预后放射学预测因子：（1）选择显着性（p <0。05）来自共识聚类和Wilcoxon秩检验的成像特征；（2）通过单变量随机森林（Uni-RF）和Pearson相关矩阵（PCM）选择最具区分性的特征；（3）根据多元随机森林（RF）从遍历特征选择（TFS）确定最具预测性的特征。使用RF构建预测模型，然后使用10倍交叉验证对预测模型进行30次验证，然后进行独立验证。根据曲线下面积（AUC），敏感性，特异性，阳性预测值（PPV）和阴性预测值（NPV）来衡量放射预测指标的性能。结果PET / CT放射性预测因子在训练拆分集上的预测准确度为0.857（AUC = 0.844），在训练拆分集上的预测准确度为0.767（AUC = 0）。722）。纳入年龄后，独立验证集的拆分集准确性提高到0.857（AUC = 0.958）和0.8（AUC = 0.73），均优于临床预测模型。我们还发现放射学特征，受体表达和肿瘤T期之间密切相关。结论结合患者年龄，来自治疗前PET / CT扫描的放射学预测因子能够预测NAC后的pCR。我们建议这些数据对于患者管理将是有价值的。结论结合患者年龄，来自治疗前PET / CT扫描的放射学预测因子能够预测NAC后的pCR。我们建议这些数据对于患者管理将是有价值的。结论结合患者年龄，来自治疗前PET / CT扫描的放射学预测因子能够预测NAC后的pCR。我们建议这些数据对于患者管理将是有价值的。