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Abstract：
OBJECTIVE: Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment.
METHODS: A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %.
CONCLUSIONS: Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.
METHODS: A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled. Radiomics features and clinicopathologic characteristics were analyzed through the analysis of variance and the least absolute shrinkage and selection operator algorithm. Finally, 24 and 28 optimal features were selected to construct machine learning models based on 6 algorithms for predicting each clinical outcome, respectively. The diagnostic performances of models were evaluated in the testing set by the area under the curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: Of the 268 patients, 94 (35.1 %) achieved breast cancer pathological complete response (bpCR) and of the 240 patients with clinical positive-node, 120 (50.0 %) achieved axillary lymph node pathological complete response (apCR). The multi-layer perception (MLP) algorithm yielded the best diagnostic performances in predicting apCR with an AUC of 0.825 (95 % CI, 0.764-0.886) and an accuracy of 77.1 %. And MLP also outperformed other models in predicting bpCR with an AUC of 0.852 (95 % CI, 0.798-0.906) and an accuracy of 81.3 %.
CONCLUSIONS: Our study established non-invasive combining models to predict the therapeutic response of primary breast cancer and axillary positive-node prior to NAC, which may help to modify preoperative treatment and determine post-NAC surgery strategy.
摘要：
目的：准确识别原发性乳腺癌和腋窝淋巴结对新辅助化疗（NAC）的阳性反应对于确定合适的手术策略很重要。我们旨在开发基于乳腺多参数磁共振成像和临床病理特征的组合模型，以预测治疗前原发性肿瘤和腋窝阳性淋巴结的治疗反应。
方法：共纳入268例完成NAC并接受手术的乳腺癌患者。通过方差分析和最小绝对收缩和选择算子算法，分析了影像组学特征和临床病理特征。最后,选择24和28个最佳特征来基于6种算法构建机器学习模型，用于预测每种临床结果，分别。在测试集中通过曲线下面积（AUC）评估模型的诊断性能，灵敏度,特异性，和准确性。
结果：在268名患者中，94例（35.1%）获得乳腺癌病理完全缓解（bpCR），240例临床淋巴结阳性患者中，120例(50.0%)达到腋窝淋巴结病理完全缓解(apCR)。多层感知（MLP）算法在预测apCR方面产生了最佳的诊断性能，AUC为0.825（95％CI，0.764-0.886），准确率为77.1％。MLP在预测bpCR方面也优于其他模型，AUC为0.852(95%CI，0.798-0.906)，准确率为81.3%。
结论：我们的研究建立了非侵入性联合模型来预测NAC之前原发性乳腺癌和腋窝阳性淋巴结的治疗反应，这可能有助于修改术前治疗和确定NAC后手术策略。
方法：共纳入268例完成NAC并接受手术的乳腺癌患者。通过方差分析和最小绝对收缩和选择算子算法，分析了影像组学特征和临床病理特征。最后,选择24和28个最佳特征来基于6种算法构建机器学习模型，用于预测每种临床结果，分别。在测试集中通过曲线下面积（AUC）评估模型的诊断性能，灵敏度,特异性，和准确性。
结果：在268名患者中，94例（35.1%）获得乳腺癌病理完全缓解（bpCR），240例临床淋巴结阳性患者中，120例(50.0%)达到腋窝淋巴结病理完全缓解(apCR)。多层感知（MLP）算法在预测apCR方面产生了最佳的诊断性能，AUC为0.825（95％CI，0.764-0.886），准确率为77.1％。MLP在预测bpCR方面也优于其他模型，AUC为0.852(95%CI，0.798-0.906)，准确率为81.3%。
结论：我们的研究建立了非侵入性联合模型来预测NAC之前原发性乳腺癌和腋窝阳性淋巴结的治疗反应，这可能有助于修改术前治疗和确定NAC后手术策略。
