PDF(Pubmed)
Abstract：
BACKGROUND: Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR).
METHODS: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots.
RESULTS: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively.
CONCLUSIONS: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.
METHODS: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots.
RESULTS: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively.
CONCLUSIONS: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.
摘要：
背景：准确评估有腋窝淋巴结转移的乳腺癌患者新辅助治疗后的腋窝状态对于选择合适的后续腋窝治疗决策非常重要。我们的目标是准确预测腋窝淋巴结转移的乳腺癌患者是否可以达到腋窝病理完全缓解（pCR）。
方法：我们收集影像学数据以提取新辅助化疗（NAC）前后的纵向CT图像特征，分析了影像组学与临床病理特征的相关性，并建立了预测腋窝淋巴结转移患者NAC后能否实现腋窝pCR的模型。通过决策曲线分析（DCA）确定模型的临床实用性。还进行了亚组分析。然后，根据具有最佳预测效率和临床实用性的模型制作了列线图,并使用校准图进行了验证.
结果：本研究共纳入549例腋窝淋巴结转移的乳腺癌患者。从LASSO回归中选择42个独立的影像组学特征构建具有临床病理特征的逻辑回归模型(LR影像组学-临床联合模型)。LR影像组学-临床组合模型预测性能的AUC在训练集中为0.861,在测试集中为0.891。对于HR+/HER2-，HER2+,和三阴性亚型，LR影像组学-临床组合模型在训练集中产生0.756、0.812和0.928的最佳预测AUC，测试集中的AUC为0.757、0.777和0.838，分别。
结论：影像组学特征与临床病理特征相结合可有效预测NAC乳腺癌患者的腋窝pCR状态。
方法：我们收集影像学数据以提取新辅助化疗（NAC）前后的纵向CT图像特征，分析了影像组学与临床病理特征的相关性，并建立了预测腋窝淋巴结转移患者NAC后能否实现腋窝pCR的模型。通过决策曲线分析（DCA）确定模型的临床实用性。还进行了亚组分析。然后，根据具有最佳预测效率和临床实用性的模型制作了列线图,并使用校准图进行了验证.
结果：本研究共纳入549例腋窝淋巴结转移的乳腺癌患者。从LASSO回归中选择42个独立的影像组学特征构建具有临床病理特征的逻辑回归模型(LR影像组学-临床联合模型)。LR影像组学-临床组合模型预测性能的AUC在训练集中为0.861,在测试集中为0.891。对于HR+/HER2-，HER2+,和三阴性亚型，LR影像组学-临床组合模型在训练集中产生0.756、0.812和0.928的最佳预测AUC，测试集中的AUC为0.757、0.777和0.838，分别。
结论：影像组学特征与临床病理特征相结合可有效预测NAC乳腺癌患者的腋窝pCR状态。
