Department of Breast Disease, Henan Breast Cancer Center, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China.
Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, 127 Dongming Road, Zhengzhou, Henan, 450008, China.
BMC Cancer. 2024 May 1;24(1):549. doi: 10.1186/s12885-024-12257-y.
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).
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.
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.
The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.
准确评估腋窝状态对于有腋窝淋巴结转移的乳腺癌患者新辅助治疗后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乳腺癌伴腋窝淋巴结转移患者是否能达到腋窝病理完全缓解（pCR）。
我们收集影像学数据，提取新辅助化疗（NAC）前后的纵向 CT 图像特征，分析放射组学特征与临床病理特征的相关性，并建立预测模型，以评估腋窝淋巴结转移的乳腺癌患者是否能达到 NAC 后的腋窝 pCR。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，根据预测效率和临床实用性最佳的模型建立列线图，并通过校准图进行验证。
共纳入 549 例腋窝淋巴结转移的乳腺癌患者。从 LASSO 回归中筛选出 42 个独立的放射组学特征，与临床病理特征构建逻辑回归模型（LR 放射组学-临床联合模型）。LR 放射组学-临床联合模型在训练集和验证集的预测性能的 AUC 分别为 0.861 和 0.891。对于 HR+/HER2-、HER2+和三阴性亚型，LR 放射组学-临床联合模型在训练集的最佳预测 AUC 分别为 0.756、0.812 和 0.928，在验证集的 AUC 分别为 0.757、0.777 和 0.838。
放射组学特征与临床病理特征的结合可以有效地预测 NAC 乳腺癌患者的腋窝 pCR 状态。
