Zhu Quing, Wang Liqun, Tannenbaum Susan, Ricci Andrew, DeFusco Patricia, Hegde Poornima
Breast Cancer Res. 2014 Oct 28;16(5):456. doi: 10.1186/s13058-014-0456-0.
The purpose of this study is to develop a prediction model utilizing tumor hemoglobin parameters measured by ultrasound-guided near-infrared optical tomography (US-NIR) in conjunction with standard pathologic tumor characteristics to predict pathologic response before neoadjuvant chemotherapy (NAC) is given.
Thirty-four patients' data were retrospectively analyzed using a multiple logistic regression model to predict response. These patients were split into 30 groups of training (24 tumors) and testing (12 tumors) for cross validation. Tumor vascularity was assessed using US-NIR measurements of total hemoglobin (tHb), oxygenated (oxyHb) and deoxygenated hemoglobin (deoxyHb) concentrations acquired before treatment. Tumor pathologic variables of tumor type, Nottingham score, mitotic index, the estrogen and progesterone receptors and human epidermal growth factor receptor 2 acquired before NAC in biopsy specimens were also used in the prediction model. The patients' pathologic response was graded based on the Miller-Payne system. The overall performance of the prediction models was evaluated using receiver operating characteristic (ROC) curves. The quantitative measures were sensitivity, specificity, positive and negative predictive values (PPV and NPV) and the area under the ROC curve (AUC).
Utilizing tumor pathologic variables alone, average sensitivity of 56.8%, average specificity of 88.9%, average PPV of 84.8%, average NPV of 70.9% and average AUC of 84.0% were obtained from the testing data. Among the hemoglobin predictors with and without tumor pathological variables, the best predictor was tHb combined with tumor pathological variables, followed by oxyHb with pathological variables. When tHb was included with tumor pathological variables as an additional predictor, the corresponding measures improved to 79%, 94%, 90%, 86% and 92.4%, respectively. When oxyHb was included with tumor variables as an additional predictor, these measures improved to 77%, 85%, 83%, 83% and 90.6%, respectively. The addition of tHb or oxyHb significantly improved the prediction sensitivity, NPV and AUC compared with using tumor pathological variables alone.
These initial findings indicate that combining widely used tumor pathologic variables with hemoglobin parameters determined by US-NIR may provide a powerful tool for predicting patient pathologic response to NAC before the start of treatment.
ClincalTrials.gov ID: NCT00908609 (registered 22 May 2009).
本研究的目的是开发一种预测模型，该模型利用超声引导近红外光学断层扫描（US-NIR）测量的肿瘤血红蛋白参数，并结合标准病理肿瘤特征，在给予新辅助化疗（NAC）之前预测病理反应。
使用多因素逻辑回归模型对34例患者的数据进行回顾性分析以预测反应。将这些患者分为30组进行训练（24个肿瘤）和测试（12个肿瘤）以进行交叉验证。使用治疗前通过US-NIR测量的总血红蛋白（tHb）、氧合血红蛋白（oxyHb）和脱氧血红蛋白（deoxyHb）浓度评估肿瘤血管生成情况。预测模型中还使用了活检标本在NAC之前获取的肿瘤类型、诺丁汉评分、有丝分裂指数、雌激素和孕激素受体以及人表皮生长因子受体2等肿瘤病理变量。根据米勒-佩恩系统对患者的病理反应进行分级。使用受试者工作特征（ROC）曲线评估预测模型的整体性能。定量指标为敏感性、特异性、阳性和阴性预测值（PPV和NPV）以及ROC曲线下面积（AUC）。
仅利用肿瘤病理变量时，测试数据得出的平均敏感性为56.8%，平均特异性为88.9%，平均PPV为84.8%，平均NPV为70.9%，平均AUC为84.0%。在包含和不包含肿瘤病理变量的血红蛋白预测指标中，最佳预测指标是tHb与肿瘤病理变量相结合，其次是oxyHb与病理变量相结合。当将tHb与肿瘤病理变量作为额外预测指标纳入时，相应指标分别提高到79%、94%、90%、86%和92.4%。当将oxyHb与肿瘤变量作为额外预测指标纳入时，这些指标分别提高到77%、85%、83%、83%和90.6%。与仅使用肿瘤病理变量相比，添加tHb或oxyHb显著提高了预测敏感性、NPV和AUC。
这些初步研究结果表明，将广泛使用的肿瘤病理变量与US-NIR测定的血红蛋白参数相结合，可能为在治疗开始前预测患者对NAC的病理反应提供一个有力工具。
ClinicalTrials.gov标识符：NCT00908609（2009年5月22日注册）。
