Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near-infrared imaging parameters and tumor pathologic criteria.
Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near-infrared imaging parameters and tumor pathologic criteria.
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DOI：
10.1186/s13058-014-0456-0
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发表时间：
2014-10-28
期刊：
影响因子：
--
通讯作者：
Hegde P
中科院分区：
文献类型：
--
作者：
Zhu Q;Wang L;Tannenbaum S;Ricci A Jr;DeFusco P;Hegde P
本研究的目的是开发一种预测模型，利用超声引导的近红外光学断层扫描（US-NIR）结合标准病理肿瘤特征测量的肿瘤血红蛋白参数，以预测新辅助化疗（NAC）前的病理反应。采用多元逻辑回归模型对34例患者的数据进行回顾性分析，以预测反应。这些患者被分成30组，分别进行训练（24个肿瘤）和测试（12个肿瘤），以进行交叉验证。使用治疗前获得的总血红蛋白（tHb）、氧合血红蛋白（oxyHb）和脱氧血红蛋白（deoxyHb）浓度的US-NIR测量值评估肿瘤血管分布。肿瘤病理变量肿瘤类型，诺丁汉评分，有丝分裂指数，雌激素和孕激素受体和人类表皮生长因子受体2前获得的NAC活检标本中也被用于预测模型。根据Miller-Payne系统对患者的病理反应进行分级。使用受试者工作特征（ROC）曲线评价预测模型的总体性能。定量指标包括灵敏度、特异性、阳性和阴性预测值（PPV和NPV）以及ROC曲线下面积（AUC）。单用肿瘤病理学变量，平均灵敏度为56.8%，平均特异度为88.9%，平均PPV为84.8%，平均NPV为70.9%，平均AUC为84.0%。在有和无肿瘤病理变量的血红蛋白预测因子中，最好的预测因子是tHb与肿瘤病理变量的组合，其次是oxyHb与病理变量的组合。当tHb与肿瘤病理学变量作为额外的预测因素时，相应的测量值分别提高到79%、94%、90%、86%和92.4%。当将氧血红蛋白与肿瘤变量作为额外预测因素时，这些指标分别提高到77%、85%、83%、83%和90.6%。与单独使用肿瘤病理学变量相比，tHb或oxyHb的添加显著提高了预测灵敏度、NPV和AUC。这些初步研究结果表明，结合广泛使用的肿瘤病理变量与血红蛋白参数确定的US-NIR可以提供一个强大的工具，用于预测患者的病理反应NAC治疗开始前。ClincalTrials.gov NCT00908609（2009年5月22日注册）本文的在线版本（doi：10.1186/s13058 - 014 - 0456 - 0）包含补充材料，授权用户可以使用。
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) The online version of this article (doi:10.1186/s13058-014-0456-0) contains supplementary material, which is available to authorized users.
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DOI：
10.1158/1078-0432.ccr-13-0799
发表时间：
2013-10-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Masuda H;Baggerly KA;Wang Y;Zhang Y;Gonzalez-Angulo AM;Meric-Bernstam F;Valero V;Lehmann BD;Pietenpol JA;Hortobagyi GN;Symmans WF;Ueno NT
通讯作者：
Ueno NT
影响因子：
45.3
作者：
Rouzier, R;Pusztai, L;Hortobagyi, GN
通讯作者：
Hortobagyi, GN
DOI：
10.1073/pnas.0611058104
发表时间：
2007-03-06
影响因子：
11.1
作者：
Cerussi, Albert;Hsiang, David;Tromberg, Bruce J.
通讯作者：
Tromberg, Bruce J.
影响因子：
11.5
作者：
Bardia, Aditya;Baselga, Jose
通讯作者：
Baselga, Jose
影响因子：
11.5
作者：
Soliman, Hany;Gunasekara, Anoma;Czarnota, Gregory J.
通讯作者：
Czarnota, Gregory J.