Abstract P1-08-41: Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near infrared imaging and tumor pathologic criteria
Authors: Q Zhu, L Wang, S Tannenbaum, A Ricci, P DeFusco, P Hegde
Journal: Cancer Research (2013)

Abstract Purpose: In previous studies, the utilization of ultrasound guided near infrared diffused light imaging (US-NIR) has shown great potential in predicting and monitoring the pathologic tumor response to neoadjuvant chemotherapy (NAC). The purpose of the current study is to develop a prediction model utilizing pretreatment tumor hemoglobin content measured by US-NIR in conjunction with standard pathologic tumor characteristics to predict pathologic response even before NAC is given. Utilizing a multiple logistic regression model, the sensitivity, specificity, positive and negative predictive values (PPV and NPV), and the area under the receiver operating characteristic curve (AUC) are determined for the models. Materials and Methods: 34 patients’ data were retrospectively analyzed using a multiple logistic regression model to predict response. These patients were split into a training group (23 patients of 24 tumors) and testing group (11 patients of 12 tumors). Tumor vascularity was assessed pre-NAC using US-NIR and measurements of total hemoglobin (tHb), oxygenated (oxyHb), and deoxygenated hemoglobin concentrations (deoxyHb) as well as tumor reduced scatter coefficients acquired before treatment. Tumor pathologic variables including the estrogen (ER) and progesterone (PR) receptors, human epidermal growth factor receptor 2 (HER2) and Nottingham score (mitotic index and grade) were acquired before NAC in biopsy specimens and were also used in the prediction model. The patients’ pathologic response was graded based on the Miller-Payne system as non- and partial-responders (grades 1-3) and near-complete and complete responders (grades 4-5). Results: Utilizing initial tumor pathologic characteristics (grade and receptor status) a sensitivity of 100%, specificity of 73.3%, PPV and NPV of 69.5% and 100%, and AUC of 0.83(95% CI: 0.637-963) were obtained from training data. When pretreatment hemoglobin parameters and reduced scatter coefficients were included as additional predictors in training data, sensitivity, specificity, PPV and NPV improved to 100% and AUC of 1.0 (95% CI: 1.0-1.0). The performance of the predictive models were validated on testing data and corresponding values were 100%, 66.7%, 75.0% and 100%, and AUC of 0.83 (CI: 0.56-1.0) when tumor pathologic parameters alone were used as predictors. While the corresponding values were 100% and AUC of 1.0 (CI: 1.0-1.0) when hemoglobin and reduced scatter parameters were added as predictors. Discussion: These initial findings indicate that combining widely used tumor pathologic variables with hemoglobin and optical scatter functional parameters determined by NIR provides a powerful tool for predicting patient response to preoperative chemotherapy before the initiation of the treatment. With the current trend to treat in the neoadjuvant setting, such a tool will be invaluable for response assessment. Plans are underway to validate this model in larger patient settings and its applicability to non-chemotherapeutic regimens. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-08-41.