Identifying an early treatment window for predicting breast cancer response to neoadjuvant chemotherapy using immunohistopathology and hemoglobin parameters

Background Breast cancer pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) varies with tumor subtype. The purpose of this study was to identify an early treatment window for predicting pCR based on tumor subtype, pretreatment total hemoglobin (tHb) level, and early changes in tHb following NAC. Methods Twenty-two patients (mean age 56 years, range 34–74 years) were assessed using a near-infrared imager coupled with an Ultrasound system prior to treatment, 7 days after the first treatment, at the end of each of the first three cycles, and before their definitive surgery. Pathologic responses were dichotomized by the Miller-Payne system. Tumor vascularity was assessed from tHb; vascularity changes during NAC were assessed from a percentage tHb normalized to the pretreatment level (%tHb). After training the logistic prediction models using the previous study data, we assessed the early treatment window for predicting pathological response according to their tumor subtype (human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), triple-negative (TN)) based on tHb, and %tHb measured at different cycles and evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Results In the new study cohort, maximum pretreatment tHb and %tHb changes after cycles 1, 2, and 3 were significantly higher in responder Miller-Payne 4–5 tumors (n = 13) than non-or partial responder Miller-Payne 1–3 tumors (n = 9). However, no significance was found at day 7. The AUC of the predictive power of pretreatment tHb in the cohort was 0.75, which was similar to the performance of the HER2 subtype as a single predictor (AUC of 0.78). A greater predictive power of pretreatment tHb was found within each subtype, with AUCs of 0.88, 0.69, and 0.72, in the HER2, ER, and TN subtypes, respectively. Using pretreatment tHb and cycle 1 %tHb, AUC reached 0.96, 0.91, and 0.90 in HER2, ER, and TN subtypes, respectively, and 0.95 regardless of subtype. Additional cycle 2 %tHb measurements moderately improved prediction for the HER2 subtype but did not improve prediction for the ER and TN subtypes. Conclusions By combining tumor subtypes with tHb, we predicted the pCR of breast cancer to NAC before treatment. Prediction accuracy can be significantly improved by incorporating cycle 1 and 2 %tHb for the HER2 subtype and cycle 1 %tHb for the ER and TN subtypes

Compact and Robust Ultrasound-guided Diffuse Optical Tomography for Breast Imaging: System and Algorithm Developments

According to the World Health Organization, breast cancer is the most common cancer among women worldwide, claiming lives of hundreds of thousands of women each year. Mammography, ultrasound (US) and magnetic resonance imaging (MRI) are widely used to detect and diagnose breast lesions and each of them have some disadvantages which motivates researchers to find a new and high sensitive imaging method. Diffuse optical tomography (DOT) is a noninvasive functional imaging modality that utilizes near-infrared (NIR) light to probe tissue optical properties. Minimal light absorption in the NIR spectrum allows for several centimeters of light penetration in soft tissue. NIR is sensitive to hemoglobin which is directly related to tumor angiogenesis. If multiple wavelengths are used, it can probe tumor oxygen saturation. However, DOT has drawbacks on location uncertainty and low light quantification accuracy due to tissue scattering of NIR light. Ultrasound (US)-guided Diffuse Optical Tomography has overcome these problems. The overall objective of my research is to move US-guided DOT one step closer from a research imaging modality to a commercial prototype for future wide use in clinics. To achieve this goal, I have overcome several challenges in both imaging hardware and software. In this Dissertation, I will discuss the progress we have made on development of two generations of US-guided DOT systems along with the software improvements. The developed systems have been improved in terms of robustness, stability, clinical safety and standards and user friendliness. In terms of software and algorithm development, automated methods for data selection, outlier removal and reference and perturbation filtering are presented. These methods have improved the robustness of the technique and increased speed of image formation by eliminating the need for an expert to perform data preprocessing and data selection. Phantom and clinical experimental results will be demonstrated to evaluate the performance of the developed systems and algorithms

Identifying an early treatment window for predicting breast cancer response to neoadjuvant chemotherapy using immunohistopathology and hemoglobin parameters

Abstract Background Breast cancer pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) varies with tumor subtype. The purpose of this study was to identify an early treatment window for predicting pCR based on tumor subtype, pretreatment total hemoglobin (tHb) level, and early changes in tHb following NAC. Methods Twenty-two patients (mean age 56 years, range 34–74 years) were assessed using a near-infrared imager coupled with an Ultrasound system prior to treatment, 7 days after the first treatment, at the end of each of the first three cycles, and before their definitive surgery. Pathologic responses were dichotomized by the Miller-Payne system. Tumor vascularity was assessed from tHb; vascularity changes during NAC were assessed from a percentage tHb normalized to the pretreatment level (%tHb). After training the logistic prediction models using the previous study data, we assessed the early treatment window for predicting pathological response according to their tumor subtype (human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), triple-negative (TN)) based on tHb, and %tHb measured at different cycles and evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Results In the new study cohort, maximum pretreatment tHb and %tHb changes after cycles 1, 2, and 3 were significantly higher in responder Miller-Payne 4–5 tumors (n = 13) than non-or partial responder Miller-Payne 1–3 tumors (n = 9). However, no significance was found at day 7. The AUC of the predictive power of pretreatment tHb in the cohort was 0.75, which was similar to the performance of the HER2 subtype as a single predictor (AUC of 0.78). A greater predictive power of pretreatment tHb was found within each subtype, with AUCs of 0.88, 0.69, and 0.72, in the HER2, ER, and TN subtypes, respectively. Using pretreatment tHb and cycle 1 %tHb, AUC reached 0.96, 0.91, and 0.90 in HER2, ER, and TN subtypes, respectively, and 0.95 regardless of subtype. Additional cycle 2 %tHb measurements moderately improved prediction for the HER2 subtype but did not improve prediction for the ER and TN subtypes. Conclusions By combining tumor subtypes with tHb, we predicted the pCR of breast cancer to NAC before treatment. Prediction accuracy can be significantly improved by incorporating cycle 1 and 2 %tHb for the HER2 subtype and cycle 1 %tHb for the ER and TN subtypes. Trial registration ClinicalTrials.gov, NCT02092636. Registered in March 2014