We propose to validate the potential role of our novel near-infrared (NIR) diffuse optical tomography guided by ultrasound (NIR/US) imaging system in assessing patient pathological response to neoadjuvant chemotherapy (NAC). NIR/US is implemented by simultaneously deploying NIR optical sensors and a commercial ultrasound transducer on a hand-held probe, and utilizing co-registered ultrasound to provide lesion structure information and guide optical tomography reconstruction. As a result, the optical tomography has overcome problems associated with intense light scattering and has provided reliable tumor hemoglobin distributions, which are directly related to tumor angiogenesis. Pilot data obtained from 32 patients who underwent NAC, which was assessed by NIR/US, have demonstrated that pretreatment tumor total hemoglobin (tHb) content predicts patient final pathological response with 79% sensitivity and 80% specificity. In addition, the percentage of total hemoglobin changes normalized to the pretreatment level (%tHb) can be used to further identify responders from non-responders at the end of cycle 1 (2-3 weeks) after the initiation of NAC. Furthermore, combining widely used tumor pathologic variables and receptor status with hemoglobin functional parameters obtained before the initiation of NAC can achieve 100% prediction sensitivity and specificity when baseline scatter data are included, or treatment regimens are categorized based on human epidermal growth factor receptor 2 (HER-2/neu) or the addition of %tHb at the end of treatment cycle 1 is assessed. In this proposal, we will: 1) Upgrade NIR imaging systems and validate NIR imaging algorithms optimized for imaging large lesions;2) Validate the initial findings through the recruitment of approximately 80 patients who are undergoing NAC at the Hartford Hospital, the University of Connecticut Health Center, and the Waterbury Hospital;and 3) Perform data analysis a) to determine the best time-window to assess response based on cycle 1 %tHb for different treatment regimens;b) to validate the prediction model developed from pilot data based on tumor pathological variables (tumor type, grade and mitotic count), tumor molecular markers of estrogen receptor (ER), progesterone receptor (PR), and HER-2/neu, and pretreatment NIR functional parameters as well as scatter data and response rate based on one cycle of %tHb. The successful completion of the project will result in a powerful tool to manage personalized breast cancer treatment. In the genomic era of personalized medicine where predicting and monitoring of early responses for outcome prediction becomes crucial, our NIR/US technology will prove to be invaluable.

Public Health Relevance

In this project, an ultrasound-guided near infrared tomography technique will be refined and validated for imaging locally advanced breast cancers in patients who are undergoing neoadjuvant chemotherapy. Approximately, 80 patients will be recruited from three hospitals and their responses to neoadjuvant treatment will be assessed pretreatment, at early treatment cycles, and prior to surgery. This larger patient pool will be use a) to determine the best time- window to assess response based on cycle 1 %tHb for different treatment regimens;b) to validate the prediction model developed from pilot data based on tumor pathological variables (tumor type, grade and mitotic count), tumor molecular markers of estrogen receptor (ER), progesterone receptor (PR), and HER-2/neu, and pretreatment NIR functional parameters as well as response rate based on one cycle of %tHb. The successful completion of the project will provide a means to improve the current clinical practice by accurately predicting an individual patient's response.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01EB002136-10
Application #
8737896
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Conroy, Richard
Project Start
Project End
Budget Start
Budget End
Support Year
10
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Connecticut
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Storrs-Mansfield
State
CT
Country
United States
Zip Code
06269
Zhu, Quing; Ricci Jr, Andrew; Hegde, Poornima et al. (2016) Assessment of Functional Differences in Malignant and Benign Breast Lesions and Improvement of Diagnostic Accuracy by Using US-guided Diffuse Optical Tomography in Conjunction with Conventional US. Radiology 280:387-97
Alqasemi, Umar; Salehi, Hassan S; Zhu, Quing (2016) Method for estimating closed-form solutions of the light diffusion equation for turbid media of any boundary shape. J Opt Soc Am A Opt Image Sci Vis 33:205-13
Xu, Chen; Vavadi, Hamed; Merkulov, Alex et al. (2016) Ultrasound-Guided Diffuse Optical Tomography for Predicting and Monitoring Neoadjuvant Chemotherapy of Breast Cancers: Recent Progress. Ultrason Imaging 38:5-18
Zhou, Feifei; Zanganeh, Saeid; Mohammad, Innus et al. (2015) Targeting tumor hypoxia: a third generation 2-nitroimidazole-indocyanine dye-conjugate with improved fluorescent yield. Org Biomol Chem 13:11220-7
Wang, Tianheng; Brewer, Molly; Zhu, Quing (2015) An overview of optical coherence tomography for ovarian tissue imaging and characterization. Wiley Interdiscip Rev Nanomed Nanobiotechnol 7:1-16
Xu, Yan; Zhu, Quing (2015) Estimation and imaging of breast lesions using a two-layer tissue structure by ultrasound-guided optical tomography. J Biomed Opt 20:066002
Yuan, Guangqian; Alqasemi, Umar; Chen, Aaron et al. (2014) Light-emitting diode-based multiwavelength diffuse optical tomography system guided by ultrasound. J Biomed Opt 19:126003
Zhu, Quing; Wang, Liqun; Tannenbaum, Susan et al. (2014) Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near-infrared imaging parameters and tumor pathologic criteria. Breast Cancer Res 16:456
Tavakoli, Behnoosh; Zhu, Quing (2013) Two-step reconstruction method using global optimization and conjugate gradient for ultrasound-guided diffuse optical tomography. J Biomed Opt 18:16006
Zanganeh, Saeid; Xu, Yan; Hamby, Carl V et al. (2013) Enhanced fluorescence diffuse optical tomography with indocyanine green-encapsulating liposomes targeted to receptors for vascular endothelial growth factor in tumor vasculature. J Biomed Opt 18:126014

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