Normalization of compression-induced hemodynamics in patients responding to neoadjuvant chemotherapy monitored by dynamic tomographic optical breast imaging (DTOBI).
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ABSTRACT: We characterize novel breast cancer imaging biomarkers for monitoring neoadjuvant chemotherapy (NACT) and predicting outcome. Specifically, we recruited 30 patients for a pilot study in which NACT patients were imaged using dynamic tomographic optical breast imaging (DTOBI) to quantify the hemodynamic changes due to partial mammographic compression. DTOBI scans were obtained pre-treatment (referred to as day 0), as well as 7 and 30 days into therapy on female patients undergoing NACT. We present data for the 13 patients who participated in both day 0 and 7 measurements and had evaluable data, of which 7 also returned for day 30 measurements. We acquired optical images over 2 minutes following 4-8 lbs (18-36 N) of compression. The timecourses of tissue-volume averaged total hemoglobin (HbT), as well as hemoglobin oxygen saturation (SO2) in the tumor vs. surrounding tissues were compared. Outcome prediction metrics based on the differential behavior in tumor vs. normal areas for responders (>50% reduction in maximum diameter) vs. non-responders were analyzed for statistical significance. At baseline, all patients exhibit an initial decrease followed by delayed recovery in HbT, and SO2 in the tumor area, in contrast to almost immediate recovery in surrounding tissue. At day 7 and 30, this contrast is maintained in non-responders; however, in responders, the contrast in hemodynamic time-courses between tumor and normal tissue starts decreasing at day 7 and substantially disappears at day 30. At day 30 into NACT, responding tumors demonstrate "normalization" of compression induced hemodynamics vs. surrounding normal tissue whereas non-responding tumors did not. This data suggests that DTOBI imaging biomarkers, which are governed by the interplay between tissue biomechanics and oxygen metabolism, may be suitable for guiding NACT by offering early predictions of treatment outcome.
SUBMITTER: Sajjadi AY
PROVIDER: S-EPMC5330555 | biostudies-literature | 2017 Feb
REPOSITORIES: biostudies-literature
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