Noninvasive Monitoring of Murine Tumor Blood Flow During and After Photodynamic Therapy Provides Early Assessment of Therapeutic Efficacy

Abstract

Purpose:To monitor tumor blood flow noninvasively during photodynamic therapy (PDT) and to correlate flow responses with therapeutic efficacy. Experimental Design: Diffuse correlation spectroscopy (DCS) was used to measure blood flow continuously in radiation-induced fibrosarcoma murine tumors during Photofrin (5 mg/kg)/ PDT (75 mW/cm 2 ,135 J/cm 2 ). Relative blood flow (rBF; i.e., normalized to preillumination values) was compared with tumor perfusion as determined by power Doppler ultrasound and was correlated with treatment durability, defined as the time of tumor growth to a volume of 400 mm 3 . Broadband diffuse reflectance spectroscopy concurrently quantified tumor hemoglobin oxygen saturation (SO 2 ). Results: DCS and power Doppler ultrasound measured similar flow decreases in animals treated with identical protocols. DCS measurement of rBF during PDT revealed a series of PDT-induced peaks and declines dominated by an initial steep increase (average F SE: 168.1 F 39.5%) and subsequent decrease (59.2 F 29.1%). The duration (interval time; range, 2.2-15.6 minutes) and slope (flow reduction rate; range, 4.4 -45.8% minute À1) of the decrease correlated significantly (P = 0.0001and 0.0002, r 2 = 0.79 and 0.67, respectively) with treatment durability. A positive, significant (P = 0.016, r 2 = 0.50) association between interval time and time-to-400 mm 3 was also detected in animals with depressed pre-PDT blood flow due to hydralazine administration. At 3 hours after PDT, rBF and SO 2 were predictive (P V 0.015) of treatment durability. Conclusion: These data suggest a role for DCS in real-time monitoring of PDT vascular response as an indicator of treatment efficacy.

The role of photodynamic therapy (PDT) physics

“…This concept has been investigated in animal models in which the blood flow and/or oxygenation were monitored and changes correlated with outcome. 58,59 …”

“…We anticipate that future clinical protocols involving real-time dosimetry will also include real-time oxygen monitoring, both to prevent PDT-induced hypoxia and to take advantage of the predictive value of PDT-induced changes in blood flow and hemoglobin oxygen saturation. 58 The monitoring of hemoglobin saturation and blood flow has been demonstrated in clinical trials. 101 The translation of measurements of hemoglobin oxygenation into a determination of the cellular oxygen concentration requires a model of oxygen diffusion and consumption.…”

Near-infrared diffuse correlation spectroscopy in cancer diagnosis and therapy monitoring

“…Patients with hypoxic tumors are well known to show only minimal improvements in response to radiation and photodynamic therapies. [3][4][5] Moreover, cancer therapy can alter tumor hemodynamic and metabolic status, which impacts further treatment outcomes. 2,5 Expectations are that functional assessment of tumor hemodynamics and metabolism before, during and after cancer therapy in each patient provides information for the early prediction and longitudinal evaluation of therapeutic outcomes, thus enabling clinicians to make treatment decisions and optimize individual treatment.…”

“…For example, DCS has been utilized in the monitoring of tumor-to-normal flow contrasts 25,30 and early hemodynamic/metabolic responses to chemotherapy 30 in human breast cancers, physiological effects of chemoradiation therapy on human head and neck cancers, 29,53 hemodynamic responses to photodynamic therapy (PDT) in human prostate cancers, 28,51 and efficacies of PDT in murine tumor models. 5,35,[55][56][57] In some of these studies, DCS was combined with NIRS in hybrid instruments for accurately extracting tumor blood flow 21 and for calculating tumor metabolic rate of oxygen consumption (TMRO 2 ) from the measured flow and oxygenation data. 30 It is expected that measurements with multiple hemodynamic and metabolic parameters have the potential to enhance sensitivity and specificity in detection of cancers and monitoring of therapies.…”

“…Intuitively, the random ballistic flow model might be considered the best model with which to fit DCS data. In practice, however, the diffusion model, i.e., hΔr 2 ðτÞi ¼ 6D B τ, fits the autocorrelation curves rather well over a broad range of tissue types, ranging from rat brain, 43,45 and mouse tumor, 5,35 to piglet brain, 39 adult human skeletal muscle, 23,26 premature brain, 34,38 adult brain, 20,27,33,41 and adult tumors. 21,29,30 Here D B is the effective Brownian diffusion coefficient.…”