Background Although Aminolevulinic Acid (ALA)-induced protoporphyrin IX (PpIX) photodynamic therapy (PDT) is an effective FDA-approved therapy for actinic keratosis (AK), a substantial fraction of patients (up to 25%) do not respond to treatment. This study examined the feasibility of using pre-treatment measurements of PpIX concentration in AK lesions to predict response of ALA-PpIX PDT. Methods A non-invasive fiber-optic fluorescence spectroscopy system was used to measure PpIX concentration in patients undergoing standard-of-care ALA-PDT for AK. All patients provided assessments of pain at the time of treatment (n=70), and a subset reported pain and erythema 48–76 hours after treatment (n=13). Results PpIX concentration was significantly higher in lesions of patients reporting high levels of pain (VAS score ≥ 5) immediately after treatment vs. patients reporting pain scores below VAS=5 (p<0.022) (n=70). However, pain was not an exclusive indicator of PpIX concentration as many patients with low PpIX concentration reported high pain. In a subpopulation of patients surveyed in the days after treatment (n=13), PpIX concentration measured on the day of treatment was uncorrelated with pain-reported immediately after treatment (r=0.17, p<0.57), but positive correlations were found between PpIX concentration and patient-reported pain (r=0.55, p < 0.051) and erythema (r=0.58, p < 0.039) in the 48–72 hr following treatment. Conclusions These data suggest that in vivo optical measurements of PpIX concentration acquired before light delivery may be an objective predictor of response to ALA-PpIX PDT. Identification of non-responding patients on the day of treatment could facilitate the use of interventions that may improve outcomes.
Bioluminescence imaging is a powerful technique for visualizing gene expression in small animals but it suffers a serious limitation: the absorption and scattering of light in tissue. Several factors influence the image: source strength and depth, effective numerical aperture of the imaging optics, and attenuation by the tissue between the source and the camera. Our overall goal is to account for these effects and to recover the actual strength and spatial location of the bioluminescence sources in vivo. An essential first step in this research is to develop a physical model that accurately predicts the light reaching the surface of the animal for an arbitrary distribution of sources and optical absorption and scattering coefficients. The calculations must be fast, so that the model can be used eventually in an iterative algorithm to solve the inverse problem. We use the diffusion approximation, valid when scattering dominates absorption and when it is not necessary to calculate the light field close to sources. The diffusion equation expresses the light fluence rate as a function of position and the spatially dependent absorption coefficient, scattering coefficient, and source function. A finite element code called NIRFAST has been developed to generate numerical solutions. NIRFAST has been implemented in MATLAB and uses a 2 or 3 dimensional model to represent the object. The absorption and scattering coefficients are specified at each node of the mesh. We have assigned “reasonable” values of the absorption and scattering coefficients to each node based on tissue identification by x‐ray CT.
Image‐guided near infrared spectroscopy (IG‐NIRS) provides deep tissue functional characterization at high resolution. This approach combines conventional imaging techniques such as MRI and CT with optical near infrared technologies, giving information directly relating to the vascular and metabolic status of tissue in‐vivo. The resultant estimates of total hemoglobin, oxygen saturation, water, lipids and scatter provide a window towards understanding the mechanisms of cancer in terms of angiogenesis, hypoxia, changes in the interstitium and cell organelle structural changes. This type of spectroscopy has been applied for breast cancer diagnosis and treatment monitoring, as well as image‐guided fluorescence in small‐animals. Optimization of these systems is essential to provide quantitative and accurate spectroscopy. This optimization encompasses system design for simultaneous multi‐modality image acquisition, methods for intelligently combining spatial anatomical structure from MRI/CT into optical recovery, image segmentation, visualization and interpretation of novel combined optical and MRI/CT parameters. This talk will provide an over‐view of these aspects of multi‐modality imaging as well as results from in‐vivo clinical applications. Learning Objectives: 1. Understanding the rationale for multi‐modality IG‐NIRS systems 2. Understanding the type of information and contrast available through these systems 3. Understanding the challenges towards clinical use of these systems.
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