Updating Photon-Based Normal Tissue Complication Probability Models for Pneumonitis in Patients With Lung Cancer Treated With Proton Beam Therapy

Purpose: No validated models for predicting the risk of radiation pneumonitis (RP) with proton beam therapy (PBT) currently exist. Our goal was to externally validate and recalibrate multiple established photon-based normal tissue complication probability models for RP in a cohort with locally advanced nonsmall cell lung cancer treated with contemporary doses of chemoradiation using PBT. Methods and Materials: The external validation cohort consisted of 99 consecutive patients with locally advanced nonsmall cell lung cancer treated with chemoradiation using PBT. RP was retrospectively scored at 3 and 6 months posttreatment. We evaluated the performance of the photon Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) pneumonitis model, the QUANTEC model adjusted for clinical risk factors, and the newer Netherlands updated QUANTEC model. A closed testing procedure was performed to test the need for model updating, either by recalibration-in-the-large (re-estimation of intercept), recalibration (re-estimation of intercept/slope), or model revision (re-estimation of all coefficients). Results: There were 21 events (21%) of ≥grade 2 RP. The closed testing procedure on the PBT data set did not detect major deviations between the models and the data and recommended adjustment of the intercept only for the photon-based Netherlands updated QUANTEC model (intercept update: –1.2). However, an update of the slope and revision of the model coefficients were not recommended by the closed testing procedure, as the deviations were not significant within the power of the data. Conclusions: The similarity between the dose-response relationship for PBT and photons for normal tissue complications has been an assumption until now. We demonstrate that the preexisting, widely used photon based models fit our PBT data well with minor modifications. These now-validated and updated normal tissue complication probability models can aid in individualizing selection of the most optimal treatment technique for a particular patient

Measuring the Physiologic Properties of Oral Lesions Receiving Fractionated Photodynamic Therapy

Photodynamic therapy (PDT) can treat superficial, early‐stage disease with minimal damage to underlying tissues and without cumulative dose‐limiting toxicity. Treatment efficacy is affected by disease physiologic properties, but these properties are not routinely measured. We assessed diffuse reflectance spectroscopy (DRS) for the noninvasive, contact measurement of tissue hemoglobin oxygen saturation (StO2) and total hemoglobin concentration ([tHb]) in the premalignant or superficial microinvasive oral lesions of patients treated with 5‐aminolevulinic acid (ALA)‐PDT. Patients were enrolled on a Phase 1 study of ALA‐PDT that evaluated fluences of 50, 100, 150 or 200 J cm−2 delivered at 100 mW cm−2. To test the feasibility of incorporating DRS measurements within the illumination period, studies were performed in patients who received fractionated (two‐part) illumination that included a dark interval of 90–180 s. Using DRS, tissue oxygenation at different depths within the lesion could also be assessed. DRS could be performed concurrently with contact measurements of photosensitizer levels by fluorescence spectroscopy, but a separate noncontact fluorescence spectroscopy system provided continuous assessment of photobleaching during illumination to greater tissue depths. Results establish that the integration of DRS into PDT of early‐stage oral disease is feasible, and motivates further studies to evaluate its predictive and dosimetric value.Diffuse reflectance spectroscopy with a contact probe was employed as part of a fluorescence and reflectance spectroscopy system to measure the tissue hemoglobin oxygen saturation and hemoglobin content of lesions of premalignant or early microinvasive cancer of the oral cavity. Studies demonstrate the feasibility of incorporating these measurements into treatment with fractionated (two‐part) photodynamic therapy (PDT) using 5‐aminolevulinic acid. Patient‐specific differences in physiologic parameters were detectable at baseline and at times during and after PDT. Photobleaching of photosensitizer was measured by its fluorescence. Results establish the utility of rationally designed spectroscopy probes toward personalized dosimetry in PDT of oral disease.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113767/1/php12475.pd

Simplified mathematical model of proton exchange membrane fuel cell based on horizon fuel cell stack

This paper presents a simplified zero-dimensional mathematical model for a self-humidifying proton exchange membrane (PEM) fuel cell stack of 1 kW. The model incorporates major electric and thermodynamic variables and parameters involved in the operation of the PEM fuel cell under different operational conditions. Influence of each of these parameters and variables upon the operation and the performance of the PEM fuel cell are investigated. The mathematical equations are modeled by using Matlab–Simulink tools in order to simulate the operation of the developed model with a commercial available 1 kW horizon PEM fuel cell stack (H-1000), which is used for the purposes of model validation and tuning of the developed model. The model can be extrapolated to higher wattage fuel cells of similar arrangements. New equation is presented to determine the impact of using air to supply the PEM fuel cell instead of pure oxygen upon the concentration losses and the output voltage when useful current is drawn from it

A novel radioresistant mechanism of galectin-1 mediated by H-Ras-dependent pathways in cervical cancer cells

Galectin-1 is a lectin recognized by galactoside-containing glycoproteins, and is involved in cancer progression and metastasis. The role of galectin-1 in radiosensitivity has not previously been investigated. Therefore, this study tests whether galectin-1 is involved in the radiosensitivity mediated by the H-Ras signaling pathway using cervical carcinoma cell lines. A knockdown of galectin-1 expression in HeLa cells decreased clonogenic survival following irradiation. The clonogenic survival increased in both HeLa and C33A cells with galectin-1 overexpression. The overexpression or knockdown of galectin-1 did not alter radiosensitivity, whereas H-Ras was silenced in both cell lines. Whereas K-Ras was knocked down, galectin-1 restored the radiosensitivity in HeLa cells and C33A cells. The knockdown of galectin-1 increased the high-dose radiation-induced cell death of HeLa cells transfected by constitutively active H-Ras. The knockdown of galectin-1 inhibited the radiation-induced phosphorylation of Raf-1 and ERK in HeLa cells. Overexpression of galectin-1 enhanced the phosphorylation of Raf-1 and ERK in C33A cells following irradiation. Galectin-1 decreased the DNA damage detected using comet assay and γ-H2AX in both cells following irradiation. These findings suggest that galectin-1 mediates radioresistance through the H-Ras-dependent pathway involved in DNA damage repair

A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response