Citations

Abstract Photon-counting computed tomography (PCCT) uses a photon counting detector to count individual photons and allocate them to specific energy bins by comparing photon energy to preset thresholds. This enables simultaneous multi-energy CT with a single source and detector. Phantom studies were performed to assess the spectral performance of a research PCCT scanner by assessing the accuracy of derived images sets. Specifically, we assessed the accuracy of iodine quantification in iodine map images and of CT number accuracy in virtual monoenergetic images (VMI). Vials containing iodine with five known concentrations were scanned on the PCCT scanner after being placed in phantoms representing the attenuation of different size patients. For comparison, the same vials and phantoms were also scanned on 2nd and 3rd generation dual-source, dual-energy scanners. After material decomposition, iodine maps were generated, from which iodine concentration was measured for each vial and phantom size and compared with the known concentration. Additionally, VMIs were generated and CT number accuracy was compared to the reference standard, which was calculated based on known iodine concentration and attenuation coefficients at each keV obtained from the U.S. National Institute of Standards and Technology (NIST). Results showed accurate iodine quantification (root mean square error of 0.5 mgI/cc) and accurate CT number of VMIs (percentage error of 8.9%) using the PCCT scanner. The overall performance of the PCCT scanner, in terms of iodine quantification and VMI CT number accuracy, was comparable to that of EID-based dual-source, dual-energy scanners.

Polycystic kidney diseases (PKD) are characterized by excessive proliferation of renal tubular epithelial cells, development of fluid-filled cysts, and progressive renal insufficiency. cAMP inhibits proliferation of normal renal tubular epithelial cells but stimulates proliferation of renal tubular epithelial cells derived from patients with PKD. Madin-Darby canine kidney (MDCK) epithelial cells, which are widely used as an in vitro model of cystogenesis, also proliferate in response to cAMP. Intracellular cAMP levels are tightly regulated by phosphodiesterases (PDE). Isoform-specific PDE inhibitors have been developed as therapeutic agents to regulate signaling pathways directed by cAMP. In other renal cell types, we have previously demonstrated that cAMP is hydrolyzed by PDE3 and PDE4, but only PDE3 inhibitors suppress proliferation by inhibiting Raf-1 activity (Cheng J, Thompson MA, Walker HJ, Gray CE, Diaz Encarnacion MM, Warner GM, Grande JP. Am J Physiol Renal Physiol 287:F940-F953, 2004.) A potential role for PDE isoform(s) in cAMP-mediated proliferation of MDCK cells has not previously been established. Similar to what we have previously found in several other renal cell types, cAMP hydrolysis in MDCK cells is directed primarily by PDE4 (85% of total activity) and PDE3 (15% of total activity). PDE4 inhibitors are more effective than PDE3 inhibitors in increasing intracellular cAMP levels in MDCK cells. However, only PDE3 inhibitors, and not PDE4 inhibitors, stimulate mitogenesis of MDCK cells. PDE3 but not PDE4 inhibitors activate B-Raf but not Raf-1, as assessed by an in vitro kinase assay. PDE3 but not PDE4 inhibitors activate the ERK pathway and activate cyclins D and E, as assessed by histone H1 kinase assay. We conclude that mitogenesis of MDCK cells is regulated by a functionally compartmentalized intracellular cAMP pool directed by PDE3. Pharmacologic agents that stimulate PDE3 activity may provide the basis for new therapies directed toward reducing cystogenesis in patients with PKD.

AbstractBackgroundIn treatment planning for intensity‐modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. Mixed‐integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose‐volume constraints. In this study, we incorporated dose‐volume constraints in an MIP model to generate treatment plans for IMPT.MethodsWe created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited‐memory Broyden–Fletcher–Goldfarb–Shanno (L‐BFGS) method while the other plan was derived with our MIP model with dose‐volume constraints. We then compared these two plans by dose‐volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose‐volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection.ResultsThe MIP model with dose‐volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial‐and‐error process. Some notable reduction in the mean doses of OARs is observed.ConclusionsThe treatment plans from our MIP model with dose‐volume constraints can meet all dose‐volume constraints for OARs and targets without any tedious trial‐and‐error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.

PurposeTo implement and evaluate a novel and fast method for proton range verification by using a planar scintillator and step wedge.MethodsA homogenous proton pencil beam plan with 35 energies was designed and delivered to a 2D flat scintillator with a step wedge. The measurement was repeated 15 times (3 different days, 5 times per day). The scintillator image was smoothed, the Bragg peak and distal fall off regions were fitted by an analytical equation, and the proton range was calculated using simple trigonometry. The accuracy of this method was verified by comparing the measured ranges to those obtained using an ionization chamber and a scanning water tank, the gold standard. The reproducibility was evaluated by comparing the ranges over 15 repeated measurements. The sensitivity was evaluated by delivering to same beam to the system with a film inserted under the wedge.ResultsThe range accuracy of all 35 proton energies measured over 3 days was within 0.2 mm. The reproducibility in 15 repeated measurements for all 35 proton ranges was ±0.045 mm. The sensitivity to range variation is 0.1 mm for the worst case. This efficient procedure permits measurement of 35 proton ranges in less than 3 min. The automated data processing produces results immediately. The setup of this system took less than 5 min. The time saving by this new method is about two orders of magnitude when compared with the time for water tank range measurements.ConclusionsA novel method using a scintillator with a step wedge to measure the proton range was implemented and evaluated. This novel method is fast and sensitive, and the proton range measured by this method was accurate and highly reproducible.

Background: Serious adverse events (SAEs) and subject replacements occur frequently in phase 1 oncology clinical trials. Whether baseline quality-of-life (QOL) or social support can predict risk for SAEs or subject replacement among these patients is not known. Methods: Between 2011–2013, 92 patients undergoing screening for enrollment into one of 22 phase 1 solid tumor clinical trials at Roswell Park Cancer Institute were included in this study. QOL Questionnaires (EORTC QLQ-C30 and FACT-G), Medical Outcomes Study Social Support Survey (MOSSSS), Charlson comorbidity scores (CCS) and Royal Marsden scores (RMS) were obtained at baseline. Frequency of dose limiting toxicities (DLTs), subject replacement and SAEs that occurred within the first 4 cycles of treatment were recorded. Fisher’s exact test and Mann-Whitney-Wilcoxon test were used to study the association between categorical and continuous variables, respectively. A linear transformation was used to standardize QOL scores. p-value ≤ 0.05 was considered statistically significant. Results: Baseline QOL, MOSSSS, CCS and RMS were not associated with subject replacement nor DLTs. Baseline EORTC QLQ-C30 scores were significantly lower among patients who encountered SAEs within the first 4 cycles (p = 0.04). Conclusions: Lower (worse) EORTC QLQ-C30 score at baseline is associated with SAE occurrence during phase 1 oncology trials.