Does the proteasome inhibitor bortezomib sensitize to DNA-damaging therapy in gastroenteropancreatic neuroendocrine neoplasms? - A preclinical assessment in vitro and in vivo.

BACKGROUND: Well-differentiated gastroenteropancreatic neuroendocrine neoplasms are rare tumors with a slow proliferation. They are virtually resistant to many DNA-damaging therapeutic approaches, such as chemo- and external beam therapy, which might be overcome by DNA damage inhibition induced by proteasome inhibitors such as bortezomib. METHODS AND RESULTS: In this study, we assessed several combined treatment modalities in vitro and in vivo. By cell-based functional analyses, in a 3D in ovo and an orthotopic mouse model, we demonstrated sensitizing effects of bortezomib combined with cisplatin, radiation and peptide receptor radionuclide therapy (PRRT). By gene expression profiling and western blot, we explored the underlying mechanisms, which resulted in an impaired DNA damage repair. Therapy-induced DNA damage triggered extrinsic proapoptotic signaling as well as the induction of cell cycle arrest, leading to a decreased vital tumor volume and altered tissue composition shown by magnetic resonance imaging and F-18-FDG-PET in vivo, however with no significant additional benefit related to PRRT alone. CONCLUSIONS: We demonstrated that bortezomib has short-term sensitizing effects when combined with DNA damaging therapy by interfering with DNA repair in vitro and in ovo. Nevertheless, due to high tumor heterogeneity after PRRT in long-term observations, we were not able to prove a therapeutic advantage of bortezomib-combined PRRT in an in vivo mouse model.

A Delphic consensus assessment: imaging and biomarkers in gastroenteropancreatic neuroendocrine tumor disease management.

The complexity of the clinical management of neuroendocrine neoplasia (NEN) is exacerbated by limitations in imaging modalities and a paucity of clinically useful biomarkers. Limitations in currently available imaging modalities reflect difficulties in measuring an intrinsically indolent disease, resolution inadequacies and inter-/intra-facility device variability and that RECIST (Response Evaluation Criteria in Solid Tumors) criteria are not optimal for NEN. Limitations of currently used biomarkers are that they are secretory biomarkers (chromogranin A, serotonin, neuron-specific enolase and pancreastatin); monoanalyte measurements; and lack sensitivity, specificity and predictive capacity. None of them meet the NIH metrics for clinical usage. A multinational, multidisciplinary Delphi consensus meeting of NEN experts (n = 33) assessed current imaging strategies and biomarkers in NEN management. Consensus (>75%) was achieved for 78% of the 142 questions. The panel concluded that morphological imaging has a diagnostic value. However, both imaging and current single-analyte biomarkers exhibit substantial limitations in measuring the disease status and predicting the therapeutic efficacy. RECIST remains suboptimal as a metric. A critical unmet need is the development of a clinico-biological tool to provide enhanced information regarding precise disease status and treatment response. The group considered that circulating RNA was better than current general NEN biomarkers and preliminary clinical data were considered promising. It was resolved that circulating multianalyte mRNA (NETest) had clinical utility in both diagnosis and monitoring disease status and therapeutic efficacy. Overall, it was concluded that a combination of tumor spatial and functional imaging with circulating transcripts (mRNA) would represent the future strategy for real-time monitoring of disease progress and therapeutic efficacy.

PET image segmentation using a Gaussian mixture model and Markov random fields.

BACKGROUND: Classification algorithms for positron emission tomography (PET) images support computational treatment planning in radiotherapy. Common clinical practice is based on manual delineation and fixed or iterative threshold methods, the latter of which requires regression curves dependent on many parameters. METHODS: An improved statistical approach using a Gaussian mixture model (GMM) is proposed to obtain initial estimates of a target volume, followed by a correction step based on a Markov random field (MRF) and a Gibbs distribution to account for dependencies among neighboring voxels. In order to evaluate the proposed algorithm, phantom measurements of spherical and non-spherical objects with the smallest diameter being 8 mm were performed at signal-to-background ratios (SBRs) between 2.06 and 9.39. Additionally (68)Ga-PET data from patients with lesions in the liver and lymph nodes were evaluated. RESULTS: The proposed algorithm produces stable results for different reconstruction algorithms and different lesion shapes. Furthermore, it outperforms all threshold methods regarding detection rate, determines the spheres' volumes more accurately than fixed threshold methods, and produces similar values as iterative thresholding. In a comparison with other statistical approaches, the algorithm performs equally well for larger volumes and even shows improvements for small volumes and SBRs. The comparison with experts' manual delineations on the clinical data shows the same qualitative behavior as for the phantom measurements. CONCLUSIONS: In conclusion, a generic probabilistic approach that does not require data measured beforehand is presented whose performance, robustness, and swiftness make it a feasible choice for PET segmentation.

Scheduling, revenue management, and fairness in an academic-hospital radiology division.

RATIONALE AND OBJECTIVES: Physician staff of academic hospitals today practice in several geographic locations including their main hospital. This is referred to as the extended campus. With extended campuses expanding, the growing complexity of a single division's schedule means that a naive approach to scheduling compromises revenue. Moreover, it may provide an unfair allocation of individual revenue, desirable or burdensome assignments, and the extent to which the preferences of each individual are met. This has adverse consequences on incentivization and employee satisfaction and is simply against business policy. MATERIALS AND METHODS: We identify the daily scheduling of physicians in this context as an operational problem that incorporates scheduling, revenue management, and fairness. Noting previous success of operations research and optimization in each of these disciplines, we propose a simple unified optimization formulation of this scheduling problem using mixed-integer optimization. RESULTS: Through a study of implementing the approach at the Division of Angiography and Interventional Radiology at the Brigham and Women's Hospital, which is directed by one of the authors, we exemplify the flexibility of the model to adapt to specific applications, the tractability of solving the model in practical settings, and the significant impact of the approach, most notably in increasing revenue by 8.2% over previous operating revenue while adhering strictly to a codified fairness and objectivity. CONCLUSIONS: We found that the investment in implementing such a system is far outweighed by the large potential revenue increase and the other benefits outlined.

Dynamic O-(2-[18F]fluoroethyl)-L-tyrosine (F-18 FET) PET for glioma grading: assessment of individual probability of malignancy.

PURPOSE: (1) To investigate the diagnostic value of some O-(2-[F]fluoroethyl)-L-tyrosine (F-18 FET) indices derived from the dynamic acquisition to differentiate low-grade gliomas from high-grade; (2) to analyze the course of tumor time-activity curves (TACs); and (3) to calculate the individual probability of a high-grade glioma using the logistic regression. METHODS: Seventeen low-grade (WHO I-II) and 15 high-grade (WHO III-IV) gliomas were studied with dynamic F-18 FET PET. Regions of interests were drawn over the tumor and contralateral brain, and TACs were analyzed. We considered early standardized uptake value (SUV), middle SUV, late SUV, early-to-middle SUV tumor ratio, early-to-late SUV tumor ratio; time to peak (Tpeak), in minutes, from the beginning of the dynamic acquisition up to the maximum SUV of the tumor; and SoD (sum of the frame-to-frame differences). To assess the individual probability of high-grade, logistic regression was also used. RESULTS: High-grade gliomas showed significantly (P < 0.0001) higher values when compared with low-grade gliomas in early SUV, early-to-middle ratio, early-to-late ratio, Tpeak, and SoD. For the grading of gliomas, the best indices were early-to-middle ratio and Tpeak providing a diagnostic accuracy of 94%. TACs analysis provided an 87% diagnostic accuracy. For individual high-grade diagnosis, the logistic regression provided 93% sensitivity, 100% specificity, and 97% accuracy. CONCLUSION: Early-to-middle SUV tumor ratio and Tpeak were the best indices for assessing the grading of gliomas. Since early-to-middle ratio derives from the first 35 minutes of the dynamic acquisition, the PET study could last half an hour instead of 1 hour. By logistic regression, it is possible to assess the individual probability of high-grade, useful for prognosis and treatment.