Stereotactic body radiation therapy for primary and metastatic liver tumors.

OBJECTIVES: The full potential of stereotactic body radiation therapy (SBRT), in the treatment of unresectable intrahepatic malignancies, has yet to be realized as our experience is still limited. Thus, we evaluated SBRT outcomes for primary and metastatic liver tumors, with the goal of identifying factors that may aid in optimization of therapy. METHODS: From 2005 to 2010, 62 patients with 106 primary and metastatic liver tumors were treated with SBRT to a median biologic effective dose (BED) of 100 Gy (42.6-180). The majority of patients received either three (47%) or five fractions (48%). Median gross tumor volume (GTV) was 8.8 cm(3) (0.2-222.4). RESULTS: With a median follow-up of 18 months (0.46-46.8), freedom from local progression (FFLP) was observed in 97 of 106 treated tumors, with 1- and 2-year FFLP rates of 93% and 82%. Median overall survival (OS) for all patients was 25.2 months, with 1- and 2-year OS of 81% and 52%. Neither BED nor GTV significantly predicted for FFLP. Local failure was associated with a higher risk of death [hazard ratio (HR) = 5.1, P = .0007]. One Child-Pugh Class B patient developed radiation-induced liver disease. There were no other significant toxicities. CONCLUSIONS: SBRT provides excellent local control for both primary and metastatic liver lesions with minimal toxicity. Future studies should focus on appropriate selection of patients and on careful assessment of liver function to maximize both the safety and efficacy of treatment.

Selective radiosensitization of p53 mutant pancreatic cancer cells by combined inhibition of Chk1 and PARP1.

We have recently shown that inhibition of HRR (homologous recombination repair) by Chk1 (checkpoint kinase 1) inhibition radiosensitizes pancreatic cancer cells and others have demonstrated that Chk1 inhibition selectively sensitizes p53 mutant tumor cells. Furthermore, PARP1 [poly (ADP-ribose) polymerase-1] inhibitors dramatically radiosensitize cells with DNA double strand break repair defects. Thus, we hypothesized that inhibition of HRR (mediated by Chk1 via AZD7762) and PARP1 [via olaparib (AZD2281)] would selectively sensitize p53 mutant pancreatic cancer cells to radiation. We also used 2 isogenic p53 cell models to assess the role of p53 status in cancer cells and intestinal epithelial cells to assess overall cancer specificity. DNA damage response and repair were assessed by flow cytometry, γH2AX, and an HRR reporter assay. We found that the combination of AZD7762 and olaparib produced significant radiosensitization in p53 mutant pancreatic cancer cells and in all of the isogenic cancer cell lines. The magnitude of radiosensitization by AZD7762 and olaparib was greater in p53 mutant cells compared with p53 wild type cells. Importantly, normal intestinal epithelial cells were not radiosensitized. The combination of AZD7762 and olaparib caused G 2 checkpoint abrogation, inhibition of HRR, and persistent DNA damage responses. These findings demonstrate that the combination of Chk1 and PARP1 inhibition selectively radiosensitizes p53 mutant pancreatic cancer cells. Furthermore, these studies suggest that inhibition of HRR by Chk1 inhibitors may be a useful strategy for selectively inducing a BRCA1/2 'deficient-like' phenotype in p53 mutant tumor cells, while sparing normal tissue.

Resident workload, pager communications, and quality of care.

With the recent regulations limiting resident work hours, it has become more important to understand how residents spend their time. The volume and content of the pages they receive provide a valuable source of information that give insight into their workload and help identify inefficiencies in hospital communication. We hypothesized that above a certain workload threshold, paging data would suggest breakdowns in communication and implications for quality of care. All pages sent to six general surgery interns at the University of Michigan over the course of one academic year (7/1/2008-6/30/2009) were retrospectively categorized by sender type, message type, message modifier, and message quality. Census, discharge, and admission information for each intern service were also collected, and intern duties were further analyzed with respect to schedule. "On-call" days were defined as days on which the intern bore responsibility for care of all admitted floor patients. The interns received a total of 9,843 pages during the study period. During on-call shifts, each intern was paged an average of 57 ± 3 times, and those on non-call shifts received an average of 12 ± 3 pages. Floor/intensive care unit (ICU) nurses represented 32% of the page volume received by interns. Interestingly, as patient volume increased, there was a decrease in the number of pages received per patient. By contrast, at higher patient volumes, there was a trend toward an increasing percentage of urgent pages per patient. At high intern workloads, our data suggest no major communication breakdowns but reveal the potential for inferior quality of care.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure.

An infrared target tracking framework is presented that consists of three main parts: mean shift tracking, its tracking performance evaluation, and position correction. The mean shift tracking algorithm, which is a widely used kernel-based method, has been developed for the initial tracking for its efficiency and effectiveness. A performance evaluation module is applied for the online evaluation of its tracking performance with a kernel- based metric to unify the tracking and performance metric within a kernel-based tracking framework. Then the tracking performance evaluation result is input into a controller in which a decision is made whether to trigger a position correction process. The position correction module employs a matching method with a new eigenvalue-based similarity measure computed from a local complexity degree weighted covariance matrix. Experimental results on real-life infrared image sequences are presented to demonstrate the efficacy of the proposed method.