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The optimal preoperative cardiac evaluation strategy for patients with end-stage liver disease (ESLD) undergoing liver transplantation remains unknown. Patients are frequently referred for cardiac catheterization, but the effects of coronary artery disease (CAD) on posttransplant mortality are also unknown. We sought to determine the contribution of CAD and multivessel CAD in particular to posttransplant mortality. We performed a retrospective study of ESLD patients undergoing cardiac catheterization before liver transplant surgery between August 1, 2004 and August 1, 2007 to determine the effects of CAD on outcomes after transplantation. Among 83 patients who underwent left heart catheterization, 47 underwent liver transplantation during the follow-up period. Twenty-one of all ESLD patients who underwent liver transplantation (45%) had CAD. Fifteen of the transplant patients with CAD (71%) had multivessel disease. Among transplant patients, the presence of multivessel CAD (versus no CAD) was predictive of mortality (27% versus 4%, P ¼ 0.046), increased length of stay (22 versus 15 days, P ¼ 0.050), and postoperative pressor requirements (27% versus 4%, P ¼ 0.029). Interestingly, neither the presence of any CAD nor the severity of stenosis in any single coronary artery predicted mortality. Furthermore, none of the traditional clinical predictors (age, gender, diabetes, creatinine, ejection fraction, and Model for End-Stage Liver Disease score) were predictive of mortality among transplant recipients. In conclusion, multivessel CAD is associated with higher mortality after liver transplantation when it is documented angiographically before transplantation, even in the absence of severe coronary artery stenosis. This study provides preliminary evidence showing that there may be significant prognostic value in coronary angiography as a part of the pretransplant workup.

Purpose: To compare two coverage-based planning (CP) techniques with standard fixed marginbased planning (FM), considering the dosimetric impact of interfraction deformable organ motion exclusively for high-risk prostate treatments. Methods: Nineteen prostate cancer patients with 8-13 prostate CT images of each patient were used to model patient-specific interfraction deformable organ changes. The model was based on the principal component analysis (PCA) method and was used to predict the patient geometries for virtual treatment course simulation. For each patient, an IMRT plan using zero margin on target structures, prostate (CTV prostate ) and seminal vesicles (CTV SV ), were created, then evaluated by simulating 1000 30-fraction virtual treatment courses. Each fraction was prostate centroid aligned. Patients whose D 98 failed to achieve 95% coverage probability objective D 98,95 ≥ 78 Gy (CTV prostate ) or D 98,95 ≥ 66 Gy (CTV SV ) were replanned using planning techniques: (1) FM (PTV prostate = CTV prostate + 5 mm, PTV SV = CTV SV + 8 mm), (2) CP OM which optimized uniform PTV margins for CTV prostate and CTV SV to meet the coverage probability objective, and (3) CP COP which directly optimized coverage probability objectives for all structures of interest. These plans were intercompared by computing probabilistic metrics, including 5% and 95% percentile DVHs (pDVH) and TCP/NTCP distributions. Results: All patients were replanned using FM and two CP techniques. The selected margins used in FM failed to ensure target coverage for 8/19 patients. Twelve CP OM plans and seven CP COP plans were favored over the other plans by achieving desirable D 98,95 while sparing more normal tissues. Conclusions: Coverage-based treatment planning techniques can produce better plans than FM, while relative advantages of CP OM and CP COP are patient-specific. C 2014 American Association of Physicists in Medicine. [http://dx

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Assumption of shift- and deformation-invariant dose distributions on an average introduces <2% error in evaluated dose-volume metrics for 6 and 18 MV IMRT prostate plans. Use of invariant dose distributions has a potential to reduce online re-planning time and permit pre-planning based on tissue deformation models.

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The variability in risks of edema and in factors impacting those risks is likely a result of differences across studies in the clinicopathologic characteristics of the patient populations, as well as differences in treatment modalities and SRS planning and delivery parameters. More studies on pooled populations, grouped by potential prognostic factors such as tumor location and prior therapy, are needed to better understand dosimetric and nondosimetric factors predictive of edema risk after SRS for meningioma.