Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization

Beam orientation optimization (BOO) is a key component in the process of IMRT treatment planning. It determines to what degree one can achieve a good treatment plan quality in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l_1 minimization. Specifically, we introduce a sparsity energy function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an energy term favors small number of beam angles. By optimizing a total energy function containing a dosimetric term and the sparsity term, we are able to identify the unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as the how the beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulted plan quality is presented and found to be better than that obtained from unoptimized (equiangular) beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for 5 prostate cases and 1 head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations and the equiangular ones. It is found that, our BOO algorithm can lead to beam configurations which attain lower FMO objective function values than corresponding equiangular cases, indicating the effectiveness of our BOO algorithm.Comment: 19 pages, 2 tables, and 5 figure

Reg3α concentrations at day of allogeneic stem cell transplantation predict outcome and correlate with early antibiotic use

Intestinal microbiome diversity plays an important role in the pathophysiology of acute gastrointestinal (GI) graft-versus-host disease (GVHD) and influences the outcome of patients after allogeneic stem cell transplantation (ASCT). We analyzed clinical data and blood samples taken preconditioning and on the day of ASCT from 587 patients from 7 German centers of the Mount Sinai Acute GVHD International Consortium, dividing them into single-center test (n = 371) and multicenter validation (n = 216) cohorts. Regenerating islet–derived 3α (Reg3α) serum concentration of day 0 correlated with clinical data as well as urinary 3-indoxylsulfate (3-IS) and Clostridiales group XIVa, indicators of intestinal microbiome diversity. High Reg3α concentration at day 0 of ASCT was associated with higher 1-year transplant-related mortality (TRM) in both cohorts (P < .001). Cox regression analysis revealed high Reg3α at day 0 as an independent prognostic factor for 1-year TRM. Multivariable analysis showed an independent correlation of high Reg3α concentrations at day 0 with early systemic antibiotic (AB) treatment. Urinary 3-IS (P = .04) and Clostridiales group XIVa (P = .004) were lower in patients with high vs those with low day 0 Reg3α concentrations. In contrast, Reg3α concentrations before conditioning therapy correlated neither with TRM nor disease or treatment-related parameters. Reg3α, a known biomarker of acute GI GVHD correlates with intestinal dysbiosis, induced by early AB treatment in the period of pretransplant conditioning. Serum concentrations of Reg3α measured on the day of graft infusion are predictive of the risk for TRM of ASCT recipients

Costlets: A Generalized Approach to Cost Functions for Automated Optimization of IMRT Treatment Plans

We present the creation and use of a generalized cost function methodology based on costlets for automated optimization for conformal and intensity modulated radiotherapy treatment plans. In our approach, cost functions are created by combining clinically relevant “costlets”. Each costlet is created by the user, using an “evaluator” of the plan or dose distribution which is incorporated into a function or “modifier” to create an individual costlet. Dose statistics, dose-volume points, biological model results, non-dosimetric parameters, and any other information can be converted into a costlet. A wide variety of different types of costlets can be used concurrently. Individual costlet changes affect not only the results for that structure, but also all the other structures in the plan (e.g., a change in a normal tissue costlet can have large effects on target volume results as well as the normal tissue). Effective cost functions can be created from combinations of dose-based costlets, dose-volume costlets, biological model costlets, and other parameters. Generalized cost functions based on costlets have been demonstrated, and show potential for allowing input of numerous clinical issues into the optimization process, thereby helping to achieve clinically useful optimized plans. In this paper, we describe and illustrate the use of the costlets in an automated planning system developed and used clinically at the University of Michigan Medical Center. We place particular emphasis on the flexibility of the system, and its ability to discover a variety of plans making various trade-offs between clinical goals of the treatment that may be difficult to meet simultaneously.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47484/1/11081_2005_Article_2066.pd

Respiratory motion models built using MR-derived signals and different amounts of MR image data from multi-slice acquisitions

MR-Linacs provide 2D cine-MR images capturing respiratory motion before and during radiotherapy treatment. Surrogate-driven respiratory motion models can estimate the 3D motion of tumour and organs-at-risk with high spatio-temporal resolution using surrogate signals extracted from 2D cine-MR images. Our motion modelling framework fits the model to unsorted 2D images producing a correspondence model and a motion-compensated super-resolution reconstruction (MCSR). This study investigates the effect of the training data size used to build the respiratory motion models, since long acquisition and processing times limit their application for MR-guided radiotherapy. Four volunteers were scanned on a 1.5T MR scanner with a 3-minute interleaved multi-slice acquisition of 2x2x10mm3 surrogate and motion images, repeated 10 times: sagittal surrogate images from a fixed location, sagittal and coronal motion images covering the thorax. Two surrogate signals were generated by applying principal component analysis to the deformation fields obtained from registering the surrogate images. For each volunteer we built motion models using data from 1, 3, 5, and 10 repetitions, generating a 2x2x2mm3 MCSR. We reconstructed super-resolution images without motion compensation (no-MCSR) to show the improvement obtained with the models. Visual assessment showed plausible estimated respiratory motion with breath-to-breath variations. We computed intensity profiles along the boundary between diaphragm and lung to assess the image quality of the super-resolution reconstructions. We calculated the mean absolute difference (MAD) between the training motion images and the corresponding model-simulated images, averaged over all images and volunteers. The MCSRs presented sharper intensity profiles than no-MCSRs indicating successful motion compensation. MAD increases with the number of repetitions and without motion compensation (for MCSRs/no-MCSRs: 2.10/2.40 with 1 repetition, 2.33/2.56 with 10 repetitions). Computational times to build the models without GPU implementation ranged from ~30 (1 repetition) to ~380 minutes (10 repetitions). Promising results indicated the feasibility of short acquisition and processing times

Evaluation of Elafin as a Prognostic Biomarker in Acute Graft-versus-Host Disease

Acute graft-versus-host disease (GVHD) is a major cause of mortality in patients undergoing hematopoietic cell transplantation (HCT) for hematologic malignancies. The skin is the most commonly involved organ in GVHD. Elafin, a protease inhibitor overexpressed in inflamed epidermis, was previously identified as a diagnostic biomarker of skin GVHD; however, this finding was restricted to a subset of patients with isolated skin GVHD. The main driver of nonrelapse mortality (NRM) in HCT recipients is gastrointestinal (GI) GVHD. Two biomarkers, Regenerating islet-derived 3a (REG3 alpha) and Suppressor of tumorigenesis 2 (ST2), have been validated as biomarkers of GI GVHD that predict long-term outcomes in patients treated for GVHD. We undertook this study to determine the utility of elafin as a prognostic biomarker in the general population of acute GVHD patients in whom GVHD may develop in multiple organs. We analyzed serum elafin concentrations as a predictive biomarker of acute GVHD outcomes and compared it with ST2 and REG3 alpha in a large group of patients treated at multiple centers. A total of 526 patients from the Mount Sinai Acute GVHD International Consortium (MAGIC) who had received corticosteroid treatment for skin GVHD and who had not been previously studied were analyzed. Serum concentrations of elafin, ST2, and REG3 alpha were measured by ELISA in all patients. The patients were divided at random into equal training and validation sets, and a competing-risk regression model was developed to model 6-month NRM using elafin concentration in the training set. Additional models were developed using concentrations of ST2 and REG3 alpha or the combination of all 3 biomarkers as predictors. Receiver operating characteristic (ROC) curves were constructed using the validation set to evaluate the predictive accuracy of each model and to stratify patients into high- and low-risk biomarker groups. The cumulative incidence of 6-month NRM, overall survival (OS), and 4-week treatment response were compared between the risk groups. Unexpectedly, patients in the low-risk elafin group demonstrated a higher incidence of 6-month NRM, although the difference was not statistically significant (17% versus 11%; P =.19). OS at 6 months (68% versus 68%; P >.99) and 4-week response (78% versus 78%; P =.98) were similar in the low-risk and high-risk elafin groups. The area under the ROC curve (AUC) was 0.55 for elafin and 0.75 for the combination of ST2 and REG3 alpha. The addition of elafin to the other 2 biomarkers did not improve the AUC. Our data indicate that serum elafin concentrations measured at the initiation of systemic treatment for acute GVHD did not predict 6-month NRM, OS, or treatment response in a multicenter population of patients treated systemically for acute GVHD. As seen in previous studies, serum concentrations of the GI GVHD biomarkers ST2 and REG3 alpha were significant predictors of NRM, and the addition of elafin levels did not improve their accuracy. These results underscore the importance of GI disease in driving NRM in patients who develop acute GVHD. (C) 2021 The American Society for Transplantation and Cellular Therapy. Published by Elsevier Inc. All rights reserved