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Eva K Lee

Publications and source records attributed to Eva K Lee.

7 recordsLinked to original sources
PubMed2006

DNA motifs associated with aberrant CpG island methylation.

Epigenetic silencing involving the aberrant methylation of promoter region CpG islands is widely recognized as a tumor suppressor silencing mechanism in cancer. However, the molecular pathways underlying aberrant DNA methylation remain elusive. Recently we showed that, on a genome-wide level, CpG island loci differ in their intrinsic susceptibility to aberrant methylation and that this susceptibility can be predicted based on underlying sequence context. These data suggest that there are sequence/structural features that contribute to the protection from or susceptibility to aberrant methylation. Here we use motif elicitation coupled with classification techniques to identify DNA sequence motifs that selectively define methylation-prone or methylation-resistant CpG islands. Motifs common to 28 methylation-prone or 47 methylation-resistant CpG island-containing genomic fragments were determined using the MEME and MAST algorithms (). The five most discriminatory motifs derived from methylation-prone sequences were found to be associated with CpG islands in general and were nonrandomly distributed throughout the genome. In contrast, the eight most discriminatory motifs derived from the methylation-resistant CpG islands were randomly distributed throughout the genome. Interestingly, this latter group tended to associate with Alu and other repetitive sequences. Used together, the frequency of occurrence of these motifs successfully discriminated methylation-prone and methylation-resistant CpG island groups with an accuracy of 87% after 10-fold cross-validation. The motifs identified here are candidate methylation-targeting or methylation-protection DNA sequences.

Algorithms↗
PubMed2005

Simultaneous beam geometry and intensity map optimization in intensity-modulated radiation therapy.

PURPOSE: In current intensity-modulated radiation therapy (IMRT) plan optimization, the focus is on either finding optimal beam angles (or other beam delivery parameters such as field segments, couch angles, gantry angles) or optimal beam intensities. In this article we offer a mixed integer programming (MIP) approach for simultaneously determining an optimal intensity map and optimal beam angles for IMRT delivery. Using this approach, we pursue an experimental study designed to (a) gauge differences in plan quality metrics with respect to different tumor sites and different MIP treatment planning models, and (b) test the concept of critical-normal-tissue-ring--a tissue ring of 5 mm thickness drawn around the planning target volume (PTV)--and its use for designing conformal plans. METHODS AND MATERIALS: Our treatment planning models use two classes of decision variables to capture the beam configuration and intensities simultaneously. Binary (0/1) variables are used to capture "on" or "off" or "yes" or "no" decisions for each field, and nonnegative continuous variables are used to represent intensities of beamlets. Binary and continuous variables are also used for each voxel to capture dose level and dose deviation from target bounds. Treatment planning models were designed to explicitly incorporate the following planning constraints: (a) upper/lower/mean dose-based constraints, (b) dose-volume and equivalent-uniform-dose (EUD) constraints for critical structures, (c) homogeneity constraints (underdose/overdose) for PTV, (d) coverage constraints for PTV, and (e) maximum number of beams allowed. Within this constrained solution space, five optimization strategies involving clinical objectives were analyzed: optimize total intensity to PTV, optimize total intensity and then optimize conformity, optimize total intensity and then optimize homogeneity, minimize total dose to critical structures, minimize total dose to critical structures and optimize conformity simultaneously. We emphasize that the objectives that include optimizing conformity make use of the critical-normal-tissue-ring. Three tumor sites: head-and-neck, pediatric brain, and prostate are used for comparison. RESULTS: The critical-normal-tissue-ring acts as a good device for enforcing conformity. Trends in the characteristics and quality of plans resulting from each model were observed. Attempts to reduce dose to critical structures tend to worsen PTV conformity (1.542 to 3.092) and homogeneity (1.223 to 1.984), depending on the relative size and spatial distance of the critical structures to the PTV. When the critical structures are relatively small compared with the PTV (as in the case for the pediatric brain tumor, where each is less than 15% in volume), dose reduction to critical structures is accompanied by much worse scores in conformity (2.482) and homogeneity (1.984). When the critical structures are larger, as in the case of head-and-neck (approximately 50%), the conformity and homogeneity deterioration is less significant (1.542 and 1.239, respectively). There is a clear tradeoff between homogeneity, conformity, and minimum dose to organs at risk (OARs). For head-and-neck and pediatric brain tumor, the model that minimizes total dose to critical structures and optimizes conformity simultaneously offers a compromise among these factors, resulting in reduced critical structure dose with conformal and homogeneous plans. In the prostate case, the tumor is smaller than the two large nearby critical structures, and all models provide very homogeneous PTV dose distribution. However, minimizing dose to critical structures worsens conformity, as it spreads the radiation to the area surrounding the PTV. The maximum dose to the critical structures also increases slightly. Compared with plans used in the clinic which generally have uniformly spaced beam angles, the optimal clinically acceptable plans obtained via the methods herein do not have equispaced beams. The optimal beam angles returned appear to be nonintuitive, and depend on PTV size and geometry and the spatial relationship between the tumor and critical structures. CONCLUSIONS: The MIP model described allows simultaneous optimization over the space of beamlet fluence weights and beam and couch angles. Based on experiments with tumor data, this approach can return good plans that are clinically acceptable and practical. This work distinguishes itself from recent IMRT research in several ways. First, in previous methods beam angles are selected before intensity map optimization. Herein, we employ 0/1 variables to model the set of candidate beams, and thereby allow the optimization process itself to select optimal beams. Second, instead of incorporating dose-volume criteria within the objective function as in previous work, herein, a combination of discrete and continuous variables associated with each voxel provides a mechanism to strictly enforce dose-volume criteria within the constraints. Third, using the construct of critical-normal-tissue-ring within the objective function can enhance the achievement of conformal plans. Based on the three tumor sites considered, it appears that volume and spatial geometry with respect to the PTV are important factors to consider when selecting objectives to optimize, and in estimating how well suited a particular model is for achieving a specified goal.

Algorithms↗
PubMed2005

Long-term results of angioplasty vs stenting in cardiac transplant recipients with allograft vasculopathy.

BACKGROUND: Allograft vasculopathy remains a major limiting factor in long-term graft survival. The absence of symptoms and diffuse nature of the disease make clinical detection and therapy more difficult. Limited data exist on the long-term outcome of percutaneous interventions in this group of patients. METHODS: Medical records and cardiac catheterizations from the Cardiac Cath Lab database were retrospectively reviewed for all cardiac transplant recipients who had undergone a percutaneous intervention. Procedural results, complications, use of stents and angiographic follow-up were recorded. Re-stenosis was defined as a lesion >50% in the target vessel at follow-up angiography. RESULTS: Thirty-three patients underwent 97 percutaneous interventions with a mean of 2.9 interventions per patient. Mean age at the time of first intervention was 52 +/- 13 (mean +/- standard deviation) years. Mean time from transplant to first intervention was 5 +/- 3.0 years. The primary procedural success rate was 99%. Thirty-four procedures involved placement of a stent, 63 were angioplasty alone. There were no procedure-related complications. Seventy percent of lesions were de novo and 30% were re-stenotic lesions. Six-month, 12-month and 5-year target vessel re-stenosis rates in the stent group were 31%, 46% and 69%, and in the percutaneous transluminal coronary angioplasty (PTCA) group were 41%, 53% and 68%, respectively. Thirteen patients (39.3%) died or were re-transplanted, at 1.9 +/- 2.29 (mean +/- SD) years after their first intervention. Twenty patients were alive at 4.5 +/- 2.99 years after the first intervention. CONCLUSIONS: Percutaneous intervention can be performed safely in cardiac transplant recipients. Stent placement reduces early and mid-term re-stenosis, but late re-stenosis occurs in 70% of lesions. Late re-stenosis, development of new coronary lesions, and need for repeat intervention are common, regardless of the method used for percutaneous intervention, emphasizing the diffuse and progressive nature of transplant coronary disease.

Aged↗
PubMed2004

Prediction of ultrasound-mediated disruption of cell membranes using machine learning techniques and statistical analysis of acoustic spectra.

Although biological effects of ultrasound must be avoided for safe diagnostic applications, ultrasound's ability to disrupt cell membranes has attracted interest as a method to facilitate drug and gene delivery. This paper seeks to develop "prediction rules" for predicting the degree of cell membrane disruption based on specified ultrasound parameters and measured acoustic signals. Three techniques for generating prediction rules (regression analysis, classification trees and discriminant analysis) are applied to data obtained from a sequence of experiments on bovine red blood cells. For each experiment, the data consist of four ultrasound parameters, acoustic measurements at 400 frequencies, and a measure of cell membrane disruption. To avoid over-training, various combinations of the 404 predictor variables are used when applying the rule generation methods. The results indicate that the variable combination consisting of ultrasound exposure time and acoustic signals measured at the driving frequency and its higher harmonics yields the best rule for all three rule generation methods. The methods used for deriving the prediction rules are broadly applicable, and could be used to develop prediciton rules in other scenarios involving different cell types or tissues. These rules and the methods used to derive them could be used for real-time feedback about ultrasound's biological effects.

Animals↗
PubMed2003

Intraoperative dynamic dose optimization in permanent prostate implants.

PURPOSE: With the advent of intraoperative optimized planning, the treatment of prostate cancer with permanent implants has reached an unprecedented level of dose conformity. However, because of well-documented (and unavoidable) inaccuracies in seed placement into the gland, carrying out a plan results in a large degree of variability relative to the intended dose distribution. This brings forth the need to periodically readjust the plan to allow for the real positions of seeds already implanted. In this paper, an algorithm for performing this task, hereby described as intraoperative dynamic dose optimization (IDDO), is presented and assessed. METHODS AND MATERIALS: The general scheme for performing IDDO consists of three steps: (1) at some point during the implant, coordinates of implanted seeds are identified; (2) seed images are projected onto the reference frame of the ultrasound images for planning; and (3) the plan is reoptimized. Work on the first two steps is reported elsewhere. Here, we focus on the strategy for implementing the reoptimization step. An optimal treatment plan is first obtained based on initial operating room-acquired ultrasound images. We analyze the sensitivity and effect of the IDDO procedure with respect to the total number of reoptimizations performed. Specifically, we consider reoptimizing 2, 3, and 4 times. When two reoptimizations are used, half of the seeds from the initial optimal plan are implanted. The first reoptimization is performed on the remaining possible seed positions, and all the seeds designated in this reoptimized plan are implanted. The second (final) reoptimization is done on the remaining unused seed positions to ensure 100% coverage of the gland and to eliminate possible cold spots in the gland. Similarly, when three reoptimization steps are used, one-third of the seeds from the initial optimized plan, one-half of the seeds from the first reoptimization, and all seeds from the second reoptimization are implanted. The third (final) reoptimization is performed to assist in eliminating possible cold spots. Reoptimizing four times proceeds in a like manner. Fifteen patient cases are used for comparison. Strict dose bounds of 100% and 120% of the prescription dose are imposed on the urethra, and 100% coverage is imposed on the prostate volume. To assist in achieving good conformity, prostate contour points are assigned a target upper dose bound of 150% of the prescription dose. RESULTS: A two-way comparison is performed: (a) initial optimized plan, (b) IDDO plan. Postimplant dose analysis, coverage and conformity measures, as well as actual dose received by urethra and rectum are used to gauge the results. The initial optimized plan consistently provides 93% prescription dose coverage to the gland with average conformity index of 1.32. The urethra dose ranges within 100% to 150%, and the maximum dose delivered to the rectum reaches 91% of the prescription dose. On average, about 50% of the urethra receives more than 120% of the prescription dose, and 19% of the rectum volume receives more than the 78% upper dose limit. For the IDDO plan, 100% postimplant coverage with 1.16 conformity is achieved. Urethra and rectum dose is maintained within the prescribed 100% to 120% range and 78% upper bound, respectively. CONCLUSIONS: With real-time treatment planning, it is possible to dynamically reoptimize treatment plans to account for actual seed positions (as opposed to planned positions) and needle-induced swelling to the gland during implantation. Postimplant analysis shows that the final seed configuration resulting from the IDDO method yields improved dosimetry. The algorithmic design ensures that one can achieve complete coverage while maintaining good conformity, thus sparing excess radiation to external tissue. The study also provides evidence of the possibility of morbidity reduction to urethra and rectum (because of reduced dose delivered to these structures) via the use of IDDO planning. Clinical studies are needed to validate the importance of our approach.

Algorithms↗
PubMed2002

Automated planning volume definition in soft-tissue sarcoma adjuvant brachytherapy.

In current practice, the planning volume for adjuvant brachytherapy treatment for soft-tissue sarcoma is either not determined a priori (in this case, seed locations are selected based on isodose curves conforming to a visual estimate of the planning volume), or it is derived via a tedious manual process. In either case, the process is subjective and time consuming, and is highly dependent on the human planner. The focus of the work described herein involves the development of an automated contouring algorithm to outline the planning volume. Such an automatic procedure will save time and provide a consistent and objective method for determining planning volumes. In addition, a definitive representation of the planning volume will allow for sophisticated brachytherapy treatment planning approaches to be applied when designing treatment plans, so as to maximize local tumour control and minimize normal tissue complications. An automated tumour volume contouring algorithm is developed utilizing computational geometry and numerical interpolation techniques in conjunction with an artificial intelligence method. The target volume is defined to be the slab of tissue r cm perpendicularly away from the curvilinear plane defined by the mesh of catheters. We assume that if adjacent catheters are over 2r cm apart, the tissue between the two catheters is part of the tumour bed. Input data consist of the digitized coordinates of the catheter positions in each of several cross-sectional slices of the tumour bed, and the estimated distance r from the catheters to the tumour surface. Mathematically, one can view the planning volume as the volume enclosed within a minimal smoothly-connected surface which contains a set of circles, each circle centred at a given catheter position in a given cross-sectional slice. The algorithm performs local interpolation on consecutive triplets of circles. The effectiveness of the algorithm is evaluated based on its performance on a collection of soft-tissue sarcoma tumour beds within various anatomical structures. For each of 15 patient cases considered, the algorithm takes approximately 2 min to generate the planning volume. Although the tumour shapes are rather different, the algorithm consistently generates planning volumes that visually demonstrate smooth curves compactly encapsulating the circles. This general-purpose contouring algorithm works well whether the catheters are all close together, spread far apart in the plane or arranged in a convoluted way. The automatic contouring algorithm significantly reduces labour time and provides a consistent and objective method for determining planning volumes for soft-tissue sarcoma. Further studies are needed to validate the significance of the resulting planning volumes in designing treatment plans and the role that sophisticated brachytherapy treatment planning optimization may have in producing good plans.

Adult↗