Rachel Sparks

UDP-glucuronosyltransferase and sulfotransferase polymorphisms, sex hormone concentrations, and tumor receptor status in breast cancer patients

IntroductionUDP-glucuronosyltransferase (UGT) and sulfotransferase (SULT) enzymes are involved in removing sex hormones from circulation. Polymorphic variation in five UGT and SULT genes – UGT1A1 ((TA)6/(TA)7), UGT2B4 (Asp458Glu), UGT2B7 (His268Tyr), UGT2B15 (Asp85Tyr), and SULT1A1 (Arg213His) – may be associated with circulating sex hormone concentrations, or the risk of an estrogen receptor-negative (ER-) or progesterone receptor-negative (PR-) tumor.MethodsLogistic regression analysis was used to estimate the odds ratios of an ER- or PR- tumor associated with polymorphisms in the genes listed above for 163 breast cancer patients from a population-based cohort study of women in western Washington. Adjusted geometric mean estradiol, estrone, and testosterone concentrations were calculated within each UGT and SULT genotype for a subpopulation of postmenopausal breast cancer patients not on hormone therapy 2–3 years after diagnosis (n = 89).ResultsThe variant allele of UGT1A1 was associated with reduced risk of an ER- tumor (P for trend = 0.03), and variants of UGT2B15 and SULT1A1 were associated with non-statistically significant risk reductions. There was some indication that plasma estradiol and testosterone concentrations varied by UGT2B15 and SULT1A1 genotypes; women with the UGT2B15 Asp/Tyr and Tyr/Tyr genotypes had higher concentrations of estradiol than women with the Asp/Asp genotype (P = 0.004). Compared with women with the SULT1A1 Arg/Arg and Arg/His genotypes, women with the His/His genotype had elevated concentrations of testosterone (P = 0.003).ConclusionsThe risk of ER- breast cancer tumors may vary by UGT or SULT genotype. Further, plasma estradiol and testosterone concentrations in breast cancer patients may differ depending on some UGT and SULT genotypes.

Rescreening for gonorrhea and chlamydial infection through the mail: a randomized trial.

Background Rescreening patients after treatment of Chlamydia trachomatis or Neisseria gonorrhoeae infection has had high yield but low rates of participation. Goal The goal of this study was to determine if rescreening for gonorrhea and chlamydial infection in a largely urban sexually transmitted disease population would be more successful if individuals were given the option of submitting a specimen for testing through the mail. Study Design We conducted a randomized clinical trial involving 122 patients of whom 62 were assigned to clinic rescreening and 60 were given the option of either mailing a specimen for testing or going to a clinic for rescreening. Results Twenty-seven patients (45%) given the option of either rescreening in the clinic or through the mail and 20 (32%) assigned to clinic rescreening were rescreened within 28 days of enrollment in the study (odds ratio, 1.7; 95% confidence interval, 0.8–3.8). Of the 60 patients randomized to the clinic rescreening or mailing option, 11 of 18 (61%) who opted to mail in a specimen and 16 of 42 (38%) who chose clinic rescreening were rescreened within 28 days of enrollment (P = 0.10). Conclusions Although not statistically significant, this study indicates that mailed rescreening could be a successful method to increase rescreening rates.

Co-Occurring Gland Angularity in Localized Subgraphs: Predicting Biochemical Recurrence in Intermediate-Risk Prostate Cancer Patients

Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive disease. By modeling the extent of the disorder, we can differentiate surgically removed prostate tissue sections from (a) benign and malignant regions and (b) more and less aggressive prostate cancer. For a cohort of 40 intermediate-risk (mostly Gleason sum 7) surgically cured prostate cancer patients where half suffered biochemical recurrence, the CGA features were able to predict biochemical recurrence with 73% accuracy. Additionally, for 80 regions of interest chosen from the 40 studies, corresponding to both normal and cancerous cases, the CGA features yielded a 99% accuracy. CGAs were shown to be statistically signicantly () better at predicting BCR compared to state-of-the-art QH methods and postoperative prostate cancer nomograms.

Accuracy of intracranial electrode placement for stereoelectroencephalography: A systematic review and meta-analysis

Stereoelectroencephalography (SEEG) is a procedure in which electrodes are inserted into the brain to help define the epileptogenic zone. This is performed prior to definitive epilepsy surgery in patients with drug‐resistant focal epilepsy when noninvasive data are inconclusive. The main risk of the procedure is hemorrhage, which occurs in 1–2% of patients. This may result from inaccurate electrode placement or a planned electrode damaging a blood vessel that was not detected on the preoperative vascular imaging. Proposed techniques include the use of a stereotactic frame, frameless image guidance systems, robotic guidance systems, and customized patient‐specific fixtures.

Utility of 3D multimodality imaging in the implantation of intracranial electrodes in epilepsy

We present a single‐center prospective study, validating the use of 3D multimodality imaging (3DMMI) in patients undergoing intracranial electroencephalography (IC‐EEG).

Statistical shape model for manifold regularization: Gleason grading of prostate histology

Gleason patterns of prostate cancer histopathology, characterized primarily by morphological and architectural attributes of histological structures (glands and nuclei), have been found to be highly correlated with disease aggressiveness and patient outcome. Gleason patterns 4 and 5 are highly correlated with more aggressive disease and poorer patient outcome, while Gleason patterns 1-3 tend to reflect more favorable patient outcome. Because Gleason grading is done manually by a pathologist visually examining glass (or digital) slides subtle morphologic and architectural differences of histological attributes, in addition to other factors, may result in grading errors and hence cause high inter-observer variability. Recently some researchers have proposed computerized decision support systems to automatically grade Gleason patterns by using features pertaining to nuclear architecture, gland morphology, as well as tissue texture. Automated characterization of gland morphology has been shown to distinguish between intermediate Gleason patterns 3 and 4 with high accuracy. Manifold learning (ML) schemes attempt to generate a low dimensional manifold representation of a higher dimensional feature space while simultaneously preserving nonlinear relationships between object instances. Classification can then be performed in the low dimensional space with high accuracy. However ML is sensitive to the samples contained in the dataset; changes in the dataset may alter the manifold structure. In this paper we present a manifold regularization technique to constrain the low dimensional manifold to a specific range of possible manifold shapes, the range being determined via a statistical shape model of manifolds (SSMM). In this work we demonstrate applications of the SSMM in (1) identifying samples on the manifold which contain noise, defined as those samples which deviate from the SSMM, and (2) accurate out-of-sample extrapolation (OSE) of newly acquired samples onto a manifold constrained by the SSMM. We demonstrate these applications of the SSMM in the context of distinguish between Gleason patterns 3 and 4 using glandular morphologic features in a prostate histopathology dataset of 58 patient studies. Identifying and eliminating noisy samples from the manifold via the SSMM results in a statistically significant improvement in area under the receiver operator characteristic curve (AUC), 0.832 ± 0.048 with removal of noisy samples compared to a AUC of 0.779 ± 0.075 without removal of samples. The use of the SSMM for OSE of newly acquired glands also shows statistically significant improvement in AUC, 0.834 ± 0.051 with the SSMM compared to 0.779 ± 0.054 without the SSMM. Similar results were observed for the synthetic Swiss Roll and Helix datasets.

Explicit shape descriptors: Novel morphologic features for histopathology classification

Object morphology, defined as shape and size characteristics, observed on medical imagery is often an important marker for disease presence and/or aggressiveness. In the context of prostate cancer histopathology, gland morphology is an integral component of the Gleason grading system which enables discrimination between low and high grade disease. However, clinicians are often unable to distinguish between subtle differences in object morphology, as evidenced by high inter-observer variability in Gleason grading. Boundary-based morphologic descriptors, such as the variance in the distance from points on the boundary of an object to its center, may not have the requisite discriminability to separate objects with subtle shape differences. In this paper, we present a set of novel explicit shape descriptors (ESDs) which are capable of distinguishing subtle shape differences between prostate glands of intermediate Gleason grades (grades 3 and 4) on prostate cancer histopathology. Calculation of ESDs involves: (1) representing object morphology using an explicit shape model (e.g. medial axis); (2) aligning the shape models via a non-rigid registration scheme with a diffeomorphic constraint and quantifying shape model dissimilarity; and (3) applying a non-linear dimensionality reduction scheme (e.g. Graph Embedding) to learn a low dimensional projection encoding the shape differences between objects. ESDs are hence the principal eigenvectors in the reduced embedding space. In this work we demonstrate that ESDs in conjunction with a Support Vector Machine classifier are able to correctly distinguish between 888 prostate glands corresponding to different Gleason grades (benign, grade 3, or grade 4) of prostate cancer from 58 needle biopsy specimens with a maximum accuracy of 0.89 and corresponding area under the receiver operating characteristic curve of 0.78.

Novel morphometric based classification via diffeomorphic based shape representation using manifold learning

Morphology of anatomical structures can provide important diagnostic information regarding disease. Implicit features of morphology, such as contour smoothness or perimeter-to-area ratio, have been used in the context of computerized decision support classifiers to aid disease diagnosis. These features are usually specific to the domain and application (e.g., margin irregularity is a predictor of malignant breast lesions on DCE-MRI). In this paper we present a framework for extracting Diffeomorphic Based Similarity (DBS) features to capture subtle morphometric differences between shapes that may not be captured by implicit features. Object morphology is represented using the medial axis model and objects are compared by determining correspondences between medial axis models using a cluster-based diffeomorphic registration scheme. To visualize and classify morphometric differences, a manifold learning scheme (Graph Embedding) is employed to identify nonlinear dependencies between medial axis model similarity and calculate DBS. We evaluated our DBS on two clinical problems discriminating: (a) different Gleason grades of prostate cancer using gland morphology on a set of 102 images, and (b) benign and malignant lesions on 44 breast DCE-MRI studies. Precision-recall curves demonstrate DBS features are better able to classify shapes belonging to the same class compared to implicit features. A support vector machine (SVM) classifier is trained to distinguish between different classes utilizing DBS. SVM accuracy was 83 +/- 4.47% for distinguishing benign from malignant lesions on breast DCE-MRI and over 80% in distinguishing between intermediate Gleason grades of prostate cancer on digitized histology.

Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy.

OBJECT The objective of this study was to evaluate the clinical utility of multitrajectory computer-assisted planning software (CAP) to plan stereoelectroencephalography (SEEG) electrode arrangements. METHODS A cohort of 18 patients underwent SEEG for evaluation of epilepsy at a single center between August 2013 and August 2014. Planning of electrodes was performed manually and stored using EpiNav software. CAP was developed as a planning tool in EpiNav. The user preselects a set of cerebral targets and optimized trajectory constraints, and then runs an automated search of potential scalp entry points and associated trajectories. Each trajectory is associated with metrics for a safety profile, derived from the minimal distance to vascular structures, and an efficacy profile, derived from the proportion of depth electrodes that are within or adjacent to gray matter. CAP was applied to the cerebral targets used in the cohort of 18 previous manually planned implantations to generate new multitrajectory implantation plans. A comparison was then undertaken for trajectory safety and efficacy. RESULTS CAP was applied to 166 electrode targets in 18 patients. There were significant improvements in both the safety profile and efficacy profile of trajectories generated by CAP compared with manual planning (p < 0.05). Three independent neurosurgeons assessed the feasibility of the trajectories generated by CAP, with 131 (78.9%) of 166 trajectories deemed suitable for implementation in clinical practice. CAP was performed in real time, with a median duration of 8 minutes for each patient, although this does not include the time taken for data preparation. CONCLUSIONS CAP is a promising tool to plan SEEG implantations. CAP provides feasible depth electrode arrangements, with quantitatively greater safety and efficacy profiles, and with a substantial reduction in duration of planning within the 3D multimodality framework.

High-throughput prostate cancer gland detection, segmentation, and classification from digitized needle core biopsies

We present a high-throughput computer-aided system for the segmentation and classification of glands in high resolution digitized images of needle core biopsy samples of the prostate. It will allow for rapid and accurate identification of suspicious regions on these samples. The system includes the following three modules: 1) a hierarchical frequency weighted mean shift normalized cut (HNCut) for initial detection of glands; 2) a geodesic active contour (GAC) model for gland segmentation; and 3) a diffeomorphic based similarity (DBS) feature extraction for classification of glands as benign or cancerous. HNCut is a minimally supervised color based detection scheme that combines the frequency weighted mean shift and normalized cuts algorithms to detect the lumen region of candidate glands. A GAC model, initialized using the results of HNCut, uses a color gradient based edge detection function for accurate gland segmentation. Lastly, DBS features are a set of morpho-metric features derived from the nonlinear dimensionality reduction of a dissimilarity metric between shape models. The system integrates these modules to enable the rapid detection, segmentation, and classification of glands on prostate biopsy images. Across 23 H & E stained prostate studies of whole-slides, 105 regions of interests (ROIs) were selected for the evaluation of segmentation and classification. The segmentation results were evaluated on 10 ROIs and compared to manual segmentation in terms of mean distance (2.6 ± 0.2 pixels), overlap (62 ± 0.07%), sensitivity (85 ± 0.01%), specificity (94 ± 0.003%) and positive predictive value (68 ± 0.08%). Over 105 ROIs, the classification accuracy for glands automatically segmented was (82.5 ± 9.10%) while the accuracy for glands manually segmented was (82.89 ± 3.97%); no statistically significant differences were identified between the classification results.