Display omitted
•Accurate locoregional evaluation of endometrial cancer is necessary for staging.•Accuracy in fusion between individual MRI and PET on PET/MRI is not perfect.•MRI scan timing obtained ...by active trigger enables accurate fusion images with PET.•BPL improves the PET degradation caused by reconstruction with MR active trigger.•MR active trigger and BPL leads accurate diagnosis of endometrial cancer on PET/MRI.
Minimal misregistration of fused PET and MRI images can be achieved with simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI). However, the acquisition of multiple MRI sequences during a single PET emission scan may impair fusion precision of each sequence. This study evaluated the diagnostic utility of time-synchronized PET/MRI using an MR active trigger and a Bayesian penalized likelihood reconstruction algorithm (BPL) to assess the locoregional extension of endometrial cancer.
Fifty-five patients with endometrial cancer who underwent pelvic 2-deoxy-2-18Ffluoro-D-glucose PET/MRI were retrospectively evaluated. The PET emission time for the BPL reconstruction was determined by the MR active trigger of each MR sequence. The concordance rates of image interpretation with pathological T-staging, diagnostic performance for deep myometrial invasion (MI), and diagnostic confidence levels were evaluated by two readers and compared between time-synchronized, overlapping (conventional and simultaneous, but not time-synchronized), and sequential (not simultaneous) PET/MRI and MRI with diffusion-weighted imaging. Misregistration of the PET/MRI-fused images was determined by evaluating the differences in bladder dimensions.
The T classification by time-synchronized PET/MRI was the most concordant with the pathological T classification for the two readers. Time-synchronized PET/MRI had a significantly higher diagnostic performance for deep MI and higher confidence level scores than overlapping PET/MRI for the novice reader (p = 0.033 and p = 0.038, respectively). The differences in bladder dimension on sequential PET/MRI were significantly larger than those on overlapping and time-synchronized PET/MRI (p <0.001).
Time-synchronized PET/MRI is superior to conventional PET/MRI for assessing the locoregional extension of endometrial cancer.
Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit the outliers. ...To overcome this problem, data often needs to be preprocessed to remove outliers, though this is difficult for applications where the data needs to be analyzed online. We present an approach to changepoint detection that is robust to the presence of outliers. The idea is to adapt existing penalized cost approaches for detecting changes so that they use loss functions that are less sensitive to outliers. We argue that loss functions that are bounded, such as the classical biweight loss, are particularly suitable-as we show that only bounded loss functions are robust to arbitrarily extreme outliers. We present an efficient dynamic programming algorithm that can find the optimal segmentation under our penalized cost criteria. Importantly, this algorithm can be used in settings where the data needs to be analyzed online. We show that we can consistently estimate the number of changepoints, and accurately estimate their locations, using the biweight loss function. We demonstrate the usefulness of our approach for applications such as analyzing well-log data, detecting copy number variation, and detecting tampering of wireless devices. Supplementary materials for this article are available online.
During the past decade, penalized likelihood methods have been widely used in variable selection problems, where the penalty functions are typically symmetric about 0, continuous and nondecreasing in ...(0, ∞). We propose a new penalized likelihood method, reciprocal Lasso (or in short, rLasso), based on a new class of penalty functions that are decreasing in (0, ∞), discontinuous at 0, and converge to infinity when the coefficients approach zero. The new penalty functions give nearly zero coefficients infinity penalties; in contrast, the conventional penalty functions give nearly zero coefficients nearly zero penalties (e.g., Lasso and smoothly clipped absolute deviation SCAD) or constant penalties (e.g., L ₀ penalty). This distinguishing feature makes rLasso very attractive for variable selection. It can effectively avoid to select overly dense models. We establish the consistency of the rLasso for variable selection and coefficient estimation under both the low- and high-dimensional settings. Since the rLasso penalty functions induce an objective function with multiple local minima, we also propose an efficient Monte Carlo optimization algorithm to solve the involved minimization problem. Our simulation results show that the rLasso outperforms other popular penalized likelihood methods, such as Lasso, SCAD, minimax concave penalty, sure independence screening, interative sure independence screening, and extended Bayesian information criterion. It can produce sparser and more accurate coefficient estimates, and catch the true model with a higher probability. Supplementary materials for this article are available online.
Summary
Penalization of the likelihood by Jeffreys’ invariant prior, or a positive power thereof, is shown to produce finite-valued maximum penalized likelihood estimates in a broad class of binomial ...generalized linear models. The class of models includes logistic regression, where the Jeffreys-prior penalty is known additionally to reduce the asymptotic bias of the maximum likelihood estimator, and models with other commonly used link functions, such as probit and log-log. Shrinkage towards equiprobability across observations, relative to the maximum likelihood estimator, is established theoretically and studied through illustrative examples. Some implications of finiteness and shrinkage for inference are discussed, particularly when inference is based on Wald-type procedures. A widely applicable procedure is developed for computation of maximum penalized likelihood estimates, by using repeated maximum likelihood fits with iteratively adjusted binomial responses and totals. These theoretical results and methods underpin the increasingly widespread use of reduced-bias and similarly penalized binomial regression models in many applied fields.
Bias approximation has played an important rôle in statistical inference, and numerous bias calculation techniques have been proposed under different contexts. We provide a unified approach to ...approximating the bias of the maximum likelihood estimator and the l2 penalized likelihood estimator for both linear and nonlinear models, where the design variables are allowed to be random and the sample size can be a stopping time. The proposed method is based on the Woodroofe–Stein identity and is justified by very weak approximations. The accuracy of the derived bias formulas is assessed by simulation for several examples. The bias of the ridge estimator in high‐dimensional settings is also discussed.
In the multiple changepoint setting, various search methods have been proposed, which involve optimizing either a constrained or penalized cost function over possible numbers and locations of ...changepoints using dynamic programming. Recent work in the penalized optimization setting has focused on developing an exact pruning-based approach that, under certain conditions, is linear in the number of data points. Such an approach naturally requires the specification of a penalty to avoid under/over-fitting. Work has been undertaken to identify the appropriate penalty choice for data-generating processes with known distributional form, but in many applications the model assumed for the data is not correct and these penalty choices are not always appropriate. To this end, we present a method that enables us to find the solution path for all choices of penalty values across a continuous range. This permits an evaluation of the various segmentations to identify a suitable penalty choice. The computational complexity of this approach can be linear in the number of data points and linear in the difference between the number of changepoints in the optimal segmentations for the smallest and largest penalty values. Supplementary materials for this article are available online.
We revisit sure independence screening procedures for variable selection in generalized linear models and the Cox proportional hazards model. Through the publicly available R package SIS, we provide ...a unified environment to carry out variable selection using iterative sure independence screening (ISIS) and all of its variants. For the regularization steps in the ISIS recruiting process, available penalties include the LASSO, SCAD, and MCP while the implemented variants for the screening steps are sample splitting, data-driven thresholding, and combinations thereof. Performance of these feature selection techniques is investigated by means of real and simulated data sets, where we find considerable improvements in terms of model selection and computational time between our algorithms and traditional penalized pseudo-likelihood methods applied directly to the full set of covariates.
PDF
