Are There Differences in Accuracy or Outcomes Scores Among Navigated, Robotic, Patient-specific Instruments or Standard Cutting Guides in TKA? A Network Meta-analysis.

BACKGROUND: Several kinds of cutting guides, including patient-specific instrumentation, navigation, standard cutting guides, accelerometer-based navigation, and robotic guidance, are available to restore a planned alignment during TKA. No previous study has simultaneously compared all of these devices; a network meta-analysis is an especially appealing method because it allows comparisons across approaches that were not compared head-to-head in individual randomized controlled trials. QUESTIONS/PURPOSES: We performed a network meta-analysis to determine whether novel approaches to achieving implant alignment, such as patient-specific instrumentation, navigation, accelerometer-based navigation, and robotic guidance, provide any advantage over standard cutting guides in terms of: (1) hip-knee-ankle (HKA) alignment outliers greater than ± 3°, (2) outcome scores (1989 - Knee Society Score and WOMAC score) measured 6 months after surgery, or (3) femoral and tibial implant malalignment (greater than ± 3°), taken separately, in the frontal and sagittal plane, as well as other secondary outcomes including validated outcome scores 1 and 2 years after surgery. METHODS: In our network meta-analysis, we included randomized controlled trials comparing the different cutting guides by using at least one of the previously specified criteria, without limitation on language or date of publication. We searched electronic databases, major orthopaedic journals, proceedings of major orthopaedic meetings, ClinicalTrials.gov, and the World Health Organization's International Clinical Trials Registry Platform until October 1, 2018. This led to the inclusion of 90 randomized controlled trials involving 9389 patients (mean age 68.8 years) with 10,336 TKAs. Two reviewers independently selected trials and extracted data. The primary outcomes were the proportion patients with malalignment of the HKA angle (defined as HKA > 3° from neutral) and the Knee Society Score and WOMAC scores at 6 months postoperatively. We combined direct and indirect comparisons using a Bayesian network meta-analysis framework to assess and compare the effect of different cutting guides on outcomes. Bayesian estimates are based on the posterior distribution of an endpoint and are called credible intervals. Usually the 95% credible interval, corresponding to a posterior probability of 0.95 that the endpoint lies in the interval, is computed. Unlike the frequentist approach, the Bayesian approach does not allow the calculation of the p value. RESULTS: The proportion of HKA outliers was lower with navigation than with patient-specific instrumentation (risk ratio 0.46 [95% credible interval (CI) 0.34 to 0.63]) and standard cutting guides (risk ratio 0.45 [95% CI 0.37 to 0.53]); however, this corresponded to an actual difference of only 12% of patients for navigation versus 21% of patients for patient-specific instrumentation, and 12% of patients for navigation versus 25% for standard cutting guides. We found no differences for other comparisons between different cutting guides, including robotics and the accelerometer. We found no differences in the Knee Society Score or WOMAC score between the different cutting guides at 6 months. Regarding secondary outcomes, navigation reduced the risk of frontal and sagittal malalignments for femoral and tibial components compared with the standard cutting guides, but none of the other cutting guides showed superiority for the other secondary outcomes. CONCLUSIONS: Navigation resulted in approximately 10% fewer patients having HKA outliers of more than 3°, without any corresponding improvement in validated outcomes scores. It is unknown whether this incremental reduction in the proportion of patients who have alignment outside a window that itself has been called into question will justify the increased costs and surgical time associated with the approach. We believe that until or unless these new approaches either (1) convincingly demonstrate superior survivorship, or (2) convincingly demonstrate superior outcomes, surgeons and hospitals should not use these approaches since they add cost, have a learning curve (during which some patients may be harmed), and have the risks associated with uncertainty of novel surgical approaches. LEVEL OF EVIDENCE: Level I, therapeutic study.

Fluid Structure Interaction on Paravalvular Leakage of Transcatheter Aortic Valve Implantation Related to Aortic Stenosis: A Patient-Specific Case.

This study investigated the impact of paravalvular leakage (PVL) in relation to the different valve openings of the transcatheter aortic valve implantation (TAVI) valve using the fluid structure interaction (FSI) approach. Limited studies were found on the subject of FSI with regards to TAVI-PVL condition, which involves both fluid and structural responses in coupling interaction. Hence, further FSI simulation with the two-way coupling method is implemented to investigate the effects of hemodynamics blood flow along the patient-specific aorta model subjected to the interrelationship between PVL and the different valve openings using the established FSI software ANSYS 16.1. A 3D patient-specific aorta model is constructed using MIMICS software. The TAVI valve identical to Edward SAPIEN XT 26 (Edwards Lifesciences, Irvine, California), at different Geometrical Orifice Areas (GOAs), is implanted into the patient's aortic annulus. The leaflet opening of the TAVI valve is drawn according to severity of GOA opening represented in terms of 100%, 80%, 60%, and 40% opening, respectively. The result proved that the smallest percentage of GOA opening produced the highest possibility of PVL, increased the recirculatory flow proximally to the inner wall of the ascending aorta, and produced lower backflow velocity streamlines through the side area of PVL region. Overall, 40% GOA produced 89.17% increment of maximum velocity magnitude, 19.97% of pressure drop, 65.70% of maximum WSS magnitude, and a decrement of 33.62% total displacement magnitude with respect to the 100% GOA.

Influence of Patient-Specific Head Modeling on EEG Source Imaging.

Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject's head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.

TTTS-GPS: Patient-specific preoperative planning and simulation platform for twin-to-twin transfusion syndrome fetal surgery.

Twin-to-twin transfusion syndrome (TTTS) is a serious condition that may occur in pregnancies when two or more fetuses share the same placenta. It is characterized by abnormal vascular connections in the placenta that cause blood to flow unevenly between the babies. If left untreated, perinatal mortality occurs in 90% of cases, whilst neurological injuries are still present in TTTS survivors. Minimally invasive fetoscopic laser surgery is the standard and optimal treatment for this condition, but is technically challenging and can lead to complications. Acquiring and maintaining the required surgical skills need consistent practice, and a steep learning curve. An accurate preoperative planning is thus vital for complex TTTS cases. To this end, we propose the first TTTS fetal surgery planning and simulation platform. The soft tissue of the mother, the uterus, the umbilical cords, the placenta and its vascular tree are segmented and registered automatically from magnetic resonance imaging and 3D ultrasound using computer vision and deep learning techniques. The proposed state-of-the-art technology is integrated into a flexible C++ and MITK-based application to provide a full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation, determining the correct entry point, training doctors' movements and trajectory ahead of operation, which allows improving upon current practice. A comprehensive usability study is reported. Experienced surgeons rated highly our TTTS planner and simulator, thus being a potential tool to be implemented in real and complex TTTS surgeries.

DOSIS: An integrated computational tool for patient-specific dosimetry in nuclear medicine by Monte Carlo and dose point kernel approaches.

The use of radiolabeled molecules for tumor targeting constitutes a remarkable technique for the treatment of systemic malignancies. An accurate patient-specific dosimetry in nuclear medicine procedures should be a relevant pre-requisite in order to achieve the required lethal damage to tumor cells while maintaining possible side-effects to normal tissues at tolerable levels. It is desired to assess in vivo the radiopharmaceutical distribution for further estimation of absorbed dose released to target and involved organs. In this context, it was developed a computational toolkit, called DOSIS, in order to perform patient-specific dosimetry based on personalized patient anatomy and biodistribution of radionuclides both obtained by currently available dual PET/CT or SPECT/CT facilities. This work is focused on comparing 3D dose distributions obtained by DOSIS performing full stochastic Monte Carlo simulations versus analogue distributions obtained with analytical approaches like dose point kernel convolution and local energy deposition, when considering non-homogeneous activity or density distributions at different scales. Mathematical virtual phantoms were created for this study in order to compare results with other calculation methods. Some of the beta-emitters radionuclides commonly used for therapy (90Y, 131I, 177Lu) were investigated, and emissions of beta-particles, conversion electrons, gamma radiation, and characteristic X-rays were considered. DOSIS implements a novel code devoted to managing radiation transport simulation by means of PENELOPE Monte Carlo general-purpose routines on voxelized geometries defined by 3D mass and activity distributions. Both distributions can be defined through patients-specific images, or pre-defined virtual phantoms. Results preliminary confirmed DOSIS as a reliable and accurate toolkit for personalized internal dosimetry along with highlighting advantages/drawbacks of the different calculation schemes proposed.

Patient-Specific Phantomless Estimation of Bone Mineral Density and Its Effects on Finite Element Analysis Results: A Feasibility Study.

OBJECTIVES: This study proposes a regression model for the phantomless Hounsfield units (HU) to bone mineral density (BMD) conversion including patient physical factors and analyzes the accuracy of the estimated BMD values. METHODS: The HU values, BMDs, circumferences of the body, and cross-sectional areas of bone were measured from 39 quantitative computed tomography images of L2 vertebrae and hips. Then, the phantomless HU-to-BMD conversion was derived using a multiple linear regression model. For the statistical analysis, the correlation between the estimated BMD values and the reference BMD values was evaluated using Pearson's correlation test. Voxelwise BMD and finite element analysis (FEA) results were analyzed in terms of root-mean-square error (RMSE) and strain energy density, respectively. RESULTS: The HU values and circumferences were statistically significant (p < 0.05) for the lumbar spine, whereas only the HU values were statistically significant (p < 0.05) for the proximal femur. The BMD values estimated using the proposed HU-to-BMD conversion were significantly correlated with those measured using the reference phantom: Pearson's correlation coefficients of 0.998 and 0.984 for the lumbar spine and proximal femur, respectively. The RMSEs of the estimated BMD values for the lumbar spine and hip were 4.26 ± 0.60 (mg/cc) and 8.35 ± 0.57 (mg/cc), respectively. The errors of total strain energy were 1.06% and 0.91%, respectively. CONCLUSIONS: The proposed phantomless HU-to-BMD conversion demonstrates the potential of precisely estimating BMD values from CT images without the reference phantom and being utilized as a viable tool for FEA-based quantitative assessment using routine CT images.

Computational modelling of resistance and associated treatment response heterogeneity in metastatic cancers.

Metastatic cancer patients invariably develop treatment resistance. Different levels of resistance lead to observed heterogeneity in treatment response. The main goal was to evaluate treatment response heterogeneity with a computation model simulating the dynamics of drug-sensitive and drug-resistant cells. Model parameters included proliferation, drug-induced death, transition and proportion of intrinsically resistant cells. The model was benchmarked with imaging metrics extracted from 39 metastatic prostate cancer patients who had 18F-NaF-PET/CT scans performed at baseline and at three cycles into chemotherapy or hormonal therapy. Two initial model assumptions were evaluated: considering only inter-patient heterogeneity and both inter-patient and intra-patient heterogeneity in the proportion of intrinsically resistant cells. The correlation between the median proportion of intrinsically resistant cells and baseline patient-level imaging metrics was assessed with Spearman's rank correlation coefficient. The impact of model parameters on simulated treatment response was evaluated with a sensitivity study. Treatment response after periods of six, nine, and 12 months was predicted with the model. The median predicted range of response for patients treated with both therapies was compared with a Wilcoxon rank sum test. For each patient, the time was calculated when the proportion of disease with a non-favourable response outperformed a favourable response. By taking into account inter-patient and intra-patient heterogeneity in the proportion of intrinsically resistant cells, the model performed significantly better ([Formula: see text]) than by taking into account only inter-patient heterogeneity ([Formula: see text]). The median proportion of intrinsically resistant cells showed a moderate correlation (ρ = 0.55) with mean patient-level uptake, and a low correlation (ρ = 0.36) with the dispersion of mean metastasis-level uptake in a patient. The sensitivity study showed a strong impact of the proportion of intrinsically resistant cells on model behaviour after three cycles of therapy. The difference in the median range of response (MRR) was not significant between cohorts at any time point (p  > 0.15). The median time when the proportion of disease with a non-favourable response outperformed the favourable response was eight months, for both cohorts. The model provides an insight into inter-patient and intra-patient heterogeneity in the evolution of treatment resistance.

Computational Fluid Dynamics as an Engineering Tool for the Reconstruction of Hemodynamics after Carotid Artery Stenosis Operation: A Case Study.

Background and objectives: Brain ischemic stroke is caused by impaired or absolutely blocked blood flow into the brain regions. Despite the large number of possible origins, there is no general strategy for preventive treatment. In this paper, we aimed to predict the hemodynamics in a patient who experienced a critical stenosis operation in the carotid artery. This is a unique study where we used medical data together with the computational fluid (CFD) technique not to plan the surgery, but to predict its outcome. Materials and Methods: AngioCT data and blood perfusion of brain tissue (CT-perfusion) together with CFD technique were applied for stroke formation reconstruction in different clinical conditions. With the use of self-made semiautomatic algorithm for image processing and 3DDoctror software, 3D-vascular geometries before and after surgical intervention were reconstructed. As the paper is focused on the analysis of stroke appearance, apparent stroke was simulated as higher and lower pressure values in the cranial part due to different outcomes of the surgical intervention. This allowed to investigate the influence of spatial configuration and pressure values on blood perfusion in the analyzed circulatory system. Results: Application of CFD simulations for blood flow reconstruction for clinical conditions in the circulatory system accomplished on average 98.5% and 98.7% accuracy for CFD results compared to US-Doppler before and after surgical intervention, respectively. Meanwhile, CFD results compared to CT-perfusion indicated an average 89.7% and 92.8% accuracy before and after surgical intervention, respectively. Thus, the CFD is a reliable approach for predicting the patient hemodynamics, as it was confirmed by postoperative data. Conclusions: Our study indicated that the application of CFD simulations for blood flow reconstruction for clinical conditions in circulatory system reached 98% and 90% accuracy for US-Doppler and CT-perfusion, respectively. Therefore, the proposed method might be used as a tool for reconstruction of specific patients' hemodynamics after operation of critical stenosis in the carotid artery. However, further studies are necessary to confirm its usefulness in clinical practice.

Sparse Representation-Based Patient-Specific Diagnosis and Treatment for Esophageal Squamous Cell Carcinoma.

Precision medicine and personalized treatment have attracted attention in recent years. However, most genetic medicines mainly target one genetic site, while complex diseases like esophageal squamous cell carcinoma (ESCC) usually present heterogeneity that involves variations of many genetic markers. Here, we seek an approach to leverage genetic data and ESCC knowledge data to forward personalized diagnosis and treatment for ESCC. First, 851 ESCC-related gene markers and their druggability were studied through a comprehensive literature analysis. Then, a sparse representation-based variable selection (SRVS) was employed for patient-specific genetic marker selection using gene expression datasets. Results showed that the SRVS method could identify a unique gene vector for each patient group, leading to significantly higher classification accuracies compared to randomly selected genes (100, 97.17, 100, 100%; permutation p values: 0.0032, 0.0008, 0.0004, and 0.0008). The SRVS also outperformed an ANOVA-based gene selection method in terms of the classification ratio. The patient-specific gene markers are targets of ESCC effective drugs, providing specific guidance for medicine selection. Our results suggest the effectiveness of integrating previous database utilizing SRVS in assisting personalized medicine selection and treatment for ESCC.

Some methods for heterogeneous treatment effect estimation in high dimensions.

When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials. Stanford Health Care alone has millions of electronic medical records that are only just recently being leveraged to inform better treatment recommendations. These data present a unique challenge because they are high dimensional and observational. Our goal is to make personalized treatment recommendations based on the outcomes for past patients similar to a new patient. We propose and analyze 3 methods for estimating heterogeneous treatment effects using observational data. Our methods perform well in simulations using a wide variety of treatment effect functions, and we present results of applying the 2 most promising methods to data from The SPRINT Data Analysis Challenge, from a large randomized trial of a treatment for high blood pressure.

C-learning: A new classification framework to estimate optimal dynamic treatment regimes.

A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies.

A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation.

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician's disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.

Dysfunction Screening in Experimental Arteriovenous Grafts for Hemodialysis Using Fractional-Order Extractor and Color Relation Analysis.

In physical examinations, hemodialysis access stenosis leading to dysfunction occurs at the venous anastomosis site or the outflow vein. Information from the inflow stenosis, such as blood pressure, pressure drop, and flow resistance increases, allows dysfunction screening from the stage of early clots and thrombosis to the progression of outflow stenosis. Therefore, this study proposes dysfunction screening model in experimental arteriovenous grafts (AVGs) using the fractional-order extractor (FOE) and the color relation analysis (CRA). A Sprott system was designed using an FOE to quantify the differences in transverse vibration pressures between the inflow and outflow sites of an AVG. Experimental analysis revealed that the degree of stenosis (DOS) correlated with an increase in fractional-order dynamic errors (FODEs). Exponential regression was used to fit a non-linear curve and can be used to quantify the relationship between the FODEs and DOS (R (2) = 0.8064). The specific ranges were used to evaluate the stenosis degree, such as DOS: <50, 50-80, and >80%. A CRA-based screening method was derived from the hue angle-saturation-value color model, which describes perceptual color relationships for the DOS. It has a flexibility inference manner with color visualization to represent the different stenosis degrees, which has average accuracy >90% superior to the traditional methods. This in vitro experimental study demonstrated that the proposed model can be used for dysfunction screening in stenotic AVGs.

A Baseline Patient Model to Support Testing of Medical Cyber-Physical Systems.

Medical Cyber-Physical Systems (MCPS) are currently a trending topic of research. The main challenges are related to the integration and interoperability of connected medical devices, patient safety, physiologic closed-loop control, and the verification and validation of these systems. In this paper, we focus on patient safety and MCPS validation. We present a formal patient model to be used in health care systems validation without jeopardizing the patient's health. To determine the basic patient conditions, our model considers the four main vital signs: heart rate, respiratory rate, blood pressure and body temperature. To generate the vital signs we used regression models based on statistical analysis of a clinical database. Our solution should be used as a starting point for a behavioral patient model and adapted to specific clinical scenarios. We present the modeling process of the baseline patient model and show its evaluation. The conception process may be used to build different patient models. The results show the feasibility of the proposed model as an alternative to the immediate need for clinical trials to test these medical systems.

A computational tool for patient specific dosimetry and radiobiological modeling of selective internal radiation therapy with (90)Y microspheres.

In recent years we have witnessed tremendous progress in selective internal radiation therapy. In clinical practice, quite often, radionuclide therapy is planned using simple models based on standard activity values or activity administered per unit body weight or surface area in spite of the admission that radiation-dose methods provide more accurate dosimetric results. To address that issue, the authors developed a Matlab-based computational software, named Patient Specific Yttrium-90 Dosimetry Toolkit (PSYDT). PSYDT was designed for patient specific voxel-based dosimetric calculations and radiobiological modeling of selective internal radiation therapy with (90)Y microspheres. The developed toolkit is composed of three dimensional dose calculations for both bremsstrahlung and beta emissions. Subsequently, radiobiological modeling is performed on a per-voxel basis and cumulative dose volume histograms (DVHs) are generated. In this report we describe the functionality and visualization features of PSYDT.

Individualized model discovery: the case of anemia patients.

The universal sequel to chronic kidney condition (CKD) is anemia. Patients of anemia have kidneys that are incapable of performing certain basic functions such as sensing of oxygen levels to secrete erythropoietin when red blood cell counts are low. Under such conditions, external administration of human recombinant erythropoietin (EPO) is administered as alternative to improve conditions of CKD patients by increasing their hemoglobin (Hb) levels to a given therapeutic range. Presently, EPO dosing strategies extensively depend on packet inserts and on "average" responses to the medication from previous patients. Clearly dosage strategies based on these approaches are, at best, nonoptimal to EPO medication and potentially dangerous to patients that do not adhere to the notion of expected "average" response. In this work, a technique called semi-blind robust identification is provided to uniquely identify models of the individual patients of anemia based on their actual Hb responses and EPO administration. Using the a priori information and the measured input-output data of the individual patients, the procedure identifies a unique model consisting of a nominal model and the associated model uncertainty for the patients. By incorporating the effects of unknown system initial conditions, considerably small measurement samples can be used in the modeling process.

Improved alignment and operating room efficiency with patient-specific instrumentation for TKA.

BACKGROUND: Achieving accurate alignment in total knee arthroplasty (TKA) remains a concern. Patient-specific instrumentation (PSI) produced using preoperative 3D models was developed to offer surgeons a simplified, reliable, efficient and customised TKA procedure. METHODS: In this prospective study, 60 patients underwent TKA with conventional instrumentation and 71 patients were operated on using PSI. The primary endpoint was surgical time. Secondary endpoints included operating room (OR) time, the number of instrument trays used and postoperative radiographic limb alignment. RESULTS: Compared to conventional instrumentation, PSI significantly reduced total surgical time by 8.9 ± 3.3 min (p=0.038), OR time by 8.6 ± 4.2 min (p=0.043), and the number of instrument trays by six trays (p<0.001). Mechanical axis malalignment of the lower limb of >3° was observed in 13% of PSI patients versus 29% with conventional instrumentation (p=0.043). PSI predicted the size of the femoral and tibial components actually used in 85.9% and 78.9% of cases, respectively. CONCLUSION: PSI improves alignment, surgical and OR time, reduces the number of instruments trays used compared to conventional instrumentation in patients undergoing TKA and results in fewer outliers in overall mechanical alignment in the coronal plane. LEVEL OF EVIDENCE II: Prospective comparative therapeutic study.

Simulation modelling analysis for small sets of single-subject data collected over time.

The behavioural data yielded by single subjects in naturalistic and controlled settings likely contain valuable information to scientists and practitioners alike. Although some of the properties unique to this data complicate statistical analysis, progress has been made in developing specialised techniques for rigorous data evaluation. There are no perfect tests currently available to analyse short autocorrelated data streams, but there are some promising approaches that warrant further development. Although many approaches have been proposed, and some appear better than others, they all have some limitations. When data sets are large enough (∼30 data points per phase), the researcher has a reasonably rich pallet of statistical tools from which to choose. However, when the data set is sparse, the analytical options dwindle. Simulation modelling analysis (SMA; described in this article) is a relatively new technique that appears to offer acceptable Type-I and Type-II error rate control with short streams of autocorrelated data. However, at this point, it is probably too early to endorse any specific statistical approaches for short, autocorrelated time-series data streams. While SMA shows promise, more work is needed to verify that it is capable of reliable Type-I and Type-II error performance with short serially dependent streams of data.

A prognostic model based on preoperative MRI predicts overall survival in patients with diffuse gliomas.

BACKGROUND AND PURPOSE: Diffuse gliomas are classified as grades II-IV on the basis of histologic features, with prognosis determined mainly by clinical factors and histologic grade supported by molecular markers. Our aim was to evaluate, in patients with diffuse gliomas, the relationship of relative CBV and ADC values to overall survival. In addition, we also propose a prognostic model based on preoperative MR imaging findings that predicts survival independent of histopathology. MATERIALS AND METHODS: We conducted a retrospective analysis of the preoperative diffusion and perfusion MR imaging in 126 histologically confirmed diffuse gliomas. Median relative CBV and ADC values were selected for quantitative analysis. Survival univariate analysis was made by constructing survival curves by using the Kaplan-Meier method and comparing subgroups by log-rank probability tests. A Cox regression model was made for multivariate analysis. RESULTS: The study included 126 diffuse gliomas (median follow-up of 14.5 months). ADC and relative CBV values had a significant influence on overall survival. Median overall survival for patients with ADC < 0.799 × 10(-3) mm(2)/s was <1 year. Multivariate analysis revealed that patient age, relative CBV, and ADC values were associated with survival independent of pathology. The preoperative model provides greater ability to predict survival than that obtained by histologic grade alone. CONCLUSIONS: ADC values had a better correlation with overall survival than relative CBV values. A preoperative prognostic model based on patient age, relative CBV, and ADC values predicted overall survival of patients with diffuse gliomas independent of pathology. This preoperative model provides a more accurate predictor of survival than histologic grade alone.