Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis.

PURPOSE: Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients. METHODS: We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019. Studies comprising at least 50 patients and examining survival were included. Pooled Cox and logistic mixed-effects models were used to compare the ability of multiomics subtyping methods to identify clinically prognostic subtypes, and a structural equation model was used to examine causal paths underlying subtyping method and mortality. RESULTS: A total of 31 studies comprising 10,848 unique patients across 32 cancers were analyzed. Latent-variable subtyping was significantly associated with overall survival (adjusted hazard ratio, 2.81; 95% CI, 1.16-6.83; P = .023) and vital status (1 year adjusted odds ratio, 4.71; 95% CI, 1.34-16.49; P = .015; 5 year adjusted odds ratio, 7.69; 95% CI, 1.83-32.29; P = .005); latent-variable-identified subtypes had greater associations with mortality across models (adjusted hazard ratio, 1.19; 95% CI, 1.01-1.42; P = .050). Our structural equation model confirmed the path from subtyping method through multiomics subtype (߈ = 0.66; P = .048) on survival (߈ = 0.37; P = .008). CONCLUSION: Multiomics methods have different abilities to define clinically prognostic cancer subtypes, which should be considered before administration of personalized therapy; preliminary evidence suggests that latent-variable methods better identify clinically prognostic biomarkers and subtypes.

Do Knee Moments Normalized to Measures of Knee Cartilage Area Better Classify the Severity of Knee Osteoarthritis?

Investigations of joint loading in knee osteoarthritis (OA) typically normalize the knee adduction moment to global measures of body size (eg, body mass, height) to allow comparison between individuals. However, such measurements may not reflect knee size. This study used a morphometric measurement of the cartilage surface area on the medial tibial plateau, which better represents medial knee size. This study aimed to determine whether normalizing the peak knee adduction moment and knee adduction moment impulse during gait to the medial tibial bone-cartilage interface could classify radiographic knee OA severity more accurately than traditional normalization techniques. Individuals with mild (N = 22) and severe (N = 17) radiographic knee OA participated. The medial tibial bone-cartilage interface was quantified from magnetic resonance imaging scans. Gait analysis was performed, and the peak knee adduction moment and knee adduction moment impulse were calculated in nonnormalized units and normalized to body mass, body weight × height, and the medial tibial bone-cartilage interface. Receiver operating characteristic curves compared the ability of each knee adduction moment normalization technique to classify participants according to radiographic disease severity. No normalization technique was superior at distinguishing between OA severities. Knee adduction moments normalized to medial knee size were not more sensitive to OA severity.

Minimum joint space width (mJSW) of patellofemoral joint on standing "skyline" radiographs: test-retest reproducibility and comparison with quantitative magnetic resonance imaging (qMRI).

OBJECTIVE: To assess the intraobserver, interobserver, and test-retest reproducibility of minimum joint space width (mJSW) measurement of medial and lateral patellofemoral joints on standing "skyline" radiographs and to compare the mJSW of the patellofemoral joint to the mean cartilage thickness calculated by quantitative magnetic resonance imaging (qMRI). MATERIALS AND METHODS: A couple of standing "skyline" radiographs of the patellofemoral joints and MRI of 55 knees of 28 volunteers (18 females, ten males, mean age, 48.5 ± 16.2 years) were obtained on the same day. The mJSW of the patellofemoral joint was manually measured and Kellgren and Lawrence grade (KLG) was independently assessed by two observers. The mJSW was compared to the mean cartilage thickness of patellofemoral joint calculated by qMRI. RESULTS: mJSW of the medial and lateral patellofemoral joint showed an excellent intraobserver agreement (interclass correlation (ICC) = 0.94 and 0.96), interobserver agreement (ICC = 0.90 and 0.95) and test-retest agreement (ICC = 0.92 and 0.96). The mJSW measured on radiographs was correlated to mean cartilage thickness calculated by qMRI (r = 0.71, p < 0.0001 for the medial PFJ and r = 0.81, p < 0.0001 for the lateral PFJ). However, there was a lack of concordance between radiographs and qMRI for extreme values of joint width and KLG. Radiographs yielded higher joint space measures than qMRI in knees with a normal joint space, while qMRI yielded higher joint space measures than radiographs in knees with joint space narrowing and higher KLG. CONCLUSIONS: Standing "skyline" radiographs are a reproducible tool for measuring the mJSW of the patellofemoral joint. The mJSW of the patellofemoral joint on radiographs are correlated with, but not concordant with, qMRI measurements.

Clinical performance of a multiparametric MRI-based post concussive syndrome index.

Introduction: Diffusion Tensor Imaging (DTI) has revealed measurable changes in the brains of patients with persistent post-concussive syndrome (PCS). Because of inconsistent results in univariate DTI metrics among patients with mild traumatic brain injury (mTBI), there is currently no single objective and reliable MRI index for clinical decision-making in patients with PCS. Purpose: This study aimed to evaluate the performance of a newly developed PCS Index (PCSI) derived from machine learning of multiparametric magnetic resonance imaging (MRI) data to classify and differentiate subjects with mTBI and PCS history from those without a history of mTBI. Materials and methods: Data were retrospectively extracted from 139 patients aged between 18 and 60 years with PCS who underwent MRI examinations at 2 weeks to 1-year post-mTBI, as well as from 336 subjects without a history of head trauma. The performance of the PCS Index was assessed by comparing 69 patients with a clinical diagnosis of PCS with 264 control subjects. The PCSI values for patients with PCS were compared based on the mechanism of injury, time interval from injury to MRI examination, sex, history of prior concussion, loss of consciousness, and reported symptoms. Results: Injured patients had a mean PCSI value of 0.57, compared to the control group, which had a mean PCSI value of 0.12 (p = 8.42e-23) with accuracy of 88%, sensitivity of 64%, and specificity of 95%, respectively. No statistically significant differences were found in the PCSI values when comparing the mechanism of injury, sex, or loss of consciousness. Conclusion: The PCSI for individuals aged between 18 and 60 years was able to accurately identify patients with post-concussive injuries from 2 weeks to 1-year post-mTBI and differentiate them from the controls. The results of this study suggest that multiparametric MRI-based PCSI has great potential as an objective clinical tool to support the diagnosis, treatment, and follow-up care of patients with post-concussive syndrome. Further research is required to investigate the replicability of this method using other types of clinical MRI scanners.

Baseline knee adduction moment interacts with body mass index to predict loss of medial tibial cartilage volume over 2.5 years in knee Osteoarthritis.

This study aimed to determine the extent to which changes over 2.5 years in medial knee cartilage thickness and volume were predicted by: (1) Peak values of the knee adduction (KAM) and flexion moments; and (2) KAM impulse and loading frequency, representing cumulative load, after controlling for age, sex and body mass index (BMI). Adults with clinical knee osteoarthritis participated. At baseline and approximately 2.5 years follow-up, cartilage thickness and volume of the medial tibia and femur were segmented from magnetic resonance imaging scans. Gait kinematics and kinetics, and daily knee loading frequency were also collected at baseline. Multiple linear regressions predicted changes in cartilage morphology from baseline gait mechanics. Data were collected from 52 participants (41 women) [age 61.0 (6.9) y; BMI 28.5 (5.7) kg/m2 ] over 2.56 (0.51) years. There were significant KAM peak-by-BMI (p = 0.023) and KAM impulse-by-BMI (p = 0.034) interactions, which revealed that larger joint loads in those with higher BMIs were associated with greater loss of medial tibial cartilage volume. In conclusion, with adjustments for age, sex, and cartilage measurement at baseline, large magnitude KAM peak and KAM impulse each interacted with BMI to predict loss of cartilage volume of the medial tibia over 2.5 years among individuals with knee osteoarthritis. These data suggest that, in clinical knee osteoarthritis, exposure to large KAMs may be detrimental to cartilage in those with larger BMIs. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2476-2483, 2017.

Scan-rescan precision of subchondral bone curvature maps from routine 3D DESS water excitation sequences: Data from the Osteoarthritis Initiative.

BACKGROUND: Subchondral bone (SCB) undergoes changes in the shape of the articulating bone surfaces and is currently recognized as a key target in osteoarthritis (OA) treatment. The aim of this study was to present an automated system that determines the curvature of the SCB regions of the knee and to evaluate its cross-sectional and longitudinal scan-rescan precision METHODS: Six subjects with OA and six control subjects were selected from the Osteoarthritis Initiative (OAI) pilot study database. As per OAI protocol, these subjects underwent 3T MRI at baseline and every twelve months thereafter, including a 3D DESS WE sequence. We analyzed the baseline and twenty-four month images. Each subject was scanned twice at these visits, thus generating scan-rescan information. Images were segmented with an automated multi-atlas framework platform and then 3D renderings of the bone structure were created from the segmentations. Curvature maps were extracted from the 3D renderings and morphed into a reference atlas to determine precision, to generate population statistics, and to visualize cross-sectional and longitudinal curvature changes. RESULTS: The baseline scan-rescan root mean square error values ranged from 0.006mm(-1) to 0.013mm(-1), and from 0.007mm(-1) to 0.018mm(-1) for the SCB of the femur and the tibia, respectively. The standardized response of the mean of the longitudinal changes in curvature in these regions ranged from -0.09 to 0.02 and from -0.016 to 0.015, respectively. CONCLUSION: The fully automated system produces accurate and precise curvature maps of femoral and tibial SCB, and will provide a valuable tool for the analysis of the curvature changes of articulating bone surfaces during the course of knee OA.

Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment.

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers. For this, Alzheimer's Disease Neuroimaging Initiative database was mined for case-control candidates. At least 652 baseline features extracted from MRI and PET analyses, biological samples, and clinical data up to February 2014 were used. A feature selection methodology that includes a genetic algorithm search coupled to a logistic regression classifier and forward and backward selection strategies was used to explore combinations of features. This generated diagnostic models with sizes ranging from 3 to 8, including well documented AD biomarkers, as well as unexplored image, biochemical, and clinical features. Accuracies of 0.85, 0.79, and 0.80 were achieved for HC-AD, HC-MCI, and MCI-AD classifications, respectively, when evaluated using a blind test set. In conclusion, a set of features provided additional and independent information to well-established AD biomarkers, aiding in the classification of MCI and AD.

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI.

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain.

On the use of coupled shape priors for segmentation of magnetic resonance images of the knee.

Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually, the training data consist of whole shapes of certain organs or structures obtained manually by clinical experts. The resulting prior models enforce segmentation accuracy uniformly over the entire structure or structures to be identified. In this paper, we consider a new coupled prior shape model which is demonstrated to provide high accuracy, specifically in the region of the interest where precision is most needed for the application of the segmentation of the femur and tibia in magnetic resonance (MR) images. Experimental results for the segmentation of MR images of human knees demonstrate that the combination of the new coupled prior shape and a directional edge force provides the improved segmentation performance. Moreover, the new approach allows for equivalent accurate identification of bone marrow lesions, a promising biomarker related to osteoarthritis, to the current state of the art but requires significantly less manual interaction.

GridMass: a fast two-dimensional feature detection method for LC/MS.

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community.

Local force model for cardiac dynamics analysis from volumetric image sequences.

We propose a local force model based on the kinetics of the left ventricle (LV) surfaces to characterize the complex nonrigid motion that the heart undergoes. The left ventricle motion is analyzed in a coarse-to-fine fashion. First, global motion and deformation are analyzed and compensated with hierarchical surface modeling. Then, we propose a physics-based model of local deformation derived from the dynamics of independent point masses driven by local external force. Experimental results show that the ensembles of the estimated point mass trajectories match well with the realistic left ventricle surface dynamics.

SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis.

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

Unsupervised segmentation and quantification of anatomical knee features: data from the Osteoarthritis Initiative.

This paper presents a fully automated method for segmenting articular knee cartilage and bone from in vivo 3-D dual echo steady state images. The magnetic resonance imaging (MRI) datasets were obtained from the Osteoarthritis Initiative (OAI) pilot study and include longitudinal images from controls and subjects with knee osteoarthritis (OA) scanned twice at each visit (baseline, 24 month). Initially, human experts segmented six MRI series. Five of the six resultant sets served as reference atlases for a multiatlas segmentation algorithm. The methodology created precise knee segmentations that were used to extract articular cartilage volume, surface area, and thickness as well as subchondral bone plate curvature. Comparison to manual segmentation showed Dice similarity coefficient (DSC) of 0.88 and 0.84 for the femoral and tibial cartilage. In OA subjects, thickness measurements showed test-retest precision ranging from 0.014 mm (0.6%) at the femur to 0.038 mm (1.6%) at the femoral trochlea. In the same population, the curvature test-retest precision ranged from 0.0005 mm(-1) (3.6%) at the femur to 0.0026 mm(-1) (11.7%) at the medial tibia. Thickness longitudinal changes showed OA Pearson correlation coefficient of 0.94 for the femur. In conclusion, the fully automated segmentation methodology produces reproducible cartilage volume, thickness, and shape measurements valuable for the study of OA progression.

The use of sequential MR image sets for determining tibiofemoral motion: reliability of coordinate systems and accuracy of motion tracking algorithm.

The use of magnetic resonance imaging has been proposed by many investigators for establishment of joint reference systems and kinematic tracking of musculoskeletal joints. In this study, the intraobserver and interobserver reliability of a strategy to establish anatomic reference systems using manually selected fiducial points were quantified for seven sets of MR images of the human knee joint. The standard error of the measurement of the intraobserver and interobserver errors were less than 2.6 degrees, and 1.2 mm for relative tibiofemoral orientation and displacement, respectively. An automated motion tracking algorithm was also validated with a controlled motion experiment in a cadaveric knee joint. The controlled displacements and rotations prescribed in our motion tracking validation were highly correlated to those predicted (Pearson's correlation = 0.99, RMS errors = 0.39 mm, 0.38 degree). Finally, the system for anatomic reference system definition and motion tracking was demonstrated with a set of MR images of in vivo passive flexion in the human knee.

Computed tomography to assess pelvic lysis after total hip replacement.

To assess the accuracy of a computer-assisted computed tomography image analysis program in determining the location and volume of periacetabular osteolysis, we designed an osteolysis model by implanting bilateral total hip replacements in human pelvic cadavers and creating osteolytic lesions of varying sizes. The volumes of 48 defects were measured physically, and axial computed tomography scans were obtained. The computed tomography images were processed with streak artifact reduction and segmentation algorithms. The location and volume of lesions were determined from these images. Eighty-one percent (39 lesions) were identified correctly from the computed tomography scans. Detection was location-dependent. More lesions were detected in the ilium (100%) and at the rim (89%) than in the ischium (78%) or the pubis (50%). Computed tomography overestimated lesion volume by a mean of 0.5 +/- 2.3 cm. The volumetric error was unrelated to lesion location but was dependent on lesion size. As lesion size increased above 10 cm, the mean percentage error decreased to 1.8%. Computed tomography image analysis can be used more accurately than plain radiographs to investigate the effectiveness of treatment and the natural history of pelvic osteolysis.