Descriptive Strength and Range of Motion in Youth Baseball Players

# Background There are limited studies reporting descriptive strength and range of motion in youth baseball players 12 years of age or younger. # Purpose To establish normative data for external (ER) and internal (IR) rotation range of motion (ROM), total arc range of motion (TROM), and isometric rotator cuff strength in youth baseball players, and to compare between the dominant throwing arm (D) to the non-dominant arm (ND). # Study Design Cross-sectional # Methods Patient population included 50 (5 to 12-year-old) uninjured, healthy athletes. ROM measurements were performed preseason using a goniometer for IR and ER in the supine position with the shoulder in 90 degrees of abduction (abd) with scapular stabilization. Isometric strength measurements for IR and ER were collected in both neutral and 90 degrees (deg) of abduction with the use of a hand-held dynamometer and recorded in pounds (lbs) utilizing a “make” test. Descriptive statistics were obtained for all measures. # Results All data were analyzed as a single group (average age: 9.02). No significant difference in average total arc of PROM (ER+IR=Total Arc) on the D side compared to the ND side (136.7 ± 12.7 deg vs. 134.3 ± 12.3 deg). There were statistically significant differences between ER ROM (102.2 ± 7.7 deg vs. 96.8 ± 7.4 deg) and IR ROM (34.4 ± 9.0 deg vs. 37.5 ± 9.5 deg) between D versus ND arms (p= .000, .006 respectively). Mean ER strength in neutral (13.6 ± 3.4 and 12.8 ± 3.6 lbs) and 90 deg abduction (12.3 ± 3.4 and 12.5 ± 4.3 lbs) did were not significantly different between D and ND arms, respectively. Mean IR strength in neutral (18.0 ± 6.0 and 15.7 ± 4.7 lbs) and 90 deg abd (16.4 ± 5.6 and 15.0 ± 5.7 lbs) was significantly greater in the D arm vs ND arm, respectively (p=.000, .001). # Conclusion These data can provide descriptive information for clinicians who treat very young baseball players. These data show sport specific adaptations occur at very young ages (5-12) and are similar to prior reports on adolescent, high school and professional baseball players regarding upper extremity ROM and rotator cuff strength. # Level of Evidence

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

Radiogenomics To Characterize Regional Genetic Heterogeneity In Glioblastoma

Background. Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods. We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results. We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion. MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma

<div><p>Background</p><p>Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.</p><p>Methods</p><p>We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.</p><p>Results</p><p>We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).</p><p>Conclusion</p><p>Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.</p></div

ML-based MRI invasion maps show tumor-rich (>80% tumor nuclei) extent throughout ENH and BAT.

<p>(A,B,C,E) Biopsy locations within the non-enhancing BAT zone (green dots, arrows) on T1+C (A,D) and T2W (B,E) images correspond with high-tumor (>80% tumor nuclei) and low-tumor (<80% tumor nuclei) tissue samples on histologic analysis. (C,F) Color overlay maps with manual tracings (green) around BAT show the probability (range 0–1) of tumor-rich (red) vs tumor-poor (green/blue) content, based on ML analysis and multi-parametric MRI in 60 training biopsies and 22 validation biopsies. The maps show correspondence between tumor-rich (B, red) and tumor-poor (D, blue/gray) biopsy samples.</p

ML-based model improves tumor-rich biopsy delineation compared with CE-MRI.

<p>(A) ML-based MRI texture model in the full dataset (n = 82, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141506#pone.0141506.t001" target="_blank">Table 1</a>) shows higher positive predictive values (PPV) (66.7% in BAT, 81.3% in ENH) for recovering tumor-rich samples compared with CE-MRI (21.2% in BAT, 59.2% in ENH). These PPVs suggest that the ML-based model would help recover tumor-rich BAT samples with over three times greater efficiency compared with CE-MRI guidance. (B) ML-based MRI texture model in the subanalysis (n = 76, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141506#pone.0141506.s002" target="_blank">S2 Appendix</a>) provides higher positive predictive values (PPV) (57.1% in BAT, 80.6% in ENH) for recovering tumor-rich samples (>80% tumor nuclei) compared with CE-MRI (13.8% in BAT, 59.6% in ENH). Based on these PPVs, the ML-based model would enable four times more efficient tumor-rich recovery from BAT compared with CE-MRI guidance.</p

Summary of selected MRI-based texture features to optimize CV training accuracy.

<p>Machine learning (ML) selected the 3 MRI-based texture features that optimized cross validation (CV) accuracy based on leave-one-out cross validation (LOOCV) of the training set data (60 biopsies, 11 patients). The overall CV accuracy based on the 3 features is 85%.</p><p>Summary of selected MRI-based texture features to optimize CV training accuracy.</p