Prediction of Subsequent Vertebral Fracture After Acute Osteoporotic Fractures from Clinical and Paraspinal Muscle Features.

To construct a nomogram based on clinical factors and paraspinal muscle features to predict vertebral fractures occurring after acute osteoporotic vertebral compression fracture (OVCF). We retrospectively enrolled 307 patients with acute OVCF between January 2013 and August 2022, and performed magnetic resonance imaging of the L3/4 and L4/5 intervertebral discs (IVDs) to estimate the cross-sectional area (CSA) and degree of fatty infiltration (FI) of the paraspinal muscles. We also collected clinical and radiographic data. We used univariable and multivariable Cox proportional hazards models to identify factors that should be included in the predictive nomogram. Post-OVCF vertebral fracture occurred within 3, 12, and 24 months in 33, 69, and 98 out of the 307 patients (10.8%, 22.5%, and 31.9%, respectively). Multivariate analysis revealed that this event was associated with percutaneous vertebroplasty treatment, higher FI at the L3/4 IVD levels of the psoas muscle, and lower relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. Area under the curve values for subsequent vertebral fracture at 3, 12, and 24 months were 0.711, 0.724, and 0.737, respectively, indicating remarkable accuracy of the nomogram. We developed a model for predicting post-OVCF vertebral fracture from diagnostic information about prescribed treatment, FI at the L3/4 IVD levels of the psoas muscle, and relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. This model could facilitate personalized predictions and preventive strategies.

Fatty Infiltration of Multifidus Muscles: An Easily Overlooked Risk Factor for the Severity of Osteoporotic Vertebral Fractures.

OBJECTIVES: Osteoporotic vertebral fractures (OVFs) are a critical public health concern requiring urgent attention, and severe OVFs impose substantial health and economic burdens on patients and society. Analysis of the risk factors for severe OVF is imperative to actively prevent the occurrence of this degenerative disorder. This study aimed to investigate the risk factors associated with the severity of OVF, with a specific focus on changes in the paraspinal muscles. METHODS: A total of 281 patients with a first-time single-level acute OVF between January 2016 and January 2023 were enrolled in the study. Clinical and radiological data were collected and analyzed. The cross-sectional area (CSA) and degree of fatty infiltration (FI) of the paraspinal muscles, including the multifidus muscles (MFMs), erector spinae muscles (ESMs), and psoas major muscles (PSMs), were measured by magnetic resonance imaging (MRI) of the L4/5 intervertebral discs. According to the classification system of osteoporotic fractures (OF classification) and recommended treatment plan, OVFs were divided into a low-grade OF group and a high-grade OF group. Univariate and multivariate logistic regression analyse s were performed to identify risk factors associated with the severity of OVF. RESULTS: Ninety-eight patients were included in the low-grade OF group, and 183 patients were included in the high-grade OF group. Univariate analysis revealed a significantly higher incidence of a high degree of FI of MFMs (OR = 1.71, p = 0.002) and ESMs (OR = 1.56, p = 0.021) in the high-grade OF group. Further multivariate logistic regression analysis demonstrated that a high degree of FI of the MFMs (OR = 1.71, p = 0.002) is an independent risk factor for the severity of OVF. CONCLUSION: A high degree of FI of the MFMs was identified as an independent risk factor for the severity of OVF. Decreasing the degree of FI in the MFMs might lower the incidence of the severity of OVF, potentially reducing the necessity for surgical intervention in OVF patients.

Deep learning-based diagnosis of osteoblastic bone metastases and bone islands in computed tomograph images: a multicenter diagnostic study.

OBJECTIVE: To develop and validate a deep learning (DL) model based on CT for differentiating bone islands and osteoblastic bone metastases. MATERIALS AND METHODS: The patients with sclerosing bone lesions (SBLs) were retrospectively included in three hospitals. The images from site 1 were randomly assigned to the training (70%) and intrinsic verification (10%) datasets for developing the two-dimensional (2D) DL model (single-slice input) and "2.5-dimensional" (2.5D) DL model (three-slice input) and to the internal validation dataset (20%) for evaluating the performance of both models. The diagnostic performance was evaluated using the internal validation set from site 1 and additional external validation datasets from site 2 and site 3. And statistically analyze the performance of 2D and 2.5D DL models. RESULTS: In total, 1918 SBLs in 728 patients in site 1, 122 SBLs in 71 patients in site 2, and 71 SBLs in 47 patients in site 3 were used to develop and test the 2D and 2.5D DL models. The best performance was obtained using the 2.5D DL model, which achieved an AUC of 0.996 (95% confidence interval [CI], 0.995-0.996), 0.958 (95% CI, 0.958-0.960), and 0.952 (95% CI, 0.951-0.953) and accuracies of 0.950, 0.902, and 0.863 for the internal validation set, the external validation set from site 2 and site 3, respectively. CONCLUSION: A DL model based on a three-slice CT image input (2.5D DL model) can improve the prediction of osteoblastic bone metastases, which can facilitate clinical decision-making. KEY POINTS: • This study investigated the value of deep learning models in identifying bone islands and osteoblastic bone metastases. • Three-slice CT image input (2.5D DL model) outweighed the 2D model in the classification of sclerosing bone lesions. • The 2.5D deep learning model showed excellent performance using the internal (AUC, 0.996) and two external (AUC, 0.958; AUC, 0.952) validation sets.

Deep learning-based artificial intelligence model for classification of vertebral compression fractures: A multicenter diagnostic study.

Objective: To develop and validate an artificial intelligence diagnostic system based on X-ray imaging data for diagnosing vertebral compression fractures (VCFs). Methods: In total, 1904 patients who underwent X-ray at four independent hospitals were retrospectively (n=1847) and prospectively (n=57) enrolled. The participants were separated into a development cohort, a prospective test cohort and three external test cohorts. The proposed model used a transfer learning method based on the ResNet-18 architecture. The diagnostic performance of the model was evaluated using receiver operating characteristic curve (ROC) analysis and validated using a prospective validation set and three external sets. The performance of the model was compared with three degrees of musculoskeletal expertise: expert, competent, and trainee. Results: The diagnostic accuracy for identifying compression fractures was 0.850 in the testing set, 0.829 in the prospective set, and ranged from 0.757 to 0.832 in the three external validation sets. In the human and deep learning (DL) collaboration dataset, the area under the ROC curves(AUCs) in acute, chronic, and pathological compression fractures were as follows: 0.780, 0.809, 0.734 for the DL model; 0.573, 0.618, 0.541 for the trainee radiologist; 0.701, 0.782, 0.665 for the competent radiologist; 0.707,0.732, 0.667 for the expert radiologist; 0.722, 0.744, 0.610 for the DL and trainee; 0.767, 0.779, 0.729 for the DL and competent; 0.801, 0.825, 0.751 for the DL and expert radiologist. Conclusions: Our study offers a high-accuracy multi-class deep learning model which could assist community-based hospitals in improving the diagnostic accuracy of VCFs.

Risk Factors for Lymphovascular Invasion in Invasive Ductal Carcinoma Based on Clinical and Preoperative Breast MRI Features: a Retrospective Study.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays an important role in the prediction of metastasis and prognosis in breast cancer (BC) patients. The present study assessed correlations between preoperative breast MRI, clinical features, and LVI in patients with invasive ductal carcinoma (IDC) and identified risk factors based on these correlation factors. MATERIALS AND METHODS: Patients confirmed with IDC between 01/2012 and 12/2021 were retrospectively reviewed at our hospital. A total of 5 clinical and 14 MRI features to characterize tumours were extracted. LVI evaluated in hematoxylin and eosin sections. T-test and chi-square tests were used to compare the differences in clinical and MRI features between the LVI positive and negative groups. The associations between individual features and LVI were analysed by univariable logistic regression analysis, and risk factors for LVI were identified by multivariable logistic regression analysis based on these correlation factors. RESULTS: This study included 353 patients with IDC, including 130 with positive LVI. Age, CEA, CA-153, amount of fibroglandular tissue (FGT), background parenchymal enhancement, tumour size, shape, skin thickening, nipple retraction, adjacent vessel sign, and axillary lymph node (ALN) size in the LVI positive group were significantly different from the LVI negative group (all p<0.05). Multivariate logistic regression analysis revealed that age (odds ratio OR = 1.030), CA-153 (OR = 1.018), heterogeneous FGT (OR = 2.484), shape (OR = 2.157), and ALN size (OR = 1.051) were risk factors for LVI (all p<0.05). CONCLUSION: Preoperative breast MRI and clinical features correlated with LVI, age, CA-153, heterogeneous FGT, shape, and ALN size are risk factors for LVI in patients with IDC.

Diagnostic Performance of Diffusion-Weighted Imaging for Differentiating Malignant From Benign Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Systematic Review and Meta-Analysis.

OBJECTIVES: To assess the diagnostic accuracy of diffusion-weighted imaging (DWI) in predicting the malignant potential in patients with intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. METHODS: A systematic search of articles investigating the diagnostic performance of DWI for prediction of malignant potential in IPMNs was conducted from PubMed, Embase, and Web of Science from January 1997 to 10 February 2020. QUADAS-2 tool was used to evaluate the study quality. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratios (PLR), negative likelihood ratios (NLR), and their 95% confidence intervals (CIs) were calculated. The summary receiver operating characteristic (SROC) curve was then plotted, and meta-regression was also performed to explore the heterogeneity. RESULTS: Five articles with 307 patients were included. The pooled sensitivity and specificity of DWI were 0.74 (95% CI: 0.65, 0.82) and 0.94 (95% CI: 0.78, 0.99), in evaluating the malignant potential of IPMNs. The PLR was 13.5 (95% CI: 3.1, 58.7), the NLR was 0.27 (95% CI: 0.20, 0.37), and DOR was 50.0 (95% CI: 11.0, 224.0). The area under the curve (AUC) of SROC curve was 0.84 (95% CI: 0.80, 0.87). The meta-regression showed that the slice thickness of DWI (p = 0.02) and DWI parameter (p= 0.01) were significant factors affecting the heterogeneity. CONCLUSIONS: DWI is an effective modality for the differential diagnosis between benign and malignant IPMNs. The slice thickness of DWI and DWI parameter were the main factors influencing diagnostic specificity.

Refracture of the cemented vertebrae after percutaneous vertebroplasty: risk factors and imaging findings.

BACKGROUND: To determine the related imaging findings and risk factors to refracture of the cemented vertebrae after percutaneous vertebroplasty (PVP) treatment. METHODS: Patients who were treated with PVP for single vertebral compression fractures (VCFs) and met this study's inclusion criteria were retrospectively reviewed from January 2012 to January 2019. The follow-up period was at least 2 years. Forty-eight patients with refracture of the cemented vertebrae and 45 non-refractured patients were included. The following variates were reviewed: age, sex, fracture location, bone mineral density (BMD), intravertebral cleft (IVC), kyphotic angle (KA), wedge angle, endplate cortical disruption, cement volume, surgical approach, non-PMMA-endplate-contact (NPEC), cement leakage, other vertebral fractures, reduction rate (RR), and reduction angle (RA). Multiple logistic regression modeling was used to identify the independent risk factors of refracture. RESULTS: Refracture was found in 48 (51.6%) patients. Four risk factors, including IVC (P = 0.005), endplate cortical disruption (P = 0.037), larger RR (P = 0.007), and NPEC (P = 0.006) were found to be significant independent risk factors for refracture. CONCLUSIONS: Patients with IVC or larger RR, NPEC, or endplate cortical disruption have a high risk of refracture in the cemented vertebrae after PVP.

Overexpression of MYBL2 promotes proliferation and migration of non-small-cell lung cancer via upregulating NCAPH.

MYB Proto-Oncogene Like 2 (MYBL2) is a highly conserved member of the Myb family of transcription factors and plays a critical role in regulating cell proliferation and survival. Here we show that overexpression of MYBL2 is frequently observed in lung adenocarcinoma (LUAD) and significantly correlates with advanced stage and poor patient survival. Knockdown of MYBL2 induced apoptosis in lung cancer cells and resulted in significant inhibition of cell proliferation, migration, and invasion. Notably, we identified Non-SMC Condensin I Complex Subunit H (NCAPH) gene as a direct target of MYBL2. NCAPH expression is highly correlated with that of MYBL2 in LUAD cases and is tightly affected by MYBL2 knockdown or overexpression in vitro. Chromatin immunoprecipitation (ChIP) assays also showed that MYBL2 directly binds to the transcription start site (TSS) of NCAPH. Moreover, we provided evidence that NCAPH functions as an oncogene in lung cancer and overexpression of NCAPH could partially rescue cell death and migration blockage induced by MYBL2 knockdown. Together, these results suggest that overexpression of MYBL2 promotes proliferation and migration of lung cancer cells via upregulating NCAPH, establishing their roles as novel prognostic biomarkers as well as potential therapeutic targets for the disease.