Peijin Xin

Biomarkers in axial spondyloarthritis diagnosis: from clinical signs to multi-omics integration

Peijin Xin, Zi Xu, Yuanyi Pan, Mudan Zhang et autres

Axial spondyloarthritis (axSpA) is a chronic inflammatory rheumatic disease primarily affecting the sacroiliac joints and spine, with an estimated global prevalence of 0.3-1.5%. Despite advances in classification criteria and imaging techniques, diagnostic delay remains a persistent clinical challenge, with patients waiting a …

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Prediction of pathological response and lymph node metastasis after neoadjuvant therapy in rectal cancer through tumor and mesorectal MRI radiomic features

Siyuan Qin, Ke Liu, Yongye Chen, Yan Zhou et autres

Establishing predictive models for the pathological response and lymph node metastasis in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT) based on MRI radiomic features derived from the tumor and mesorectal compartment (MC). This study included 209 patients with LARC …

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A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study

Ruixin Yan, Siyuan Qin, Jiajia Xu, Weili Zhao et autres

Abstract Background Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. …

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Broken-fat pad sign: a characteristic radiographic finding to distinguish between knee rheumatoid arthritis and osteoarthritis

Qizheng Wang, Weili Zhao, Xiaoxi Ji, Yongye Chen et autres

OBJECTIVES: Diagnostic imaging plays an important role in the pre-treatment workup of knee osteoarthritis (OA) and rheumatoid arthritis (RA). Herein, we identified a useful MRI sign of infrapatellar fat pad (IPFP) to improve diagnosis. METHODS: Eighty-one age- and sex-matched RA and OA …

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Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model

Peijin Xin, Qizheng Wang, Ruixin Yan, Yongye Chen et autres

BACKGROUND: Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported …

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MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases

Yongye Chen, Siyuan Qin, Weili Zhao, Qizheng Wang et autres

OBJECTIVE: This study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT). METHODS: Patients with spinal metastases who …

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Assessment of Hidden Blood Loss in Spinal Metastasis Surgery: A Comprehensive Approach with MRI ‐Based Radiomics Models

Weili Zhao, Siyuan Qin, Qizheng Wang, Yongye Chen et autres

BACKGROUND: Patients undergoing surgery for spinal metastasis are predisposed to hidden blood loss (HBL), which is associated with poor surgical outcomes but unpredictable. PURPOSE: To evaluate the role of MRI-based radiomics models for assess the risk of HBL in patients undergoing spinal …

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Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study

Qizheng Wang, Weili Zhao, Xiaoying Xing, Ying Wang et autres

OBJECTIVES: To evaluate the image quality and diagnostic performance of AI-assisted compressed sensing (ACS) accelerated two-dimensional fast spin-echo MRI compared with standard parallel imaging (PI) in clinical 3.0T rapid knee scans. METHODS: This prospective study enrolled 130 consecutive participants between March and …

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Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI

Ke Liu, Siyuan Qin, Jinlai Ning, Peijin Xin et autres

We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal …

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Predictive model based on DCE-MRI and clinical features for the evaluation of pain response after stereotactic body radiotherapy in patients with spinal metastases

Yongye Chen, Qizheng Wang, Guangjin Zhou, Ke Liu et autres

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Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach

Qizheng Wang, Yongye Chen, Siyuan Qin, Xiaoming Liu et autres

The receptor activator of the nuclear factor kappa B ligand (RANKL) is the therapeutic target of denosumab. In this study, we evaluated whether radiomics signature and machine learning analysis can predict RANKL status in spinal giant cell tumors of bone (GCTB). This …

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Differentiation of predominantly osteolytic from osteoblastic spinal metastases based on standard magnetic resonance imaging sequences: a comparison of radiomics model versus semantic features logistic regression model findings

Ke Liu, Yang Zhang, Qizheng Wang, Yongye Chen et autres

Background: The aim of this study was to compare the ability of a standard magnetic resonance imaging (MRI)-based radiomics model and a semantic features logistic regression model in differentiating between predominantly osteolytic and osteoblastic spinal metastases. Methods: We retrospectively analyzed standard MRIs …

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