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Yinyan Wang

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6 papers

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YNICL Journal 2026 Journal Article

Functional gradient analysis reveals potential therapeutic mechanisms of nrTMS for postoperative motor deficits in glioma patients: A randomized controlled trial

Yuzhe Li
Jiangwei Wang
Zhong Zhang
Xing Fan
Yinyan Wang
Wenbin Ma
Tao Jiang
Shengyu Fang

OBJECTIVE: This study aimed to investigate the therapeutic effects and neural mechanisms of high-frequency neuro-navigated repetitive transcranial magnetic stimulation (nrTMS) targeting the hand knob in glioma patients with postoperative motor deficits, using functional gradient analysis to characterize cortical reorganization. METHODS: Thirty patients with postoperative motor deficits were randomized to receive nrTMS or sham stimulation targeting the ipsilateral hand knob. Motor function was assessed using Fugl-Meyer Assessment (FMA) and muscle strength. Resting-state fMRI was acquired to compute principal functional gradients. Control/tumor, nrTMS/sham, and Pre-TMS/Post-TMS gradient changes were analyzed. Correlation and regression analyses related to motor recovery were performed. RESULTS: The nrTMS group showed significantly greater improvement in muscle strength (Post-treatment: nrTMS: 3.533 ± 0.720, Sham: 2.067 ± 0.572, p = 0.019, d = 1.082; 3-month follow-up: nrTMS: 4.600 ± 0.408, Sham: 3.733 ± 0.609, p = 0.035, d = 1.012). Gradient analysis revealed increased sensorimotor network (SMN) gradient scores following nrTMS (Pre-TMS: -0.707 ± 0.108; Post-TMS: -0.636 ± 0.077; p = 0.016), and HH_SomMot_22 within upper limb motor cortex is most strongly correlated with motor recovery. CONCLUSIONS: High-frequency nrTMS targeting the hand knob accelerated the motor recovery. Gradient analysis findings provide novel insights into therapeutic mechanisms of nrTMS and underscore the value of the hand knob as a stimulation target.

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YNICL Journal 2018 Journal Article

A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas

Xing Liu
Yiming Li
Zenghui Qian
Zhiyan Sun
Kaibin Xu
Kai Wang
Shuai Liu
Xing Fan

OBJECTIVE: The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. METHODS: In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS. RESULTS: There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P < 0.001, multivariable Cox regression) and validation (P = 0.045, multivariable Cox regression) cohorts. Radiogenomic analysis revealed that the radiomics signature was associated with the immune response, programmed cell death, cell proliferation, and vasculature development. A nomogram established using the radiomics signature and clinicopathologic risk factors demonstrated high accuracy and good calibration for prediction of PFS in both the training (C-index, 0.684) and validation (C-index, 0.823) cohorts. CONCLUSIONS: PFS can be predicted non-invasively in patients with LGGs by a group of radiomics features that could reflect the biological processes of these tumors.

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YNICL Journal 2018 Journal Article

MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

Yiming Li
Zenghui Qian
Kaibin Xu
Kai Wang
Xing Fan
Shaowu Li
Tao Jiang
Xing Liu

Background: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. Methods: = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets. Results: The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features. Conclusions: These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.

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YNICL Journal 2018 Journal Article

Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas

Zhenyu Liu
Yinyan Wang
Xing Liu
Yang Du
Zhenchao Tang
Kai Wang
Jingwei Wei
Di Dong

Purpose To investigate the association between imaging features and low-grade gliomas (LGG) related epilepsy, and to propose a radiomics-based model for the prediction of LGG-associated epilepsy. Methods This retrospective study consecutively enrolled 286 patients with LGGs (194 in the primary cohort and 92 in the validation cohort). T2-weighted MR images (T2WI) were used to characterize risk factors for LGG-related epilepsy: Tumor location features and 3-D imaging features were determined, following which the interactions between these two kinds of features were analyzed. Elastic net was applied to generate a radiomics signature combining key imaging features associated with the LGG-related epilepsy with the primary cohort, and then a nomogram incorporating radiomics signature and clinical characteristics was developed. The radiomics signature and nomogram were validated in the validation cohort. Results A total of 475 features associated with LGG-related epilepsy were obtained for each patient. A radiomics signature with eleven selected features allowed for discriminating patients with epilepsy or not was detected, which performed better than location and 3-D imaging features. The nomogram incorporating radiomics signature and clinical characteristics achieved a high degree of discrimination with area under receiver operating characteristic (ROC) curve (AUC) at 0. 8769 in the primary cohort and 0. 8152 in the validation cohort. The nomogram also allowed for good calibration in the primary cohort. Conclusion We developed and validated an effective prediction model for LGG-related epilepsy. Our results suggested that radiomics analysis may enable more precise and individualized prediction of LGG-related epilepsy.

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YNICL Journal 2018 Journal Article

Voxel-based comparison of brain glucose metabolism between patients with Cushing's disease and healthy subjects

Shuai Liu
Yinyan Wang
Kaibin Xu
Fan Ping
Fang Li
Renzhi Wang
Xin Cheng

F]-fluorodeoxyglucose positron emission tomography (FDG PET), between 92 patients with CD and 118 normal subjects on a voxel-wise basis. Pearson correlation was performed to evaluate the association between cerebral FDG uptake and serum cortisol level in patients with CD. We demonstrated that certain brain regions in patients with CD showed significantly increased FDG uptake, including the basal ganglia, anteromedial temporal lobe, thalamus, precentral cortex, and cerebellum. The clusters that demonstrated significantly decreased uptake were mainly located in the medial and lateral frontal cortex, superior and inferior parietal lobule, medial occipital cortex, and insular cortex. The metabolic rate of the majority of these regions was found to be significantly correlated with the serum cortisol level. Our findings may help to explain the underlying mechanisms of cognitive impairment and psychiatric symptoms in patients exposed to excessive glucocorticoids and evaluate the efficacy of treatments during follow-up.

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YNICL Journal 2016 Journal Article

Brain glucose metabolism is associated with hormone level in Cushing's disease: A voxel-based study using FDG-PET

Shuai Liu
Yinyan Wang
Kaibin Xu
Fan Ping
Renzhi Wang
Fang Li
Xin Cheng

Chronic exposure to elevated levels of glucocorticoids can exert a neurotoxic effect in patients, possibly manifesting as molecular imaging alterations in patients. The aim of this study was to investigate the potential association between brain metabolism and elevated hormone level using (18)F-fluorodeoxyglucose positron emission tomography. We retrospectively enrolled 92 consecutive patients with confirmed diagnosis of Cushing's disease. A voxel-based analysis was performed to investigate the association between cerebral (18)F-fluorodeoxyglucose uptake and serum cortisol level. Relatively impaired metabolism of specific brain regions correlated with serum cortisol level was found. Specifically, notable correlations were found in the hippocampus, amygdala, and cerebellum, regions considered to be involved in the regulation and central action of glucocorticoids. Moreover, some hormone-associated regions were found in the frontal and occipital cortex, possibly mediating the cognitive changes seen in Cushing's disease. Our findings link patterns of perturbed brain metabolism relates to individual hormone level, thus presenting a substrate for cognitive disturbances seen in Cushing's disease patients, as well as in other conditions with abnormal cortisol levels.

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