Publications by authors named "Yunbo Zhan"
Article Synopsis
- Isocitrate dehydrogenase (IDH) mutation and 1p19q codeletion are important genetic markers for assessing therapy options and prognosis in lower-grade glioma (LGG), and this study created a machine learning model to predict different molecular subtypes of LGG using MRI data.
- The model was trained on a sample of 269 LGG patients using 5,929 extracted MRI features, and it improved accuracy by combining these features with qualitative assessments and clinical data.
- The final model demonstrated strong predictive performance, achieving area under the curve (AUC) values over 0.80 for key molecular subtypes when incorporating various factors, which suggests a promising non-invasive approach for preoperative diagnosis of LGG.
Article Synopsis
- A deep learning signature (DLS) was created using diffusion tensor imaging (DTI) to predict overall survival in patients with infiltrative gliomas and explore related biological pathways.
- The DLS demonstrated a strong association with survival, serving as an independent predictor and outperforming existing risk systems when combined, showing improved accuracy in survival predictions.
- Five significant biological pathways were linked to the DLS, indicating that therapies targeting neuron-to-brain tumor communication could be particularly beneficial for high-risk glioma patients identified by the DTI-derived DLS.
The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively.
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- A radiomics signature was created using MRI data from patients with medulloblastoma to predict overall survival (OS) and progression-free survival (PFS), showing promising results for both predictions.
- The study involved a training cohort of 83 patients and testing cohort of 83, confirming the increased predictive power of combining radiomic and clinico-molecular data compared to either alone.
- Key biological pathways linked to the radiomics signature were identified, highlighting their potential role in patient risk stratification and improving survival prognosis.
Inflammation and immunoreaction markers were correlated with the survival of patients in many tumors. However, there were no reports investigating the relationships between preoperative hematological markers and the prognosis of medulloblastoma (MB) patients based on the molecular subgroups (WNT, SHH, Group 3, and Group 4). A total 144 MB patients were enrolled in the study.
View Article and Find Full Text PDFBackground: Gliomas was the most common primary central nervous system tumors which have an increased morbidity in recent years. And the clinical prognosis of high-grade gliomas (HGG, WHO grade III to IV) was most with an average survival rate of only dozens of months. Many researchers concluded that the level of preoperative albumin-to-globulin ratio (AGR) could predict the clinical outcome of patients with solid malignant tumors.
View Article and Find Full Text PDFThe prediction of clinical outcome for patients with infiltrative gliomas is challenging. Although preoperative hematological markers have been proposed as predictors of survival in glioma and other cancers, systematic investigations that combine these data with other relevant clinical variables are needed to improve prognostic accuracy and patient outcomes. We investigated the prognostic value of preoperative hematological markers, alone and in combination with molecular pathology, for the survival of 592 patients with Grade II-IV diffuse gliomas.
View Article and Find Full Text PDFDuring neurological surgery, neurosurgeons have to transform the two-dimensional (2D) sectional images into three-dimensional (3D) structures at the cognitive level. The complexity of the intracranial structures increases the difficulty and risk of neurosurgery. Mixed reality (MR) applications reduce the obstacles in the transformation from 2D images to 3D visualization of anatomical structures of central nervous system.
View Article and Find Full Text PDFGlioma is the most common malignant tumor in the central nervous system (CNS). Lower-grade gliomas (LGG) refer to Grade II and III gliomas. In LGG patients, seizure often appears as an initial symptom and play an important role in clinical performance and quality of life of the patients.
View Article and Find Full Text PDFBackground/aims: CDH18 (cadherin 18) is specifically expressed in the central nervous system and associated with various neuropsychiatric disorders. In this study, the role of CDH18 in glioma carcinogenesis and progression was investigated.
Methods: The expression of CDH18 and its prognostic value in patients with gliomas were analyzed in public database and validated by real-time PCR/immunohistochemical staining (IHC) in our cohort.
Backgrounds: HOX (homologous box) is known as the dominant gene of vertebrate growth and cell differentiation. Abnormal expression of HOX gene in various tumors has attracted the attention of scholars. As a component of HOX clusters, HOXD4 plays a controversial role in the tumorigenesis of central nervous system.
View Article and Find Full Text PDFA previous study revealed that ubiquitin-like with PHD and RING finger domains 1 (UHRF1) promoted cell proliferation and was a potential biomarker in medulloblastoma (MB). In the present study, we reported that miR-378 inhibited the expression of UHRF1 to affect the proliferation of MB through competitive binding to the same region of its 3'-UTR. We found that the expression of miR-378 was significantly downregulated in MB tissues and inversely correlated with the expression of UHRF1.
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