Publications by authors named "Dongling Pei"
Aims: Although radiotherapy is a core treatment modality for various human cancers, including glioblastoma multiforme (GBM), its clinical effects are often limited by radioresistance. The specific molecular mechanisms underlying radioresistance are largely unknown, and the reduction of radioresistance is an unresolved challenge in GBM research.
Methods: We analyzed and verified the expression of nuclear autoantigenic sperm protein (NASP) in gliomas and its relationship with patient prognosis.
Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features.
View Article and Find Full Text PDFBackground: To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes.
Methods: A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set.
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs.
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- * The researchers aimed to create a DTI-based model that not only predicts patient prognosis but also clarifies the biological significance of various DTI features.
- * The findings indicate that the developed DTI-based radiomic signature is a strong standalone predictor of survival and, when combined with clinical data, significantly enhances prognostic accuracy related to key biological pathways in GBM.
Objectives: To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI.
Methods: We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models.
Background: Genetic testing for molecular markers of gliomas sometimes is unavailable because of time-consuming and expensive, even limited tumor specimens or nonsurgery cases.
Purpose: To train a three-class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q-noncodeleted (IDHmut-noncodel), IDH wild-type (IDHwt), IDH-mutant and 1p/19q-codeleted (IDHmut-codel) of adult gliomas and investigate whether radiomic features from diffusion-weighted imaging (DWI) could bring additive value.
Study Type: Retrospective.
Background: Tumor immune microenvironment (TIM) plays a critical role in tumorigenesis and progression. Recently, therapies based on modulating TIM have made great breakthroughs in cancer treatment. Polo-like kinase 1 (PLK1) is a crucial regulatory factor of the cell cycle process and its dysregulations often cause various pathological processes including tumorigenesis.
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- Researchers developed a deep learning imaging signature (DLIS) using multi-parametric MRI to predict survival risk in patients with glioblastoma multiforme (GBM) and explored its biological foundations.
- The DLIS was validated across multiple sets, showing strong associations with survival rates, and improved predictive capabilities over traditional clinicomolecular models.
- The findings linked the DLIS to important cancer pathways, like P53 and RB, as well as key genetic alterations, emphasizing its potential for enhancing personalized treatment strategies in GBM patients.
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.
Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102).
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- 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.