ArticleThe Journal of international medical research2020
Radiomic prediction models for the level of Ki-67 and p53 in glioma.
Article in The Journal of international medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 33 citations in OpenAlex.
- A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.PloS one · 2026Article
- Clinical value of habitat radiomics within the 2-cm edema zone surrounding the postoperative residual cavity in predicting glioma recurrence.Frontiers in oncology · 2026Article
- Preoperative stratification of Ki-67 proliferation index in intrahepatic mass-forming cholangiocarcinoma via tumor-halo radiomic phenotyping on biparametric MRI: a multicenter diagnostic model.BMC medical imaging · 2025Article
- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.Cancer medicine · 2025Article
- MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study.Frontiers in neurology · 2025Article
- Clinicopathological Parameters and Immunohistochemical Profiles in Correlation with MRI Characteristics in Glioblastomas.International journal of molecular sciences · 2024Article
- The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma.BMC medical imaging · 2024Article
- Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.Journal of radiation research · 2024Article
- Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma.Journal of translational medicine · 2023Article
- Contrast enhanced magnetic resonance imaging-based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study.Quantitative imaging in medicine and surgery · 2023Article
- Expression analysis of human glioma susceptibility gene andAnnals of translational medicine · 2023Article
- Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model.Oxidative medicine and cellular longevity · 2022Article
- An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma.Frontiers in neurology · 2022Article
- Resveratrol Enhances the Radiosensitivity by Inducing DNA Damage and Antitumor Immunity in a Glioblastoma Rat Model under 3 T MRI Monitoring.Journal of oncology · 2022Article
- Review
- Clinical-Radiomic Analysis for Pretreatment Prediction of Objective Response to First Transarterial Chemoembolization in Hepatocellular Carcinoma.Liver cancer · 2021Article
- The Feasibility of Combining ADC Value With Texture Analysis of TFrontiers in oncology · 2021Article
- Advanced Diagnosis of Glioma by Using Emerging Magnetic Resonance Sequences.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo identify glioma radiomic features associated with proliferation-related Ki-67 antigen and cellular tumour antigen p53 levels, common immunohistochemical markers for differentiating benign from malignant tumours, and to generate radiomic prediction models.
methodsPatients with glioma, who were scanned before therapy using standard brain magnetic resonance imaging (MRI) protocols on T1 and T2 weighted imaging, were included. For each patient, regions-of-interest (ROI) were drawn based on tumour and peritumoral areas (5/10/15/20 mm), and features were identified using feature calculations, and used to create and assess logistic regression models for Ki-67 and p53 levels.
resultsA total of 92 patients were included. The best area under the curve (AUC) for the Ki-67 model was 0.773 for T2 weighted imaging in solid glioma (sensitivity, 0.818; specificity, 0.833), followed by a less reliable AUC of 0.773 (sensitivity, 0.727; specificity 0.667) in 20-mm peritumoral areas. The highest AUC for the p53 model was 0.709 (sensitivity, 1; specificity, 0.4) for T2 weighted imaging in 10-mm peritumoral areas.
conclusionUsing T2-weighted imaging, the prediction model for Ki-67 level in solid glioma tissue was better than the p53 model. The 20-mm and 10-mm peritumoral areas in the Ki-67 and p53 model, respectively, showed predictive effects, suggesting value in further research into areas without conventional MRI features.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.