ArticleAmerican journal of cancer research2023
The application of decision tree model based on clinicopathological risk factors and pre-operative MRI radiomics for predicting short-term recurrence of glioblastoma after total resection: a retrospective cohort study.
Article in American journal of cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The use of artificial intelligence and machine learning to predict tumor recurrence in high-grade gliomas: a systematic review.Neurosurgical review · 2026Pooled it
- Pre-Radiotherapy Synthetic MRI-Derived Quantitative Heterogeneity and Early Recurrence in Glioblastoma.Journal of magnetic resonance imaging : JMRI · 2026Observational
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Review
- A deep learning model to predict glioma recurrence using integrated genomic and clinical data.Communications medicine · 2025Article
- Enhancement of the nontumor component in newly diagnosed glioblastoma as a more accurate predictor of local recurrence location: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To develop a decision tree model based on clinical information, molecular genetics information and pre-operative magnetic resonance imaging (MRI) radiomics-score (Rad-score) to investigate its predictive value for the risk of recurrence of glioblastoma (GBM) within one year after total resection. Patients with pathologically confirmed GBM at Huashan Hospital, Fudan University between November 2017 and June 2020 were retrospectively analyzed, and the enrolled patients were randomly divided into training and test sets according to the ratio of 3:1. The relevant clinical and MRI data of patients before, after surgery and follow-up were collected, and after feature extraction on preoperative MRI, the LASSO filter was used to filter the features and establish the Rad-score. Using the training set, a decision tree model for predicting recurrence of GBM within one year after total resection was established by the C5.0 algorithm, and scatter plots were generated to evaluate the prediction accuracy of the decision tree during model testing. The prediction performance of the model was also evaluated by calculating area under the receiver operating characteristic (ROC) curve (AUC), ACC, Sensitivity (SEN), Specificity (SPE) and other indicators. Besides, two external validation datasets from Wuhan union hospital and the second affiliated hospital of Xuzhou Medical University were used to verify the reliability and accuracy of the prediction model. According to the inclusion and exclusion criteria, 134 patients with GBM were finally identified for inclusion in the study, and 53 patients recurred within one year after total resection, with a mean recurrence time of 5.6 months. According to the importance of the predictor variables, a decision tree model for predicting recurrence based on five important factors, including patient age, Rad-score, O
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37693142PMC10492119What OpenQuestion holds
Registered trials
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