ArticleNeurosurgical review2025
Integrating diffusion-weighted MRI radiomics features to predict brain invasion of meningiomas.
Article in Neurosurgical review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Intraventricular giant cavernous malformation mimicking central neurocytoma characterized by digital subtraction angiography: illustrative case.Journal of neurosurgery. Case lessons · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Brain invasion is an independent diagnostic criterion for WHO grade 2 meningiomas, and preoperative prediction of brain invasion in meningiomas is crucial for guiding treatment decisions. Therefore, we constructed a radiomics model that integrated structural and diffusion-weighted images to predict brain invasion of meningiomas. Seven hundred and twenty-three consecutive patients with pathologically confirmed meningiomas between 2013 and 2022 were retrospectively studied. Radiomics features of the brain-to-tumor interface region were extracted from structural MRI and DWI-derived apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) method was utilized to select radiomics features. A linear predictor of brain invasion was constructed using a logistic regression classifier. The model's performance was evaluated using receiver operating characteristic (ROC) curve analysis. Additionally, decision curve analysis (DCA) was performed to evaluate the clinical utility of the established models. A nomogram was developed for a combined model that incorporates clinical features, along with radiomics scores derived from structural images and ADC maps. DeLong test and integrated discrimination improvement (IDI) were used to compare the diagnostic efficiency of different models. Six radiomics features from structural MRI, six radiomics features from ADC, the volume of peritumoral edema, and gender were selected to construct the combined model. This model achieved the highest AUC and sensitivity for predicting brain invasion in both the training (AUC = 0.897, 95%CI: 0.857 to 0.936, sensitivity = 0.911) and test sets (AUC = 0.871, 95%CI: 0.806 to 0.936, sensitivity = 0.895). It outperformed the structural model (AUC = 0.691) and the structural and clinical model (AUC = 0.812). The IDI demonstrated a significant improvement in predictive value when ADC radiomic features were added to the combined model. The incorporation of ADC radiomics into the MRI radiomic model improved the diagnostic performance for identifying brain invasion in meningiomas.
Indexed as
Identifiers
41354891What OpenQuestion holds
Registered trials
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.