ArticleJournal of cancer research and clinical oncology2023
The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Impact of deep learning image reconstruction on ADC quantification and histogram metrics: a phantom study.European radiology experimental · 2026Article
- Retrospective analysis of recurrence patterns and clinical outcomes in grade I-III meningiomas after surgery.Surgical neurology international · 2025Article
- The value of whole tumor apparent diffusion coefficient histogram parameters in predicting meningiomas progesterone receptor expression.Neurosurgical review · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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Abstract
objectiveTo investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence. MATERIALS AND
methodsSeventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017-June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curve analysis, and receiver operating characteristic curve, respectively.
resultsMaximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892-0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curve analysis indicated good clinical availability of the model.
conclusionThis model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment.
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