ArticleEuropean radiology2023
Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06498778 (Prediction of Etiology and Prognosis Based on Hematoma Location of Spontaneous Intracerebral Hemorrhage), which is not on this map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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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.
Prediction of Etiology and Prognosis Based on Hematoma Location of Spontaneous Intracerebral Hemorrhage
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Spatial pattern-driven interpretable model and biological correlates in brain glioblastoma-lymphoma differentiation.iScience · 2026Article
- Prediction of etiology and prognosis based on hematoma location of spontaneous intracerebral hemorrhage: a multicenter diagnostic study.Neuroradiology · 2025Article
- Review
- Brain arteriovenous malformations: the long way ahead in physics, chemistry, and MR angiography practice before fully replacing digital subtraction angiography.European radiology · 2023Article
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Authors and funding
16 authors.
Funding
Abstract
objectivesTo assess the value of coordinatized lesion location analysis (CLLA), in empowering ROI-based imaging diagnosis of gliomas by improving accuracy and generalization performances.
methodsIn this retrospective study, pre-operative contrasted T1-weighted and T2-weighted MR images were obtained from patients with gliomas from three centers: Jinling Hospital, Tiantan Hospital, and the Cancer Genome Atlas Program. Based on CLLA and ROI-based radiomic analyses, a fusion location-radiomics model was constructed to predict tumor grades, isocitrate dehydrogenase (IDH) status, and overall survival (OS). An inter-site cross-validation strategy was used for assessing the performances of the fusion model on accuracy and generalization with the value of area under the curve (AUC) and delta accuracy (ACC) (ACC
resultsA total of 679 patients (mean age, 50 years ± 14 [standard deviation]; 388 men) were enrolled. Based on tumor location probabilistic maps, fusion location-radiomics models (averaged AUC values of grade/IDH/OS: 0.756/0.748/0.768) showed the highest accuracy in contrast to radiomics models (0.731/0.686/0.716) and location models (0.706/0.712/0.740). Notably, fusion models ([median Delta ACC: - 0.125, interquartile range: 0.130]) demonstrated improved generalization than that of radiomics model ([- 0.200, 0.195], p = 0.018).
conclusionsCLLA could empower ROI-based radiomics diagnosis of gliomas by improving the accuracy and generalization of the models. CLINICAL RELEVANCE STATEMENT: This study proposed a coordinatized lesion location analysis for glioma diagnosis, which could improve the performances of the conventional ROI-based radiomics model in accuracy and generalization. KEY POINTS: • Using coordinatized lesion location analysis, we mapped anatomic distribution patterns of gliomas with specific pathological and clinical features and constructed glioma prediction models. • We integrated coordinatized lesion location analysis into ROI-based analysis of radiomics to propose new fusion location-radiomics models. • Fusion location-radiomics models, with the advantages of being less influenced by variabilities, improved accuracy, and generalization performances of ROI-based radiomics models on predicting the diagnosis of gliomas.
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