{
  "abstract": "Accurate differentiation between low-grade gliomas and encephalitis remains challenging due to overlapping MRI features. This study investigated whether MRI-based radiomics derived from routine non-contrast MRI could improve the differentiation of these entities in diagnostically challenging cases presenting in emergency settings. This retrospective single-center study included 41 patients with ambiguous MRI findings who underwent 3T MRI within 48 h of symptom onset. Radiomic features extracted from T2-FLAIR, DWI, and T1-TSE images, together with laboratory data available at presentation, were analyzed. Random Forest classifiers were developed using selected radiomic features, and model performance was evaluated using nested cross-validation, independent validation, receiver operating characteristic (ROC) analysis, and DeLong’s test. The cohort included 23 patients with histologically proven low-grade gliomas and 18 with confirmed encephalitis and was divided into training ( n = 31) and validation ( n = 10) sets. FLAIR-based models showed the best discriminatory performance, achieving an AUC of 0.82 (95% CI: 0.54–1.00) for both the radiomics-only and radiomics-clinical models outperforming T1-weighted (AUC 0.52 for radiomics-only and 0.60 for radiomics-clinical model) and DWI models (AUC 0.72 for both radiomics-only and radiomics-clinical approaches). The combined-sequence radiomics-clinical model achieved an AUC of 0.80 (95% CI: 0.41–1.00), with 100% sensitivity, 80% specificity, and 90% accuracy. Comparison of the ROC curves using DeLong’s test did not demonstrate statistically significant differences between the radiomics-only and radiomics-clinical models for any MRI sequence. This proof-of-concept study suggests that radiomic features extracted from routine non-contrast MRI may help differentiate low-grade gliomas from encephalitis in diagnostically challenging cases; however, these preliminary findings warrant validation in larger multicenter cohorts before clinical implementation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Eliseo Picchi",
      "orcid": "0000-0002-2524-9764"
    },
    {
      "name": "Carlo Di Donna",
      "orcid": "0000-0001-9241-1859"
    },
    {
      "name": "Armando Ugo Cavallo",
      "orcid": "0000-0001-8390-7721"
    },
    {
      "name": "Silvia Minosse",
      "orcid": "0000-0002-9921-6287"
    },
    {
      "name": "Alessandro Castelli",
      "orcid": "0000-0003-2671-5410"
    },
    {
      "name": "Chiara Giuseppina Bonomi",
      "orcid": "0000-0002-0881-8435"
    },
    {
      "name": "Francesca Izzi",
      "orcid": "0000-0003-2490-8761"
    },
    {
      "name": "Alessia Amatruda",
      "orcid": "0009-0006-5042-6423"
    },
    {
      "name": "Valerio Da Ros",
      "orcid": "0000-0001-7167-7594"
    },
    {
      "name": "Francesco Garaci"
    },
    {
      "name": "Francesca Di Giuliano",
      "orcid": "0000-0002-6067-183X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1rrkw2v3v3jc4.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-74097-z",
  "fulltext_url": null,
  "id": "1rrkw2v3v3jc4",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-74097-z",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7221335705",
  "pdf_url": "https://www.nature.com/articles/s41598-026-74097-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/1rrkw2v3v3jc4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:25Z",
      "native_id": "10.1038/s41598-026-74097-z",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-74097-z"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Radiomics and machine learning analysis to differentiate low grade glioma and encephalitis on non-contrast MRI",
  "title_text": "Radiomics and machine learning analysis to differentiate low grade glioma and encephalitis on non-contrast MRI",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:25Z",
  "url": "https://research.draconitic.com/paper/1rrkw2v3v3jc4",
  "venue": "Scientific Reports",
  "versions": []
}