{
  "abstract": "Objective: This study aimed to analyze postoperative periresidual FLAIR hyperintensity and assess radiomic features from periresidual enhancing and non-enhancing subregions for predicting early GBM recurrence (pre-CRT RANO criteria). We also evaluated the incremental prognostic value of multi-subregion radiomic signatures over conventional clinical and MRI features.\n\nMethods: Adult patients with surgically confirmed GBM were retrospectively enrolled and randomly split into training ( n = 128) and test ( n = 56) cohorts at a 7:3 ratio. Postoperative pre-radiotherapy CE-T1WI and FLAIR images were fused to semi-automatically delineate periresidual enhancing (ROI-1) and non-enhancing hyperintense (ROI-2) regions using 3D Slicer. Radiomic features were extracted from both subregions and their fusion area. LASSO regression was used to select optimal features related to early recurrence from ROI-1, ROI-2, and combined subregion datasets. Five machine learning algorithms (DT, LR, NB, SVM, XGBoost) were applied to build radiomic prediction models. To evaluate the incremental value of radiomic signatures, a clinical/MRI-only baseline model was constructed using conventional clinical and morphological MRI features. Multivariate logistic regression was performed to identify independent clinical and MRI predictors. A quantitative Rad-score was generated based on the optimal combined radiomic features, and an integrated radiomics-clinical model with a predictive nomogram was established by incorporating the Rad-score and independent clinical/MRI variables. Ten-fold cross-validation was used for internal validation. Model performance was comprehensively assessed using ROC curves, calibration curves, and decision curve analysis in terms of discrimination, calibration, and clinical utility.\n\nResults: A logistic regression analysis of 184 GBM cases found subventricular zone involvement [HR 4.868 (1.657–14.308), p = 0.004] and partial resection [HR 3.640 (1.341–9.883), p = 0.011] as predictors of early recurrence. The baseline model achieved an AUC of 0.701. Among five models using ROI-1 features, XGBoost performed best with validation AUC of 0.663. For ROI-2, the DT model excelled with 0.713. Combining ROI-1 and ROI-2, XGBoost achieved 0.752. Integrating radiomics with clinical and MRI data, XGBoost reached 0.817. DCA indicated the combined model had the highest clinical effectiveness. DCA showed that a threshold probability of 0.1 to 0.6 yields a net gain.\n\nConclusion: In-depth analysis of the enhanced and non-enhanced regions, within the FLAIR hyperintense surrounding the residual cavity of GBM before pre-CRT using ML, has revealed certain value for the early prediction of GBM recurrence. Furthermore, a combined model incorporating radiomic features, clinical and conventional MRI attributes enhances the predictive accuracy for survival prognosis.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medical Imaging, The Second Hospital of Hebei Medical University",
    "Department of Clinical Radiology, Kuopio University Hospital",
    "Department of Neurology, University of Eastern Finland",
    "Department of Ultrasound, The Second Hospital of Hebei Medical University",
    "Department of Experimental Center, College of Hebei Medical University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lijuan Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Tao Yuan"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Yawu Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiao Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Bocong Gao"
    },
    {
      "affs": [
        3
      ],
      "name": "Xiaoyun Yang"
    },
    {
      "affs": [
        4
      ],
      "name": "Yiming Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Guanmin Quan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/42jh1zh4resan.bib",
  "cited_by": 0,
  "doi": "10.3389/fneur.2026.1909375",
  "fulltext_url": null,
  "id": "42jh1zh4resan",
  "issns": [
    "1664-2295"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fneur.2026.1909375/full",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/42jh1zh4resan.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T00:21:25Z",
      "native_id": "10.3389/fneur.2026.1909375",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fneur.2026.1909375/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:1664-2295",
      "subfield": "neurology"
    }
  ],
  "title": "Decoding glioblastoma recurrence: harnessing radiomics and machine learning for early prediction",
  "title_text": "Decoding glioblastoma recurrence: harnessing radiomics and machine learning for early prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T00:23:06Z",
  "url": "https://research.draconitic.com/paper/42jh1zh4resan",
  "venue": "Frontiers in Neurology",
  "versions": []
}