{
  "abstract": "Background: Distinguishing true progression (TP) from pseudoprogression (PsP) in glioblastoma (GBM) remains a clinical challenge as both often manifest as indeterminate contrast-enhancing lesions on post-treatment MRIs. However, integrating dynamic contrast-enhanced (DCE)-MRI with analytical methods such as clustering and machine learning offers a promising approach for distinguishing TP from PsP.\n\nMethods: Quantitative analysis of DCE-MRI data from 82 patients with TP (n = 53) and PsP (n = 29) was performed using 5 pharmacokinetic models. Subsequently, a parsimonious selection method was used to identify the best fit for each voxel. In the next step, clustering analysis using K-means++ was performed to classify pixels into low-, medium-, and high-intensity regions. Tumors were segmented into enhancing, non-enhancing, and edema regions using automated nnU-Net algorithm, and clusters were extracted from these regions. Significant features derived from clustering analysis and descriptive statistics (mean, median, and skewness) of DCE-MRI parameters and tumor volume were incorporated as input variables for classifier development. Finally, 5 machine learning classifiers were employed to differentiate between TP and PsP.\n\nResults: Univariable clustering analysis of DCE-MRI data identified Ktrans, Ve, Vp, τi, and tumor volumes as significant parameters for distinguishing TP from PsP. Multivariable clustering revealed that combined high-Ktrans-high-Ve is a key distinctive variable. Among machine learning classifiers tested using these parameters, Random Forest demonstrated the best diagnostic performance, achieving an accuracy of 83% and an AUC of 0.85.\n\nConclusion: Parsimonious analysis of DCE-MRI data in conjunction with clustering and machine learning could serve as a valuable method for accurately distinguishing TP from PsP in GBMs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Sourav Basak"
    },
    {
      "name": "Subhanon Bera"
    },
    {
      "name": "Sankar Narayan Misra"
    },
    {
      "name": "Archith Rajan",
      "orcid": "0000-0001-6549-2198"
    },
    {
      "name": "Harish Poptani"
    },
    {
      "name": "Sanjeev Chawla",
      "orcid": "0000-0001-6978-7284"
    },
    {
      "name": "Sourav Bhaduri",
      "orcid": "0000-0001-9935-835X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c0nqy724m9t8w.bib",
  "cited_by": 0,
  "doi": "10.1093/noajnl/vdag254",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13726692/",
  "id": "c0nqy724m9t8w",
  "issns": [
    "2632-2498"
  ],
  "landing_url": "https://doi.org/10.1093/noajnl/vdag254",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/MED00300"
    ],
    [
      "heading",
      "AcademicSubjects/MED00310"
    ],
    [
      "heading",
      "Clinical Investigations"
    ],
    [
      "openalex",
      "2716|Glioma Diagnosis and Treatment"
    ]
  ],
  "openalex": "W7224953915",
  "pdf_url": "https://academic.oup.com/noa/advance-article-pdf/doi/10.1093/noajnl/vdag254/72608739/vdag254.pdf",
  "pmcid": "PMC13726692",
  "pmid": "42974199",
  "published": "2026-10-29",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/c0nqy724m9t8w.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:22:58Z",
      "native_id": "PMC13726692",
      "source": "pmc",
      "url": "https://doi.org/10.1093/noajnl/vdag254"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2716|Glioma Diagnosis and Treatment",
      "subfield": "human-genetics"
    }
  ],
  "title": "Differentiating true progression from pseudoprogression in glioblastomas: Dynamic contrast-enhanced MRI habitat analysis with parsimonious pharmacokinetic model",
  "title_text": "Differentiating true progression from pseudoprogression in glioblastomas: Dynamic contrast-enhanced MRI habitat analysis with parsimonious pharmacokinetic model",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:22:35Z",
  "url": "https://research.draconitic.com/paper/c0nqy724m9t8w",
  "venue": "Neuro-Oncology Advances",
  "versions": []
}