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Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy

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اقرأ على الانترنت اقرأ أكثر حفظ في قائمتي
  • معلومة اضافية
    • Publisher Information:
      Oxford University Press 2024-09-05
    • نبذة مختصرة :
      BACKGROUND Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. METHODS Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of Brain Metastases (AURORA) retrospective study (training cohort: 253 patients from two centers; external test cohort: 99 patients from five centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set. RESULTS The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (p < 0.001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. CONCLUSIONS A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.
    • الموضوع:
      Clinic for Radiation Oncology; 610 Medicine & health; Journal Article; PeerReviewed; info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion
    • Availability:
      Open access content. Open access content
      info:eu-repo/semantics/openAccess
      Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
      http://creativecommons.org/licenses/by-nc/4.0
      info:eu-repo/semantics/openAccess
    • Note:
      application/pdf
      info:doi/10.5167/uzh-261428
      English
      English
    • Other Numbers:
      CHUZH oai:www.zora.uzh.ch:261428
      https://www.zora.uzh.ch/id/eprint/261428/1/2024_Buchner_38813990.pdf
      info:doi/10.5167/uzh-261428
      info:doi/10.1093/neuonc/noae098
      info:pmid/38813990
      urn:issn:1522-8517
      1482457539
    • Contributing Source:
      HAUPTBIBLIOTHEK UNIV OF ZURICH
      From OAIster®, provided by the OCLC Cooperative.
    • الرقم المعرف:
      edsoai.on1482457539
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