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Updated: Jun 13, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Predicting Failure of Active Surveillance in Desmoid-Type Fibromatosis Using Radiomics: An International Multi-center

Stefanie N Hakkesteegt1, Douwe J Spaanderman2, Chiara Colombo3

  • 1Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.

Annals of Surgical Oncology
|June 11, 2026
PubMed

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Radiologic response assessment in patients with desmoid-type fibromatosis treated with percutaneous cryoablation.

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Summary

Radiomics can predict active surveillance failure in desmoid-type fibromatosis (DTF) patients. This approach shows potential for personalizing treatment by identifying patients who will not benefit from active surveillance.

Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Active surveillance (AS) is the primary approach for desmoid-type fibromatosis (DTF).
  • Approximately 30% of DTF patients require active treatment, necessitating methods to identify them early.
  • Predicting AS failure is crucial for personalized treatment strategies in DTF.

Purpose of the Study:

  • To assess the efficacy of radiomics in predicting AS failure in DTF patients.
  • To determine if radiomics features from MRI can differentiate between patients who will respond to AS and those who will not.
  • To develop a predictive model for AS failure in DTF.

Main Methods:

  • A multicenter study involving data from the Netherlands, Italy, and Canada.
  • Extraction of radiomics features from T1-weighted and T2-weighted MRI scans of extra-abdominal DTF patients managed with AS.

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  • Development and validation of machine-learning models for AS failure prediction using internal and external cross-validation.

    • Main Results:

    • The study included 200 DTF patients, with 26% experiencing AS failure.
    • The radiomics model achieved an AUC of 0.69 in internal validation and AUCs of 0.58 (Netherlands), 0.76 (Italy), and 0.77 (Canada) in external validation.
    • Incorporating clinical features did not enhance the predictive performance of the radiomics models.

    Conclusions:

    • Radiomics demonstrated reasonable performance in predicting AS failure for DTF.
    • The developed model generalized effectively to Italian and Canadian patient cohorts.
    • The radiomics model holds potential for identifying DTF patients who may not benefit from AS, guiding personalized treatment decisions.