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Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.

Shunli Liu1, Weikai Sun1, Shifeng Yang2

  • 1Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.

European Radiology
|August 27, 2021
PubMed
Summary

Deep learning radiomic nomogram (DLRN) models accurately predict soft tissue sarcoma (STS) recurrence after surgery. These validated tools stratify patients into risk groups, aiding treatment decisions.

Keywords:
Deep learningMagnetic resonance imagingRadiomic nomogramRecurrenceSoft tissue sarcomas

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Area of Science:

  • Radiology
  • Oncology
  • Artificial Intelligence

Background:

  • Soft tissue sarcomas (STS) pose a significant challenge due to potential for tumor relapse after surgical resection.
  • Accurate prediction of recurrence is crucial for guiding postoperative management and treatment strategies.

Purpose of the Study:

  • To evaluate the performance of deep learning radiomic nomogram (DLRN) models in predicting tumor relapse in patients with STS post-surgery.
  • To compare the predictive capability of DLRNs against established predictors like staging systems and Ki67.

Main Methods:

  • Retrospective enrollment of 282 STS patients from three centers, with a subset undergoing contrast-enhanced MRI.
  • Development and external validation of two MRI-based DLRN models using radiomics features and clinical data.
  • Comparison of DLRN performance against traditional prediction models.

Main Results:

  • Both DLRN models demonstrated superior prognostic capability (C-index ≥ 0.721, median AUC ≥ 0.746) and reduced prediction error (integrated Brier score ≤ 0.159) compared to other models.
  • Decision curve analysis confirmed greater clinical utility of DLRNs over staging systems and Ki67.
  • DLRNs successfully stratified patients into low, medium, and high-risk groups for recurrence.

Conclusions:

  • MRI-based DLRNs are reliable and externally validated tools for predicting STS recurrence.
  • These models facilitate targeted postoperative management by identifying high-risk patients.
  • Early prediction of recurrence aids in determining the necessity for more aggressive treatment interventions.