Extremity Soft Tissue Sarcoma Reconstruction Nomograms: A Clinicoradiomic, Machine Learning-Powered Predictor of Postoperative Outcomes.

Journal: JCO clinical cancer informatics
Published Date: Jun 11, 2025

Abstract

PURPOSE: The choice of wound closure modality after limb-sparing extremity soft-tissue sarcoma (eSTS) resection is fraught with uncertainty. Leveraging machine learning and clinicoradiomic data, we developed Sarcoma Reconstruction Nomograms (SARCON), a tool that provides probabilistic estimates of five adverse outcomes on the basis of the selected reconstructive modality.

Authors

  • Rami Elmorsi
    Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Luis D Camacho
    Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • David D Krijgh
    Department of Plastic, Reconstructive, and Hand Surgery, University Medical Center Utrecht, Utrecht, the Netherlands.
  • Heather Lyu
    Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Margaret S Roubaud
    Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Keila Torres
    Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Valerae Lewis
    Department of Orthopedic Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Christina L Roland
    Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
  • Alexander F Mericli
    Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.

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