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Development and external validation of a machine learning model for prediction of survival in undifferentiated pleomorphic sarcoma

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Abstract

Purpose

Machine learning (ML) algorithms to predict cancer survival have recently been reported for a number of sarcoma subtypes, but none have investigated undifferentiated pleomorphic sarcoma (UPS). ML is a powerful tool that has the potential to better prognosticate UPS.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologically confirmed undifferentiated pleomorphic sarcoma (UPS) (n = 665). Patient, tumor, and treatment characteristics were recorded, and ML models were developed to predict 1-, 3-, and 5-year survival. The best performing ML model was externally validated using an institutional cohort of UPS patients (n = 151).

Results

All ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.67–0.69 at the 5-year time point. The Multi-Layer Perceptron Neural Network (MLP) model was the best performing model and used for external validation. Similarly, the MLP model performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.85 and 0.81, respectively. The MLP model was well calibrated on external validation. The MLP model has been made publicly available at https://rachar.shinyapps.io/ups_app/.

Conclusion

Machine learning models perform well for survival prediction in UPS, though this sarcoma subtype may be more difficult to prognosticate than other subtypes. Future studies are needed to further validate the machine learning approach for UPS prognostication.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA

    L. Lee, T. Yi, M. Fice, R. K. Achar, C. Jones, E. Klein, N. Buac, M. W. Colman, S. Gitelis & A. T. Blank

  2. Department of Pathology, Rush University Medical Center, Chicago, IL, USA

    N. Lopez-Hisijos

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  1. L. Lee
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Contributions

LL (data collection, analysis, interpretation, conception, drafting, editing, revisions); TY (analysis, interpretation, drafting, editing, revisions); MF (data collection, interpretation, drafting, editing, revisions); CJ (data collection, interpretation, drafting, editing, revisions); EK (data collection, interpretation, drafting, editing, revisions); NB (data collection, interpretation, drafting, editing, revisions); NLH (data collection, oversight, editing, revisions); MWC (oversight, editing, revisions); SG (oversight, editing, revisions); ATB (conception, oversight, editing, revisions).

Corresponding author

Correspondence to L. Lee.

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Conflict of interest

ATB: (BMJ Case Reports: Editorial or governing board; Clinical Orthopaedics and Related Research: Editorial or governing board; exparel/pacira: Stock or stock Options; Journal of Oncology Practice: Editorial or governing board; Journal of Surgical Oncology: ad hoc reviewer; Lancet—Oncology: Editorial or governing board; Musculoskeletal Tumor Society: Board or committee member; Onkos Surgical: Paid consultant; Pediatric Blood and Cancer: Editorial or governing board; Rare Tumors: Editorial or governing board; Rush Orthopedic Journal: Editorial or governing board; Swim Across America Cancer Research Grant: Research support); SG: (Onkos Surgical: Paid consultant; Stock or stock Options; USMI: Stock or stock Options); MWC: (Alphatec Spine: IP royalties; Paid consultant; AO Spine North America: Board or committee member; Research support; Cervical Spine Research Society: Board or committee member; CSRS: Research support; DePuy, A Johnson & Johnson Company: Paid presenter or speaker; K2M: Paid presenter or speaker; Musculoskeletal Tumor Society: Board or committee member; North American Spine Society: Board or committee member; Orthofix, Inc.: Paid presenter or speaker; Spinal Elements: Paid consultant. All other authors have no pertinent financial disclosures or pertinent conflicts of interest.

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Rush University Medical Center obtained individual Institutional Review Board approval prior to beginning any research efforts.

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Lee, L., Yi, T., Fice, M. et al. Development and external validation of a machine learning model for prediction of survival in undifferentiated pleomorphic sarcoma. Musculoskelet Surg 108, 77–86 (2024). https://doi.org/10.1007/s12306-023-00795-w

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