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Development and external validation of a machine learning model for prediction of survival in undifferentiated pleomorphic sarcoma.
Lee, L; Yi, T; Fice, M; Achar, R K; Jones, C; Klein, E; Buac, N; Lopez-Hisijos, N; Colman, M W; Gitelis, S; Blank, A T.
Affiliation
  • Lee L; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA. linus.h.lee@gmail.com.
  • Yi T; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
  • Fice M; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
  • Achar RK; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
  • Jones C; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
  • Klein E; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
  • Buac N; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
  • Lopez-Hisijos N; Department of Pathology, Rush University Medical Center, Chicago, IL, USA.
  • Colman MW; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
  • Gitelis S; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
  • Blank AT; Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
Musculoskelet Surg ; 108(1): 77-86, 2024 Mar.
Article in En | MEDLINE | ID: mdl-37658174
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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sarcoma / Soft Tissue Neoplasms Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Musculoskelet Surg Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sarcoma / Soft Tissue Neoplasms Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Musculoskelet Surg Year: 2024 Document type: Article