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A Mouse Model of Incompletely Resected Soft Tissue Sarcoma for Testing Neoadjuvant Therapies

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Published on: July 28, 2020

A prognostic model for resectable soft tissue and cutaneous angiosarcoma.

Andrew J Sinnamon¹, Madalyn G Neuwirth¹, Matthew T McMillan¹

¹Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.

Journal of Surgical Oncology

|July 6, 2016

Summary

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This summary is machine-generated.

This study identified key risk factors for angiosarcoma survival, developing a model to predict patient outcomes after surgical resection. The new risk stratification is more effective than current staging systems.

Area of Science:

Oncology
Surgical Oncology
Cancer Research

Background:

Angiosarcoma is an aggressive cancer with increasing incidence, potentially linked to therapeutic radiation.
Prognostic factors for angiosarcoma have not been well-defined due to its rarity.

Purpose of the Study:

To identify prognostic factors for overall survival (OS) in patients with localized angiosarcoma.
To develop and validate a risk model for predicting OS in angiosarcoma patients undergoing resection.

Main Methods:

Retrospective analysis of 821 patients with localized angiosarcoma from 2002-2012 using the National Cancer Data Base.
Cox proportional hazards modeling and bootstrap resampling to identify factors associated with poor OS.
Construction of a risk model to stratify patients into low-, intermediate-, and high-risk groups.

Keywords:

angiosarcoma prognosis risk factor survival

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Main Results:

Median OS was 3.4 years. Preoperative factors associated with worse OS included age >70, black race, head and neck primary tumor, grade 3 tumors, and larger tumor size (>3 cm).
Postoperative factors, including positive resection margins (microscopic or macroscopic), were also linked to worse OS.
The developed risk group stratification demonstrated superior OS discrimination compared to the AJCC staging system.

Conclusions:

Identified significant risk factors for poor OS in angiosarcoma.
Developed a clinically useful risk model for prognostication in localized angiosarcoma patients post-resection.
This model offers improved prognostic capability over traditional staging methods.