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CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept

CardioVascular and Interventional Radiology volume 42, pages 1771–1776 (2019)Cite this article

Abstract

Introduction

To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool.

Materials and Methods

This is a pilot retrospective study of patients with adrenal metastases undergoing ablation. Clinical variables were collected. Thirty-two texture features were extracted from manually segmented adrenal tumors. A univariate cox proportional hazard model was used for prediction of local progression and survival. A linear support vector machine (SVM) learning technique was applied to the texture features and clinical variables, with leave-one-out cross-validation. Receiver operating characteristic analysis and the area under the curve (AUC) were used to assess performance between using clinical variables only versus clinical variables and texture features.

Results

Twenty-one patients (61% male, age 64.1 ± 10.3 years) were included. Mean time to local progression was 29.8 months. Five texture features exhibited association with progression (p < 0.05). The SVM model based on clinical variables alone resulted in an AUC of 0.52, whereas the SVM model that included texture features resulted in an AUC 0.93 (p = 0.01). Mean overall survival was 35 months. Fourteen texture features were associated with survival in the univariate model (p < 0.05). While the trained SVM model based on clinical variables resulted in an AUC of 0.68, the SVM model that included texture features resulted in an AUC of 0.93 (p = 0.024).

Discussion

Pre-ablation texture analysis and machine learning improve local tumor progression and survival prediction in patients with adrenal metastases who undergo ablation.

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Funding

This study was not supported by any funding.

Author information

Author notes

  1. Dania Daye and Pedro V. Staziaki contributed equally to this work.

Affiliations

  1. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA

    Dania Daye, Azadeh Tabari, Florian J. Fintelmann, Nathan Elie Frenk, Ronald Arellano, Michael S. Gee & Raul Nirmal Uppot

  2. Department of Radiology, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA

    Pedro V. Staziaki

  3. Department of Internal Medicine, University of Massachusetts, UMass, Worcester, MA, USA

    Vanessa Fiorini Furtado

  4. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Paul Shyn, Kemal Tuncali & Stuart Silverman

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  1. Dania Daye
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  2. Pedro V. Staziaki
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  4. Azadeh Tabari
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  5. Florian J. Fintelmann
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Correspondence to Dania Daye.

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Daye, D., Staziaki, P.V., Furtado, V.F. et al. CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept. Cardiovasc Intervent Radiol 42, 1771–1776 (2019). https://doi.org/10.1007/s00270-019-02336-0

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  • DOI: https://doi.org/10.1007/s00270-019-02336-0

Keywords

  • Radiomics
  • Machine learning
  • Prognostication
  • Texture analysis
  • Ablation
  • Adrenal metastasis