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

  • Dania DayeEmail author
  • Pedro V. Staziaki
  • Vanessa Fiorini Furtado
  • Azadeh Tabari
  • Florian J. Fintelmann
  • Nathan Elie Frenk
  • Paul Shyn
  • Kemal Tuncali
  • Stuart Silverman
  • Ronald Arellano
  • Michael S. Gee
  • Raul Nirmal Uppot
Clinical Investigation Other
Part of the following topical collections:
  1. Other



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.


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).


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


Radiomics Machine learning Prognostication Texture analysis Ablation Adrenal metastasis 



This study was not supported by any funding.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Supplementary material

270_2019_2336_MOESM1_ESM.docx (69 kb)
Supplementary material 1 (DOCX 69 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE) 2019

Authors and Affiliations

  • Dania Daye
    • 1
    Email author
  • Pedro V. Staziaki
    • 2
  • Vanessa Fiorini Furtado
    • 3
  • Azadeh Tabari
    • 1
  • Florian J. Fintelmann
    • 1
  • Nathan Elie Frenk
    • 1
  • Paul Shyn
    • 4
  • Kemal Tuncali
    • 4
  • Stuart Silverman
    • 4
  • Ronald Arellano
    • 1
  • Michael S. Gee
    • 1
  • Raul Nirmal Uppot
    • 1
  1. 1.Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonUSA
  2. 2.Department of RadiologyBoston Medical Center, Boston University School of MedicineBostonUSA
  3. 3.Department of Internal MedicineUniversity of Massachusetts, UMassWorcesterUSA
  4. 4.Department of RadiologyBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

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