Special Section on Radiomics and Imaging Genomics

Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy

[+] Author Affiliations
Sarah A. Mattonen, Aaron D. Ward

The University of Western Ontario, Department of Medical Biophysics, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

Shyama Tetar, Suresh Senan

VU University Medical Center, Department of Radiation Oncology, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands

David A. Palma

The University of Western Ontario, Department of Medical Biophysics, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

London Regional Cancer Program, Division of Radiation Oncology, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

Alexander V. Louie

London Regional Cancer Program, Division of Radiation Oncology, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

J. Med. Imag. 2(4), 041010 (Nov 12, 2015). doi:10.1117/1.JMI.2.4.041010
History: Received April 1, 2015; Accepted October 6, 2015
Text Size: A A A

Abstract.  Benign radiation-induced lung injury (RILI) is not uncommon following stereotactic ablative radiotherapy (SABR) for lung cancer and can be difficult to differentiate from tumor recurrence on follow-up imaging. We previously showed the ability of computed tomography (CT) texture analysis to predict recurrence. The aim of this study was to evaluate and compare the accuracy of recurrence prediction using manual region-of-interest segmentation to that of a semiautomatic approach. We analyzed 22 patients treated for 24 lesions (11 recurrences, 13 RILI). Consolidative and ground-glass opacity (GGO) regions were manually delineated. The longest axial diameter of the consolidative region on each post-SABR CT image was measured. This line segment is routinely obtained as part of the clinical imaging workflow and was used as input to automatically delineate the consolidative region and subsequently derive a periconsolidative region to sample GGO tissue. Texture features were calculated, and at two to five months post-SABR, the entropy texture measure within the semiautomatic segmentations showed prediction accuracies [areas under the receiver operating characteristic curve (AUC): 0.70 to 0.73] similar to those of manual GGO segmentations (AUC: 0.64). After integration into the clinical workflow, this decision support system has the potential to support earlier salvage for patients with recurrence and fewer investigations of benign RILI.

Figures in this Article
© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Sarah A. Mattonen ; Shyama Tetar ; David A. Palma ; Alexander V. Louie ; Suresh Senan, et al.
"Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy", J. Med. Imag. 2(4), 041010 (Nov 12, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.4.041010


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.