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

Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

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

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References (38)

Publisher
SPIE
Copyright
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Subject
Special Section on Radiomics and Imaging Genomics; Paper
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.JMI.2.4.041010
pmid
26835492
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of Medical Imaging – SPIE

Published: Oct 1, 2015

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