Texture analysis of automatic graph cuts segmentations for detection of lung cancer recurrence after stereotactic radiotherapy

Sarah A. Mattonen; David A. Palma; Cornelis J. A. Haasbeek; Suresh Senan; Aaron D. Ward

doi:10.1117/12.2081427

17 March 2015 Texture analysis of automatic graph cuts segmentations for detection of lung cancer recurrence after stereotactic radiotherapy

Sarah A. Mattonen, David A. Palma, Cornelis J. A. Haasbeek, Suresh Senan, Aaron D. Ward

Author Affiliations +

Proceedings Volume 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging; 941719 (2015) https://doi.org/10.1117/12.2081427
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Stereotactic ablative radiotherapy (SABR) is a treatment for early-stage lung cancer with local control rates comparable to surgery. After SABR, benign radiation induced lung injury (RILI) results in tumour-mimicking changes on computed tomography (CT) imaging. Distinguishing recurrence from RILI is a critical clinical decision determining the need for potentially life-saving salvage therapies whose high risks in this population dictate their use only for true recurrences. Current approaches do not reliably detect recurrence within a year post-SABR. We measured the detection accuracy of texture features within automatically determined regions of interest, with the only operator input being the single line segment measuring tumour diameter, normally taken during the clinical workflow. Our leave-one-out cross validation on images taken 2–5 months post-SABR showed robustness of the entropy measure, with classification error of 26% and area under the receiver operating characteristic curve (AUC) of 0.77 using automatic segmentation; the results using manual segmentation were 24% and 0.75, respectively. AUCs for this feature increased to 0.82 and 0.93 at 8–14 months and 14–20 months post SABR, respectively, suggesting even better performance nearer to the date of clinical diagnosis of recurrence; thus this system could also be used to support and reinforce the physician’s decision at that time. Based on our ongoing validation of this automatic approach on a larger sample, we aim to develop a computer-aided diagnosis system which will support the physician’s decision to apply timely salvage therapies and prevent patients with RILI from undergoing invasive and risky procedures.

Citation Download Citation

Sarah A. Mattonen, David A. Palma, Cornelis J. A. Haasbeek, Suresh Senan, and Aaron D. Ward "Texture analysis of automatic graph cuts segmentations for detection of lung cancer recurrence after stereotactic radiotherapy", Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 941719 (17 March 2015); https://doi.org/10.1117/12.2081427

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