Early prediction of lung cancer recurrence after stereotactic radiotherapy using second order texture statistics

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

doi:10.1117/12.2042700

13 March 2014 Early prediction of lung cancer recurrence after stereotactic radiotherapy using second order texture statistics

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

Author Affiliations +

Proceedings Volume 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging; 90380T (2014) https://doi.org/10.1117/12.2042700
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Benign radiation-induced lung injury is a common finding following stereotactic ablative radiotherapy (SABR) for lung cancer, and is often difficult to differentiate from a recurring tumour due to the ablative doses and highly conformal treatment with SABR. Current approaches to treatment response assessment have shown limited ability to predict recurrence within 6 months of treatment. The purpose of our study was to evaluate the accuracy of second order texture statistics for prediction of eventual recurrence based on computed tomography (CT) images acquired within 6 months of treatment, and compare with the performance of first order appearance and lesion size measures. Consolidative and ground-glass opacity (GGO) regions were manually delineated on post-SABR CT images. Automatic consolidation expansion was also investigated to act as a surrogate for GGO position. The top features for prediction of recurrence were all texture features within the GGO and included energy, entropy, correlation, inertia, and first order texture (standard deviation of density). These predicted recurrence with 2-fold cross validation (CV) accuracies of 70–77% at 2– 5 months post-SABR, with energy, entropy, and first order texture having leave-one-out CV accuracies greater than 80%. Our results also suggest that automatic expansion of the consolidation region could eliminate the need for manual delineation, and produced reproducible results when compared to manually delineated GGO. If validated on a larger data set, this could lead to a clinically useful computer-aided diagnosis system for prediction of recurrence within 6 months of SABR and allow for early salvage therapy for patients with recurrence.

Citation Download Citation

Sarah A. Mattonen, David A. Palma M.D., Cornelis J. A. Haasbeek, Suresh Senan, and Aaron D. Ward "Early prediction of lung cancer recurrence after stereotactic radiotherapy using second order texture statistics", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 90380T (13 March 2014); https://doi.org/10.1117/12.2042700

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