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Proceedings Paper

Early detection of lung cancer recurrence after stereotactic ablative radiation therapy: radiomics system design
Author(s): Salma Dammak; David Palma; Sarah Mattonen; Suresh Senan; Aaron D. Ward
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Paper Abstract

Stereotactic ablative radiotherapy (SABR) is the standard treatment recommendation for Stage I non-small cell lung cancer (NSCLC) patients who are inoperable or who refuse surgery. This option is well tolerated by even unfit patients and has a low recurrence risk post-treatment. However, SABR induces changes in the lung parenchyma that can appear similar to those of recurrence, and the difference between the two at an early follow-up time point is not easily distinguishable for an expert physician. We hypothesized that a radiomics signature derived from standard-of-care computed tomography (CT) imaging can detect cancer recurrence within six months of SABR treatment. This study reports on the design phase of our work, with external validation planned in future work. In this study, we performed cross-validation experiments with four feature selection approaches and seven classifiers on an 81-patient data set. We extracted 104 radiomics features from the consolidative and the peri-consolidative regions on the follow-up CT scans. The best results were achieved using the sum of estimated Mahalanobis distances (Maha) for supervised forward feature selection and a trainable automatic radial basis support vector classifier (RBSVC). This system produced an area under the receiver operating characteristic curve (AUC) of 0.84, an error rate of 16.4%, a false negative rate of 12.7%, and a false positive rate of 20.0% for leaveone patient out cross-validation. This suggests that once validated on an external data set, radiomics could reliably detect post-SABR recurrence and form the basis of a tool assisting physicians in making salvage treatment decisions.

Paper Details

Date Published: 27 February 2018
PDF: 8 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057503 (27 February 2018); doi: 10.1117/12.2292444
Show Author Affiliations
Salma Dammak, London Regional Cancer Program (Canada)
David Palma, London Regional Cancer Program (Canada)
The Univ. of Western Ontario (Canada)
Sarah Mattonen, Stanford Univ. School of Medicine (United States)
Suresh Senan, Vrije Univ. Medical Ctr. (Netherlands)
Aaron D. Ward, London Regional Cancer Program (Canada)
The Univ. of Western Ontario (Canada)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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