Radiomics versus physician assessment for the early prediction of local cancer recurrence after stereotactic radiotherapy for lung cancer

Sarah A. Mattonen, Carol Johnson, David A. Palma, George Rodrigues, Alexander V. Louie, Suresh Senan, Timothy P.C. Yeung, Aaron D. Ward

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Stereotactic ablative radiotherapy (SABR) has recently become a standard treatment option for patients with early-stage lung cancer, which achieves local control rates similar to surgery. Local recurrence following SABR typically presents after one year post-treatment. However, benign radiological changes mimicking local recurrence can appear on CT imaging following SABR, complicating the assessment of response. We hypothesize that subtle changes on early post- SABR CT images are important in predicting the eventual incidence of local recurrence and would be extremely valuable to support timely salvage interventions. The objective of this study was to extract radiomic image features on post-SABR follow-up images for 45 patients (15 with local recurrence and 30 without) to aid in the early prediction of local recurrence. Three blinded thoracic radiation oncologists were also asked to score follow-up images as benign injury or local recurrence. A radiomic signature consisting of five image features demonstrated a classification error of 24%, false positive rate (FPR) of 24%, false negative rate (FNR) of 23%, and area under the receiver operating characteristic curve (AUC) of 0.85 at 2-5 months post-SABR. At the same time point, three physicians assessed the majority of images as benign injury for overall errors of 34-37%, FPRs of 0-4%, and FNRs of 100%. These results suggest that radiomics can detect early changes associated with local recurrence which are not typically considered by physicians. We aim to develop a decision support system which could potentially allow for early salvage therapy of patients with local recurrence following SABR.

Original languageEnglish
Title of host publicationMedical Imaging 2016
Subtitle of host publicationComputer-Aided Diagnosis
ISBN (Electronic)9781510600201
Publication statusPublished - 1 Jan 2016
EventMedical Imaging 2016: Computer-Aided Diagnosis - San Diego, United States
Duration: 28 Feb 20162 Mar 2016


ConferenceMedical Imaging 2016: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego

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