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Time Series Analysis of EPID Images to Identify Patients in Need of Treatment Adaptation

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L Archambault

L Archambault*, O Piron , N Varfalvy , CHUQ Pavillon Hotel-Dieu de Quebec, Quebec, QC


SU-D-BRA-5 (Sunday, July 31, 2016) 2:05 PM - 3:00 PM Room: Ballroom A

Purpose: to evaluate if time series analysis of portal dose images can be used to identify patients undergoing important anatomical changes.

Methods: daily EPID images of every treatment fields were acquired for 48 patients treated for lung cancer. In addition, CBCT were acquired on a regular basis (weekly or biweekly). Gamma analysis was performed relative to the first fraction given that no significant anatomical change was observed on the CBCT of the first fraction compared to the planning CT. Several parameters were extracted from the gamma analysis (e.g. average gamma value, standard deviation, percent above 1). The gamma parameters formed a patient-specific time series that was analyzed. The first 24 patients were retrospectively evaluated to establish an action threshold that was then applied on the remaining 24 patients. Dosimetric evaluation of patients above that threshold was performed to as assess the level of degradation compared to the initial treatment plan.

Results: after performing a clinical retrospective analysis of the first 24 cases, an action threshold on the average gamma value was established at 0.6 and a warning level was set at 0.4. These thresholds were then applied to the remaining 24 cases. Of these, 6 patients (25%) were above the warning level and 4 (17%) were above the action threshold. Dosimetric evaluation was performed on the CBCT for all these 6 patients. Three of these patients had changes above 3% in their PTV coverage, one had changes of about 2% and the remaining two had negligible changes. Patients with the strongest changes all had clear trending in their gamma parameters that could be classified with techniques such as hidden Markov models.

Conclusion: by using time series analysis of relative EPID image it was possible to identify a subset of patient most likely to benefit from treatment adaptation.

Funding Support, Disclosures, and Conflict of Interest: This work was funded in part by Varian Medical Systems

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