TU‐G‐BRA‐08: Understanding the Performance of Control Limit‐Based Monitoring of Respiratory Surrogate Tumor Motion Models

Abstract

Purpose: To develop an understanding of mechanisms underlying the performance of a control‐limit‐based monitoring technique for detecting errors in respiratory surrogate tumor displacement models. Methods: Five lung cancer patients underwent 13 dynamic magnetic resonance imaging sessions on a 1.5 T scanner using a TrueFISP sequence (200 images, 5 sagittal slices, 8mm slice thickness, interleaved acquisition, TE 1.29 msec, TR 2.57 ms, 60° flip angle, matrix 176×256 matrix, in‐plane spatial resolution 1.6–2.2 mm each direction, 1028 bandwidth). Tumors were localized in the images at 0.4 Hz for 500 sec. Five respiratory surrogates affixed to the abdomen were optically tracked during imaging. Surrogate‐based tumor motion models were created by applying partial‐least‐squares regression to the first 30 sec of data. Hotelling's statistic and the input variable squared‐prediction‐error for each subsequent sample were compared to training data‐based control limits to predict errors >3mm. The experiment was repeated in tumor motion and respiratory surrogate signal simulations that isolated measurement precision, period variations, amplitude variations, end‐exhale variations, tumor drift, and gross patient motion. Sampling rates of 0.1–30 Hz (0.1–0.4 Hz for patient data) were evaluated. Results: For patient data sampled at 0.4 Hz and 0.1 Hz and 95% sensitivity, specificities were 8% and 17%. In comprehensive simulations tuned to 95% sensitivity, specificity increased from 33% at 0.1 Hz to 69% at 30 Hz. With measurement noise or tumor drifts alone, specificities were 99–100% and sensitivities were 0–1%. Gross patient motion was detected with sensitivity of 100% and specificity of 97%. For end‐exhale variations, sensitivity was 97%, and specificity was 57%. Respiratory cycle amplitude and period variations had no effect on monitoring performance. Conclusions: Contributors to control‐limit‐based monitoring performance included sampling rate, end‐exhale variations, measurement noise, and tumor drifts but not patient motion, period variations, or amplitude variations. Simulation results were qualitatively in agreement with patient results. Supported in part by grant CA124766 from the NIH/NCI and the Achievement Rewards for College Scientists scholarship.