Evaluation of the reliability of markerless tumor tracking with single‐energy and dual‐energy imaging using machine learning
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| Formato: | Artículo Open Access |
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Wiley
2026
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| _version_ | 1867012092039528448 |
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| author | Ha Nguyen Jason Luce Liangjia Zhu Mathias Lehmann Hyejoo Kang Matthew M. Harkenrider John C. Roeske |
| author_facet | Ha Nguyen Jason Luce Liangjia Zhu Mathias Lehmann Hyejoo Kang Matthew M. Harkenrider John C. Roeske Ha Nguyen Jason Luce Liangjia Zhu Mathias Lehmann Hyejoo Kang Matthew M. Harkenrider John C. Roeske |
| collection | Wiley Open Access |
| contents | Evaluation of the reliability of markerless tumor tracking with single‐energy and dual‐energy imaging using machine learning Ha Nguyen Jason Luce Liangjia Zhu Mathias Lehmann Hyejoo Kang Matthew M. Harkenrider John C. Roeske Journal of Applied Clinical Medical Physics Abstract Background Markerless tumor tracking (MTT) using single‐energy (SE) kilovoltage (kV) imaging has been proposed as a technique for lung tumor motion management. However, bony structures can obscure the tumor and make tracking challenging. Dual‐energy (DE) subtraction imaging can suppress the bone and therefore enhance tumor visibility, potentially improving tracking accuracy. Purpose To develop a machine learning model that predicts the reliability of MTT for SE and DE imaging using phantom and patient data. Methods Images were collected at high and low energy (120/60 kV) using fast‐kV switching on dynamic thorax phantom with 5–15 diameter targets and 20 lung cancer patients, including one patient with two tumor locations. DE images were then generated off‐line using weighted logarithmic subtraction. Separately, a template‐based tracking algorithm was used to track the tumor on both phantom and patient images. For phantom data, the ground truth (GT) was defined by the cos 4 waveform programmed into the motion controller. Since the GT was unknown for patient data, it was estimated using a Kalman filter and further refined using visual inspection. The tracking success rate (TSR) was subsequently calculated. Based on TSR, patients were stratified into three tracking groups: good, moderate, and poor. A logistic regression (LR) model was implemented with tracking features, including match score, peak to side‐peak ratio (PSR), x ‐ and y ‐velocity. To address the distribution differences between phantom and patient data, a domain indicator and interaction features were also incorporated. The model was trained on phantom and 17 patient data using leave‐one‐out cross‐validation, and its performance was evaluated on three held‐out patients, one from each tracking group. Separate LR models were developed for each imaging modality, resulting in SE‐LR and DE‐LR models. For each held‐out patient, the confusion matrix, sensitivity, and specificity were reported. Results For both SE‐LR and DE‐LR models, PSR has the largest coefficient value, indicating that it is the most influential factor when determining the reliability of tracking. At a specificity of 95%, the DE‐LR model achieved significantly higher sensitivity than the SE‐LR model (0.908 vs. 0.731, p < 0.01 for phantom data and 0.657 vs. 0.589, p < 0.01 for patient data). In the three held‐out patients, the DE‐LR model outperformed the SE‐LR model across all metrics. The largest gains were found in the patient with good tracking quality. Conclusions The proposed LR models successfully predicted the reliability of SE‐ and DE‐MTT. Across phantom and patient datasets, the DE‐LR model consistently demonstrated improved performance compared with the SE‐LR model, suggesting that DE imaging may be beneficial for MTT applications. 10.1002/acm2.70621 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/acm2.70621 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_acm2_70621 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Evaluation of the reliability of markerless tumor tracking with single‐energy and dual‐energy imaging using machine learning Ha Nguyen Jason Luce Liangjia Zhu Mathias Lehmann Hyejoo Kang Matthew M. Harkenrider John C. Roeske Journal of Applied Clinical Medical Physics Evaluation of the reliability of markerless tumor tracking with single‐energy and dual‐energy imaging using machine learning Ha Nguyen Jason Luce Liangjia Zhu Mathias Lehmann Hyejoo Kang Matthew M. Harkenrider John C. Roeske Journal of Applied Clinical Medical Physics Abstract Background Markerless tumor tracking (MTT) using single‐energy (SE) kilovoltage (kV) imaging has been proposed as a technique for lung tumor motion management. However, bony structures can obscure the tumor and make tracking challenging. Dual‐energy (DE) subtraction imaging can suppress the bone and therefore enhance tumor visibility, potentially improving tracking accuracy. Purpose To develop a machine learning model that predicts the reliability of MTT for SE and DE imaging using phantom and patient data. Methods Images were collected at high and low energy (120/60 kV) using fast‐kV switching on dynamic thorax phantom with 5–15 diameter targets and 20 lung cancer patients, including one patient with two tumor locations. DE images were then generated off‐line using weighted logarithmic subtraction. Separately, a template‐based tracking algorithm was used to track the tumor on both phantom and patient images. For phantom data, the ground truth (GT) was defined by the cos 4 waveform programmed into the motion controller. Since the GT was unknown for patient data, it was estimated using a Kalman filter and further refined using visual inspection. The tracking success rate (TSR) was subsequently calculated. Based on TSR, patients were stratified into three tracking groups: good, moderate, and poor. A logistic regression (LR) model was implemented with tracking features, including match score, peak to side‐peak ratio (PSR), x ‐ and y ‐velocity. To address the distribution differences between phantom and patient data, a domain indicator and interaction features were also incorporated. The model was trained on phantom and 17 patient data using leave‐one‐out cross‐validation, and its performance was evaluated on three held‐out patients, one from each tracking group. Separate LR models were developed for each imaging modality, resulting in SE‐LR and DE‐LR models. For each held‐out patient, the confusion matrix, sensitivity, and specificity were reported. Results For both SE‐LR and DE‐LR models, PSR has the largest coefficient value, indicating that it is the most influential factor when determining the reliability of tracking. At a specificity of 95%, the DE‐LR model achieved significantly higher sensitivity than the SE‐LR model (0.908 vs. 0.731, p < 0.01 for phantom data and 0.657 vs. 0.589, p < 0.01 for patient data). In the three held‐out patients, the DE‐LR model outperformed the SE‐LR model across all metrics. The largest gains were found in the patient with good tracking quality. Conclusions The proposed LR models successfully predicted the reliability of SE‐ and DE‐MTT. Across phantom and patient datasets, the DE‐LR model consistently demonstrated improved performance compared with the SE‐LR model, suggesting that DE imaging may be beneficial for MTT applications. 10.1002/acm2.70621 http://creativecommons.org/licenses/by/4.0/ |
| title | Evaluation of the reliability of markerless tumor tracking with single‐energy and dual‐energy imaging using machine learning |
| topic | Journal of Applied Clinical Medical Physics |
| url | https://aapm.onlinelibrary.wiley.com/doi/10.1002/acm2.70621 |