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  5. The Impact of the Different Breathing Conditions On Knowledge-Based Treatment Planning for Breast Cancer Radiotherapy

The Impact of the Different Breathing Conditions On Knowledge-Based Treatment Planning for Breast Cancer Radiotherapy

J Xu1*, J Wang2 , W Hu2 , Z Lu1 , F Zhao1 , S Yan1 , (1) Department of Radiation Oncology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China (2) Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

Presentations

(Monday, 7/15/2019) 9:30 AM - 10:00 AM

Room: Exhibit Hall | Forum 6

Purpose: To assess the impact of breathing condition on knowledge-based treatment planning.

Methods: Two kernel density estimation (KDE) models were developed based on 40 left-sided breast cancer patients with two different CT scans of free breathing (FB) and deep inspiration breath hold (DIBH). Each KDE model was used to predict the achievable DVHs for 10 new patients. For each new patient, six treatment plans generated: two manually optimized plans based on CT scans of FB and DIBH (Plan_Manual(FBCT), Plan_Manual(DIBHCT)); two plans base on FB-CT scan by FB and DIBH models (Plan_FBmodel(FBCT), Plan_DIBHmodel(FBCT)); two plans base on DIBH-CT scan by FB and DIBH models (Plan_FBmodel(DIBHCT), Plan_DIBHmodel(DIBHCT)). And the predicted DVHs were also reordered for analysis. In treatment planning, the PTV requirement was same. The constrain of OAR was come from KDE model. Mean doses to the heart, left anterior descending coronary artery (LADCA), and ipsilateral lung were evaluated and compared using the T-test.

Results: The T-test is applied to test the consistency hypothesis on the six treatment plans and the predicted DVHs of the 10 new left-breast patients. The acceptable P-values indicate that the two sets of data in Plan_FBmodel(FBCT) and Plan_Manual(FBCT) are consistent from the statistical point of view (P>0.05). For the heart, LADCA and left lung, there is no significant differences were observed in Dmean between Plan_DIBHmodel(DIBHCT) and Plan_Manual(DIBHCT) (all P>0.05). However, significant differences were observed between Predict_DIBHmodel(FBCT) and Plan_DIBHmodel(FBCT), with a counsel of perfection. We also observed a significantly inferior mean dose of the heart, LADCA and left lung during Plan_FBmodel(DIBHCT), which not achieves the optimization, compared with Plan_Manual(DIBHCT).

Conclusion: In order to obtain superior performance in a knowledge-based treatment planning, a specific KDE model should be developed for different breathing conditions.

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