## 4 Discussion  
EAT is mainly composed of brown adipose tissue [11] and has an impact on the clinical outcomes of patients with metabolic diseases such as coronary artery disease [20] and diabetes mellitus [21]. EAT is a metabolically active tissue releasing various bioactive factors that can affect the prognosis of metabolic diseases [9]. Local expression of chemokine (monocyte chemotactic protein (MCP)-1) and inflammatory cytokines (interleukin (IL)-1\(\beta\), IL-6, and tumor necrosis factor (TNF)-\(\alpha\)) was observed in CAD patients. Significant changes in IL-1\(\beta\), IL-6, MCP-1, and TNF-\(\alpha\) mRNA and protein were observed in the epicardial adipose stores [9; 22]. Ionizing radiation is known to induce inflammatory reactions and lipid remodeling of adipose tissues [23; 24; 25], but its role in brown adipose tissue or EAT remains unclear. Our investigations indicate that lower radiation exposure intensity may have a correlation with better survival. Whether this correlation resulted from radiation-induced inflammatory reactions or radiation-modulated metabolic changes still needs to be clarified.  
The EAT-REI defined as mean radiation dose divided by volume of EAT is a unique biomarker for radiobiological effect derived from conventional physic dosimetric parameters. The radiation effects from exposure dose and irradiated volume may simultaneously contribute to this radiobiological marker. Growing evidence demonstrated that local radiation may have a systemic effect due to the release of soluble mediators within the radiation field. To determine whether EAT-REI has a role in this effect, which is compatible with the simultaneously irradiated dose and volume concerned, further in vivo studies using experimental animals are warranted.  
In the era of dose painting RT, the use of CT scan images for simulation and RT planning software is a routine process. Analysis of the volume of EAT and the radiation dose distribution to EAT using these imaging data, therefore, is feasible and applicable. After analyzing the correlation between radiation dosimetric parameters and clinical outcomes, we found that EAT-REI might be a novel imaging biomarker from the radiobiological aspect. This implies that EAT-REI may have the potential to be adopted as a constraint when planning for RT. To the best of our knowledge, this is the first study to report EAT-REI as a constraint to RT planning.  
The limitations of this study were mainly due to the retrospective analysis of both clinical factors and radiotherapy parameters. To ensure the estimated radiotherapy parameters were retrieved from the same RT planning algorithm and system, and the sample size of eligible patients was relatively small (N = 36). Another limitation of this retrospective  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{1}{c}{**EAT-REI \(\geq\) 68.8 (\(n\) = 23)**} & \multicolumn{1}{c}{**EAT-REI \(<\) 68.8 (\(n\) = 13)**} & \multicolumn{1}{c}{\(p\) **Value**} \\ \hline Clinical T stage & & & 0.69 \\ cT1–2 & 5 (21.7\%) & 4 (30.8\%) & \\ cT3–4 & 18 (78.3\%) & 9 (69.2\%) & \\ Clinical N stage & & & 0.92 \\ cN0–1 & 12 (52.2\%) & 7 (53.8\%) & \\ cN2–3 & 11 (47.8\%) & 6 (46.2\%) & \\ cTNM stage & & & 0.72 \\ II & 7 (30.4\%) & 3 (23.1\%) & \\ III & 16 (69.6\%) & 10 (76.9\%) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._analysis was the lack of measurement of additional biochemical parameters related to EAT-REI. These study limitations could be overcome by further prospective investigations.