# Dose Estimation by Geant4-Based Simulations for Cone-Beam CT Applications: A Systematic Review
## 3 Results and Discussion
In order to answer this question, special attention is paid to the most influential references and the author's productivity and relevance. Productivity was measured by the number of publications of each author. Influence was taken into account by the number of total citations (TC) received by the articles included in our selection from documents indexed on the different bibliographic databases. Table 4 presents the top 10 cited articles per author and journal, comparing the TC, TC per year, and the Local citations (LC) and its percentage. LC are citations received by a certain article internally in our selection. The three most influential papers in dosimetry studies by Geant4 simulation were [PERSON] et al. [121], with 63 TC, an average of 5.25 cites per year, 4 LC, and 6.35% of LC; [PERSON] et al. [123], with 37 TC and an average of 2.64 cites per year; and [PERSON] et al. [122], with 26 TC and an average of 2.0 cites per year. The main topic addressed in his authored and co-authored publications corresponds to Cone-beam breast CT applications. Figure 2 illustrates the progression of publications available in the selected databases in the period 2008-2020. Despite the fact that our research was conducted retrospectively from 1975, since CBCT is a quite new development in clinical practice, and Geant4 is a recent simulation toolkit, the first publications combining both technologies appear in 2008. There was an upsurge in publications, from just 2 documents published in 2008 to 7 documents in 2017. The main reason for this surge may be attributed to the increase in CPU power and the development of MC simulations integrating new techniques to reduce the high computational cost required as explained in References [90; 93; 94; 96; 99; 100; 104]. The dashed blue line denotes the average TC per year showing two peaks in 2010 and 2016 corresponding to the references [110; 121], respectively. A further insight into the most relevant publications in terms of author's productivity, author's co-citation network, collaboration countries and co-occurrence average citation of the most relevant author's keywords is described in the Supplementary Material. \begin{table}
\begin{tabular}{l c c c c} \hline
**Paper** & **TC** & **TC per Year** & **LC** & **LC\%** \\ \hline [PERSON] et al., 2010, Med. Phys. [121] & 63 & 5.25 & 4 & 6.35 \\ [PERSON] et al., 2008, Med. Phys. [123] & 37 & 2.64 & 0 & 0.0 \\ [PERSON] et al., 2009, Med. Phys. [122] & 26 & 2.0 & 0 & 0.0 \\ [PERSON] et al., 2012, Med. Phys. [120] & 20 & 2.0 & 20 & 10.0 \\ [PERSON] et al., 2016, Med. Phys. [110] & 19 & 3.17 & 0 & 0.0 \\ [PERSON] et al., 2013, Med. Phys. [117] & 19 & 2.11 & 0 & 0.0 \\ [PERSON] et al., 2013, Z Med. Phys. [119] & 19 & 2.11 & 0 & 0.0 \\ [PERSON] et al., 2018, Med. Phys. [100] & 15 & 3.75 & 2 & 13.33 \\ [PERSON] et al., 2017, Med. Phys. [105] & 14 & 2.80 & 1 & 7.14 \\ [PERSON] et al., 2014 Med. Phys. [108] & 11 & 2.20 & 0 & 0.00 \\ [PERSON], 2014, Med. Phys. [115] & 11 & 1.4 & 0 & 0.0 \\ \hline \end{tabular}
\end{table} 
Table 4: Top 10 cited articles per author and journal. TC column shows the total citations, TC per year are the total citations per year, and LC are local citations, while LC % are the percentage of local citations. Figure 2: Annual publication trend of selected documents retrieved from WOS, PubMed, and Scopus in the period 2008–2021. The blue dashed line correspond to the average TC per year. RQ2: Which Are the Most Common CBCT Applications that Use Geant4 Simulations to Estimate the Dose, and How Have They Changed over the Years? To identify the main research themes, the co-occurrence keyword network map shown in Figure 3 was created by selecting co-occurrence as the target variable [87]. The size of a node is determined by the frequency of its occurrence as a keyword, while the line thickness connection of two terms shows how frequently they co-occurred as keywords. The most frequent keywords, such as \"MC simulation\", \"CBCT\", \"dosimetry\", \"computed-tomography\", \"radiotherapy\", or \"Geant4\", have the biggest sizes.