# A Game-Theoretical Approach to Clinical Decision Making with Immersive Visualisation
## 3 Proposed Method
### Data Sets  
For this study we limited ourselves to studying data derived from patient cohorts diagnosed with acute myeloid leukaemia (AML). The data used in this study were from publicly available cancer genomic data sets, including The Cancer Genome Atlas (TCGA) Research Network ([[https://www.cancer.gov/tcga](https://www.cancer.gov/tcga)]([https://www.cancer.gov/tcga](https://www.cancer.gov/tcga)) assessed on 31 May 2021), HOVON ([[http://www.hovon.nl/](http://www.hovon.nl/)]([http://www.hovon.nl/](http://www.hovon.nl/)) assessed on 31 May 2021), National Cancer Institute Office of Cancer Genomic--TARGET\" data set ([[https://ocg.cancer.gov/programs/target/data-matrix](https://ocg.cancer.gov/programs/target/data-matrix)]([https://ocg.cancer.gov/programs/target/data-matrix](https://ocg.cancer.gov/programs/target/data-matrix)) assessed on 31 May 2021), and [PERSON] et al. (2018) consolidated data set ([[https://www.cbioportal.org/study/summary?id=aml_ohsu_2018](https://www.cbioportal.org/study/summary?id=aml_ohsu_2018)]([https://www.cbioportal.org/study/summary?id=aml_ohsu_2018](https://www.cbioportal.org/study/summary?id=aml_ohsu_2018)) assessed on 31 May 2021).  
The data included bulk ribonucleic acid (RNA) sequencing data, patient metadata, and clinical history. The owners did not restrict the use of their data and had obtained ethics approval for their clinical trials. The data sets also contained the patients' genomic profiles, which had undergone a feature selection process using the LSC17 gene score [44]. The LSC17 gene score was used to rapidly identify AML patients who had not responded well to the standard AML therapy due to initial therapy resistance. The gene score could be part of newer novel strategies. Table 1 shows two samples of data from the data set.  
In this work, we further reduced the LSC17 genes, inspired by [PERSON], [PERSON] [44], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [45]'s work on clustering their patients' data, to LSC3. According [PERSON] and [PERSON] [44], LSC17 score analysis can be used to predict AML patient medical conditions. LSC17 has a list of 17 genes identified to be significant in [PERSON] and [PERSON]'s [44] analysis and predictive model. The full list of the genes identified by LSC17 is DNMT3B, ZBTB46, NYNRIN, ARHGAP22, LAPTM4B, MMRN1,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Patient** & **DNMT3B** & **ZBTB46** & **NYNRIN** & **ARHGAP2** & \(\ldots\) & **CD34** & **AKR1C3** & **GPR56** \\ \hline aml\_ohsu\_2018\_15-00626 & 5.125 & 5.081 & 3.678 & 3.785 & & 9.080 & 4.879 & 7.1123 \\ \hline aml\_ohsu\_2018\_15-00674 & 3.702 & 4.607 & 3.131 & 3.560 & & 6.403 & 3.2101 & 5.427 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patient gene data with their gene expression value. The data partially show the LSC17 gene list. The first column is the patient id, and the numerical value is the actual gene expression value of the listed gene. The table’s header is the name of the genes selected for the study.  
DPYSL3, KIAA0125, CDK6, CPXM1, SOCS2, SMIM24, EMP1, NGFRAP1, CD34, AKR1C3, and GPR56. The paediatric LSC3 (pLSC3) score model (LSC3), on the other hand, is derived from DNMT3B-CD34-GPR56 As a Prognostic Tool to Predict AML Patient Outcome [45]. The LSC3 lists a subset of the genes identified in LSC17.  
The LSC17 score analysis can be used to predict the medical condition of AML patients [44]. Their study involved diverse AML subtypes (\(n\) = 908) from five independent cohorts. The LSC17 score allows the clinician to rapidly identify AML patients who do not respond to standard therapy due to initial therapy resistance and should be part of trials evaluating novel upfront or post-remission strategies. All the combinations of three genes from LSC17 were used for our case. Other well-known biomarkers, such as CD34, were also used in the experiments.