## 2 Materials and Methods
### Data Preprocessing  
TCGA mutation analysis was used to identify relevant genetic mutations from the data present amongst the patient populations. The TCGA lists this process as first involving \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Cancer Type** & \begin{tabular}{c} **Cancer** \\ **Location** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Patients** \\ \end{tabular} & \begin{tabular}{c} **Imaging** \\ **Available** \\ \end{tabular} & \begin{tabular}{c} **Other Data** \\ **Available** \\ \end{tabular} \\ \hline CPTAC-LUAD & Adenocarcinoma & Lung & 244 & \begin{tabular}{c} CT, MR, PT, \\ CR, pathology \\ \end{tabular} & \begin{tabular}{c} Clinical, \\ genomics, \\ proteomics \\ \end{tabular} \\ TCGA-LUSC & Cell & Lung & 37 & \
\begin{tabular}{c} CT, NM, PT, \\ pathology \\ \end{tabular} &
\begin{tabular}{c} Clinical, \\ genomics \\ \end{tabular} \\ Total & – & – & 281 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of top 12 genes present across a large majority of patients present in the data and with linkage to cancer severity or treatment. Figure 2: Workflow for new algorithm proposed and evaluated in this study. This study attempted to build a custom clustering/classification methodology that related the imaging features present within medical imaging data for lung cancer to the genetic mutation data present for lung cancer by clustering initial genetic clusters, running an image classification model on the results of the clusters, and re-clustering the data should the accuracy of the model not perform well. This was carried out until an optimal preset accuracy and AUC were reached. data collection from both the primary tumor and matched normal tissue samples, then through pathological review, and nucleic acid extraction (DNA and RNA), the sequencing itself was carried out through next-generation (NGS), microarray, or Sanger sequencing; variant calling; and annotation; and the final analysis was conducted through pan-cancer, gene-level, pathway, or survival evaluation [33; 34]. This project used the mutation analysis from the primary tumor sequenced via NGS for DNA at gene-level analysis. Mutations were ranked by total patients afflicted across the two datasets (CPTAC-LUAD and TCGA-LUSC). Next, the top 100 mutations for each group were selected as potential candidates to be fed into the custom clustering/classification algorithm. These top 100 mutations were evaluated for linkage to cancer severity or potential targeted genetic treatments. A final 38 initial genes were identified that were present across most patients, had severity linkage, or had associated specific treatments. Genes were grouped for each patient into a binary matrix such that presence of mutation was indicated by \"1\" and lack of mutation for that patient was indicated by \"0\". CT image data for the lung cancer patients were collected from the TCIA website for the above patients. Single-slice CT images for each patient with identified lung tumor were identified for input into the image processing model. Single-slice CT tumor was selected based on presence of tumor and TCIA criteria. When available, single-slice selections and annotations were validated through the IDC Zenodo automatic segmentations as provided on the TCIA website for TCGA LUAD and LUSC [35; 36]. However, radiologist annotations were considered paramount when available. If a patient had multiple images with tumors, each was treated as an individual image. These images, however, were not separated into separate training and testing sets. What this meant was that if a patient had multiple lung CT slices, it was ensure that each patient sample remained in the same cohort (training or testing). This was carried out to ensure patient data did not appear separately across both cohorts to prevent risk of bias and overfitting, or specifically to keep the model predicting patient data and not imaging data. This came out to be 22,785 images across 281 patients and the 2 datasets.