## 4 Discussion  
This study detailed a custom genetic clustering and image classification algorithm and then presented the results of running it on a small set of lung CT and genetic data. This algorithm was fast and efficient, and successfully grouped genetic clusters, and it was able to link the genetic patterns to the imaging data. This methodology is especially interesting in that it could take a traditional supervised machine learning/deep learning problem (image classification) and leverage a clustering methodology to make the final labels also function in an unsupervised way. Although no prior algorithm had attempted to perform this same process on medical imaging and cancer data for lung cancer in this way, many similar algorithms persist in the literature. [PERSON] et al. fed 10 genes associated with genetic mutations for lung cancer into a Fuzzy C-Means algorithm and generated five groupings based on image input before reporting the Silhouette Coefficients (SCs) (0.68 for their study) and Davies-Bouldin Index (DBI) (2.35 for their study) [37]. [PERSON] et al. used a combined clustering/generative image strategy by segmenting lung tumors using a U-Net, using a CNN with genetic and imaging input to predict gene from imaging data and then using a Generalized Adversarial Network to generate new tumor data [38]. [PERSON] et al. used a CNN and transfer learning combined with a pre-identified set of genomic clusters to predict said clusters using MRI data (AUC 0.6-0.7) [39]. [PERSON] et al. used a U-Net to segment images, two radiologist-defined imaging features, and k-means to generate two clusters from 11 genomic signatures to create a cluster where >50% of patients in one cluster had high risks of cancer [40]. These studies used a small number of pre-selected genes (10) and frequently only generated as few as five clusters. Some of the studies ([PERSON] et al.) [37] that were most similar to this algorithm reported worse accuracy than our study (0.9 > 0.7 AUC). It is important to note that this algorithm was not the same as this and the other likeminded studies did not report comparable metrics such as the AUC or accuracy. It would be interesting in future work to compare how the results presented here compare to others published should these other studies also report the accuracy and AUC for any classification methods used. These past studies also did not take into account the clinical relevance of the genetic clusters or whether they had any linkage to cancer severity. One major advantage of the algorithm \begin{table}
\begin{tabular}{c c c} \hline  & **LUAD** & **LUSC** \\ \hline Demographic & & \\ \hline Not reported & 3\% & 0\% \\ American Indian or Alaska Native & 1\% & 0\% \\ Asian & 1\% & 1\% \\ Black or African American & 10\% & 9\% \\ Not reported & 10\% & 17\% \\ Unknown & 1\% & 0\% \\ White & 75\% & 72\% \\ \hline Stages & & \\ \hline Not reported & 45\% & 36\% \\ Stage I & 1\% & 1\% \\ Stage IA & 12\% & 11\% \\ Stage IB & 13\% & 17\% \\ Stage II & 0\% & 1\% \\ Stage IIA & 6\% & 9\% \\ Stage IIB & 9\% & 12\% \\ Stage III & 0\% & 1\% \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._and methods presented here is that they consider clinical relevance and do not require the manual selection of genes or imaging input. Large groupings of genes can be retrospectively analyzed. By iteratively re-clustering using the accuracy of an image classification model, this algorithm was also replicable across three different initialization strategies (random, binary matrix factorization, and k-means), and despite differing initial accuracies of the image classification algorithm, got to a similar classification accuracy (>90%) after only a short number of runs (nine). This study was limited in that it used a small dataset and only tested a small number of possible outputs. This framework could be applied to a large number of additional problems and would benefit from the inclusion of more data.