# An Intelligent Radomic Approach for Lung Cancer Screening
## 4 Discussion  
Intelligent artificial methods applied to medical imaging have to face two key drawbacks. The available small amount of labelled data and the obligation that methods must ensure good rates avoiding false positives. In order to overcome with these two main challenges, we have proposed an hybrid method that combines an embedded radiomic texture features to characterize nodules and an optimized feedforward network for nodule diagnosis. The nodule embedding step is based on selecting those radiomic features that significantly correlate to malignancy ensuring reproducibility with minimal training data. The fully connected network architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power to ensure maximum clinical outcome. Results demonstrate the power of the two main contributions. Table 8 results demonstrate the power of using \(t\)-test analysis for statistical significance and nodule embedding, as best results are achieved on those models (3, 6, 9, 12). Table 9 confirms that the whole hybrid strategy outperforms in Accuracy (96.30), Sensitivity (100) and F1 Score (97.67) the state of the art methods. Notice that in computer-aided diagnose, sensitivity is significant because correctly finding out patients with malignant nodules is crucial. A remarkable outcome is that our approach outperforms deep approaches with only requiring 290 parameters (in contrast to the thousands required by deep methods). This has a direct impact with the small training data needed. Our work could be improved in the following aspects. Nodule embedding bases on a combination of simple \(t\)-tests as a first approach to a statistical selection of features, which disregards correlations across GLCM features. Future work will explore the use of other statistics (like regression models) taking into account multiple comparisons across features. In addition, it is planed to increase the database and introduce our own-defined metrics as the loss function of the fully connected network to guide training to ensure maximum clinical outcome. Conceptualization, A.R. and D.G. ; Data curation, G.T., S.B., C.S., I.G. and D.G. ; Formal analysis, D.G. ; Funding acquisition, A.R. and D.G. ; Investigation, G.T., S.B., C.S., I.G., A.R. and D.G. ; Methodology, G.T., S.B., C.S., I.G., A.R. and D.G. ; Project administration, A.R. and D.G. ; Resources, S.B., I.G., A.R. and D.G. ; Software, G.T., C.S. and D.G. ; Supervision, C.S., A.R. and D.G. ; Validation, G.T., C.S. and D.G. ; Visualization, G.T., C.S. and D.G. ; Writing--original draft, G.T., S.B., C.S., I.G. and D.G. ; Writing--review & editing, G.T., C.S., A.R. and D.G. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approaches** & **Accuracy** & **Sensitivity** & **Specificity** & \
\begin{tabular}{c} **F1** \\ **Score** \\ \end{tabular} & **AUC** &
\begin{tabular}{c} **Param.** \\ **(M)** \\ \end{tabular} \\ \hline
**Radiomics** & & & & & & \\ [PERSON] et al. [5] & – & 90.40 & 85.50 & – & 0.939 & **<0.29** \\ \hline
**Machine Learning** & & & & & & \\ [PERSON] et al. [6] & 96.09 & 96.84 & **95.34** & – & **0.979** & **<0.29** \\ \hline
**Deep CNN** & & & & & & \\ Multicrop [8] & 87.14 & 77.00 & 93.00 & – & 0.930 & – \\ Nodule-level 2D [9] & 87.30 & 88.50 & 86.00 & 87.23 & 0.937 & – \\ Vanilla 3D [9] & 87.40 & 89.40 & 85.20 & 87.25 & 0.947 & – \\ DeepLung [10] & 90.44 & 81.42 & – & – & – & 141.57 \\ AE-DPN [11] & 90.24 & 92.04 & 88.94 & 90.45 & 0.933 & 678.69 \\ NASLung [12] & 90.77 & 85.37 & 95.04 & 89.04 & – & 16.84 \\ \hline
**Hybrid** & & & & & & \\ model3 (Our) & **96.30** & **100** & 83.33 & **97.67** & 0.940 & 0.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of our method compared to the state of the art with malignant nodules as positive cases.