## 3 Present
### CAD in Lung Cancer Diagnosis
#### 3.1.2 Algorithms Proposed for Nodular Classification
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**CAD Model** & **Method** & **Function** & **Data Base** & **N\({}^{*}\)Scanners** & **N\({}^{*}\)N Nodules** & **Sensitivity** & **Specificity** & **FP/Scan** & **Accuracy** & **AUC** & **Year** \\ \hline [PERSON] [28] & Rademics-CT & Classification & Private & 373 & 373 & 89.00\% & 74.00\% & NA & 77.00\% & 0.84 & 2019 \\ \hline [PERSON] et al. [29] & Rademics-LDCT & Classification & LIDC-IDBI & 1018 & 1226 & 85.80\% & 90.20\% & NA & 88.90\% & 0.935 & 2019 \\ \hline [PERSON] et al. [30] & CAD + DL + CNN & Detection & Classification & LIDC-IDBI & 1018 & NA & 97.93\% & 96.32\% & NA & 95.62\% & NA & 2020 \\ \hline [PERSON] et al. [31] & CAD + DL + CNN & Detection and Classification & LIDC-IDBI & 1018 & NA & 98.00\% & 95.63\% & NA & 97.27\% & 0.996 & 2020 \\ \hline [PERSON] et al. [22] & CAD + DL + Detection and ResNet & Detection and Classification & LUNA16 & 888 & NA & 87.10\% & 89.46\% & NA & 88.33\% & 0.88 & 2020 \\ \hline [PERSON] et al. [33] & CAD + DL + TL & Detection and Classification & LUNA16 & 888 & 1397 & NA & 96.40\% & 99.40\% & 0.6 & 97.00\% & NA & 2020 \\ \hline [PERSON] et al. [34] & CAD + DL + CNN & Detection & LUNA16 & 888 & NA & 81.20\% & & 0.125 & & & \\  & & & & & & 98.93\% & NA & 1 & NA & 2019 \\ \hline [PERSON] et al. [34] & CAD + DL + CNN & Detection & LIDC-IDBI & \multirow{2}{*}{1190} & \multirow{2}{*}{NA} & \multirow{2}{*}{98.40\%} & \multirow{2}{*}{NA} & \multirow{2}{*}{2.1} & \multirow{2}{*}{NA} & \multirow{2}{*}{NA} & \multirow{2}{*}{2019} \\  & & & & & & & & & & & \\ \hline [PERSON] & \multirow{2}{*}{CAD + DL-CNN} & Detection and Classification & LIDC-IDBI & 1200 & 3250 & 94.00\% & 90.00\% & NA & 91.13\% & 0.99 & 2019 \\ \hline [PERSON] & \multirow{2}{*}{CAD + DL-CNN} & Detection and Classification & LIDC-IDBI & 1018 & 2562 & 93.97\% & 89.93\% & NA & 88.97\% & NA & 2019 \\ \hline [PERSON] et al. [36] & CAD + DL + DNN & Detection and Classification & LIDC-IDBI & 1018 & NA & NA & NA & NA & 96.00\% & NA & 2019 \\ \hline [PERSON] et al. [77] & CAD + DL + CNN & Detection and Classification & LUNA16 & 757 & 855 & 84.4\% & 83.00\% & NA & 83.70\% & 0.803 & 2019 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Some authors defend that the radiological information obtained from CT may be the key to the correct classification of nodules as benign or malignant. They argue that the aforementioned classification models are based on the statistical study of various image values without taking into account relevant clinical data, and without a detailed analysis that could demonstrate correlations between phenotype and histology or genetics that would provide valuable information about these pulmonary nodules. [PERSON] et al. [26] investigate and develop a radiological nomogram to assist in the diagnosis of potentially malignant pulmonary nodules. This would constitute a non-invasive diagnostic tool that helps make clinical and surgical decisions. They bet on the use of a huge number of radiological features as a determining factor in the best performance of the model. In this study, the private database of a hospital is used, establishing as an inclusion criterion that patients have or are going to have a definitive histological diagnosis. This brings reliability to the data set studied as well as new biases, such as the possible fact that presumably healthy patients do not choose to undergo a biopsy or surgery. [PERSON] et al. [27] affirm that despite the amount of available knowledge nowadays, there are almost no studies that specifically focus on the usefulness of radiomics in predicting malignancy of small and solid nodules in screening for lung cancer with LDCT. Small nodules have a much lower number of specific features compared to large ones, and malignant solid nodules progress much faster than subsolid nodules. For these reasons, the purpose of the study is to develop a predictive radiological model to diagnose small solid lung nodules (6-15 mm), since it is something that has not been exhaustively investigated. [PERSON] et al.