## 4 Discussion  
We developed a novel method for classifying pulmonary tumors, which combines CT and PET images, as well as patient-related information. The classification results in Figure 3 show that the four cases that could not be classified correctly using our previous method were correctly identified using the currently proposed method. One case of granulomas, OP, cryptococcus, and other were included in each of these four cases. In all of these cases, the tumor was near the chest wall. Images that were affected by the chest wall may have been correctly identified by utilizing patient information. We analyzed the details of the patient information that was classified correctly using the proposed method. It is generally known that lung cancer often occurs in elderly men. Of these four cases, three cases were female. Additionally, two of these cases were a 48-year-old male and a 63-year-old female, who were relatively young. Furthermore, the amount of an enzyme called lactate dehydrogenase (LD) is often \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Input Features**} & \multicolumn{3}{c}{**Classification Accuracy [\%]**} & \multirow{2}{*}{**AUC**} \\ \cline{2-3} \cline{5-10}  & **Patient Information** & & & & & & **Benign** & **Malignant** & **Overall** \\ \hline I & \(\bullet\) & - & - & ANN & 33.3 & 61.1 & 47.2 & 0.520 \\ II & \(\bullet\) & \(\bullet\) & - & SVM & 66.7 & 94.4 & 80.6 & 0.881 \\ III & \(\bullet\) & \(\bullet\) & \(\bullet\) (E) & SVM & 83.3 & 88.9 & 86.1 & 0.920 \\ IV & \(\bullet\) & \(\bullet\) & \(\bullet\) (E, D) & SVM & 100 & 88.9 & 94.1 & 0.901 \\ V & - & \(\bullet\) & - & RF & 72.2 & 61.1 & 66.7 & 0.730 \\ VI & - & \(\bullet\) & \(\bullet\) (E) & RF & 72.2 & 83.3 & 77.8 & 0.860 \\ VII & - & \(\bullet\) & \(\bullet\) (E, D) & RF & 77.8 & 77.8 & 77.8 & 0.895 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results for different input features. The results of I-IV were obtained using the proposed method. The results of V-VII were obtained using the previous method [18]. Figure 3: Classification results for Model #4 (patient information, CT images, and early-and delayed-phase PET images); (**a**) Benign; (**b**) Malignant. increased in the blood, because tumors require large amounts of energy. The LD value was normal in two of these four cases. For a sufficiently high malignant tumor classification rate (0.944), the correct classification rate for benign tumors was the highest using the proposed method. In particular, when patient information was combined with CT images, an improvement of 44.5% was obtained, compared with the method that only uses CT images. These results indicate that information about the patient metabolism and inflammation (which can be extracted from PET images) might have been supplemented by blood-related information (which can be extracted from the patient metadata). Therefore, when only CT examination can be performed as an imaging examination, patient information may be useful to improve the classification accuracy of lung tumors. Comparing Model #3 and #4, Model #3 (without delay-phased PET images) had slightly higher AUROC (Table 4). Delay-phased PET images are generally useful information for diagnosing lung cancer. However, some inflammatory diseases do not necessarily contribute to an improvement in classification accuracy because these SUV increase, similar to malignant tumors. The reason why Model #3 had the highest AUROC was that the information contained in delay-phased PET images was sufficiently complemented by providing the patient information. We also analyzed effective features for classification of the image features and the patient information. Among the image features, the effectiveness of \(\text{ESUV}_{\text{max}}\), \(\text{DSUV}_{\text{max}}\), \(\text{ESUV}_{\text{peak}}\), \(\text{DSUV}_{\text{peak}}\), \(\text{\Delta SUV}_{\text{max}}\), and \(\text{\Delta SUV}_{\text{peak}}\) was high.