## 4 Discussion
Various quantitative CT features have been tested to predict the growth and improve the risk stratification of SSNs [9; 14; 17; 18; 19; 20; 21; 22; 23; 37; 38]. However, most of these studies have considered PGGNs [17; 18; 19; 22; 37; 38] or SSNs without a distinction between PGGNs and PSNs [9; 21]. To the best of our knowledge, this study is the first to perform quantitative CT analysis for predicting the behavior of PSNs with a solid component \(<\)6 mm and total diameter \(<\)15 mm. Our study is also the first to use shape descriptors (circularity and solidity) and the LMD for the quantitative CT analysis of SSNs. Circularity and solidity were used to objectively define the nodule margins and the nodule shape. The LMD was used to accurately define the growth rate of PSNs during follow-up. The main advantage of the LMD is that this feature combines size and density measurements into a single value; therefore, we considered this feature appropriate for measuring changes in the SSNs on CT images, particularly when three-dimensional software is not available. The present study demonstrates that the quantitative CT analysis performed with open-source image processing software was helpful for predicting the growth in a select group of PSNs with a solid component \(<\)6 mm and a total diameter \(<\)15 mm. In particular, we found that the shape descriptors (circularity and solidity) and the size features (area, perimeter, mean Feret diameter, and LMD) were significantly correlated with LMD-DT and consequently with the growth rate of PSNs (Table 2). In addition, the same quantitative features showed statistical significance when we compared the groups of growing and nongrowing PSNs (Table 3). Therefore, the more irregular and nonspherical a PSN, the greater the likelihood of PSN growth (Figures 5 and 6). Additionally, this likelihood increases as the size of the PSN increases. Figure 5: Nongrowing part-solid nodule (PSN) in the right upper lobe in a 43-year-old woman (nonsmoker and without an oncologic history). The PSN exhibits a rounded shape. The interval between the baseline (**left**) and last follow-up CT scan (**right**) was 789 days. The computerized analysis showed a relative linear mass density (LMD) variation of +7.9% in the nodule (less than the cut-off of 11.3%) with a LMD-doubling time (LMD-DT) of 7285 days. The purple box (top left) shows the main quantitative features obtained from the baseline CT image. No significant correlation was found between the LMD-DT and the quantitative features related to CT attenuation (mean, median, mode, standard deviation, skewness and kurtosis) (Table 2). No significant difference was also found in the density-related CT features (mean, median, mode, skewness and kurtosis) between the groups of growing and nongrowing PSNs. In contrast to these results, data reported in the literature on PGGNs indicated that some density-related CT features could be helpful in predicting the future behavior of PGGNs [17; 18; 19; 22]. This difference might be related to the different methods of analysis and SSN selection. The only discrepancy observed in our data was related to the standard deviation of CT attenuation, which showed a statistically significant difference only when comparing the groups of growing and nongrowing PSNs (Table 3). However, we considered the nonsignificant correlation between the standard deviation and LMD-DT to be statistically more robust as the doubling time integrates information about both time and growth into a single value. We also found that the median time interval to detect growth in the growing group was 143 days (IQR, 101-217 days). On the other hand, no significant change in the LMD was found in the nongrowing group after a median time interval of 349 days (IQR, 193-1026 days). From a clinical point of view, we consider these results very interesting, as they could affect the guidelines for nodule management in terms of defining a \"tailored\" timing for the follow-up of PSNs with a small solid component.