## 2 Materials and Methods
### Computerized Analysis  
For each patient, the two thoracic radiologists (A.B. and A.S.) selected thin-section CT images containing the PSNs on the baseline and on the last follow-up CT exam. Before selecting the images, the same observers assessed the lobe location of the PSNs and the absence or presence of emphysema. Then, the selected CT images were exported in DICOM format, transferred to a personal computer and processed using open-source image processing software (Imagej software, version 1.52g, U.S. National Institutes of Health, Bethesda, MD, USA, 2018) [24]. The segmentation process and the computerized analysis of PSNs consisted of different steps (Figure 1). On the baseline CT images, the following quantitative features were considered in the analysis: area, perimeter, mean, median, mode and standard deviation of CT attenuation, skewness, kurtosis and mean Feret diameter (i.e., average between the maximum Feret diameter and the minimum Feret diameter). The maximum Feret diameter corresponded to the longest distance between any two parallel tangents along the nodule contours; in other words, this measure represented the maximum diameter [24; 25; 26]. The minimum Feret diameter corresponded to the shortest distance between any two parallel tangents along the nodule contours; in other words, this measure represented the minimum diameter [24; 25; 26]. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Characteristic**} \\ \hline Age (years) & \(65.5\pm 10.5\) \\ \hline Sex & \\ Male & 24 (48.0) \\ Female & 26 (52.0) \\ \hline Smoking habits & \\ Current/former & 35 (70.0) \\ Never & 15 (30.0) \\ \hline Oncologic history & 23 (46.0) \\ Non-small cell lung cancer & 10 (43.5) \\ Breast cancer & 4 (17.4) \\ Head and neck cancer & 3 (13.0) \\ Other malignancy & 6 (26.1) \\ \hline \hline \end{tabular} Data are presented as the number (percentage) or mean \(\pm\) standard deviation.  
\end{table} 
Table 1: Characteristics of the selected patients (\(n=50\)). Since the segmentation was performed on only the largest cross-sectional area of the PSNs, the mass was not calculated [27; 28]. As a viable alternative, we used the linear mass density (LMD), which was defined as the amount of mass per unit length. Typically, this quantitative feature is used to describe the characteristics of one-dimensional objects. However, the LMD may also be used to describe the density of a three-dimensional object along one particular dimension. Therefore, we considered the LMD to be adaptable for the quantitative analysis of PSNs. The LMD was calculated with the following Equation (1):  
Figure 1: Example of segmentation process and computerized analysis performed using Image) software in a part-solid nodule (PSN). Step 1: importing the computed tomography (CT) images in DICOM format to Image) software (**a**). Step 2: drawing a polygonal region of interest (ROI) that outlined the nodule contours on the largest cross-sectional area of the lesion (**b**). Step 3: checking and manually correcting any segmentation error. Step 4: clicking “Image” on the ImageJ toolbar and selecting “Adjust \(\rightarrow\) Threshold”. Step 5: choosing a suitable CT attenuation value to excluded pixels with air attenuation around and within the PSNs (CT attenuation value, \(-800\) HU) (**c**). Step 6: clicking “Analyze” on the toolbar, selecting “Set Measurement” and checking the quantitative features of interest (**d**). Step 7: clicking “Analyze” again and then clicking “Measure”, to automatically calculate the selected quantitative features. LMD (mg/mm) = [Area \(\times\) (Mean CT Attenuation + 1000)]/1000  
We also considered the following shape descriptors typically used in particle analysis: circularity and solidity [24; 25; 26]. Circularity is a dimensionless value that defines the degree to which a nodule resembles a circle [24; 25; 26]. The circularity value ranges between 0.0 and 1.0 [24; 25; 26].