Much progress had been made in cancer prevention and early detection, resulting in the reduction in mortality rates and improved survival rates [6]. Evangelista et al.’s study [8] showed a follow-up scheme guideline for pure GGNs detected cancer incidentally or at lung cancer screening. To be biennial, CT follow-up for GGN must be less than 5 mm, annual for GGN 5–10 mm, and 3–6 months for GGN more than 10 mm. There must also be a downgrading of CT follow-up for stable nodules and upgrading of CT follow-up duration for nodules that increase in size and/or density. Nodules after 3–6 months follow-up that increased in size necessitate surgical resection. There must be continuous CT surveillance using unenhanced, low-dose techniques until life expectancy.
In this study, we evaluated CT chest scans of 72 patients, and 33.3% were non-neoplastic nodules. All of them were inflammatory, yet 66.7% were neoplastic nodules. Correlation of lesion size, behavior, density, shape, and histogram analysis with the final diagnosis provides basic features for pulmonary nodules, which help in determining the likelihood of these nodules representing neoplastic disease.
According to the current international guidelines, size and growth rate represented the main indicators to determine the nature of a pulmonary nodule, but the first radiological predictor of malignancy is the size [9, 10]. In this study, there was statistically significant relation between mean size of the nodule and final diagnosis. The mean long-axis diameter in cases of malignant nodules was 6.66 mm, yet the mean long-axis diameter of benign nodules was 11.25 mm (P-value < 0.002). This was explained that the nodules were pGGNs, and the neoplastic pGNN is detected more in preinvasive lesions, which is the early stage and small in size, which when it increases in size, solid component increased and will be excluded from this study. Truong et al. [11] reported that in terms of size, the likelihood of malignancy positively correlates with nodule diameter. As the diameter of a nodule increases, so does the likelihood of malignancy; however, a small nodule diameter does not exclude malignancy. Small nodules (< 4 mm) have less than 1% chance of being malignant. Furthermore, the risk for malignancy increases to 10–20% for nodules with the size of 8 mm.
Nodule growth, determined by imaging surveillance, could be used as a diagnostic tool for assessing malignancy [12,13,14]. Truong et al.’s study showed that after reassessing the nodule with CT, lesions that resulted from infectious or noninfectious inflammatory causes mostly regressed or resolved in the interval; however, nodule increased in diameter in follow-up and the risk of malignancy increased. Therefore, follow-up was the only strong predictor for malignancy with sensitivity and specificity of 71% and 95%, respectively [15]. Moreover, persistent partial nodules are more likely to be malignant, but they may also be benign like in cases of focal interstitial fibrosis and organizing pneumonia. Kim et al.’s study [16] also showed that 75% of persistent ground-glass appearance was malignant in origin. They explained this great variation by the different types of the original malignancy. In a small study, Lindell et al. reported that 11 out of 18 malignant nodules showed a primary decrease in size at some point on their growth curves.
The nodule appearance in terms of density affects the probability of malignancy, reflecting histological differences between lesions [11, 15]. In this study, regarding pGGNs, there was no statistically significant relation between mean density with the final diagnosis. The prevalence of malignancy was higher among solid lung nodules, particularly when considering semisolid than solid ones [17]. Yet, malignant nodules mostly have a lepidic growth pattern (which arises from and spread along alveolar walls without invasion), which there is an inclusion of air or an air bronchogram or degenerative changes such as necrosis or hemorrhage within the tumor. Fat attenuation (− 40 to − 120 HU) is frequently seen in as many as 50% of neoplasms at CT. Other causes of fat attenuation in pulmonary nodules included pulmonary metastases [11].
Zhang et al. [18] found statistically significant density deviation in the nonsolid component that may help to identify invasive GGNs. The MIA and IA and the mean, maximum, and minimum density and density deviation had a positive correlation over time, while benign and preinvasive GGNs showed a negative correlation for these features. A diagnostic model based on three GGN features (increasing in diameter, mean density, and density deviation) identified invasive GGNs with a sensitivity, specificity, and area under the ROC curve (AUC) of 83.7%, 61.9%, and 0.786, respectively (P < 0.001). This was consistent with this study that showed the change in density over time was significantly affecting the diagnosis as the mean density of persistent neoplastic lung nodules was − 91.52 HU with cutoff level − 99.5 HU. However, in cases of benign nodule, it was − 247.13 HU after nodule follow-up (P < 0.001).
Regarding nodule multiplicity, in a study in the University of Chicago Medical Center, they postulated high statistically significant correlation between multiplicities of nodules and increased likelihood of malignancy [10]; however, in this study, there was no significant relation to nodule multiplicity.
The morphological features of lung nodules had a great role in defining whether it is malignant or benign [15]. According to this study, there was a significant positive relationship between the shape and margin of nodules in the CT chest and increases their susceptibility to be malignant. The regular and well-defined lung nodules are highly significantly related to malignant (P < 0.001 and 0.019, respectively). Metastatic nodules often exhibit a largely uniform growth rate and homogenous invasion in all directions. These features contribute to a round or quasi-circular contour, whereas nonmetastatic lesions, including benign lesions and primary lung cancer, have irregular shapes due to uneven growth rates at various sites [19]. Metastatic etiology exceeded primary adenocarcinoma in this study. Truong et al. stated that benign conditions that resulted from infection or inflammation, focal atelectasis, tuberculoma, and progressive massive fibrosis may also have a speculated margin. In addition, smooth margins do not exclude malignancy; as many pulmonary metastasis as 20% of primary lung malignancies have smooth margins [11]. Chun et al. reported that both benign and malignant nodules could exhibit smooth edges [13]. Moreover, this was in agreement with Tsutsui who found that 87% of focal pure ground-glass opacities (GGOs) had a well-defined border or round shape. Nambu et al.’s study showed that a well-defined border and rounded shape of focal GGO is more frequently seen in neoplastic lesions (well-differentiated adenocarcinoma) than in non-neoplastic inflammatory lesions [6, 20].
In recent years, several studies had applied texture analysis of GGNs. In this study, there was statistically significant correlation between peak frequency percentage of the histogram and the final diagnosis with no significant result regarding other performed visual assessment of histogram features. To the best of our knowledge, this study is the first to study the relationship between histogram features as qualitative assessment of kurtosis, skewness, and peak frequency percentage measures for evaluating pGGNs. We expected that skewness or kurtosis would help distinguish the neoplastic pGGNs from benign pGGNs. However, the distinction was not accomplished with skewness and kurtosis. Chae et al. [21] found that higher kurtosis and smaller mass are significant differentiators of preinvasive lesions from invasive pulmonary aspergillosis; however, they only focused on partly solid GGOs and few texture features, as histogram, volumetric, and morphological features. According to Mao et al., regarding the histogram analysis between benign and malignant in all types of pulmonary nodules, there was a significant difference found in the kurtosis values. The greater kurtosis and reduced skewness in malignant nodules may be related to the greater heterogeneity than benign nodules, although they appear to be uniformly solid nodules on CT images. These differences in internal density homogeneity are reflected by the differences in the kurtosis measurements but are not detected by conventional visual assessments [22].
Further studies are recommended for pGGNs on large-scale cases with more quantitative measures of histogram to be measured, comprehensive and robust radiomics evaluation which is a limitation in this study as it was not applicable in our workstation.
