Image analysis of small pulmonary nodules identified by computed tomography

A model of malignant risk prediction for solitary pulmonary nodules on 18 F-FDG PET/CT: Building and estimating

BACKGROUND

To develop a model of malignant risk prediction of solitary pulmonary nodules (SPNs) using metabolic characteristics of lesions.

METHODS

A total of 362 patients who underwent PET/CT imaging from January 2013 to July 2017 were analyzed. Differences in the clinical and imaging characteristics were analyzed between patients with benign SPNs and those with malignant SPNs. Risk factors were screened by multivariate nonconditional logistic regression analysis. The self-verification of the model was performed by receiver operating characteristic (ROC) curve analysis, and out-of-group verification was performed by k-fold cross-validation.

RESULTS

There were statistically significant differences in age, maximum standardized uptake value (SUV_max ), size, lobulation, spiculation, pleural traction, vessel connection, calcification, presence of vacuoles, and emphysema between patients with benign nodules and those with malignant nodules (all P < 0.05). The risk factors for malignant nodules included age, SUV_max , size, lobulation, calcification and vacuoles. The logistic regression model was as follows: P = l/(1 + e^-x ), x = - 5.583 + 0.039 × age + 0.477 × SUV_max  + 0.139 × size + 1.537 × lobulation - 1.532 × calcification + 1.113 × vacuole. The estimated area under the curve (AUC) for the model was 0.915 (95% CI: 0.883-0.947), the sensitivity was 89.7%, and the specificity was 78.9%. K-fold cross-validation showed that the training accuracy was 0.899 ± 0.011, and the predictive accuracy was 0.873 ± 0.053.

CONCLUSIONS

The risk factors for malignant nodules included age, SUV_max , size, lobulation, calcification and vacuoles. After verification, the model has satisfactory accuracy, and it may assist clinics make appropriate treatment decisions.

Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations

BACKGROUND

The computed tomography (CT) features of small solid lung cancers and their changing regularity as they grow have not been well studied. The purpose of this study was to analyze the CT features of solid lung cancerous nodules (SLCNs) with different sizes and their variations.

METHODS

Between February 2013 and April 2018, a consecutive cohort of 224 patients (225 nodules) with confirmed primary SLCNs was enrolled. The nodules were divided into four groups based on tumor diameter (A: diameter ≤ 1.0 cm, 35 lesions; B: 1.0 cm < diameter ≤ 1.5 cm, 60 lesions; C: 1.5 cm < diameter ≤ 2.0 cm, 63 lesions; and D: 2.0 cm < diameter ≤ 3.0 cm, 67 lesions). CT features of nodules within each group were summarized and compared.

RESULTS

Most nodules in different groups were located in upper lobes (groups A - D:50.8%-73.1%) and had a gap from the pleura (groups A - D:89.6%-100%). The main CT features of smaller (diameter ≤ 1 cm) and larger (diameter > 1 cm) nodules were significantly different. As nodule diameter increased, more lesions showed a regular shape, homogeneous density, clear but coarse tumor-lung interface, lobulation, spiculation, spinous protuberance, vascular convergence, pleural retraction, bronchial truncation, and beam-shaped opacity (p < 0.05 for all). The presence of halo sign in all groups was similar (17.5%-22.5%; p > 0.05).

CONCLUSIONS

The CT features vary among SLCNs with different sizes. Understanding their changing regularity is helpful for identifying smaller suspicious malignant nodules and early determining their nature in follow-up.

Looking for Lepidic Component inside Invasive Adenocarcinomas Appearing as CT Solid Solitary Pulmonary Nodules (SPNs): CT Morpho-Densitometric Features and 18-FDG PET Findings

Objective

To investigate CT morphologic and densitometric features and 18-FDG PET findings of surgically excised lung adenocarcinomas "mixed subtype" with predominant lepidic component, appearing as solid solitary pulmonary nodules (SPNs) on CT scan.

Materials and Methods

Approval for this study was given from each local institutional review board according to its retrospective nature. Nodules pathologically classified as lung adenocarcinoma mixed subtype with bronchioloalveolar otherwise lepidic predominant component, in three different Italian institutions (Napoli; Varese; Parma), were retrospectively selected.

Results

22 patients were identified. The number of SPNs with smooth margins was significantly lower with respect to the number of SPNs with spiculated margins (p: 0.033), radiating spiculations (p: 0.019), and notch sign (p: 0.011). Mean contrast enhancement (CE) was 53.34 HU (min 5.5 HU, max 112 HU); considering 15 HU as cut-off value, CE was positive in 20/22 cases. No significant correlation was found between size and CE. Mean SUVmax was 2.21, ranging from 0.2 up to 7.5 units; considering 2.5 units as cut-off, SUVmax was positive in 7/22 cases. The number of SPNs with positive CE was significantly higher than the number of SPNs with positive SUVmax (p: 0.0005).

Conclusion

CT generally helps in identifying solid SPN suspicious for malignancy but 18-FDG PET may result in false-negative evaluation; when 18-FDG PET findings of a solid SPN are negative even though CT morphology and CE suggest malignancy, radiologist should consider that lepidic component may be present inside the invasive tumor, despite the absence of ground glass.