Predictors of malignancy in patients with solitary pulmonary nodules undergoing pulmonary resection

Quantitative parameters of HRCT target scan to predict the risk of lung adenocarcinoma based on the detection of lung ground-glass nodules

BACKGROUND

To explore the value of high-resolution computed tomography (HRCT) in the differential diagnosis of benign and malignant ground-glass nodules (GGNs), and to provide a theoretical basis for the clinical application of HRCT.

METHODS

A total of 208 patients with GGN who had been clinically confirmed by surgical pathology and clinical confirmation were collected, and HRCT target scanning technology was used to scan and collect general information of patients, and observe the distribution of GGN, GGN size, GGN cross-sectional area, diameter, transverse diameter, solid composition, relationship with bronchi, and relationship with blood vessels and other indicators. Multivariate regression analysis and risk factor prediction are performed.

RESULTS

The differences were statistically significant in multivariate regression analysis, such as nodule location, maximum diameter, maximum cross-sectional area, GGN status, nodule boundary and relationship with blood vessels (P < 0.05). The results of ROC curve showed that the AUC value of nodule site and nodule boundary was greater than 0.5, and the nodule boundary AUC value was 0.676, which was more sensitive to predict whether GGN deteriorated to lung adenocarcinoma (LUAD).

CONCLUSION

Nodule site and nodule boundary are effective risk predictors for LUAD in patients with GGN, and nodule boundary is the most valuable independent predictor.

Qualitative and Semiquantitative Parameters of 18F-FDG-PET/CT as Predictors of Malignancy in Patients with Solitary Pulmonary Nodule

This study aims to evaluate the reliability of qualitative and semiquantitative parameters of ¹⁸F-FDG PET-CT, and eventually a correlation between them, in predicting the risk of malignancy in patients with solitary pulmonary nodules (SPNs) before the diagnosis of lung cancer. A total of 146 patients were retrospectively studied according to their pre-test probability of malignancy (all patients were intermediate risk), based on radiological features and risk factors, and qualitative and semiquantitative parameters, such as SUVmax, SUVmean, TLG, and MTV, which were obtained from the FDG PET-CT scan of such patients before diagnosis. It has been observed that visual analysis correlates well with the risk of malignancy in patients with SPN; indeed, only 20% of SPNs in which FDG uptake was low or absent were found to be malignant at the cytopathological examination, while 45.45% of SPNs in which FDG uptake was moderate and 90.24% in which FDG uptake was intense were found to be malignant. The same trend was observed evaluating semiquantitative parameters, since increasing values of SUVmax, SUVmean, TLG, and MTV were observed in patients whose cytopathological examination of SPN showed the presence of lung cancer. In particular, in patients whose SPN was neoplastic, we observed a median (MAD) SUVmax of 7.89 (±2.24), median (MAD) SUVmean of 3.76 (±2.59), median (MAD) TLG of 16.36 (±15.87), and a median (MAD) MTV of 3.39 (±2.86). In contrast, in patients whose SPN was non-neoplastic, the SUVmax was 2.24 (±1.73), SUVmean 1.67 (±1.15), TLG 1.63 (±2.33), and MTV 1.20 (±1.20). Optimal cut-offs were drawn for semiquantitative parameters considered predictors of malignancy. Nodule size correlated significantly with FDG uptake intensity and with SUVmax. Finally, age and nodule size proved significant predictors of malignancy. In conclusion, considering the pre-test probability of malignancy, qualitative and semiquantitative parameters can be considered reliable tools in patients with SPN, since cut-offs for SUVmax, SUVmean, TLG, and MTV showed good sensitivity and specificity in predicting malignancy.

A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks

Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so they are difficult to extract accurately. Moreover, the number of spiculation samples is limited, so it is difficult to build a stable network structure. Thus, a novel pulmonary nodule spiculation recognition algorithm is proposed. MCA (morphological component analysis) model is built to segment pulmonary nodules in accordance with the composition of pulmonary CT images. Subsequently, the maximum density projection mechanism is introduced to characterize the boundary features of pulmonary nodules to the maximum extent. Inspired by time series dynamic programming, this paper proposes DTW (dynamic time warping) distance to measure data similarity. Lastly, a semisupervised generative adversarial network is built to solve the problem of insufficient positive samples, and it is capable of recognizing pulmonary nodule spiculation. As revealed by the experimental result, the proposed algorithm exhibited strong robustness.