CT-Detected Subsolid Nodules: A Predictor of Lung Cancer Development at Another Location?

Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors' evaluation in lung cancer screening

OBJECTIVE

To evaluate the consistency between doctors and artificial intelligence (AI) software in analysing and diagnosing pulmonary nodules, and assess whether the characteristics of pulmonary nodules derived from the two methods are consistent for the interpretation of carcinomatous nodules.

MATERIALS AND METHODS

This retrospective study analysed participants aged 40-74 in the local area from 2011 to 2013. Pulmonary nodules were examined radiologically using a low-dose chest CT scan, evaluated by an expert panel of doctors in radiology, oncology, and thoracic departments, as well as a computer-aided diagnostic(CAD) system based on the three-dimensional(3D) convolutional neural network (CNN) with DenseNet architecture(InferRead CT Lung, IRCL). Consistency tests were employed to assess the uniformity of the radiological characteristics of the pulmonary nodules. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy. Logistic regression analysis is utilized to determine whether the two methods yield the same predictive factors for cancerous nodules.

RESULTS

A total of 570 subjects were included in this retrospective study. The AI software demonstrated high consistency with the panel's evaluation in determining the position and diameter of the pulmonary nodules (kappa = 0.883, concordance correlation coefficient (CCC) = 0.809, p = 0.000). The comparison of the solid nodules' attenuation characteristics also showed acceptable consistency (kappa = 0.503). In patients diagnosed with lung cancer, the area under the curve (AUC) for the panel and AI were 0.873 (95%CI: 0.829-0.909) and 0.921 (95%CI: 0.884-0.949), respectively. However, there was no significant difference (p = 0.0950). The maximum diameter, solid nodules, subsolid nodules were the crucial factors for interpreting carcinomatous nodules in the analysis of expert panel and IRCL pulmonary nodule characteristics.

CONCLUSION

AI software can assist doctors in diagnosing nodules and is consistent with doctors' evaluations and diagnosis of pulmonary nodules.

Sequential pulmonary resections by uniportal video-assisted thoracic surgery for bilateral multiple pulmonary nodules

Objective

The aim of this study was to evaluate the effect of sequential pulmonary resections by uniportal video-assisted thoracoscopic surgery (VATS) for bilateral multiple pulmonary nodules (BMPNs).

Methods

A single-center, prospective, nonrandomized study was performed on patients who underwent one-stage or two-stage operations by uniportal VATS. The clinical, pathological and perioperative data were summarized and analyzed from January 2021 to December 2021.

Results

A total of 80 patients were included during the study period. Sequential pulmonary resection by uniportal VATS was underwent in 40 patients. There were no perioperative deaths and serious complications, 2 patients had postoperative pneumonia, 3 patients had transient atrial fibrillation, 1 patient had persistent severe air leakage, 1 patient occurred hemoptysis. The one-stage group had less operative time, surgical blood loss, pleural drainage, chest tube duration and postoperative admission duration(P<0.05). The results of pathological examination of pulmonary nodules revealed adenocarcinoma in situ (n=12), minimally invasive adenocarcinoma (n=24), invasive adenocarcinoma (n=42), squamous carcinoma (n=1),and benign nodules (n=10). The pathological diagnosis included multiple primary lung cancers (30/40, 75%), single primary lung cancer (6/40, 15%). The most advanced pathologic stage of the primary lung cancer was classified as IA (n=19), IB (n=5), II (n=3), and IIIA (n=2).

Conclusion

For patients with excellent pulmonary function, sequential pulmonary resection by uniportal VATS is a safe and feasible for BMPNs. Strict control of surgical indications, reasonable preoperative planning, accurate intraoperative operation, and standardized perioperative management can effectively reduce complications and maximize benefits for suitable patients.