Objective To investigate the clinical and pathological characteristics, prognosis and treatment strategies of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA). Methods We retrospectively analyzed the clinical data of 489 patients with AIS and MIA in our hospital from January 2007 to August 2015. There were 122 males and 367 females with an average age of 26–78 (51±9) years. According to the pathological types, they were divided into the AIS group (246 patients) and the MIA group (243 patients). In the AIS group, there were 60 males and 186 females with an average age of 50±7 years. In the MIA group, there were 62 males and 181 females with an average age of 54±5 years. The clinicopathological features, surgical methods and prognosis of the two groups were compared. Results There were significant differences in age, value of carcino-embryonic antigen (CEA), nodule shape and nodule size between the AIS and MIA groups (P<0.05). AIS patients were mostly under the age of 60 years with the value of CEA in the normal range which often appeared as pure ground-glass opacity lung nodules <1 cm in diameter on the CT scan. MIA often appeared as mixed ground-glass nodules <1.5 cm in diameter, accompanied by bronchiectasis and pleural indentation. The 5-year disease-free survival rate of the AIS and MIA groups reached 100%, and there was no statistical difference in the prognosis between the two groups after subtotal lobectomy (pulmonary resection and wedge resection) and lobectomy, systematic lymph node dissection and mediastinal lymph node sampling. Conclusion The analysis of preoperative clinical and imaging features can predict the AIS and MIA and provide individualized surgery and postoperative treatment program.
ObjectiveTo evaluate the effectiveness of the artificial intelligence-assisted diagnosis and treatment system in distinguishing benign and malignant lung nodules and the infiltration degree.MethodsClinical data of 87 patients with pulmonary nodules admitted to the First Affiliated Hospital of Xiamen University from January 2019 to August 2020 were retrospectively analyzed, including 33 males aged 55.1±10.4 years, and 54 females aged 54.5±14.1 years. A total of 90 nodules were included, which were divided into a malignant tumor group (n=80) and a benign lesion group (n=10), and the malignant tumor group was subdivided into an invasive adenocarcinoma group (n=60) and a non-invasive adenocarcinoma group (n=20). The malignant probability and doubling time of each group were compared and its ability to predict the benign and malignant nodules and the invasion degree was analyzed.ResultsBetween the malignant tumor group and the benign lesion group, the malignant probability was significantly different, and the malignant probability could better distinguish malignant nodules and benign lesions (87.2%±9.1% vs. 28.8%±29.0%, P=0.000). The area under the curve (AUC) was 0.949. The maximum diameter of nodules in the benign lesion group was significantly longer than that in the malignant tumor group (1.270±0.481 cm vs. 0.990±0.361 cm, P=0.026); the doubling time of benign lesions was significantly longer than that of malignant nodules (1 083.600±258.180 d vs. 527.025±173.176 d, P=0.000), and the AUC was 0.975. The maximum diameter of the nodule in the invasive adenocarcinoma group was longer than that of the non-invasive adenocarcinoma group (1.350±0.355 cm vs. 0.863±0.271 cm, P=0.000), and there was no statistical difference in the probability of malignancy between the invasive adenocarcinoma group and the non-invasive adenocarcinoma group (89.7%±5.7% vs. 86.4%±9.9%, P=0.082). The AUC was 0.630. The doubling time of the invasive adenocarcinoma group was significantly shorter than that of the non-invasive adenocarcinoma group (392.200±138.050 d vs. 571.967±160.633 d, P=0.000), and the AUC was 0.829.ConclusionThe malignant probability and doubling time of lung nodules calculated by the artificial intelligence-assisted diagnosis and treatment system can be used in the assessment of the preoperative benign and malignant lung nodules and the infiltration degree.