A retrospective diagnostic test study on circulating tumor cells and artificial intelligence imaging in patients with lung adenocarcinoma

BACKGROUND: Either tumor volume or folate-receptor-positive circulating tumor cells (FR(+)CTC) has been proven effective in predicting tumor cell invasion. However, it has yet to be documented to use FR(+)CTC along with artificial intelligence (AI) tumor volume to differentiate between pathological subtypes of lung adenocarcinoma (LUAD). Therefore, this study is aimed to evaluate the accuracy of FR(+)CTC and AI tumor volume for classifying the invasiveness of LUAD. METHODS: A total of 226 patients who were diagnosed with LUAD were enrolled. The inclusion criteria were: (I) FR(+)CTC detection and AI imaging before anticancer therapy, and (II) definite histopathologic diagnosis, which is the gold diagnosis of LUAD and its subtypes. Use the CytoploRare(®) Detection Kit to quantify FR(+)CTC and the AI-assisted diagnosis system, ScrynPro, to measure tumor volume. The clinical data were used to construct univariate and multivariate logistic regression models. A nomogram was drawn based on the multivariate logistic regression model. The validity is evaluated by the calibration curve and Hosmer-Lemeshow goodness-of-fit test. RESULTS: The mean age of 146 patients (96 males, 49 females and 1 gender missing) retrospectively enrolled was 56.6. In the cohort, 41 and 105 patients were assigned to adenocarcinoma in situ (AIS) + minimally invasive adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IPA), respectively. There was no significant difference between the sex distribution and smoking history of the two groups (P=0.155 and P=0.442, respectively). In univariate analysis, the nodules type, maximum density, tumor volume and FR(+)CTC level were statistically significant with the invasiveness of LUAD (P<0.05). The multivariate analysis showed significant differences in FR(+)CTC and AI tumor volume (P<0.001). The area under the curves (AUCs) of FR(+)CTC and AI tumor volume in diagnosing tumor invasiveness were 0.659 and 0.698, respectively. A predictive model combining FR(+)CTC with AI tumor volume showed a sensitivity of 86.89% and a specificity of 70.94%, and the AUC was 0.841. The nomogram had good agreement with actual observation, and the Hosmer-Lemeshow test yielded non-significant goodness-of-fit. CONCLUSIONS: FR(+)CTC and/or AI tumor volume are independent indicators of the invasiveness of LUAD, and the nomogram based on them can be used for the preoperative screening of patients.