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

Abstract

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.

The development and validation of a circulating tumor cells-based integrated model for improving the indeterminate lung solid nodules diagnosis

“…Several recent studies have reported that the combination of CTCs and additional dimensional features (e.g., nodules radiological size, serum tumor marker) could significantly improve the differentiation ability of small lung nodules with high sensitivity and specificity ( 13 , 17 , 19 ). Besides, the combination of CTCs and artificial intelligence imaging was also shown to be an independent indicator for lung adenocarcinoma invasiveness in our previously published study ( 20 ). However, compared with traditional radiological features, radiomics has been shown to be a more effective clinical application tool for differentiating lung nodules in the early screening for lung cancer ( 21 ), since it can quickly extract a larger number of quantitative features from radiological images using high-throughput calculations ( 22 - 24 ).…”

Recent Progress in Enhanced Cancer Diagnosis, Prognosis, and Monitoring Using a Combined Analysis of the Number of Circulating Tumor Cells (CTCs) and Other Clinical Parameters

“…Based on these past experiences, an attempt was made to combine the number of FR + CTCs with the AI-assisted diagnosis tumor volume system (ScrynPro) to predict lung cancers with different invasive abilities. After the analysis, compared with using FR + CTCs and tumor volume alone, the combination of the two can be used to predict the invasion ability of lung cancer more accurately (AUC = 0.841) [153].…”

Multi-classification-assisted diagnosis of multi-scale lung adenocarcinoma pathological maps based on Scalenet101

“…In medical image proce ssing, DL techniques have advantages such as high accuracy and strong adapta bility. In recent years, with the rapid development of DL technology, an increa sing number of scholars have harnessed it for medical domain, and great stride s have been made [21][22][23] . However，there are relatively few studies on the constr uction of lung adenocarcinoma data set and intelligent detection，which conflict s with the urgent treatment needs.…”