Integrated CT Radiomics and Circulating Tumor Cell Analysis in Predicting Lung Adenocarcinoma Invasion: A Dual-Center...

This study aimed to construct a risk prediction model based on radiomics, circulating tumor cells (CTCs), and dual-center clinical data to predict the invasiveness of lung adenocarcinoma, specifically for discriminating between minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). The clinical value of this model in the precise diagnosis of early-stage lung adenocarcinoma was investigated to provide a reference for formulating reasonable treatment plans.

Clinical data, imaging data, CTCs, and pathological information from 202 patients with lung adenocarcinoma were retrospectively collected and analyzed from two medical centers between May 2022 and July 2023. The 146 cases from medical center 1 were randomly divided into a development set and an internal test set at a 3:2 ratio. The 56 cases from medical center 2 served as an external validation set. Machine learning was employed to analyze preoperative CTC counts and CT radiomic features. A feature selection method based on LASSO regression (with λ determined by the minimum criterion) was used to screen out 12 radiomic features. These features were subsequently incorporated into logistic regression to construct three prediction models: (1) a radiomics model based on radiomic features; (2) a CTCs-clinical data model based on the total development set; and (3) a composite clinical data-radiomics-CTCs model integrating the former two. The optimal model was selected to construct a nomogram. Its goodness-of-fit was assessed using a calibration curve (Hosmer-Lemeshow goodness-of-fit test), and its predictive performance was validated in the external validation set.

A total of 107 radiomic features were extracted and categorized into 7 groups: 18 (16.8%) first-order features, 24 (22.4%) gray-level co-occurrence matrix (GLCM) features, 14 (13.1%) gray-level dependence matrix (GLDM) features, 16 (15.0%) each for gray-level run length matrix (GLRLM) and gray-level size zone matrix (GLSZM) features, 5 (4.7%) neighboring gray-tone difference matrix (NGTDM) features, and the remaining (13.1%) were shape-based features. In the total development set, significant differences were observed in clinical-imaging semantic features including CEA, CK19, CTC count, and lesion diameter, which were used to construct the clinical model. The area under the curve (AUC) for the radiomics model was 0.896 95% CI:0.832-0.960. The CTCs-clinical model demonstrated superior performance AUC:0.960, 95% CI:0.926-0.994. The composite clinical-radiomics-CTCs model showed the highest predictive accuracy AUC:0.980, 95% CI:0.960-1.000. According to decision curve analysis and the Akaike information criterion, the composite clinical-radiomics-CTCs model outperformed any single clinical or radiomic feature in terms of clinical predictive capability.

For assessing the invasiveness of early-stage lung adenocarcinoma, the radiomics approach can effectively discriminate between MIA and IAC. However, compared to single-modality methods, the composite clinical-radiomics-CTCs model offers a novel auxiliary diagnostic method for evaluating the risk of invasiveness in early-stage lung cancer.