Combining tumor habitat radiomics and circulating tumor cell data for predicting high-grade pathological components in lung adenocarcinoma

Abstract

Objectives

Lung cancer is the leading cause of cancer incidence and mortality. Early surgical resection significantly improves patient prognosis. Studies have shown that high-grade components in lung adenocarcinoma (LUAD) severely impact patient outcomes. Therefore, early prediction of these high-grade components is crucial for clinical surgical decision-making.

Methods

We delineated tumor subregions using k-means clustering and excluded features with low reproducibility by calculating the intraclass correlation coefficient (ICC). Stratified sampling ensured a consistent sample distribution between the training and testing datasets, and Borderline synthetic minority over-sampling technique (BorderlineSMOTE) addressed the data imbalance in the training dataset. Normality tests were conducted, followed by feature selection using independent sample t tests, Mann‒Whitney U tests, and Spearman rank correlation. Principal component analysis (PCA) of reduced dimensionality and model integration were performed using a stacking approach. Model predictive performance was evaluated using the area under the curve (AUC), and significant differences between models were assessed using the DeLong test.

Results

The combined Habitat-circulating tumor cell (CTC) model showed the best predictive performance for high-grade components in both the training and validation datasets, achieving AUCs of 0.98 [95 % CI: 0.95–1.00] and 0.91 [95 % CI: 0.82–1.00], respectively. In the training dataset, the combined model's AUC of 0.98 [95 % CI: 0.95–1.00] was notably higher than that of the single CTC model, which achieved an AUC of 0.75 [95 % CI: 0.64–0.85], and the single sub-region model, which had an AUC of 0.94 [95 % CI: 0.88–1.00]. Decision-curve analysis demonstrated maximal net benefit at threshold probabilities of 0.2–0.4. In the independent cohort (n = 29), AUC reached 1.00 [95 % CI: 1.00–1.00].

Conclusion

Combining habitat radiomics and CTC-related clinical models allows for more precise prediction of high-grade pathological components, aiding in clinical preoperative decision-making.