Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma

Abstract

Abstract Background: This study aims to quantify intratumoral heterogeneity(ITH) using preoperative CT scans and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC). Methods: In this retrospective study, 457 patients postoperatively diagnosed with clinical stage I solid LADC were included from two medical centers, comprising a training set (center 1, n=304) and a test set (center 2, n=153). Sub-regions within the tumor were identified using the K-means method. Both intratumoral ecological diversity features (hereafter referred to as ITH) and conventional radiomics (hereafter referred to as C-radiomics) were extracted to generate ITH scores and C-radiomics scores. Next, univariate and multivariate logistic regression analyses were employed to identify clinical-radiological (Clin-Rad) features associated with the MP/S (+) group for constructing the Clin-Rad classification. Subsequently, a hybrid model which presented as a nomogram was developed, integrating the Clin-Rad classification and ITH score. The performance of models was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), accuracy, sensitivity, and specificity were determined. Results: The ITH score outperformed both C-radiomics scores and Clin-Rad classification, as indicated by higher AUC values in the training (0.820 versus 0.810 and 0.700) and test sets (0.805 versus 0.771 and 0.732), respectively. Notably, the hybrid model consistently demonstrated robust predictive capabilities in identifying MP/S (+), achieving AUCs of 0.830 in the training set and 0.849 in the test sets. Conclusion: The ITH of sub-regions within the intratumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC. This finding holds the potential to make a significant contribution to clinical decision-making processes.