Topologically distinct 2D and 3D intratumoral heterogeneity scores for preoperatively predicting invasiveness in stage I lung adenocarcinoma: A multicenter study

This multicenter study aims to enhance the preoperative prediction of pathological invasiveness in clinical stage I lung adenocarcinoma (LUAD) by developing and validating topologically distinct 2D and 3D intratumoral heterogeneity (ITH) scores derived from chest CT imaging. Patients with histopathologically confirmed LUAD were enrolled from three medical centers. We established a dual-scale computational framework to quantify ITH: the 2D ITH score was derived by integrating local radiomics features with global pixel distribution patterns on the largest cross-sectional slice, while the 3D ITH score captured volumetric heterogeneity using a voxel-based topology-aware approach. Subsequently, six machine learning models integrating clinicoradiologic (CR) features with these heterogeneity scores were developed. Model performance was optimized based on the area under the curve (AUC) across a training set and validated in both an internal test set and an independent external validation set. A total of 1,238 eligible patients were enrolled. Centers 1 and 2 provided 1,053 patients (Training: n=737; Internal Test: n=316), while Center 3 provided 185 patients for external validation. The CatBoost classifier integrating 2D/3D ITH scores with CR features (2DITH-3DITH-CR CatBoost) exhibited superior diagnostic performance, achieving AUCs of 0.867 in the internal test set and 0.881 in the external validation set. The integration of topologically distinct 3D ITH scores significantly improves the preoperative stratification of LUAD invasiveness. The 2DITH-3DITH-CR CatBoost model serves as a robust, non-invasive tool to guide individualized surgical decision-making in clinical practice.Author summary: Lung adenocarcinoma is the predominant form of lung cancer, and for early-stage patients, surgical decisions hinge on accurately predicting tumor invasiveness. Distinguishing between non-invasive lesions suitable for limited resection and invasive tumors requiring lobectomy remains challenging with standard subjective CT interpretation. To address this, we developed a quantitative framework that analyzes the internal “texture” and structural complexity of lung nodules. A key innovation of our study is the introduction of a “3D intratumoral heterogeneity score,” which uses advanced topological analysis to map the spatial connectivity and fragmentation of tumor tissue across the entire volume, rather than just a single 2D slice. We integrated these scores into a machine learning model and validated its performance on a large cohort of 1,238 patients from three different medical centers. Our results confirm that this 3D approach significantly outperforms traditional methods in identifying invasive cancer. This non-invasive, robust tool offers clinicians a powerful objective metric to guide personalized surgical planning, helping to avoid overtreatment and preserve vital lung function for patients.