ArticleDigital health
Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study.
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Who cites it
12 citing papers in PubMed.
- CT-based Intratumoral Heterogeneity Score for Preoperative Prediction of Lymphovascular Invasion and Prognosis in Lung Adenocarcinoma: A Multicenter Validation and Radiogenomic Interpretation.Annals of surgical oncology · 2026Article
- Topologically distinct intratumoral heterogeneity scores for predicting high-risk pathological grades in invasive lung adenocarcinoma: A multicenter study across four institutions.PLOS digital health · 2026Article
- Ensemble machine learning classifiers based on computed tomography radiomics for predicting spread through air spaces in lung adenocarcinoma: a multicenter retrospective cohort study with transcriptomic interpretation.Quantitative imaging in medicine and surgery · 2026Article
- Topologically distinct 2D and 3D intratumoral heterogeneity scores for preoperatively predicting invasiveness in stage I lung adenocarcinoma: A multicenter study.PLOS digital health · 2026Article
- 3D intratumoral heterogeneity-based quantitative score from chest CT for preoperative prediction of visceral pleural invasion in lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
- Intratumoral heterogeneity score enhances invasiveness prediction in pulmonary ground-glass nodules via stacking ensemble machine learning.Insights into imaging · 2025Article
- Nomogram integrating clinical-radiological and radiomics features for differentiating invasive from non-invasive pulmonary adenocarcinomas presenting as ground-glass nodules.American journal of cancer research · 2025Article
- Intratumoral Heterogeneity Scores as Predictors of Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules: Insights from Explainable Machine Learning-Based Ternary Classification Models.Technology in cancer research & treatmentArticle
- Stacking Model-Based Preoperative Prediction of Lymphovascular Invasion in Lung Adenocarcinoma by Integrating Intratumoral Heterogeneity Score and Clinicoradiological Features: A Multicenter Study.Technology in cancer research & treatmentArticle
- Time-to-Event Machine Learning Model Incorporating MRI-Derived Intratumoral Heterogeneity Score for Predicting Invasive Breast Cancer Recurrence: A Dual-Center Study.Technology in cancer research & treatmentArticle
- Decoding Intratumoral Heterogeneity in Breast Cancer: The Evolution From Radiomics to Topological Quantification for Predicting Lymphovascular Invasion.Cancer control : journal of the Moffitt Cancer CenterReview
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5 authors.
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Abstract
Objective: The invasiveness of lung adenocarcinoma significantly impacts clinical decision-making. However, assessing this invasiveness preoperatively, especially when it manifests as pure ground-glass nodules (pGGN) on CT scans, poses challenges. This study aims to quantify intratumor heterogeneity (ITH) and determine whether the ITH score can enhance the accuracy of invasiveness predictions. Methods: A total of 524 patients with lung adenocarcinomas presenting as pGGN were enrolled in the study, with 177 (33.78%) receiving a pathologic diagnosis of invasiveness. Four diagnostic approaches were developed to predict the invasiveness of lung adenocarcinoma presenting as pGGN: (1) conventional lesion size, (2) ITH score, (3) clinical-radiological features (ClinRad), and (4) integration of the ITH score with ClinRad. ClinRad alone or in combination with the ITH score served as the input for 11 machine learning approaches. The trained models were evaluated in an independent validation cohort, and the area under the curve (AUC) was calculated to assess classification performance. Results: The conventional lesion size showed the lowest performance, with an AUC of 0.826 (95% confidence interval [CI]: 0.758-0.894), while the ITH score outperformed it with an AUC of 0.846 (95% CI: 0.787-0.905). The CatBoost model performed best when the ITH score and ClinRad were both used as input features, leading to the development of an ITH-ClinRad-guided CatBoost classifier. CatBoost also excelled with ClinRad alone, resulting in a ClinRad-guided CatBoost classifier with an AUC of 0.830 (95% CI: 0.764-0.896), surpassed by the ITH-ClinRad-guided CatBoost classifier with an AUC of 0.871 (95% CI: 0.818-0.924). Conclusion: The ITH-ClinRad-guided CatBoost classifier emerges as a promising tool with significant potential to revolutionize the management of lung adenocarcinomas presenting as pGGNs.
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