ArticleBMC cancer2025
Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- 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
- Integrated tumor habitat and metabolic hot spot displacement analysis on [¹⁸F]FDG PET/CT for predicting treatment response and progression in unresectable locally advanced NSCLC: a two-center competing risk study.European journal of nuclear medicine and molecular imaging · 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
- 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
- A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images.BMC cancer · 2026Article
- CT-Based Nested Habitats Analysis for Early Recurrence Prediction and Risk Stratification in Hepatocellular Carcinoma: Development and Multicenter Validation Across Four Cohorts.Annals of surgical oncology · 2026Article
- An interpretable machine learning model based on habitat radiomics combined with deep learning for predicting the WHO/ISUP grade of patients with clear cell renal cell carcinoma.BMC medical imaging · 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
- Prediction of tumor-infiltrating lymphocytes through habitat radiomics and exploration of response mechanisms in neoadjuvant immunochemotherapy-treated lung cancer.Cancer immunology, immunotherapy : CII · 2026Article
- Article
- 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
- Explainable multimodal fusion model integrating clinical and quantitative CT features for preoperative prediction of high-grade histologic patterns in invasive lung adenocarcinoma.Frontiers in oncology · 2026Article
- Novel insights into predicting the presence of micropapillary and solid components in stage IA lung adenocarcinoma using machine learning models of modifiable risk factors.Annals of medicine · 2025Article
- Assessment of intratumor heterogeneity in non-small cell lung cancer by unsupervised K-means clustering of radiomics features based on multiphase computed tomography images.Quantitative imaging in medicine and surgery · 2025Article
- Intratumoral heterogeneity score enhances invasiveness prediction in pulmonary ground-glass nodules via stacking ensemble machine learning.Insights into imaging · 2025Article
- Article
- Multiparametric MRI-Derived Habitat Radiomics in Subregional Analysis for Predicting Axillary Lymph Node Metastatic Burden in Breast Cancer.Technology in cancer research & treatmentArticle
- Ensemble Machine Learning Classifiers Combining CT Radiomics and Clinical-Radiological Features for Preoperative Prediction of Pathological Invasiveness in Lung Adenocarcinoma Presenting as Part-Solid Nodules: A Multicenter Retrospective Study.Technology in cancer research & treatmentArticle
- Article
- 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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7 authors.
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Abstract
backgroundThis study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image 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).
methodsIn this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (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.
resultsThe ITH score outperformed both C-radiomics scores and Clin-Rad classification, as evidenced by higher AUC values in the training set (0.820 versus 0.810 and 0.700, p = 0.049 and p = 0.031, respectively) and in the test set (0.805 versus 0.771 and 0.732, p = 0.041 and p = 0.025, respectively). Finally, the hybrid model consistently demonstrated robust predictive capabilities in identifying presence of MP/S components, achieving AUC of 0.830 in the training set and 0.849 in the test set (all p < 0.05).
conclusionThe ITH derived from sub-region within the tumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC.
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