ArticleScientific reports2024
Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Multi-habitat radiomics on T2-FS MRI for identifying spatial patterns of axial spondyloarthritis-associated bone marrow edema.Clinical rheumatology · 2026Article
- A radiomics-based machine learning model for the preoperative differentiation of lung adenocarcinoma subtypes.Quantitative imaging in medicine and surgery · 2026Article
- Development and validation of a radiomics-habitat model for preoperatively predicting poorly differentiated stage IA lung adenocarcinoma.Journal of thoracic disease · 2026Article
- Habitat model based on CEUS for noninvasive prediction of EGFR mutation status in peripheral NSCLC.BMC cancer · 2026Article
- Heterogeneity Analyzed by CT-Based Habitat Analysis for Clinical Management of Cancers: A Narrative Review.The Kaohsiung journal of medical sciences · 2026Review
- Multiparametric MRI-Based Habitat Radiomics Combined with Deep Transfer Learning for Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma.Journal of imaging informatics in medicine · 2026Article
- Development of a Prediction Model for Progression Risk in High-Grade Gliomas Based on Habitat Radiomics and Pathomics.Annals of clinical and translational neurology · 2026Article
- MRI-based habitat and peritumoral radiomics for predicting HER2 status in breast cancer: a multicenter study.Scientific reports · 2026Article
- Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.European journal of nuclear medicine and molecular imaging · 2026Article
- Review
- Temporal-spatial evolution of tumor habitat analysis: a bibliometric study on research hotspots and trends in medical imaging (2014-2025).Translational cancer research · 2026Article
- A multicenter study: habitat imaging and radiomics to guide precision and individualized surgical treatment in chronic osteomyelitis.BMC medical imaging · 2026Article
- Commentary on "Intratumoral and peritumoral heterogeneity based on CT to predict the pathological response after neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma".International journal of surgery (London, England) · 2026Article
- The predictive value ofFrontiers in medicine · 2026Article
- Habitat Radiomics Analysis Based on Non-contrast CT in Differentiation of Parotid Pleomorphic Adenoma and AdenolymphomaCurrent medical imaging · 2026Article
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- Contrast-enhanced CT-based habitat radiomics for distinguishing low-risk thymomas from high-risk thymomas: a multicenter study.Japanese journal of radiology · 2026Article
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9 authors.
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Abstract
Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included 438 patients who underwent curative surgery and completed driver-gene mutation tests for stage I NSCLC from four academic medical centers. Predictive models were established by extracting and analyzing radiomic features in intratumoral, peritumoral, and habitat regions of CT images to identify EGFR mutation status in stage I NSCLC. Additionally, three deep learning models based on the intratumoral region were constructed. A nomogram was developed by integrating representative radiomic signatures, deep learning, and clinical features. Model performance was assessed by calculating the area under the receiver operating characteristic (ROC) curve. The established habitat radiomics features demonstrated encouraging performance in discriminating between EGFR mutant and wild-type, with predictive ability superior to other single models (AUC 0.886, 0.812, and 0.790 for the training, validation, and external test sets, respectively). The radiomics-based nomogram exhibited excellent performance, achieving the highest AUC values of 0.917, 0.837, and 0.809 in the training, validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the nomogram provided a higher net benefit than other radiomics models, offering valuable information for treatment.
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