ArticleCancer medicine2018
Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial.
Article in Cancer medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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28 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
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- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.Scientific reports · 2025Article
- Data augmented lung cancer prediction framework using the nested case control NLST cohort.Frontiers in oncology · 2025Article
- Differential Radiomics-Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort.Cancer medicine · 2024Article
- Development and validation of an interpretable delta radiomics-based model for predicting invasive ground-glass nodules in lung adenocarcinoma: a retrospective cohort study.Quantitative imaging in medicine and surgery · 2024Article
- Review
- Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules.Scientific reports · 2023Article
- Deep learning-based algorithm improves radiologists' performance in lung cancer bone metastases detection on computed tomography.Frontiers in oncology · 2023Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Multi-Biomarkers Panel in Identifying Benign and Malignant Lung Diseases and Pathological Types of Lung Cancer.Journal of Cancer · 2023Article
- Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers.Cancer biomarkers : section A of Disease markers · 2022Article
- Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging.European radiology · 2021Article
- Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine.Cold Spring Harbor perspectives in medicine · 2021Review
- Reproducibility of lung nodule radiomic features: Multivariable and univariable investigations that account for interactions between CT acquisition and reconstruction parameters.Medical physics · 2021Article
- Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer.Frontiers in oncology · 2021Article
- Radiomics Improves Cancer Screening and Early Detection.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Review
- Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future.Computers in biology and medicine · 2020Article
- Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening.Scientific reports · 2020Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundCurrent guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow-up screens and performed size-specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm [small], 6-16 mm [intermediate], and ≥16 mm [large]).
methodsWe extracted 219 features from baseline (T0) nodules and 219 delta features which are the change from T0 to first follow-up (T1). Nodules were identified for 160 incidence cases diagnosed with lung cancer at T1 or second follow-up screen (T2) and for 307 nodule-positive controls that had three consecutive positive screens not diagnosed as lung cancer. The cases and controls were split into training and test cohorts; classifier models were used to identify the most predictive features.
resultsThe final models revealed modest improvements for baseline and delta features when compared to only baseline features. The AUROCs for small- and intermediate-sized nodules were 0.83 (95% CI 0.76-0.90) and 0.76 (95% CI 0.71-0.81) for baseline-only radiomic features, respectively, and 0.84 (95% CI 0.77-0.90) and 0.84 (95% CI 0.80-0.88) for baseline and delta features, respectively. When intermediate and large nodules were combined, the AUROC for baseline-only features was 0.80 (95% CI 0.76-0.84) compared with 0.86 (95% CI 0.83-0.89) for baseline and delta features.
conclusionsWe found modest improvements in predicting lung cancer incidence by combining baseline and delta radiomics. Radiomics could be used to improve current size-based screening guidelines.
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