ArticleOncotarget2017
Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 52 citations in OpenAlex.
- Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images.NPJ precision oncology · 2025Article
- Impact of deep learning based reconstruction algorithms on CT radiomic features of carotid plaques.Journal of applied clinical medical physics · 2025Article
- Article
- Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach.Frontiers in oncology · 2024Article
- Combined whole-lesion radiomic and iodine analysis for differentiation of pulmonary tumors.Scientific reports · 2022Article
- Differentiation of adrenal adenomas from adrenal metastases in single-phased staging dual-energy CT and radiomics.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Article
- Robustness of dual-energy CT-derived radiomic features across three different scanner types.European radiology · 2022Article
- Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT.European radiology · 2022Article
- Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy.Frontiers in oncology · 2022Article
- CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation.Journal of clinical medicine · 2021Article
- A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.Journal for immunotherapy of cancer · 2021Article
- Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics.Cancers · 2021Article
- Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes.European radiology · 2021Article
- Article
- CT-Based Radiomics Signature: A Potential Biomarker for Predicting Postoperative Recurrence Risk in Stage II Colorectal Cancer.Frontiers in oncology · 2021Article
- Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT.European radiology · 2020Article
- Radiomics predict postoperative survival of patients with primary liver cancer with different pathological types.Annals of translational medicine · 2020Article
- The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma.BMC cancer · 2020Article
- Review
- Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab.Oncology letters · 2020Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the usefulness of surrogate biomarkers as predictors of histopathologic tumor grade and aggressiveness using radiomics data from dual-energy computed tomography (DECT), with the ultimate goal of accomplishing stratification of early-stage lung adenocarcinoma for optimal treatment.
resultsPathologic grade was divided into grades 1, 2, and 3. Multinomial logistic regression analysis revealed i-uniformity and 97.5th percentile CT attenuation value as independent significant factors to stratify grade 2 or 3 from grade 1. The AUC value calculated from leave-one-out cross-validation procedure for discriminating grades 1, 2, and 3 was 0.9307 (95% CI: 0.8514-1), 0.8610 (95% CI: 0.7547-0.9672), and 0.8394 (95% CI: 0.7045-0.9743), respectively. MATERIALS AND
methodsA total of 80 patients with 91 clinically and radiologically suspected stage I or II lung adenocarcinoma were prospectively enrolled. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. Quantitative CT and PET imaging characteristics were evaluated using a radiomics approach. Significant features for a tumor aggressiveness prediction model were extracted and used to calculate diagnostic performance for predicting all pathologic grades.
conclusionsQuantitative radiomics values from DECT imaging metrics can help predict pathologic aggressiveness of lung adenocarcinoma.
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