ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023
Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Accuracy of Radiomics-Based Machine Learning for Predicting Risk of Recurrence in Non-Small Cell Lung Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Interpretable machine learning model integrating delta-radiomics enhances postoperative recurrence prediction in early-stage lung adenocarcinoma.Journal of thoracic disease · 2026Article
- An explainable multimodal 2.5D deep learning-radiomics model for predicting extranodal extension in lung adenocarcinoma using preoperative CT: a multicenter retrospective cohort study.BMC medical imaging · 2026Article
- Advancing Lung Cancer Imaging with Self-Supervised Foundation Models.Radiology. Imaging cancer · 2026Article
- A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.Radiology. Imaging cancer · 2026Article
- Preoperative CT-based Radiomics for Predicting Response to Neoadjuvant Chemoimmunotherapy in Esophageal Squamous Cell Carcinoma.Radiology. Imaging cancer · 2026Article
- CT-based intratumoral habitat and peritumoral radiomics model to predict spread through air spaces in solid lung adenocarcinoma with diameter ≤ 2 cm: a dual-center study.Frontiers in oncology · 2026Article
- CT-based body composition and inflammatory nutritional biomarker nomogram for predicting early postoperative recurrence of non-small cell lung cancer: a multicenter study.Journal of thoracic disease · 2025Article
- A deep learning-clinical combined model with SHapley Additive exPlanations (SHAP) method for assessing the tumor spread through air spaces in lung adenocarcinoma: a multicohort retrospective study.Quantitative imaging in medicine and surgery · 2025Article
- CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study.Insights into imaging · 2025Article
- Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adenocarcinoma: a retrospective pilot study.Frontiers in oncology · 2025Article
- Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma.Frontiers in oncology · 2025Article
- Development and validation of machine learning models for predicting STAS in stage I lung adenocarcinoma with part-solid and solid nodules: a two-center study.Frontiers in oncology · 2025Article
- Preoperative CT-based Intratumoral and Peritumoral Radiomics Prediction for Vasculogenic Mimicry in Lung Adenocarcinoma.Current medical imaging · 2025Article
- Performance of deep learning model and radiomics model for preoperative prediction of spread through air spaces in the surgically resected lung adenocarcinoma: a two-center comparative study.Translational lung cancer research · 2024Article
- Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study.Gland surgery · 2024Article
- Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A deep learning approach for predicting visceral pleural invasion in cT1 lung adenocarcinoma.Journal of thoracic disease · 2024Article
- CT-based radiomics and clinical characteristics for predicting bone metastasis in lung adenocarcinoma patients.Translational lung cancer research · 2024Article
- Prediction of iodine-125 seed implantation efficacy in lung cancer using an enhanced CT-based nomogram model.PloS one · 2024Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). MATERIALS AND
methodsThe most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort.
resultsA total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762-0.932) and validation cohort (AUC = 0.817, 95% CI 0.625-1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits.
conclusionWe pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies.
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