SynthesisEuropean radiology2023
Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.
Synthesis in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- A Novel Tumor Burden Index to Predict the Prognosis in Stage IA Lung Adenocarcinoma.Annals of surgical oncology · 2026Article
- Immediate post-microwave ablation computed tomography model may better predict short-term response of stage I non-small cell lung cancer.World journal of radiology · 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
- Radiomics in Lung Cancer Imaging: A Narrative Review of Current Evidence.Journal of imaging · 2026Review
- Article
- Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.medRxiv : the preprint server for health sciences · 2026Article
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
- Construction of a postoperative disease-free survival prediction model for non-small cell lung cancer patients based on dual-energy computed tomography parameters and blood inflammatory indicators.Translational cancer research · 2026Article
- CT-based subregional and peritumoral radiomics for predicting pathological T stage of clear cell renal cell carcinoma: an exploratory study of biological mechanisms.Insights into imaging · 2026Article
- Quantitative peri-lesional densitometry mapping via thin-slice volume rendering enhances differentiation of pneumonic-type lung cancer and inflammatory pneumonia.Frontiers in oncology · 2026Article
- Foundation model-enhanced multimodal radiomics for predicting response to chemo-immunotherapy in advanced lung squamous cell carcinoma.Frontiers in oncology · 2026Article
- Noninvasive prediction of Glypican-3 expression in hepatocellular carcinoma using Habitat-based and peritumoral CT radiomics: a nomogram approach.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Article
- Research on the Precise Differentiation of Pathological Subtypes of Non-Small Cell Lung Cancer Based onCancers · 2025Article
- A radiomics-based machine learning model and SHAP for predicting spread through air spaces and its prognostic implications in stage I lung adenocarcinoma: a multicenter cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Utilizing Clinicopathological and Radiomic Features for Risk Stratification of Lung Cancer Recurrence.Academic radiology · 2025Article
- Exploring the Predictive Value of Grading in Regions Beyond Peritumoral Edema in Gliomas Based on Radiomics.Current medical imaging · 2025Article
- Article
- Radiomic Feature Characteristics of Ovine Pulmonary Adenocarcinoma.Veterinary sciences · 2025Article
- Prediction of radiosensitivity in non-small cell lung cancer based on computed tomography and tumor genomics: a multiple real world cohort study.Respiratory research · 2025Article
Corrections and comments
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5 authors.
Funding
Abstract
objectivesTo provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies.
methodsThe PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST).
resultsThirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4-19), while all of included studies were assessed as having a high risk of bias (ROB) overall.
conclusionsPeritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed. KEY POINTS: • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4-19), and all of studies were considered to have a high risk of bias overall.
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