ReviewTranslational lung cancer research2023
Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer.
Review in Translational lung cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Radiomics-Based AI for Predicting Neoadjuvant Immunochemotherapy Pathological Response in Non-Small Cell Lung Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- 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
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Externally validated explainable machine learning for postoperative recurrence prediction in early-stage NSCLC.Frontiers in oncology · 2026Article
- Applying Radiomics to Predict Outcomes in Patients with High-Grade Retroperitoneal Sarcoma Treated with Preoperative Radiotherapy.Journal of imaging · 2025Article
- A Vision Toward Risk-Stratified Postoperative Surveillance for Lung Cancer: Harnessing CT and AI.Journal of the American College of Radiology : JACR · 2025Article
- MetaPredictomics: A Comprehensive Approach to Predict Postsurgical Non-Small Cell Lung Cancer Recurrence Using Clinicopathologic, Radiomics, and Organomics Data.Clinical nuclear medicine · 2025Article
- Artificial Intelligence as a Diagnostic Tool in Preoperative Surgical Planning for Early Non-Small Cell Lung Cancer: A Single-Center Experience.Journal of clinical medicine · 2025Article
- Review
- Utilizing Clinicopathological and Radiomic Features for Risk Stratification of Lung Cancer Recurrence.Academic radiology · 2025Article
- Effectiveness of Artificial Intelligence Models in Predicting Lung Cancer Recurrence: A Gene Biomarker-Driven Review.Cancers · 2025Review
- Radiomic Analysis of Molecular Magnetic Resonance Imaging of Aortic Atherosclerosis in Rabbits.Current medical science · 2025Article
- Personalized prediction model for scar response after radionuclide therapy: development and validation in a Chinese cohort.Frontiers in medicine · 2025Article
- Circulating Tumor cells and multiple indicators combined to identify the risk of poorer prognosis in patients with resected non-small cell lung cancer.BMC cancer · 2024Article
- Correlations between 14-gene RNA-level assay and clinical and molecular features in resectable non-squamous non-small cell lung cancer: a cross-sectional study.Translational lung cancer research · 2024Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Computed tomography radiomics study of invasion and instability of lung adenocarcinoma manifesting as ground glass nodule.Journal of thoracic disease · 2024Article
- Predictors of disease recurrence after radical resection and adjuvant chemotherapy in patients with stage IIb-IIIa squamous cell lung cancer: A retrospective analysis.World journal of experimental medicine · 2024Article
- Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Evaluating peritumoral and intratumoral radiomics signatures for predicting lymph node metastasis in surgically resectable non-small cell lung cancer.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Radiomics is an emerging field of advanced image analysis that has shown promise as a non-invasive, companion diagnostic in predicting clinical outcomes and response assessments in solid tumors. Radiomics aims to extract high-content information from medical images not visible to the naked eye, especially in early-stage non-small cell lung cancer (NSCLC) patients. Although these patients are being identified by early detection programs, it remains unclear which patients would benefit from adjuvant treatment versus active surveillance. Having a radiomic signature(s) that could predict early recurrence would be beneficial. In this review, an overview of the basic radiomic approaches used to evaluate solid tumors on radiologic scans, including NSCLC is provided followed by a review of relevant literature that supports the use of radiomics to help predict tumor recurrence in early-stage NSCLC patients. Methods: A review of the radiomic literature from 1985 to present focusing on the prediction of disease recurrence in early-stage NSCLC was conducted. PubMed database was searched using key terms for radiomics and NSCLC. A total of 41 articles were identified and 13 studies were considered suitable for inclusion based upon study population, patient number (n>50), use of well described radiomic methodologies, suitable model building features, and well-defined testing/training and validation where feasible. Key Content and Findings: Examples of using radiomics in early-stage NSCLC patients will be presented, where disease free survival is a primary consideration. A summary of the findings demonstrates the importance of both the intratumor and peritumoral radiomic signals as a marker of outcomes. Conclusions: The value of radiomic information for predicting disease recurrence in early-stage NSCLC patients is accumulating. However, overcoming several challenges along with the lack of prospective trials, has inhibited it use as a clinical decision-making support tool in early-stage NSCLC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.