ReviewQuantitative imaging in medicine and surgery2024
Current status and quality of prognosis prediction models of non-small cell lung cancer constructed using computed tomography (CT)-based radiomics: a systematic review and radiomics quality score 2.0 assessment.
Review in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Radiomics in Lung Cancer Imaging: A Narrative Review of Current Evidence.Journal of imaging · 2026Review
- Exploring the evolving landscape of radiomics in lung cancer: a comprehensive bibliometric analysis [2008-2024].Quantitative imaging in medicine and surgery · 2025Article
- A novel computed tomography-based multi-parameter decision tree algorithm model for preoperatively predicting the risk of lymph node metastasis in surgically resectable synchronous multiple primary lung cancer.Quantitative imaging in medicine and surgery · 2025Article
- Predictive modeling for metastasis in oncology: current methods and future directions.Annals of medicine and surgery (2012) · 2025Review
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Authors and funding
4 authors.
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Abstract
Background: Radiomics extracts specific quantitative data from medical images and explores the characteristics of tumors by analyzing these representations and making predictions. The purpose of this paper is to review computed tomography (CT)-based radiomics articles related to prognostic outcomes in non-small cell lung cancer (NSCLC), assess their scientificity and quality by the latest radiomics quality score (RQS) 2.0 scoring criteria, and provide references for subsequent related studies. Methods: CT-based radiomics studies on NSCLC prognosis published from 1 November 2012 to 30 November 2022 in English were screened through the databases of the Cochrane Library, Embase, and PubMed. By excluding criteria such as non-original studies, small sample sizes studies, positron emission tomography (PET)/CT only, and methodological studies only, 17 studies in English were included. The RQS proposed in 2017 is a quality evaluation index specific to radiomics following the PRISMA guidelines, and the latest update of RQS 2.0 has improved the scientificity and completeness of the score. Each checkpoint either belongs to handcrafted radiomics (HCR), deep learning, or both. Results: The 17 included studies covered most treatments for NSCLC, including radiotherapy, chemotherapy, surgery, radiofrequency ablation, immunotherapy, and targeted therapy, and predicted outcomes such as overall survival (OS), progression-free survival (PFS), distant metastases, and disease-free survival (DFS). The median score rate for the included studies was 28%, with a range of 12% to 44%. The quality of studies in HCR is not high, and only 4 studies have been validated with independent cohorts. Conclusions: The value of radiomics studies needs to be increased, such that clinical application will be possible, and the field of radiomics still has much room for growth. To make prediction models more reliable and stable in forecasting the prognosis of NSCLC and advancing the individualized treatment of NSCLC patients, more clinicians must participate in their development and clinical testing.
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