ReviewLung cancer (Amsterdam, Netherlands)2020
Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.
Review in Lung cancer (Amsterdam, Netherlands), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 6 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
71 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.European radiology · 2023Pooled it
- Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.European journal of nuclear medicine and molecular imaging · 2022Pooled it
- Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.The British journal of radiology · 2021Pooled it
- A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.European radiology · 2021Pooled it
- Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study.JMIR formative research · 2026Article
- Feature Selection and Machine Learning Strategies for CT Radiomics-Based Survival Prediction in Non-Small Cell Lung Cancer: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Application Value of a nomogram integrating contrast-enhanced CT radiomics and clinical indicators in evaluating lymph node metastasis in pediatric peripheral neuroblastoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- The diagnostic value of enhanced CT radiomics and deep learning in differentiating pediatric peripheral neuroblastoma from ganglioneuroblastoma.BMC cancer · 2026Article
- Exploring the evolving landscape of radiomics in lung cancer: a comprehensive bibliometric analysis [2008-2024].Quantitative imaging in medicine and surgery · 2025Article
- Mapping the future: bibliometric analysis of omics research trends in non-small cell lung cancer.Discover oncology · 2025Article
- Article
- Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review.Cureus · 2025Review
- Prognostic modeling in head and neck cancer: deep learning or handcrafted radiomics?BJR artificial intelligence · 2025Review
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.Insights into imaging · 2024Article
- Multicentric development and evaluation of [European journal of nuclear medicine and molecular imaging · 2024Article
- Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis.NPJ precision oncology · 2024Article
- Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with 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.