ReviewCold Spring Harbor perspectives in medicine2021
Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine.
Review in Cold Spring Harbor perspectives in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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
80 citing papers in PubMed, 107 citations in OpenAlex.
- Prediction of distant metastasis in esophageal cancer using a radiomics-clinical model.European journal of medical research · 2022Trial
- Review
- High-fidelity super-resolution CT radiomics for non-invasive EGFR mutation prediction in lung adenocarcinoma: a multi-center pooled analysis.La Radiologia medica · 2026Observational
- Review
- Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.BMC pulmonary medicine · 2026Article
- Advances in translational lung cancer research in 2025: a narrative review.Translational lung cancer research · 2026Review
- Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.Multidisciplinary respiratory medicine · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Theoretical applications of artificial intelligence in smart infusion pump technology: Expert panel insights.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2026Article
- Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.Cancer research · 2026Article
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
- Explainable deep learning approaches and clinical insights for cancer biomarker identification.Frontiers in oncology · 2026Review
- Non-invasive Prediction of Lung Cancer Histological Differentiation via Radiomics and Multi-binary Classification ModelsCurrent medical imaging · 2026Article
- Exploring the evolving landscape of radiomics in lung cancer: a comprehensive bibliometric analysis [2008-2024].Quantitative imaging in medicine and surgery · 2025Article
- Harnessing multi-omics approaches to decipher tumor evolution and improve diagnosis and therapy in lung cancer.Biomarker research · 2025Review
- [Advances in radiomics for early diagnosis and precision treatment of lung cancer].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- A Comprehensive Review on the Application of Artificial Intelligence for Predicting Postsurgical Recurrence Risk in Early-Stage Non-Small Cell Lung Cancer Using Computed Tomography, Positron Emission Tomography, and Clinical Data.Journal of medical radiation sciences · 2025Review
- Virtual biopsy through CT imaging: can radiomics differentiate between subtypes of non-small cell lung cancer?La Radiologia medica · 2025Article
- Plasma-derived circALG8 and circCAMTA1 as a panel for early diagnosis of non-small cell lung cancer.Biomarkers in medicine · 2025Article
20 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
3 authors at 1 institution in 1 country.
Funding
Abstract
Medical imaging is the standard-of-care for early detection, diagnosis, treatment planning, monitoring, and image-guided interventions of lung cancer patients. Most medical images are stored digitally in a standardized Digital Imaging and Communications in Medicine format that can be readily accessed and used for qualitative and quantitative analysis. Over the several last decades, medical images have been shown to contain complementary and interchangeable data orthogonal to other sources such as pathology, hematology, genomics, and/or proteomics. As such, "radiomics" has emerged as a field of research that involves the process of converting standard-of-care images into quantitative image-based data that can be merged with other data sources and subsequently analyzed using conventional biostatistics or artificial intelligence (AI) methods. As radiomic features capture biological and pathophysiological information, these quantitative radiomic features have shown to provide rapid and accurate noninvasive biomarkers for lung cancer risk prediction, diagnostics, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics and emerging AI methods in lung cancer research are highlighted and discussed including advantages, challenges, and pitfalls.
Indexed as
Identifiers
What OpenQuestion holds
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.