ReviewDiagnostics (Basel, Switzerland)2020
Imaging-Based Prediction of Molecular Therapy Targets in NSCLC by Radiogenomics and AI Approaches: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled 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 Robust Deep Learning Approach for COPD Automated Detection.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence inCancers · 2026Review
- Noninvasive assessment of core metastatic genes in lung adenocarcinoma: development of a predictive model integrating single-cell transcriptomics and radiomics.Frontiers in oncology · 2026Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Radiomic features and tumor immune microenvironment associated with anaplastic lymphoma kinase-rearranged lung adenocarcinoma and their prognostic value.Frontiers in genetics · 2025Article
- Dual-Phase Enhanced CT-Derived Radiomics Nomogram for Progression-Free Survival Prediction in Stage IV Lung Adenocarcinoma.Cancer medicine · 2024Article
- From pixels to predictions: the scrying power of molecular imaging.European journal of nuclear medicine and molecular imaging · 2024Article
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Radiomics based onFrontiers in oncology · 2024Article
- MRI/RNA-Seq-Based Radiogenomics and Artificial Intelligence for More Accurate Staging of Muscle-Invasive Bladder Cancer.International journal of molecular sciences · 2023Article
- Article
- Establishing a predictive model for tumor mutation burden status based on CT radiomics and clinical features of non-small cell lung cancer patients.Translational lung cancer research · 2023Article
- Multiparameter prediction model of immune checkpoint inhibitors combined with chemotherapy for non-small cell lung cancer based on support vector machine learning.Scientific reports · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer.Journal of personalized medicine · 2022Article
- Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.Journal of clinical medicine · 2022Article
- Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future.Diagnostics (Basel, Switzerland) · 2022Review
- Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas.The Journal of investigative dermatology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this systematic review was to analyze the current state of the art of imaging-derived biomarkers predictive of genetic alterations and immunotherapy targets in lung cancer. We included original research studies reporting the development and validation of imaging feature-based models. The overall quality, the standard of reporting and the advancements towards clinical practice were assessed. Eighteen out of the 24 selected articles were classified as "high-quality" studies according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The 18 "high-quality papers" adhered to Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) with a mean of 62.9%. The majority of "high-quality" studies (16/18) were classified as phase II. The most commonly used imaging predictors were radiomic features, followed by visual qualitative computed tomography (CT) features, convolutional neural network-based approaches and positron emission tomography (PET) parameters, all used alone or combined with clinicopathologic features. The majority (14/18) were focused on the prediction of epidermal growth factor receptor (EGFR) mutation. Thirty-five imaging-based models were built to predict the EGFR status. The model's performances ranged from weak (n = 5) to acceptable (n = 11), to excellent (n = 18) and outstanding (n = 1) in the validation set. Positive outcomes were also reported for the prediction of ALK rearrangement, ALK/ROS1/RET fusions and programmed cell death ligand 1 (PD-L1) expression. Despite the promising results in terms of predictive performance, image-based models, suffering from methodological bias, require further validation before replacing traditional molecular pathology testing.
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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.