ArticleJournal of thoracic imaging2020
A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study.
Article in Journal of thoracic imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for COVID-19: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Development and multicentric external validation of a prognostic COVID-19 severity model based on thoracic CT.BMC medical informatics and decision making · 2025Article
- Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics.Bioengineering (Basel, Switzerland) · 2025Article
- A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia.BMC medical imaging · 2024Article
- Novel machine-learning analysis of SARS-CoV-2 infection in a subclinical nonhuman primate model using radiomics and blood biomarkers.Scientific reports · 2023Article
- Artificial Intelligence and Infectious Disease Imaging.The Journal of infectious diseases · 2023Article
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- A CT-Based Radiomics Model for Prediction of Prognosis in Patients with Novel Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Study.Diagnostics (Basel, Switzerland) · 2023Article
- Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.Academic radiology · 2022Article
- COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.Computers in biology and medicine · 2022Article
- Clinical Characteristics of COVID-19 Patients and Application to an Artificial Intelligence System for Disease Surveillance.Journal of clinical medicine · 2022Article
- An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study.Frontiers in radiology · 2022Article
- Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia.La Radiologia medica · 2021Article
- Artificial intelligence-driven assessment of radiological images for COVID-19.Computers in biology and medicine · 2021Review
- CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias.La Radiologia medica · 2021Article
- The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.Diagnostics (Basel, Switzerland) · 2021Review
- CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions.The British journal of radiology · 2021Article
- Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study.Abdominal radiology (New York) · 2021Article
- Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.European journal of radiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection.
methodsThe clinical and imaging data of 64 patients with confirmed diagnoses of COVID-19 were retrospectively selected and divided into a stable group and a progressive group according to the data obtained from the ongoing treatment process. Imaging features from whole-lung images from baseline computed tomography (CT) scans were extracted and dimensionality reduction was performed. Support vector machines were used to construct radiomics signatures and to compare differences between the 2 groups. We also compared the differences of signature scores in the clinical, laboratory, and CT image feature subgroups and finally analyzed the correlation between the radiomics features of the constructed signature and the other features including clinical, laboratory, and CT imaging features.
resultsThe signature has a good classification effect for the stable group and the progressive group, with area under curve, sensitivity, and specificity of 0.833, 80.95%, and 74.42%, respectively. Signature score differences in laboratory and CT imaging features between subgroups were not statistically significant (P>0.05); cough was negatively correlated with GLCM Entropy_angle 90_offset4 (r=-0.578), but was positively correlated with ShortRunEmphhasis_AllDirect_offset4_SD (r=0.454); C-reactive protein was positively correlated with Cluster Prominence_ AllDirect_offset 4_ SD (r=0.47).
conclusionThe radiomics signature of the whole lung based on machine learning may reveal the changes of lung microstructure in the early stage and help to indicate the progression of the disease.
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.