ArticleBMC bioinformatics2020
A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures.Radiation oncology (London, England) · 2022Trial
- Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis.BMC medical imaging · 2025Article
- Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma.Radiation oncology (London, England) · 2024Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- AI Evaluation of Imaging Factors in the Evolution of Stage-Treated Metastases Using Gamma Knife.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- How Machine Learning is Powering Neuroimaging to Improve Brain Health.Neuroinformatics · 2022Review
- The CT delta-radiomics based machine learning approach in evaluating multiple primary lung adenocarcinoma.BMC cancer · 2022Article
- A diagnosis model in nasopharyngeal carcinoma based on PET/MRI radiomics and semiquantitative parameters.BMC medical imaging · 2022Article
- Radiomics Analysis on Gadoxetate Disodium-Enhanced MRI Predicts Response to Transarterial Embolization in Patients with HCC.Diagnostics (Basel, Switzerland) · 2022Article
- Review
- Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation.Korean journal of radiology · 2022Article
- Automatic detection and delineation of pediatric gliomas on combined [Frontiers in nuclear medicine · 2022Article
- Fully automated identification of brain abnormality from whole-body FDG-PET imaging using deep learning-based brain extraction and statistical parametric mapping.EJNMMI physics · 2021Article
- Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images.Journal of imaging · 2021Article
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Reproducibility of Baseline Tumour Metabolic Volume Measurements in Diffuse Large B-Cell LymphomA: Is There a Superior Method?Metabolites · 2021Article
- Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging.Applied sciences (Basel, Switzerland) · 2021Article
- The Role of the Immune Response in Brain Metastases: Novel Imaging Biomarkers for Immunotherapy.Frontiers in oncology · 2021Review
- Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.PloS one · 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
9 authors.
Funding
Abstract
backgroundPositron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure. This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[
resultsFor predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUV
conclusionsThe proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.
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.