ArticleEuropean journal of nuclear medicine and molecular imaging2020
Imaging phenotype using
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Insights into pet-based radiogenomics in oncology: an updated systematic review.European journal of nuclear medicine and molecular imaging · 2025Pooled it
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Stereotactic body radiation therapy for the primary tumor and oligometastases versus the primary tumor alone in patients with metastatic pancreatic cancer.Radiation oncology (London, England) · 2024Article
- Ability ofCancers · 2023Article
- Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review.Healthcare (Basel, Switzerland) · 2022Review
- Therapeutic Aspects and Molecular Targets of Autophagy to Control Pancreatic Cancer Management.Biomedicines · 2022Review
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Review
- Frontiers and hotspots ofFrontiers in oncology · 2022Article
- Review
- Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection.Journal of translational medicine · 2021Article
- Review
- A computed tomography (CT)-derived radiomics approach for predicting primary co-mutations involvingAnnals of translational medicine · 2021Article
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- A Novel Validated Recurrence Stratification System Based onFrontiers in oncology · 2021Article
- The impact of radiomics in diagnosis and staging of pancreatic cancer.Therapeutic advances in gastrointestinal endoscopyReview
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
purposeThis study aimed to determine if major gene mutations including in KRAS, SMAD4, TP53, and CDKN2A were related to imaging phenotype using
methodsData on 48 PDAC patients with pretreatment FDG PET/CT who underwent genomic analysis of their tumor tissue were retrospectively analyzed. A total of 35 unique quantitative radiomic features were extracted from PET images, including imaging phenotypes such as pixel intensity, shape, and textural features. Targeted exome sequencing using a customized cancer panel was used for genomic analysis. To assess the predictive performance of genetic alteration using PET-based radiomics, areas under the receiver operating characteristic curve (AUC) were used.
resultsMutation frequencies were KRAS 87.5%, TP53 70.8%, SMAD4 25.0%, and CDKN2A 18.8%. KRAS gene mutations were significantly associated with low-intensity textural features, including long-run emphasis (AUC = 0.806), zone emphasis (AUC = 0.794), and large-zone emphasis (AUC = 0.829). SMAD4 gene mutations showed significant relationships with standardized uptake value skewness (AUC = 0.727), long-run emphasis (AUC = 0.692), and high-intensity textural features such as run emphasis (AUC = 0.775), short-run emphasis (AUC = 0.736), zone emphasis (AUC = 0.750), and short-zone emphasis (AUC = 0.725). No significant associations were seen between the imaging phenotypes and genetic alterations in TP53 and CDKN2A.
conclusionGenetic alterations of KRAS and SMAD4 had significant associations with FDG PET-based radiomic features in PDAC. PET-based radiomics may help clinicians predict genetic alteration status in a noninvasive way.
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.