Trial reportEuropean journal of nuclear medicine and molecular imaging2025
Evaluation of locoregional invasiveness of early lung adenocarcinoma manifesting as ground-glass nodules via [
Trial report in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04588064 (Comparison of 18F-FDG and 68Ga-FAPI PET/CT in Patients with Lung Adenocarcinoma), which is not on this map. Cited by 3 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.
Comparison of 18F-FDG and 68Ga-FAPI PET/CT in Patients with Lung Adenocarcinoma
Who cites it
3 citing papers in PubMed.
- Gallium-Containing Agents for Tumor Diagnosis and Therapy: Current Status and Future Prospects.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Advancing lung adenocarcinoma diagnosis using peri-nodular features: a CT radiomics study for determining invasiveness in sub-centimeter pure ground glass nodules.Journal of thoracic disease · 2026Article
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
purposeAccurate differentiation of the histologic invasiveness of early-stage lung adenocarcinoma is crucial for determining surgical strategies. This study aimed to investigate the potential of [
methodsThis prospective study (NCT04588064) was conducted between July 2020 and July 2022, focusing on GGNs that were confirmed postoperatively to be either invasive adenocarcinoma (IAC), minimally invasive adenocarcinoma (MIA), or precursor glandular lesions (PGL). A total of 45 patients with 53 pulmonary GGNs were included in the study: 19 patients with GGNs associated with PGL-MIA and 34 with IAC. Lung nodules were segmented using the Segment Anything Model in Medical Images (MedSAM) and the PET Tumor Segmentation Extension. Clinical characteristics, along with conventional and high-throughput radiomics features from High-resolution CT (HRCT) and PET scans, were analysed. The predictive performance of these features in differentiating between PGL or MIA (PGL-MIA) and IAC was assessed using 5-fold cross-validation across six machine learning algorithms. Model validation was performed on an independent external test set (n = 11). The Chi-squared, Fisher's exact, and DeLong tests were employed to compare the performance of the models.
resultsThe maximum standardised uptake value (SUVmax) derived from [
conclusionThe SUVmax derived from [ CLINICAL TRIAL REGISTRATION NUMBER: NCT04588064; URL: https://clinicaltrials.gov/study/NCT04588064 .
Indexed as
Identifiers
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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.