ArticleMolecular imaging and biology2019
How Often Do We Fail to Classify the Treatment Response with [
Article in Molecular imaging and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- [A bidirectional feature mapping classification model based on multi-constrained latent representation learning for differential diagnosis of pneumonia].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Prognostic significance of the harmonized maximum standardized uptake value of 18F-FDG-PET/CT in patients with resectable oral tongue squamous cell carcinoma: a multicenter study.Dento maxillo facial radiology · 2023Article
- Harmonization of standard uptake values across different positron emission tomography/computed tomography systems and different reconstruction algorithms: validation in oncology patients.EJNMMI physics · 2023Article
- Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.An international journal on information fusion · 2022Article
- Harmonized pretreatment quantitative volume-based FDG-PET/CT parameters for prognosis of stage I-III breast cancer: Multicenter study.Oncotarget · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTumor response evaluated by 2-deoxy-2-[ PROCEDURES: We retrospectively identified 95 PET pairs (pre- and post-treatment) performed on different scanners (Biograph mCT, Siemens; GEMINI GXL, Philips) in 73 oncological patients (52F; 57.8 ± 16.3 years). An 8-mm Gaussian filter was applied for the Biograph protocol to meet the EANM/EARL harmonization standard; no filter was needed for GXL. SUVmax and SUVmaxEQ of the same target lesion in the pre- and post-treatment PET/CT were noted. For each PET pair, the metabolic response classification (responder/non-responder), derived from combining the EORTC response categories, was evaluated twice (with and without harmonization). In discordant cases, the association of each metabolic response classification with final clinical response assessment and survival data (2-year disease-free survival, DFS) was assessed.
resultsOn Biograph, SUVmaxEQ of all target lesions was significantly lower (p = 0.001) than SUVmax (8.5 ± 6.8 vs 12.5 ± 9.6; - 38.6 %). A discordance between the two metabolic response classifications (harmonized and non-harmonized) was found in 19/95 (20 %) PET pairs. In this subgroup (n = 19; mean follow-up, 33.9 ± 9 months), responders according to harmonized classification (n = 9) had longer DFS (47.5 months, 88.9 %) than responders (n = 10) according to non-harmonized classification (26.3 months, 50.0 %; p = 0.01). Moreover, harmonized classification showed a better association with final clinical response assessment (17/19 PET pairs).
conclusionsThe harmonized metabolic response classification is more associated with the final clinical response assessment, and it is able to better predict the DFS than the non-harmonized classification. EQ·PET is a useful harmonization tool for evaluating metabolic tumor response using different PET/CT scanners, also in different departments or for multicenter studies.
Indexed as
Identifiers
30850971What 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.