ArticleAnnals of nuclear medicine2023
Added prognostic value of molecular imaging parameters over proliferation index in typical lung carcinoid: an [18F]FDG PET/CT and SSTR imaging study.
Article in Annals of nuclear medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study was performed to evaluate the prognostic meaning of volumetric and semi-quantitative parameters measured using [18F]FDG PET/CT and somatostatin receptor (SSTR) imaging in patients with typical lung carcinoid (TC), and their relationship with proliferative index (Ki67).
methodsWe retrospectively reviewed 67 patients (38-94 years old, mean: 69.7) with diagnosis of TC who underwent [18F]FDG PET/CT and/or SSTR scintigraphy/SPECT with [111In]DTPA-Octreotide plus contrast-enhanced CT (CECT) at staging evaluation. All patients had Ki67 measured and a follow-up (FU) of at least 1 year. SSTR density (SSTRd) was calculated as the percentage difference of tumor/non-tumor ratio at 4 and 24 h post-injection. At PET/CT, metabolic activity was measured using SUVmax and SUVratio; volumetric parameters included MTV and TLG of the primary tumor, measured using the threshold SUV41%. ROC analysis, discriminant analysis and Kaplan-Meier curves (KM) were performed.
results11 patients died during FU. Disease stage (localized versus advanced), SUVratio, SUVmax, Ki67, MTV and TLG were significantly higher in non-survivors than in survivors. ROC curves resulted statistically significant for Ki67, SUVratio, SUVmax, MTV and TLG. On multivariate analysis, stage of disease and TLG were significant independent predictors of overall survival (OS). In KM curves, the combination of disease stage and TLG identified four groups with significantly different outcomes (p < 0.005). Metabolic activity (SUVmax and SUVratio) was confirmed as significant independent prognostic factor for OS also in patients with advanced disease, with the best AUC using SUVmax. In patients with advanced and localized disease, SSTRd proved to be the best imaging prognostic factor for progression and for disease-free survival (DFS), respectively. In localized disease, SSTRd 31.5% identified two subgroups of patients with significant different DFS distribution and in advanced disease, a high cutoff value (58.5%) was a significant predictor of adverse prognosis.
conclusionVolumetric and semi-quantitative parameters measured using [18F]FDG PET/CT and SSTR imaging combined with Ki67 may provide a reference for prognosis evaluation of patients with TC, to better stratify risk groups with the goal of developing individualized therapeutic strategies.
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.