SynthesisCancer imaging : the official publication of the International Cancer Imaging Society2026
Elastography for distinguishing lymphoma from benign and metastatic lymphadenopathy: a systematic review and Bayesian meta-analysis.
Synthesis in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. 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.
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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferentiating lymphoma from benign and metastatic lymphadenopathies using conventional ultrasound remains clinically challenging. Despite the utility of ultrasound elastography in assessing tissue stiffness, the intermediate stiffness profile of lymphoma frequently leads to misclassification. To address this diagnostic gap, we conducted a comprehensive systematic review and Bayesian diagnostic meta-analysis to evaluate the performance of various elastography modalities.
methodsFollowing PRISMA 2020 guidelines (PROSPERO: CRD420251184980), we systematically searched PubMed, Embase, Web of Science, and the Cochrane Library. Original studies evaluating elastography against valid reference standards were included, and methodological quality was assessed using the QUADAS-2 tool. Diagnostic performance was synthesized via a robust two-tiered Bayesian bivariate hierarchical random-effects model to calculate pooled sensitivity, specificity, diagnostic odds ratios (DOR), and likelihood ratios (PLR/NLR) for both overall and modality-specific analyses.
resultsTwenty studies encompassing 2,342 patients and 2,626 superficial lymph nodes (981 benign, 895 lymphoma, 750 metastatic) were included. In differentiating lymphoma from benign lymphadenopathy, Shear Wave Velocity (SWV) exhibited the highest sensitivity (0.913) and lowest negative likelihood ratio (NLR, 0.117). Conversely, aggregated Strain Elastography (SE) scoring achieved the highest specificity (0.909) and positive likelihood ratio (PLR, 7.59). When distinguishing lymphoma from metastatic nodes, Virtual Touch Tissue Imaging (VTI) techniques demonstrated robust diagnostic yield for identifying lymphoma; specifically, the VTI ratio achieved the highest diagnostic odds ratio (DOR, 39.4), with a sensitivity of 0.92 and an NLR of 0.11.
conclusionUltrasound elastography provides significant modality-specific diagnostic value for stratifying lymphadenopathy. SWV effectively excludes lymphoma from benign nodes, whereas SE scoring optimizes its confirmation. Furthermore, VTI techniques robustly distinguish lymphoma from metastasis. However, given substantial inter-study heterogeneity, elastography should be integrated as a complementary multiparametric adjunct to guide biopsy triage, rather than replace definitive histopathological evaluation.
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.