ArticleTranslational cancer research2026
A pretreatment
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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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.
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Authors and funding
5 authors.
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Abstract
Background: It is challenging to differentiate between primary gastric cancer (GC) and primary gastric diffuse large B-cell lymphoma (PG-DLBCL) with traditional imaging modalities. This study aimed to develop a predictive nomogram model for differential diagnosis of primary GC and PG-DLBCL by integrating semiquantitative metabolic parameters from pre-treatment Methods: A total of 111 patients with histologically confirmed gastric malignancies who underwent pre-treatment Results: Primary lesiontype, infra-renal lymph node involvement, and TLR-SUV were identified as independent discriminators between GC and PG-DLBCL. The integrated nomogram model combining CT-based morphological characteristics and PET-derived metabolic metrics achieved an AUC of 0.877 [95% confidence interval (CI): 0.796-0.957] for distinguishing GC from PG-DLBCL, demonstrating robust discrimination and excellent calibration. DCA further confirmed the model's potential to provide valuable clinical decision support across a range of risk thresholds. Conclusions: The nomogram model constructed by integrating concurrent CT morphological features with PET metabolic parameters exhibited high diagnostic efficacy in distinguishing GC from PG-DLBCL, and serves as a promising non-invasive imaging tool.
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