Evidence map›Paper›PMID 42640400›Full record

ReviewEndocrine pathology2026

Neuroendocrine Neoplasms of the Urinary Bladder: Integrating Molecular Advances into a Refined Classification System.

Anandi Lobo, Liang Cheng

Abstract readReview
PubMed Publisher
In one paragraph

Review in Endocrine pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Anandi LoboDepartment of Pathology and Laboratory Medicine, Kapoor Center for Pathology and Urology, Raipur, India.
Liang ChengDepartment of Pathology and Laboratory Medicine, Department of Surgery (Urology), University Warren Alpert Medical School, the Legorreta Cancer Center at Brown University, and Brown University Health, 593 Eddy Street, APC 12-105, Providence, RI, 02903, USA. liang_cheng@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuroendocrine neoplasms (NENs) of the urinary bladder are rare but highly aggressive tumors that account for under 1% of bladder malignancies. The 2022 WHO classification recognizes small cell neuroendocrine carcinoma (SCNEC), large cell neuroendocrine carcinoma (LCNEC), mixed neuroendocrine neoplasms (MiNEN), well-differentiated neuroendocrine tumor (NET), and paraganglioma; however, this framework is largely extrapolated from other organ systems and does not fully capture the biological complexity of bladder NENs. Most cases represent poorly differentiated neuroendocrine carcinomas, frequently admixed with urothelial carcinoma and characterized by recurrent TP53 and RB1 inactivation, TERT promoter mutations, and epigenetic dysregulation. Emerging transcriptomic data further identify lineage-defined subgroups within SCNEC and LCNEC based on ASCL1, NEUROD1, and POU2F3 expression, with potential prognostic and therapeutic relevance. This review integrates histopathologic, immunophenotypic, and molecular data to highlight diagnostic challenges, biologic heterogeneity, and limitations of current classification schemes. We propose a refined, bladder-specific framework that incorporates proliferative indices, molecular alterations, and recognition of mixed neuroendocrine-non-neuroendocrine neoplasms, with the goal of improving diagnostic reproducibility and informing future biomarker-driven therapeutic strategies.

Indexed as

Neuroendocrine TumorsUrinary Bladder NeoplasmsBiomarkers, TumorHumansBiomarkers, TumorBladderClassificationLarge cell carcinomaNeuroendocrine neoplasmsParagangliomaSmall cell carcinomaWell-differentiated neuroendocrine tumor

Identifiers

What OpenQuestion holds

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Registered trials

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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.