Evidence map›Paper›PMID 42348504›Full record

ArticleNeuroendocrinology2026

Pathological Classification and Clinical Characteristics of Growth Hormone-Secreting PitNETs.

Zhenwei Li, Yinzi Wu, Yike Gao, Ming Feng, Kan Deng, Bing Xing, Wei Lian, Yong Yao, Renzhi Wang, Jian Sun and 1 more

Abstract read
In one paragraph

Article in Neuroendocrinology, 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

11 authors.

Zhenwei LiDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yinzi WuDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yike GaoPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ming FengDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Kan DengDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Bing XingDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Wei LianDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yong YaoDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Renzhi WangDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jian SunPeking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, sunjian@pumch.cn.
Xinjie BaoDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, baoxinjie1@pumch.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAcromegaly/gigantism is a rare disease primarily caused by growth hormone (GH)-secreting pituitary neuroendocrine tumors (PitNETs). With the update in the WHO 5th edition classification of pituitary tumors, particularly the introduction of the novel PIT1/SF1 co-expressing tumor subtype, our understanding of these neoplasms has significantly advanced. However, systematic pathological classification and clinical characterization studies of GH-secreting PitNETs remain relatively scarce.

methodsA retrospective study was conducted on 143 patients with acromegaly/gigantism who underwent surgical treatment at Peking Union Medical College Hospital between June 2022 and December 2024. Tumor specimens were re-evaluated for pathological subtyping. Demographic data, radiological characteristics, hormone profiles, immunohistochemical findings, and clinical outcomes were collected and compared among subtypes.

resultsForty-five cases (32%) were classified as pure GH-secreting tumors, while the remainder showed co-expression of other hormones and transcription factors. Among PIT1-lineage tumors, sparsely granulated somatotroph tumors (SGSTs) demonstrated greater cavernous sinus invasion, larger maximal tumor diameter, lower GH secretion index, and immunostaining intensity, and reduced SSTR2 expression, suggesting a more aggressive biological behavior. Co-expression of prolactin increased the risk of hyperprolactinemia (OR = 2.843), though only 23.7% of mammosomatotroph tumors and 32.6% of mixed somatotroph-lactotroph tumors presented with hyperprolactinemia. Additionally, 19 PIT1/SF1 co-expressing tumors were identified, showing diverse hormonal profiles and significantly higher cavernous sinus invasion compared to PIT1-lineage tumors.

conclusionGH-secreting PitNETs exhibit marked heterogeneity in pathological subtypes. Current classification systems require clearer cutoff criteria to improve diagnostic consistency. SGSTs are associated with a more invasive phenotype, warranting close clinical monitoring and long-term follow-up. PIT1/SF1 co-expressing tumors represent a distinct and heterogeneous entity that should be considered in future classification frameworks.

Indexed as

AcromegalyGrowth hormoneInvasive pituitary adenomasPituitary neuroendocrine tumorWHO classification

Identifiers

PMID42348504
PMCPMC13446884

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.