Evidence map›Paper›PMID 38090038›Full record

ReviewMediastinum (Hong Kong, China)2023

Pathological snapshots of thymic epithelial tumors with invasion into neighboring structures: preparing for the forthcoming revision of the TNM classification.

Yosuke Yamada, Hironori Haga

Abstract readReview
In one paragraph

Review in Mediastinum (Hong Kong, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Yosuke YamadaDepartment of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan.
Hironori HagaDepartment of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment decision-making of thymic epithelial tumors (TETs) after surgery is based on the pathological stage. Currently, most institutions use both the Masaoka-Koga system and the 8th edition of the tumor, node, metastasis (TNM) classification. Because these two systems separate each stage according to the same concept, namely, the "levels" of tumor extension, precise pathological evaluation of the presence or absence of tumor invasion into stage-defining structures is necessary. This review provides representative pathological snapshots of tumors invading neighboring structures to provide references that might be helpful to readers; the snapshots will cover features that correspond to those of "locally advanced TETs", the topic of this series. Tumor subtype, whether thymoma or thymic carcinoma, is another factor influencing treatment decisions. Accumulating evidence has indicated that most thymomas and thymic carcinomas have biologically distinct features. Representative results were achieved by a study conducted as part of The Cancer Genome Atlas (TCGA) project, and subsequent studies with the help of the TCGA data have further reported on these distinctive features. Here, we also introduce newly recognized features of TETs, mainly focusing on the difference between epithelial-rich thymomas and thymic squamous cell carcinoma. The new (9th) edition of the TNM classification will be launched in January 2024. Therefore, sharing current pathological features of TETs will help readers, not only in their daily practice but also in preparing for the upcoming classification system.

Indexed as

the 8th edition of the TNM classification (TNM-8)the Masaoka-Koga stage classificationthymic carcinomathymic epithelial tumors (TETs)Thymoma

Identifiers

PMID38090038
PMCPMC10713236

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.