Evidence map›Paper›PMID 36414086›Full record

ArticleThe American journal of pathology2023

Whole-Slide Cytometric Feature Mapping for Distinguishing Tumor Genomic Subtypes in Head and Neck Squamous Cell Carcinoma Whole-Slide Images.

Stephanie J Blocker, Samantha Morrison, Jeffrey I Everitt, James Cook, Sheng Luo, Tammara L Watts, Yvonne M Mowery

Open access · greenAbstract read
In one paragraph

Article in The American journal of pathology, 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
0.1field-weighted citation impact, top 44% of its field
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 citations in OpenAlex.

  1. Article
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

7 authors at 1 institution in 1 country.

Stephanie J BlockerCenter for In Vivo Microscopy, Department of Radiology, Duke University School of Medicine, Durham, North Carolina. Electronic address: stephanie.blocker@duke.edu.
Samantha MorrisonDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
Jeffrey I EverittDepartment of Pathology, Duke University School of Medicine, Durham, North Carolina.
James CookCenter for In Vivo Microscopy, Department of Radiology, Duke University School of Medicine, Durham, North Carolina.
Sheng LuoDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
Tammara L WattsDepartment of Head and Neck Surgery and Communication Sciences, Duke University School of Medicine, Durham, North Carolina.
Yvonne M MoweryDepartment of Head and Neck Surgery and Communication Sciences, Duke University School of Medicine, Durham, North Carolina; Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina.
Duke University · US

Funding

NRSA Training CoreTL1TR002555 · NCATS · DUKE UNIVERSITY · PI EDELMAN, DAVID · 2018 to 2022
$3.7M
Employing Novel Primary Mouse Models of Head and Neck Cancer to Overcome Chemoradiation ResistanceK08DE029887 · NIDCR · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MOWERY, YVONNE MARIE · 2020 to 2024
$804k
NCATS NIH HHS TL1 TR002555NIDCR NIH HHS K08 DE029887
6 · The paper itself

Abstract

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease where, in advanced stages, clinical and pathologic stages do not correlate with outcome. Molecular and genomic biomarkers for HNSCC classification have shown promise for prognostic and therapeutic applications. This study utilized automated image analysis techniques in whole-slide images of HNSCC tumors to identify relationships between cytometric features and genomic phenotypes. Hematoxylin and eosin-stained slides of HNSCC tumors (N = 49) were obtained from The Cancer Imaging Archive, along with accompanying clinical, pathologic, genomic, and proteomic reports. Automated nuclear detection was performed across the entirety of slides, and cytometric feature maps were generated. Forty-one cytometric features were evaluated for associations with tumor grade, tumor stage, tumor subsite, and integrated genomic subtype. Thirty-two features demonstrated significant association with integrated genomic subtype when corrected for multiple comparisons. In particular, the basal subtype was visually distinguishable from the chromosomal instability and immune subtypes based on cytometric feature measurements. No features were significantly associated with tumor grade, stage, or subsite. This study provides preliminary evidence that features derived from tissue pathology slides could provide insights into genomic phenotypes of HNSCC.

Indexed as

Head and Neck NeoplasmsBiomarkers, TumorGenomicsHumansPrognosisProteomicsSquamous Cell Carcinoma of Head and NeckBiomarkers, Tumor

Identifiers

PMID36414086
PMCPMC9885294
OpenAlexW4309407834

What OpenQuestion holds

Textmetadata
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.