Evidence map›Paper›PMID 38425542›Full record

ArticleJournal of pathology informatics2024

External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study.

Sebastian Stenman, Sylvain Bétrisey, Paula Vainio, Jutta Huvila, Mikael Lundin, Nina Linder, Anja Schmitt, Aurel Perren, Matthias S Dettmer, Caj Haglund and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pathology informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.4field-weighted citation impact, top 17% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. The actual and future role of molecular tests in thyroid pathology.Virchows Archiv : an international journal of pathology · 2026
    Review
  2. 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

12 authors at 6 institutions in 4 countries.

Sebastian StenmanInstitute for Molecular Medicine Finland - FIMM, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland.
Sylvain BétriseyInstitute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008 Bern, Switzerland.
Paula VainioDepartment of Pathology, University of Turku, Turku University Hospital, Kiinamyllykatu 10, 20520 Turku, Finland.
Jutta HuvilaDepartment of Pathology, University of Turku, Turku University Hospital, Kiinamyllykatu 10, 20520 Turku, Finland.
Mikael LundinInstitute for Molecular Medicine Finland - FIMM, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland.
Nina LinderInstitute for Molecular Medicine Finland - FIMM, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland.
Anja SchmittInstitute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008 Bern, Switzerland.
Aurel PerrenInstitute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008 Bern, Switzerland.
Matthias S DettmerInstitute of Tissue Medicine and Pathology, University of Bern, Murtenstrasse 31, 3008 Bern, Switzerland.
Caj HaglundDepartment of Surgery, Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
Johanna ArolaHUSLAB, Department of Pathology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Haartmaninkatu 3C, 000290 HUS Helsinki, Finland.
Johan LundinInstitute for Molecular Medicine Finland - FIMM, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Finland.
University of Bern · CHInstitute for Molecular Medicine Finland · FIUniversity of Helsinki · FIHelsinki University Hospital · FITurku University Hospital · FIUniversity of Turku · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.

Indexed as

Artificial intelligenceDeep learningDigital pathologyPapillary thyroid carcinoma

Identifiers

PMID38425542
PMCPMC10901856
OpenAlexW4391425656

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.