Evidence map›Paper›PMID 42310431›Full record

ArticleScientific reports2026

3D radiomics profiling of thyroid tumors using micro-CT.

Kiarash Tajbakhsh, Olga Stanowska, Marija Buljan, Antonia Neels, Aurel Perren, Robert Zboray

Abstract read
In one paragraph

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

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0citing papers in PubMed
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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

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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.

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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

6 authors.

Kiarash TajbakhshCenter for X-ray Analytics, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, 8600, Switzerland.
Olga StanowskaInstitute of Tissue Medicine and Pathology, University of Bern, Bern, 3008, Switzerland.
Marija BuljanNanomaterials in Health, Swiss Federal Laboratories for Materials Science and Technology (Empa), St. Gallen, 9014, Switzerland.
Antonia NeelsCenter for X-ray Analytics, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, 8600, Switzerland.
Aurel PerrenInstitute of Tissue Medicine and Pathology, University of Bern, Bern, 3008, Switzerland.
Robert ZborayCenter for X-ray Analytics, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf, 8600, Switzerland. Robert.Zboray@empa.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor heterogeneity plays a central role in treatment resistance, disease progression, and diagnostic uncertainty. However, it may be overlooked by traditional 2D histology. Accurate 3D assessment of tumor microarchitecture is therefore important for capturing its spatial complexity. Micro-CT, a well-established imaging modality now emerging for high-resolution 3D virtual histology of soft tissues, provides a promising alternative. Combined with radiomics, this technique enables interpretable, quantitative characterization of tumor biology beyond visual inspection. In this study, we analyzed radiomics signatures of a large cohort of thyroid tumors (418 patients) using micro-CT imaging of tissue microarrays. We achieved robust classification of (i) neoplastic versus non-neoplastic thyroid tissues, (ii) papillary thyroid carcinoma versus follicular thyroid neoplasm, and (iii) BRAF V600E mutation status. Shapley additive explanations were used to reveal key visual traits driving these classification decisions. Exploratory analysis in a limited TERT cohort (8 mutated vs 103 wild-type) identified prospective radiomics patterns associated with TERT promoter mutations, suggesting potential surrogate imaging biomarkers that warrant further investigation. Micro-CT radiomics shows promise as a complementary tool for diagnostic classification in thyroid cancer and offers a platform for quantitative 3D tissue characterization pending broader validation.

Indexed as

Imaging, Three-DimensionalThyroid Cancer, PapillaryThyroid NeoplasmsX-Ray MicrotomographyAdenocarcinoma, FollicularFemaleHumansMaleMiddle AgedMutationProto-Oncogene Proteins B-rafRadiomicsTelomeraseBRAF protein, humanProto-Oncogene Proteins B-rafTelomeraseTERT protein, humanBRAF V600EMicro-CTRadiomicsTERTThyroid neoplasms

Identifiers

PMID42310431
PMCPMC13542263

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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.