Follicular Thyroid Carcinoma Relapse Cases - Revisited by X-ray 3D Virtual Histology

The diagnosis and prognosis of follicular thyroid neoplasms are based on the identification of capsular and vascular invasion. Although conventional histology allows accurate classification in most cases, its inherent limitations in tissue sampling can result in misjudgment of both the presence and extent of invasion, and occasionally result in diagnostic inaccuracies. Consequently, unexpected tumor recurrence or overtreatment still represent significant clinical challenges. To mitigate these limitations, emerging diagnostic approaches are exploring advanced imaging modalities. X-ray 3D virtual histology has been reported to enable non-destructive and comprehensive sampling of the entire tumor volume embedded in formalin-fixed paraffin-embedded blocks. In thisstudy, the X-ray 3D virtual histology technique is first evaluated by classifying 99 follicular thyroid carcinomas and 31 follicular adenomas, achieving an accuracy of 89.2%. It is then applied to tissue blocks from five relapse cases that were postoperatively diagnosed as adenomas using conventional histology. Three of five tumors exhibited at least one unequivocal focus of vascular invasion, reinforcing that even a single well-defined focus portends significant risk of recurrence and distant metastasis. Although histological confirmation at this stage remained necessary, X-ray 3D virtual histology proved to be a valuable screening method.

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Publication Details

Journal
Endocrine Pathology
Published
2025-11-24
DOI
https://doi.org/10.1007/s12022-025-09891-y
Primary Topic
Thyroid Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
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article

Follicular Thyroid Carcinoma Relapse Cases - Revisited by X-ray 3D Virtual Histology

Reto Martin Kaderli, Martina T. Mogl, Jonas Bossart, Marija Buljan et al.
Endocrine Pathology
Thyroid Cancer Diagnosis and Treatment
article

Follicular Thyroid Carcinoma Relapse Cases - Revisited by X-ray 3D Virtual Histology

Reto Martin Kaderli, Martina T. Mogl, Jonas Bossart, Marija Buljan, Michael Hermann, Olga Stanowska, Robert Zboray, Aurel Perren, Sabine Kirchnawy, Kiarash Tajbakhsh, Wolfgang Hulla, Catarina Alisa Kunze, A. Neels, G Klein, Rene Brillmann
article en

Abstract

The diagnosis and prognosis of follicular thyroid neoplasms are based on the identification of capsular and vascular invasion. Although conventional histology allows accurate classification in most cases, its inherent limitations in tissue sampling can result in misjudgment of both the presence and extent of invasion, and occasionally result in diagnostic inaccuracies. Consequently, unexpected tumor recurrence or overtreatment still represent significant clinical challenges. To mitigate these limitations, emerging diagnostic approaches are exploring advanced imaging modalities. X-ray 3D virtual histology has been reported to enable non-destructive and comprehensive sampling of the entire tumor volume embedded in formalin-fixed paraffin-embedded blocks. In thisstudy, the X-ray 3D virtual histology technique is first evaluated by classifying 99 follicular thyroid carcinomas and 31 follicular adenomas, achieving an accuracy of 89.2%. It is then applied to tissue blocks from five relapse cases that were postoperatively diagnosed as adenomas using conventional histology. Three of five tumors exhibited at least one unequivocal focus of vascular invasion, reinforcing that even a single well-defined focus portends significant risk of recurrence and distant metastasis. Although histological confirmation at this stage remained necessary, X-ray 3D virtual histology proved to be a valuable screening method.

Endocrine PathologyVol. 36(1)
University of Bern (CH), SIB Swiss Institute of Bioinformatics (CH), Bern University of Applied Sciences (CH), University of Fribourg (CH), Board of the Swiss Federal Institutes of Technology (CH), University Hospital of Bern (CH), Humboldt-Universität zu Berlin (DE), Klinik Hietzing (AT), Wiener Krankenanstaltenverbund (AT), Swiss Federal Laboratories for Materials Science and Technology (CH), Freie Universität Berlin (DE), Charité - Universitätsmedizin Berlin (DE)
Openalex Percentile: Top 52%
Thyroid Cancer Diagnosis and Treatment
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