MRI-Based Radiomics as a Promising Noninvasive Diagnostic Technique for Adenomyosis

Laurin Burla, Elisabeth Sartoretti, Manoj Mannil, Stefan Seidel, Thomas Sartoretti, Harald Krentel, Rudy Leon De Wilde, Patrick Imesch
Journal of clinical medicine · 2024 · vol. 13(8) , pp. 2344 · doi:10.3390/jcm13082344 · PMID:38673617 · W4394920710
article OA: gold CC0 ⤵ 4 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study found that radiomics features extracted from T2w MRI can accurately differentiate uteri with adenomyosis from those without, achieving an area under the curve of 1.0 in machine learning models.

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Abstract

Background: MRI diagnostics are important for adenomyosis, especially in cases with inconclusive ultrasound. This study assessed the potential of MRI-based radiomics as a novel tool for differentiating between uteri with and without adenomyosis. Methods: This retrospective proof-of-principle single-center study included nine patients with and six patients without adenomyosis. All patients had preoperative T2w MR images and histological findings served as the reference standard. The uterus of each patient was segmented in 3D using dedicated software, and 884 radiomics features were extracted. After dimension reduction and feature selection, the diagnostic yield of individual and combined features implemented in the machine learning models were assessed by means of receiver operating characteristics analyses. Results: Eleven relevant radiomics features were identified. The diagnostic performance of individual features in differentiating adenomyosis from the control group was high, with areas under the curve (AUCs) ranging from 0.78 to 0.98. The performance of ML models incorporating several features was excellent, with AUC scores of 1 and an area under the precision-recall curve of 0.4. Conclusions: The set of radiomics features derived from routine T2w MRI enabled accurate differentiation of uteri with adenomyosis. Radiomics could enhance diagnosis and furthermore serve as an imaging biomarker to aid in personalizing therapies and monitoring treatment responses.

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MUSA

Condition tags

adenomyosis

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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