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Uterine mesenchymal tumors: development and preliminary results of a magnetic resonance imaging (MRI) diagnostic algorithm

  • Magnetic Resonance Imaging
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Abstract

Purpose

The aim of our study is to propose a diagnostic algorithm to guide MRI findings interpretation and malignancy risk stratification of uterine mesenchymal masses with a multiparametric step-by-step approach.

Methods

A non-interventional retrospective multicenter study was performed: Preoperative MRI of 54 uterine masses was retrospectively evaluated.

Firstly, the performance of MRI with monoparametric and multiparametric approach was assessed. Reference standard for final diagnosis was surgical pathologic result (n = 53 patients) or at least 1-year MR imaging follow-up (n = 1 patient).

Subsequently, a diagnostic algorithm was developed for MR interpretation, resulting in a Likert score from 1 to 5 predicting risk of malignancy of the uterine lesion. The accuracy and reproducibility of the MRI scoring system were then tested: 26 preoperative pelvic MRI were double-blind evaluated by a senior (SR) and junior radiologist (JR).

Diagnostic performances and the agreement between the two readers with and without the application of the proposed algorithm were compared, using histological results as standard reference.

Results

Multiparametric approach showed the best diagnostic performance in terms of accuracy (94.44%,) and specificity (97.56%).

DWI was confirmed as the most sensible parameter with a relative high specificity: low ADC values (mean 0.66) significantly correlated to uterine sarcomas diagnosis (p < 0.01).

Proposed algorithm allowed to improve both JR and SR performance (algorithm-aided accuracy 88.46% and 96%, respectively) and determined a significant increase in inter-observer agreement, helping even the less-experienced radiologist in this difficult differential diagnosis.

Conclusions

Uterine leiomyomas and sarcomas often show an overlap of clinical and imaging features.

The application of a diagnostic algorithm can help radiologists to standardize their approach to a complex myometrial mass and to easily identify suspicious MRI features favoring malignancy.

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Abbreviations

MRI:

Magnetic resonance imaging

LMS:

Leiomyosarcomas

LG ESS:

Low-grade endometrial stromal sarcoma

HG ESS:

High-grade endometrial stromal sarcoma

US:

Undifferentiated sarcoma

AD:

Adenosarcoma

STUMP:

Smooth muscle tumor of uncertain malignant potential

ESUR:

European Society of Urogenital Radiology

WI:

Weighted imaging

FS:

Fat suppression

DWI:

Diffusion-weighted imaging

ADC:

Apparent diffusion coefficient

SR:

Senior radiologist

SI:

Signal intensity

CE:

Contrast enhancement

ROI:

Region of interest

JR:

Junior radiologist

ROC:

Receiver operating characteristic

CIs:

Confidence intervals

PPV:

Positive predictive value

NPV:

Negative predictive value

FN:

False negative

FP:

False positive

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Author notes

  1. F. Rosa and C. Martinetti have contributed equally to this work.

Authors and Affiliations

  1. Diagnostic Imaging Department, San Paolo Hospital-ASL 2, via Genova, 30, Savona, Italy

    Francesca Rosa

  2. Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy

    Carola Martinetti, Stefania Migone, Daria Schettini & Nicoletta Gandolfo

  3. Diagnostic Imaging Unit, Esperia Medical Center, Porcia, PN, Italy

    Silvia Magnaldi

  4. Service of Radiology, Imaging Institute of Southern Switzerland, Clinica di Radiologia EOC, 6900, Lugano, Switzerland

    Stefania Rizzo

  5. Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), via Buffi 23, 6900, Lugano, Switzerland

    Stefania Rizzo

  6. Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, 00185, Rome, Italy

    Lucia Manganaro

  7. Anatomic Pathology Unit, San Paolo Hospital-ASL 2, via Genova, 30, Savona, Italy

    Silvia Ardoino

  8. Obstetrics and Gynecology Unit, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy

    Pierangelo Marchiolè

  9. Obstetrics and Gynecology Department, IRCCS ospedale Policlinico San Martino, Genoa, Italy

    Pierangelo Marchiolè

  10. Anatomic Pathology Unit, Villa Scassi Hospital-ASL 3, Corso Scassi 1, Genoa, Italy

    Tommaso Ragusa

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  1. Francesca Rosa
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rosa Francesca and Martinetti Carola. The first draft of the manuscript was written by Rosa Francesca and Martinetti Carola, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Francesca Rosa.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

This non-interventional, observational, and retrospective study was approved by the Internal Review Board of the Diagnostic Imaging Department and by the Regional Ethics Committee (N. CER Liguria: 78/2023-DB id 12833).

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Informed consent was obtained from all individual participants included in the study.

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Fig. 1

Pelvic peritoneal implant of carcinosis. MRI shows Pelvic peritoneal nodularity (arrow) in the Douglas pouch and irregular thickening of anterior peritoneal reflection at utero-vescical pouch (dashed arrows) with ascites (*). Sagittal T2wi (a), DWI (b=1000), (b) and ADC map (c)

Table 1

MRI protocol

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Rosa, F., Martinetti, C., Magnaldi, S. et al. Uterine mesenchymal tumors: development and preliminary results of a magnetic resonance imaging (MRI) diagnostic algorithm. Radiol med 128, 853–868 (2023). https://doi.org/10.1007/s11547-023-01654-1

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  • DOI: https://doi.org/10.1007/s11547-023-01654-1

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