1.Differentiation of uterine fibroids and sarcomas by MRI and serum LDH levels: a multicenter study of the KAMOGAWA study
Yukio YAMANISHI ; Yasushi KOTANI ; Aki KIDO ; Tomoyuki OTANI ; Yuki HIMOTO ; Yasuhisa KURATA ; Kosuke MURAKAMI ; Hisamitsu TAKAYA ; Masahiro SUMITOMO ; Ikuko EMOTO ; Motonori MATSUBARA ; Naoki HORIKAWA ; Kana AKAGI ; Kentaro ISHIDA ; Aya TAKAORI ; Azusa SAKURAI ; Kaoru ABIKO ; Koji YAMANOI ; Masaki MANDAI ; Noriomi MATSUMURA
Journal of Gynecologic Oncology 2025;36(4):e58-
Objective:
In the differential diagnosis between uterine fibroids and uterine sarcomas, realworld magnetic resonance imaging (MRI) diagnostic information is scarce; furthermore, high diagnostic sensitivity is important in clinical practice. We previously developed a diagnostic algorithm to detect uterine sarcoma with high sensitivity using simple MRI images and serum lactate dehydrogenase (LDH) levels. In this multicenter study, we investigated the preoperative diagnosis of sarcoma in the real world and further validated the usefulness of our diagnostic algorithm.
Methods:
Of 154 uterine sarcomas and 154 uterine fibroids treated at 15 centers between January 2006 and December 2020, 139 sarcomas (16 smooth muscle tumors of uncertain malignant potential) and 141 fibroids with diffusion-weighted imaging information were included in the analysis. The diagnostic algorithm was validated by 3 radiologists who were blinded to the clinical information and pathologic diagnoses and who read the MRIs.
Results:
The sensitivity/specificity of preoperative diagnosis was 77.7%/92.9% for the preoperative report; 92.1%/72.3% for algorithm A; and 82.0%/85.8% for algorithm B (McNemar’s test p<0.05). Comparison of overall survival rates among 3 groups (Group 1: negative A, Group 2: positive A and negative B; Group 3: positive B) using algorithms A and B showed p=0.012. On multivariate analysis, stage, and serum LDH level were independent prognostic factors.
Conclusion
MRI is useful for preoperative diagnosis of uterine sarcoma, and the sarcoma diagnostic algorithm presented in this study is an option for diagnosing sarcoma with greater sensitivity. This information should be shared with patients.
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