Magnetic Resonance Spectrum Imaging of Endometrial Lesions: A Diagnostic Performance Comparative Study with Pathological Findings
Year: 2026
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: To evaluate the diagnostic value and significance of magnetic resonance spectroscopy (MRS) in endometrial pathology. METHODS: In this retrospective study, the 1H-MRS records of patients with suspected abnormal endometrial conditions were analyzed. Single-voxel spectra acquired from the largest region of the lesions were evaluated. The peak heights and areas under the curves (AUCs) for different metabolic peaks (total choline (tCho), creatine (Cr), lactate, lipid, N-acetylaspartate (NAA)) were recorded. The patients were classified into two groups: benign (hyperplasia or polyp) and malignant. Statistical differences in each peak between the two groups were analyzed individually. RESULTS: Tumor lesions showed higher tCho (p=0.006), Cr(p=0.007), and lipid peaks (p=0.042) than benign endometrial lesions, together with increased tCho/Cr (p=0.021) and tCho/NAA ratios (p=0.016). In endometrial cancer, higher tCho(AUC=0.64), lipid (AUC=0.618), and tCho/Cr (AUC=0.649) were associated with higher tumor grade. Lipid and tCho/NAA also demonstrated value for differentiating endometrioid from nonendometrioid subtypes, with AUCs of 0.761 and 0.756, respectively. Lactate and an NAA-like peak were detectable in a subset of endometrial lesions. Multivariable logistic regression revealed that the incorporation of the proton MRS tCho metric into a baseline clinico-radiological model (age, body mass index (BMI), cancer antigen 125 (CA125)) significantly enhanced diagnostic performance (AUC = 0.797). DISCUSSION: tCho- and lipid-related MRS metrics may aid non-invasive characterization of malignancy and tumor aggressiveness, supporting preoperative subtype assessment and risk stratification. CONCLUSION: MRS reflects the basic metabolic characteristics of tissues and aids in determining the nature and invasiveness of endometrial lesions.
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7170055455