EP22.01: Our trial of preoperative prediction of uterine sarcoma using magnetic resonance imaging and its accuracy
Year: 2024
Type: conference-abstract
Abstract: Uterine sarcoma is a rare disease with a poor prognosis, and its preoperative prediction is difficult. We previously presented the finding that a tumour with high tumour-to-subcutaneous fat signal intensity ratio on MRI T2 weighted imaging (TFSIR) (over 0.515) and low ADC (under1.280) is highly suspected of sarcoma. In the previous meeting, we introduced the new preoperative prediction method using these 2 parameters i.e. the tumour with both positive parameters is highly suspected as sarcoma. This time, we report our trial of preoperative prediction of uterine sarcoma using this method. In addition, we describe its accuracy. The materials are 90 uterine tumours which performed MRI preoperatively and confirmed the pathological diagnosis in our hospital for recent 3 years (from 2021 to 2023). Both TFSIR and ADC had calculated for all tumours. The tumours with both positive parameters are predicted as sarcomas, and other tumours are predicted as benign tumours. After the operations, the preoperative predictions were matched to the pathological results. The sensitivity, specificity, positive predictive rate and negative predictive rate of this preoperative prediction method were calculated. Among 90 uterine tumours, 4 tumours were predicted as sarcomas and 3 of those tumours were indeed sarcomas. Thus 1 false positive case resulted. Its pathology was cellar leiomyoma. The other 86 tumours predicted as benign tumours were indeed benign. The sensitivity of this method is 100%, the specificity is 99%, the positive predictive rate is 75% and the negative predictive rate is 100%. Preoperative prediction of uterine sarcoma is possible using both TFSIR and ADC on MRI. However, we have to be careful for false positive cases especially in cases which the patient wishes to remain fertile.
DOI: 10.1002/uog.28890
Institution: Komatsu (Japan)
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4402360899