CHICAGO (Reuters), Jul 8 - An advanced type of MRI scan may help doctors more accurately diagnose a severe form of endometriosis, researchers said on Tuesday, potentially allowing some women to avoid invasive pelvic surgery.
They said a new kind of magnetic resonance imaging scan called 3 tesla, or 3T MRI, helped doctors rule out deep endometriosis in 93% of young women studied.
Endometriosis is a chronic disease that results when uterine tissue, called endometrium, grows outside the uterus, causing pain in the pelvis and lower back, painful menstrual cramps, fatigue, and infertility. It affects 5 million American women.
In most cases, doctors can remove excess tissue using laparoscopic surgery, in which a lighted instrument is inserted through a small incision.
But in women with deep endometriosis, in which uterine tissue attaches itself to other organs such as the ovaries, fallopian tubes, and colon, excess tissue must be removed through a large incision in the abdomen, a riskier procedure that requires longer follow-up.
A team lead by Dr. Nathalie Hottat of the Universite Libre de Bruxelles in Brussels, Belgium, wanted to see if the high-tech MRI scanners could help surgeons better predict which women needed more invasive surgery.
Hottat's team performed MRI scans on 41 women ages 20 to 46 with suspected endometriosis prior to surgery.
They found MRI accurately diagnosed 26 of 27 cases of deep endometriosis, helping doctors to plan their surgery, the team reported in the journal Radiology.
The scans were highly accurate at determining which women would benefit from less-invasive surgery, ruling out deep endometriosis in 93% of cases and allowing surgeons to perform a less invasive laparoscopic procedure.
Source: Radiology, online July 7, 2009.
Last Updated: 2009-07-07 16:29:06 -0400 (Reuters Health)
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
