Thursday, December 1 | 11:40 a.m.-11:50 a.m. | SSQ12-08 | Room E451A
In case you're wondering, SONK stands for spontaneous osteonecrosis of the knee, which is related to a subchondral fracture. In this Thursday session, researchers will discuss how MRI can help assess this pesky break and predict how well patients will recover."MRI has been proven as an excellent diagnostic tool for spontaneous osteonecrosis of the knee," said Dr. Jared Nesbitt from Stony Brook Medicine in Stony Brook, NY. "Unfavorable clinical outcomes for SONK include progression of osteoarthritis and arthroplasty. Rapidly progressive osteoarthritis has become increasingly recognized. It is known that SONK/subchondral fractures are important risk factors; hence, we tried to identify MRI features that may put patients at risk for unfavorable clinical outcomes."
Nesbitt and colleagues evaluated MRI scans of 43 knees from 37 patients who had spontaneous osteonecrosis of the knee. A review for clinical outcomes had an average follow-up time of 13.3 months (range, 0-88 months). Poor outcomes were defined as progression to articular surface collapse, continued complaints leading to surgical knee replacement, or another episode of SONK.
Six patients (14%) had another episode of SONK, while 11 (26%) showed no improvement and needed an injection rather than arthroscopy, according to the researchers. Four patients (9%) required arthroplasty, and 22 (51%) had no negative outcomes. Reasons for poor clinical outcomes included a significantly higher average body mass index and several other conditions related to SONK.
"MRI can identify a series of findings related to SONK, and our results have shown that the combination of subchondral fracture line, loss of subchondral articular surface contour, meniscal tear with extrusion, and adjacent soft-tissue edema are more indicative of unfavorable clinical outcomes than just one key feature," Nesbitt told AuntMinnie.com.
MRI can help predict how well patients recover from SONK, and more aggressive treatment could help certain patients minimize their risk, the researchers concluded.
![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.auntminnie.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)
