Validity of soft-tissue thickness of calf measured using MRI for assessing unilateral lower extremity lymphoedema secondary to cervical and endometrial cancer treatments

•The soft tissue thickness of calves on MRI could quantitatively assess secondary LEL.•Calf soft tissue thickness indicated concurrent or construct validity of calf volume.•The difference of subcutaneous tissue thickness of calves could be used to stage LEL.

AimTo determine whether soft-tissue thickness of the calf measured using MRI could be valid for assessing unilateral lower extremity lymphoedema (LEL) secondary to cervical and endometrial cancer treatments.Materials and methodsSeventy women with unilateral LEL and 25 without LEL after cervical or endometrial cancer treatments underwent MRI examinations of their calves. Total thickness of soft-tissue (TT), muscle thickness (MT), and subcutaneous tissue thickness (STT) of the calf, and the difference between the affected and contralateral unaffected calf regarding TT (DTT), MT (DMT), and STT (DSTT) were obtained using fat-suppressed T2-weighted imaging in the middle of the calves. The volume of the calf and difference in volume (DV) between calves were obtained by the method of water displacement. Statistical analysis was performed to determine the validity of MRI measurements by volume measurements in staging LEL.ResultsThere was a close correlation between volume and TT for the affected (r = 0.927) or unaffected calves (r = 0.896). STT of the affected calf, and DTT or DSTT of the calves were closely correlated with volume of the affected calf or DV of the calves (all p < 0.05). Multivariate analysis showed significant differences in TT, STT, volume of the affected calf, DTT, DSTT, and DV between stages except in volume of the affected calf or in DV between stage 0 and 1. For staging LEL, DSTT showed the best discrimination ability among all the parameters.ConclusionsSoft-tissue thickness of the calf measured at MRI could be valid for quantitatively staging unilateral LEL, and DSTT of the calves could be the best classifying factor.