Multiexponential T2 relaxometry of benign and malignant adipocytic tumours

Abstract

Abstract Background We investigated a recently proposed multiexponential (Mexp) fitting method applied to T2 relaxometry magnetic resonance imaging (MRI) data of benign and malignant adipocytic tumours and healthy subcutaneous fat. We studied the T2 distributions of the different tissue types and calculated statistical metrics to differentiate benign and malignant tumours. Methods Twenty-four patients with primary benign and malignant adipocytic tumours prospectively underwent 1.5-T MRI with a single-slice T2 relaxometry (Carr-Purcell-Meiboom-Gill sequence, 25 echoes) prior to surgical excision and histopathological assessment. The proposed method adaptively chooses a monoexponential or biexponential model on a voxel basis based on the adjusted R2 goodness of fit criterion. Linear regression was applied on the statistical metrics derived from the T2 distributions for the classification. Results Healthy subcutaneous fat and benign lipoma were better described by biexponential fitting with a monoexponential and biexponential prevalence of 0.0/100% and 0.2/99.8% respectively. Well-differentiated liposarcomas exhibit 17.6% monoexponential and 82.4% biexponential behaviour, while more aggressive liposarcomas show larger degree of monoexponential behaviour. The monoexponential/biexponential prevalence was 47.6/52.4% for myxoid tumours, 52.8/47.2% for poorly differentiated parts of dedifferentiated liposarcomas, and 24.9/75.1% pleomorphic liposarcomas. The percentage monoexponential or biexponential model prevalence per patient was the best classifier distinguishing between malignant and benign adipocytic tumours with a 0.81 sensitivity and a 1.00 specificity. Conclusions Healthy adipose tissue and benign lipomas showed a pure biexponential behaviour with similar T2 distributions, while decreased adipocytic cell differentiation characterising aggressive neoplasms was associated with an increased rate of monoexponential decay curves, opening a perspective adipocytic tumour classification.