Multiexponential T2 relaxometry of benign and malignant adipocytic tumours

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.

Keywords: Lipoma; Liposarcoma; Magnetic resonance imaging; Neoplasms (adipose tissue); Subcutaneous fat.

Fig. 1

Study workflow

Fig. 2

a Whole slice Mexp analysis for a lower limb sarcoma where ROI_1 is indicative for healthy adipose tissue area and ROI_2 corresponds to a malignant region. b–d Mexp-derived spectra for ROI_1 and ROI_2. e T2-weighted images. f, g Whole slice T2_i parametric maps

Fig. 3

Relative contribution of monoexponential and biexponential behaviour for each tumour subtype

Fig. 4

Derived Mexp spectra from purely biexponential tissue samples for subcutaneous fat (a), lipoma (b), and well-differentiated liposarcoma (c)

Fig. 5

Per-patient analysis of T2 relaxometry data with inverse Laplace method (a) and Mexp method (b) for five lipoma patients

Fig. 6

Derived spectra from mixed monoexponential and biexponential tissue samples for myxoid liposarcoma monoexponential spectrum (a), poorly differentiated liposarcoma monoexponential spectrum (b), pleomorphic liposarcoma monoexponential spectrum (c), myxoid liposarcoma biexponential spectrum (d), poorly differentiated liposarcoma biexponential spectrum (e), and pleomorphic liposarcoma biexponential spectrum (f). exp Exponential

Fig. 7

Voxelwise model classification from Mexp for five patients with different histopathologically proven lipomatous neoplasms: benign lipoma (a), well-differentiated liposarcoma (b), myxoid liposarcoma (c), poorly differentiated liposarcoma (d), pleomorphic liposarcoma (e)