Disease heterogeneity in patients with malignant pleural mesothelioma (MPM), which presents with both solid tumour and pleural effusion, makes it challenging to characterise solid disease and assess response following treatment. Computed Diffusion-Weighted MRI (cDWI) provides improved contrast between disease and background tissues, and facilitates total disease segmentation [1]. Mixture modelling of joint histograms of within-tumour Apparent Diffusion Coefficient (ADC) and transverse relaxation rate (R2) may offer a viable method to charactierise disease heterogeneity. The purposes of this study were to investigate this novel methodology to assess disease heterogeneity in mesothelioma, and demonstrate its utility on a paired pre/post-treatment dataset.

MR image acquisition: Diffusion- and T2-weighted images were acquired in five patients with MPM, before and 4 weeks after anti-tumour treatment, using 1.5T Magnetom Avanto (Siemens Healthcare, Erlangen, Germany). A 2-station EPI sequence (covering the whole chest) was used for both acquisitions. The DWI parameters were: 30 axial slices, slice thickness 5mm, TR/TE=9000/82 ms, FOV=273*380 mm, 4 averages, matrix 92*128, parallel acquisition (Grappa acc. factor 2, ref lines 30), receiver bandwidth = 1860Hz/Px, fat suppression SPAIR, b =100/500/800 mm^-2s. T2-weighted images were acquired using the same sequence and matrix, with b-value of 0 and TEs 60/82/177 ms.

Segmentation/ mixture modelling: The total disease volume was segmented using a semi-automatic tool (in-house software based on the GrowCut algorithm [2]) using cDWI images with b = 150 s/mm² and echo time 20ms (in-house software [1]), chosen such that the two components of MPM had similar signal intensities, whilst maintaining good disease/background contrast. Segmented ROIs were reviewed by a senior radiologist and then transferred to calculated ADC and R2 maps, jointly estimated from the diffusion- and T2-weighted data [1]. Two-dimensional scatter plots of ADC/R2 voxel values were generated and a 2-class Gaussian Mixture Model (GMM) was applied to the global multivariate data, using the Expectation Maximization (EM) algorithm for parameter estimation [3]. Following estimation of these multi-modal probability distributions, deriving the posterior probability, P_ij, of a voxel i belonging to each class Cj, allowed construction of tissue classification maps and calculation of proportional disease volume of each class.

Patient 1: It can be seen (Figure 1) that the two pre-treatment baseline data have very similar shapes of ADC/R2 distribution on the joint histogram and are both well fitted by GMM (contour lines) with two Gaussian distributions (mean vectors shown on the figure). The 3rd column of Figure 1 displays the regions of solid disease (red) and pleural effusion (green) on the ADC maps, while the 4th column shows maps of the posterior probabilities for the two classes (C1: solid disease; C2: pleural effusion). In addition, regions and their probability maps show a high correspondence between the two baseline studies. The proportional volumes of the two classes have < 1% difference in the two pre-treatment baseline scans (Patient 1 in Table 1), suggesting a repeatable method, despite an apparent difference in centroid ADC for C1.

Patient 2: After 4 weeks treatment, the joint parameter distribution changed with increased dispersion, with increasing scatter observed on the left corner of the joint histogram, representing post-treatment changes in class 1 (lower ADC and lower R2) (Figure 2). There was a visible shift of the centre of cluster 1 (solid disease) to the lower left, ADC from 1538 to 1411 s/mm^-2 while R2 from to 13.6 to 12.3 s^-1 (Figure 2 and Table 1). After treatment, both the absolute and proportional volume of class 1 (solid disease) increased while the pleural effusion decreased (Patient 2 in Table 1).

In these two cases, we have demonstrated that it is possible to assess disease heterogeneity by using joint ADC/R2 modelling combined with a semi-automatic segmentation tool on cDWI. This method can be used to observe changes in the mean ADC and R2 values after treatment within a disease volume for each tissue class independently and evaluate the heterogeneous treatment response. Further investigation of the model will be performed in a larger clinical cohort. The mixture (ADC and R2) modelling methodology successfully characterised response heterogeneity for two patients with malignant pleural mesothelioma and can provide additional quantitative functional disease response characterisation compared with using only a single parameter.