Infiltrating Glioblastoma Multiforme (GBM) cells typically move fastest along existing structures in brain – vessels and white-matter tracts¹. Matrix metalloproteinases (MMPs) and/or ADAMs (A-Disintegrin-And-Metalloproteinase) are up-regulated in GBM and are employed to overcome structural barriers in the extracellular extravascular matrix (EEM)^2,3. Lysis of the EEM by MMPs is accompanied by inflammation, and in the brain is accompanied by alteration of the permeability of the Blood-Brain-Barrier(BBB) to small molecules³. However, it is a matter of common experience that, in regions with significant infiltration, even Gd-DTPA(molecular-weight 512) does not penetrate the BBB in any detectable amount over a period of tens of minutes. Unfortunately, in humans the central lesion, with its avid angiogenesis and accompanying leakage of vascular proteins and copious amounts of water, produces an extensive edema that masks subtle changes in local vascular permeability that accompany the presence of infiltrating tumor cells. While any one mode of MRI imaging (diffusion-T1- and T2-weighted, post-contrast-T1-weighted,dynamic studies) fails to discriminate tissue with infiltrating tumor cells, preliminary investigations demonstrate a multimodal analysis to be effective in estimating tumor burden in a Patient-Derived Mouse Xenografts (PDX) model of GBM. We suggest that MRI sequences sensitive to the presence of tissue water, tissue water exchange across microvascular boundaries, and magnetization exchange between tissue structures and tissue water will generate a tissue signature characteristic of local tumor burden. Also, MRI measures of vascular volume and tissue metabolism will reflect local tumor burden. This pilot study investigates the feasibility of constructing an Adaptive Model (AM) as probabilistic predictor of infiltrating tumor burden in PDX model of GBM using multiple MRI imaging.Eight mice implanted with GBM CSC HF2927 were studied. MRI studies were performed in a Direct-Drive-Varian (Santa-Clara, CA) 7Tesla, 20cm bore system with a 24mm² field of view. Gradient maximum strengths and rise times were 250mT/m and 120µs. The following image sets were acquired: high-resolution T1- and T2-weighted (matrix:256x192, 27slices, 0.5mm thickness, NE=1, NA=4, TE/TR=16/800 and NE=4,TE/TR = (20,40,60,80)/3000 ms); MT-weighted fast spin-echo, matrix:256x128, 27slices (0.4,0.1 mm gap), TR/TE 2248/11.4, 32 segments, 4 echoes, NEX=6. Magnevist (0.25mmol/kg i.p) was injected about 5 minutes before the post-contrast T1-weighted image set was acquired. The animal was sacrificed immediately after MRI and stained for the presence of human GBM cells. We used the following MRI sequences to establish a basis set for training the AM: pre- and post-contrast (Magnevist, I.P.) T1-weighted, 4-echo T2, MT, 3-direction, 3 b-value diffusion-weighted. Maps of T2 and proton density (T2-PD) were produced by fitting the T2 data. All MRI images were skull-stripped manually and co-registered to the T2-PD map using an affine transform. The histology image was warped and co-registered to the T2-PD image. MR image sets were normalized to the white-matter area of the brain, and each voxel profile extracted from the 9 image set was normalized to the summation of two normalized T2 images (echo 2 and 3), following which the normalized profile along with the co-registered histology was used for training and testing of an artificial neural network (ANN)⁴ with Multi-Layer-perceptron architecture to predict the presence of local tumor burden in form of tumorous cell density. About 6000 samples were used to train the ANN. The Area-Under-the-Receiver-Operating-Characteristic (AUROC) along with K-fold-cross-validation(KFCV) with 20 folds (about 300 samples-per-fold) technique [4] were used to validate, optimize, and estimate the predictive power of the ANN.The optimal ANN(10:3:1) was successfully trained and validated using the 9 MR modalities. Results imply that the chosen MRI feature-set contains adequate information for training the ANN. The predictive power of the ANN was ~0.81. Figure 1-A shows the ANN’s probabilistic prediction of tumor burden using a set of MRI images in an exemplary case. The correlation coefficient for the association between the predicted map and histology was ~0.85. Figure 1-B illustrates MHC stain(brown) for human cells. The spread of the implanted glioma cells from the tumor mass(black dashed-line) below the injection site(arrow) to the contralateral side of the brain(yellow dashed-line) is evident. The histology demonstrates that GBM cells were densely present near the injection-site and had infiltrated broadly throughout the brain. Figure 1-C, shows the post-contrast T1-weighted image. Note the lack of enhancement in the image. Thus, in a model of infiltrating tumor with minimal breakdown of the BBB, we have generated a probabilistic estimate of tumor burden. Given the success of training an ANN to predict infiltrating tumor burden, it may be possible to identify similar or different basis set of MRI images that can robustly predict both infiltrating and solid tumor burden in human GBM.