Quantification of tumor heterogeneity using PET/MRI and machine learning

Despite a broad understanding that solid tumors exhibit significant tissue heterogeneity, clinical trials have not seen a remarkable development in techniques that aid in characterizing cancer. Needle biopsies often represent only a partial view of the tumor profile, lacking the ability to comprehensively reflect spatiotemporal phenotypic changes. Recent multimodal multiparametric imaging techniques could provide further valuable insights if the complementary imaging information is sufficiently analyzed. Therefore, in this work I developed and applied machine learning methods on multiparametric positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, acquired using mice bearing subcutaneous tumors, to obtain a precise spatio-temporal characterization of intratumor heterogeneity

When are Neural ODE Solutions Proper ODEs?

A key appeal of the recently proposed Neural Ordinary Differential Equation(ODE) framework is that it seems to provide a continuous-time extension of discrete residual neural networks. As we show herein, though, trained Neural ODE models actually depend on the specific numerical method used during training. If the trained model is supposed to be a flow generated from an ODE, it should be possible to choose another numerical solver with equal or smaller numerical error without loss of performance. We observe that if training relies on a solver with overly coarse discretization, then testing with another solver of equal or smaller numerical error results in a sharp drop in accuracy. In such cases, the combination of vector field and numerical method cannot be interpreted as a flow generated from an ODE, which arguably poses a fatal breakdown of the Neural ODE concept. We observe, however, that there exists a critical step size beyond which the training yields a valid ODE vector field. We propose a method that monitors the behavior of the ODE solver during training to adapt its step size, aiming to ensure a valid ODE without unnecessarily increasing computational cost. We verify this adaption algorithm on two common bench mark datasets as well as a synthetic dataset. Furthermore, we introduce a novel synthetic dataset in which the underlying ODE directly generates a classification task

A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

PURPOSE: We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies. PROCEDURES: Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2–3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology. RESULTS: While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (r(necrotic) = 0.92, r(peri-necrotic) = 0.82 and r(viable) = 0.98) with the histology. CONCLUSIONS: The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11307-016-1009-y) contains supplementary material, which is available to authorized users