Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques.

No abstract

Select adhesion proteins control the development of synapses and modulate their structural and functional properties. Despite these important roles, the extent to which different synapse-organizing mechanisms act across brain regions to establish connectivity and regulate network properties is incompletely understood. Further, their functional roles in different neuronal populations remain to be defined. Here, we applied diffusion tensor imaging (DTI), a modality of magnetic resonance imaging (MRI), to map connectivity changes in knock-out (KO) mice lacking the synaptogenic cell adhesion protein SynCAM 1. This identified reduced fractional anisotropy in the hippocampal CA3 area in absence of SynCAM 1. In agreement, mossy fiber refinement in CA3 was impaired in SynCAM 1 KO mice. Mossy fibers make excitatory inputs onto postsynaptic specializations of CA3 pyramidal neurons termed thorny excrescences and these structures were smaller in the absence of SynCAM 1. However, the most prevalent targets of mossy fibers are GABAergic interneurons and SynCAM 1 loss unexpectedly reduced the number of excitatory terminals onto parvalbumin (PV)-positive interneurons in CA3. SynCAM 1 KO mice additionally exhibited lower postsynaptic GluA1 expression in these PV-positive interneurons. These synaptic imbalances in SynCAM 1 KO mice resulted in CA3 disinhibition, in agreement with reduced feedforward inhibition in this network in the absence of SynCAM 1-dependent excitatory drive onto interneurons. In turn, mice lacking SynCAM 1 were impaired in memory tasks involving CA3. Our results support that SynCAM 1 modulates excitatory mossy fiber inputs onto both interneurons and principal neurons in the hippocampal CA3 area to balance network excitability.

Purpose: To establish magnetic resonance (MR)-based molecular imaging paradigms for the noninvasive monitoring of extracellular pH (pH e ) as a functional surrogate biomarker for metabolic changes induced by locoregional therapy of liver cancer.Experimental Design: Thirty-two VX2 tumor-bearing New Zealand white rabbits underwent longitudinal imaging on clinical 3T-MRI and CT scanners before and up to 2 weeks after complete conventional transarterial chemoembolization (cTACE) using ethiodized oil (lipiodol) and doxorubicin. MR-spectroscopic imaging (MRSI) was employed for pH e mapping. Multiparametric MRI and CT were performed to quantify tumor enhancement, diffusion, and lipiodol coverage of the tumor posttherapy. In addition, incomplete cTACE with reduced chemoembolic doses was applied to mimic undertreatment and exploit pH e mapping to detect viable tumor residuals. Imaging findings were correlated with histopathologic markers indicative of metabolic state (HIF-1a, GLUT-1, and LAMP-2) and viability (proliferating cell nuclear antigen and terminal deoxynucleotidyl-transferase dUTP nick-end labeling).Results: Untreated VX2 tumors demonstrated a significantly lower pH e (6.80 AE 0.09) than liver parenchyma (7.19 AE 0.03, P < 0.001). Upregulation of HIF-1a, GLUT-1, and LAMP-2 confirmed a hyperglycolytic tumor phenotype and acidosis. A gradual tumor pH e increase toward normalization similar to parenchyma was revealed within 2 weeks after complete cTACE, which correlated with decreasing detectability of metabolic markers. In contrast, pH e mapping after incomplete cTACE indicated both acidic viable residuals and increased tumor pH e of treated regions. Multimodal imaging revealed durable tumor devascularization immediately after complete cTACE, gradually increasing necrosis, and sustained lipiodol coverage of the tumor.Conclusions: MRSI-based pH e mapping can serve as a longitudinal monitoring tool for viable tumors. As most liver tumors are hyperglycolytic creating microenvironmental acidosis, therapy-induced normalization of tumor pH e may be used as a functional biomarker for positive therapeutic outcome.

In this project, our goal is to classify different types of liver tissue on 3D multi-parameter magnetic resonance images in patients with hepatocellular carcinoma. In these cases, 3D fully annotated segmentation masks from experts are expensive to acquire, thus the dataset available for training a predictive model is usually small. To achieve the goal, we designed a novel deep convolutional neural network that incorporates auto-context elements directly into a U-net-like architecture. We used a patch-based strategy with a weighted sampling procedure in order to train on a sufficient number of samples. Furthermore, we designed a multi-resolution and multi-phase training framework to reduce the learning space and to increase the regularization of the model. Our method was tested on images from 20 patients and yielded promising results, outperforming standard neural network approaches as well as a benchmark method for liver tissue classification.