Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Generative Adversarial Networks in Brain Imaging: A Narrative Review

Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases. Generative Adversarial Networks have been proposed as one of the most exciting applications of deep learning in radiology. GANs are a new approach to deep learning that leverages adversarial learning to tackle a wide array of computer vision challenges. Brain radiology was one of the first fields where GANs found their application. In neuroradiology, indeed, GANs open unexplored scenarios, allowing new processes such as image-to-image and cross-modality synthesis, image reconstruction, image segmentation, image synthesis, data augmentation, disease progression models, and brain decoding. In this narrative review, we will provide an introduction to GANs in brain imaging, discussing the clinical potential of GANs, future clinical applications, as well as pitfalls that radiologists should be aware of

COMPOSITIONS AND METHODS FOR TARGETING CELLS

The present invention provides compositions and methods for targeting cells for therapeutic and/or diagnostic purposes, e.g., delivery of therapeutic and/or diagnostic agents to a cell. Nanoparticles and polymers functionalized with capture molecules, reporter molecules, and/or therapeutic agents are provided for the treatment or prevention of disease, including neurological diseases associated with neuroinflammation, and cancer

Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases

Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an un- met need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steato- hepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2–3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three ra- diomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI

Radiosynthesis and preliminary biological evaluation of [18F]VC701, a radioligand for translocator protein. G. Di Grigoli, C. Monterisi, S. Belloli, V. Masiello, L. S. Politi, S. Valenti, M. Paolino, M. Anzini, M. Matarrese, A. Cappelli, R.M. Moresco

Positron emission tomography (PET) can be used to monitor in vivo translocator protein (TSPO) expression by using specific radioligands. Recently, several [11C]PK11195 analogues have been synthesized to improve binding stability and brain availability. [18F]VC701 was synthesized and validated in CD healthy rats by biodistribution and inhibition analysis. Imaging studies were also conducted on animals injected unilaterally in the striatum with quinolinic acid (QA) to evaluate the TSPO ligand uptake in a neuroinflammation/neurodegenerative model. [18F]VC701 was synthesized with a good chemical and radiochemical purity and specific activity higher than 37 GBq/mmol. Kinetic studies performed on healthy animals showed the highest tracer biodistribution in TSPO-rich organs, and preadministration of cold PK11195 caused an overall radioactivity reduction. Metabolism studies showed the absence of radio metabolites in the rat brain of QA lesioned rats, and biodistribution analysis revealed a progressive increase in radioactivity ratios (lesioned to nonlesioned striatum) during time, reaching an approximate value of 5 4 hours after tracer injection. These results encourage further evaluation of this TSPO radioligand in other models of central and peripheral diseases

Precision Oncology in Lower-Grade Gliomas: Promises and Pitfalls of Therapeutic Strategies Targeting IDH-Mutations

Mutations in isocitrate dehydrogenase (IDH)1 and its homolog IDH2 are considered an earliest “driver” genetic event during gliomagenesis, representing now the molecular hallmark of lower-grade gliomas (LGGs). IDH-mutated genes encode for a neomorphic enzyme that converts α-ketoglutarate to the oncometabolite D-2-hydroxyglutarate (2-HG), which accumulates to high concentrations and alters cellular epigenetics and metabolism. Targeting IDH mutations is the first attempt to apply “precision oncology” in LGGs. Two distinct strategies have been proposed so far and are under intense clinical investigation: (i) reducing the amount of intratumoral 2-HG by directly blocking the function of mutant IDH enzyme; (ii) exploiting the selective epigenetic and metabolic cellular vulnerabilities as a consequence of 2-HG accumulation. The present review describes the physiopathological mechanisms by which IDH mutations lead to tumorigenesis, discussing their prognostic significance and pivotal role in the gliomas diagnostic classification system. We critically review preclinical evidence and available clinical data of first-generation mutant-selective IDH inhibitors and novel IDH-targeted vaccines. Finally, as an alternative and attractive approach, we present the rationale to take advantage of selective 2-HG related epigenetic and metabolic weaknesses. The results of ongoing clinical trials will help us clarify the complex scenario of IDH-targeted therapeutic approaches in gliomas

18F-VC701-PET and MRI in the in vivo neuroinflammation assessment of a mouse model of multiple sclerosis

BACKGROUND: Positron emission tomography (PET) using translocator protein (TSPO) ligands has been used to detect neuroinflammatory processes in neurological disorders, including multiple sclerosis (MS). The aim of this study was to evaluate neuroinflammation in a mouse MS model (EAE) using TSPO-PET with 18F-VC701, in combination with magnetic resonance imaging (MRI). METHODS: MOG35-55/CFA and pertussis toxin protocol was used to induce EAE in C57BL/6 mice. Disease progression was monitored daily, whereas MRI evaluation was performed at 1, 2, and 4 weeks post-induction. Microglia activation was assessed in vivo by 18F-VC701 PET at the time of maximum disease score and validated by radioligand ex vivo distribution and immunohistochemistry at 2 and 4 weeks post-immunization. RESULTS: In vivo and ex vivo analyses show that 18F-VC701 significantly accumulates within the central nervous system (CNS), particularly in the cortex, striatum, hippocampus, cerebellum, and cervical spinal cord of EAE compared to control mice, at 2 weeks post-immunization. MRI confirmed the presence of focal brain lesions at 2 weeks post-immunization in both T1-weighted and T2 images. Of note, MRI abnormalities attenuated in later post-immunization phase. Neuropathological analysis confirmed the presence of microglial activation in EAE mice, consistent with the in vivo increase of 18F-VC701 uptake. CONCLUSION: Increase of 18F-VC701 uptake in EAE mice is strongly associated with the presence of microglia activation in the acute phase of the disease. The combined use of TSPO-PET and MRI provided complementary evidence on the ongoing disease process, thus representing an attractive new tool to investigate neuronal damage and neuroinflammation at preclinical levels

Cellular magnetic resonance with iron oxide nanoparticles: long-term persistence of SPIO signal in the CNS after transplanted cell death

Aim: To study the specificity of cellular MRI based on superparamagnetic iron oxide particles (SPIOs), especially within the CNS. Materials & methods: A microglial cell line was engineered for the expression of a suicide gene, the receptor of diphtheria toxin (DT), and two reporter genes, green fluorescent protein and luciferase, in order to induce, in a controlled manner, cell death and test it through bioluminescence. SPIO-labeled DT-sensitive and control DT-insensitive cells were transplanted into the brains of mice, which underwent serial MRI and bioluminescence studies before and up to 90 days after DT-induced cell death. Results: No variations in SPIO signal voids were detected along longitudinal monitoring in brain hemispheres transplanted with DT-sensitive cells. Ex vivo analyses showed persistence of iron nanoparticle deposits at transplantation sites. Conclusion: Due to the long-term persistence of signal after transplanted cell death, caution is advised when SPIOs are employed for cell tracking. Original submitted 27 January 2014; Revised submitted 18 April 2014