Search

Over the past decade rapid advancements in molecular imaging (MI) and artificial intelligence (AI) have revolutionized traditional musculoskeletal radiology. Molecular imaging refers to the ability of various methods to in vivo characterize and quantify biological processes, at a molecular level. The extracted information provides the tools to understand the pathophysiology of diseases and thus to early detect, to accurately evaluate the extend and to apply and evaluate targeted treatments. At present, molecular imaging mainly involves CT, MRI, radionuclide, US, and optical imaging and has been reported in many clinical and preclinical studies. Although originally MI techniques targeted at central nervous system disorders, later on their value on musculoskeletal disorders was also studied in depth. Meaningful exploitation of the large volume of imaging data generated by molecular and conventional imaging techniques, requires state-of-the-art computational methods that enable rapid handling of large volumes of information. AI allows end-to-end training of computer algorithms to perform tasks encountered in everyday clinical practice including diagnosis, disease severity classification and image optimization. Notably, the development of deep learning algorithms has offered novel methods that enable intelligent processing of large imaging datasets in an attempt to automate decision-making in a wide variety of settings related to musculoskeletal trauma. Current applications of AI include the diagnosis of bone and soft tissue injuries, monitoring of the healing process and prediction of injuries in the professional sports setting. This review presents the current applications of novel MI techniques and methods and the emerging role of AI regarding the diagnosis and evaluation of musculoskeletal trauma., Competing Interests: Declaration of Competing Interest No conflict of interest to disclose for all authors, (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Segmentation, denoising, and partial volume correction (PVC) are three major processes in the quantification of uptake regions in post-reconstruction PET images. These problems are conventionally addressed by independent steps. In this study, we hypothesize that these three processes are dependent; therefore, jointly solving them can provide optimal support for quantification of the PET images. To achieve this, we utilize interactions among these processes when designing solutions for each challenge. We also demonstrate that segmentation can help in denoising and PVC by locally constraining the smoothness and correction criteria. For denoising, we adapt generalized Anscombe transformation to Gaussianize the multiplicative noise followed by a new adaptive smoothing algorithm called regional mean denoising. For PVC, we propose a volume consistency-based iterative voxel-based correction algorithm in which denoised and delineated PET images guide the correction process during each iteration precisely. For PET image segmentation, we use affinity propagation (AP)-based iterative clustering method that helps the integration of PVC and denoising algorithms into the delineation process. Qualitative and quantitative results, obtained from phantoms, clinical, and pre-clinical data, show that the proposed framework provides an improved and joint solution for segmentation, denoising, and partial volume correction., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Introduction: In vivo imaging biomarkers of various HIV neuropathologies, including dopaminergic dysfunction, are still lacking. Towards developing dopaminergic biomarkers of brain involvement in HIV, we assessed the pre and postsynaptic components of the dopaminergic system in the HIV-1 transgenic rat (Tg), a well-characterized model of treated HIV+ patients, using small-animal PET imaging., Methods: Fifteen to 18 month-old Tg and wild type (WT) rats were imaged with both [18F]-FP-CMT, a dopamine transporter (DAT) ligand (n=16), and [18F]-Fallypride, a D2/D3 dopamine receptor (D2/D3DR) ligand (n=16). Five to 8 month-old Tg and WT rats (n=18) were also imaged with [18F]-FP-CMT. A subset of animals was imaged longitudinally at 7 and 17 months of age. Multiplex immunohistochemistry staining for DAT, tyrosine hydroxylase, D2DR, D3DR , GFAP, Iba1 and NeuN was performed on a subgroup of the scanned animals., Results: [18F]-FP-CMT and [18F]-Fallypride binding potential (BP ND ) values were significantly lower in 15-18 month-old Tg compared to age-matched WT rats (p<0.0001 and 0.001, respectively). [18F]-FP-CMT BP ND values in 5-8 month-old rats, however, were not significantly different. Longitudinal age-related decrease in [18F]-FP-CMT BP ND was exacerbated in the Tg rat. Immunohistochemistry showed decreased staining of dopaminergic markers in Tg rats. Rats with higher serum gp120 had lower mean BP ND values for both ligands., Conclusions: We found presynaptic and postsynaptic dopaminergic dysfunction/loss in older Tg compared to WT rats. We believe this to be related to neurotoxicity of viral proteins present in the Tg rats' serum and brain., Advances in Knowledge: Our findings confirm prior reports of neurobehavioral abnormalities suggestive of dopaminergic dysfunction in this model. They also suggest similarities between the Tg rat and HIV+ patients as far as dopaminergic dysfunction., Implications for Patient Care: The Tg rat, along with the above-described quantitative PET imaging biomarkers, can have a role in the evaluation of HIV neuroprotective therapies prior to human translation., (Published by Elsevier Inc.)

Objective: Insulin-like growth factor (IGF)-axis is involved in human oncogenesis and metastasis development for various solid tumors including breast cancer. Aim of this study was to assess the association between IGF-1, IGF-binding protein-3 (IGFBP-3) serum levels and the presence of circulating tumor cells (CTCs) in the peripheral blood of women diagnosed with early breast cancer (EBC), before and after adjuvant chemotherapy., Design: 171 patients with early-stage breast adenocarcinomas were retrospectively evaluated. Immunoradiometric (IRMA) assays were employed for the in-vitro determination of IGF-1 and IGFBP-3 serum levels in blood samples collected after surgical treatment and before initiation of adjuvant chemotherapy. CTCs' presence was assessed through detection of cytokeratin-19 (CK-19) mRNA transcripts using quantitative real time reverse transcription polymerase chain reaction (RT-PCR). IGF-1, IGFBP-3 serum levels were correlated with CTCs' presence before and after adjuvant chemotherapy as well as with tumor characteristics including tumor size, axillary lymph node status, oestrogen (ER)/progestorene (PR) and human epidermural growth factor receptor 2 (HER2) receptor status. Log-rank test was applied to investigate possible association between IGF-1, IGFBP-3 serum levels and disease-free interval (DFI) and overall survival (OS)., Results: Before initiation of adjuvant therapy IGF-1, IGFBP-3 serum levels were moderately associated (Spearman's rho=0.361, p<0.001) with each other, while presenting significant differences across age groups (all p values<0.05). IGF-1 serum levels did not correlate with the presence of CTCs before initiation (p=0.558) or after completion (p=0.474) of adjuvant chemotherapy. Similarly, IGFBP-3 serum levels did not show significant association with detectable CTCs either before (p=0.487) or after (p=0.134) completion of adjuvant chemotherapy. There was no statistically significant association between the clinical outcome of patients in terms of DFI, OS and IGF-1(DFI: p=0.499; OS: p=0.220) or IGFBP-3 (DFI: p=0.900; OS: p=0.406) serum levels., Conclusions: IGF-1 and IGFBP-3 serum levels before initiation of adjuvant chemotherapy are not indicative of CTCs' presence in the blood and do not correlate with clinical outcome of women with early-stage breast cancer., (Copyright © 2017 Elsevier Ltd. All rights reserved.)