Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice

A Unique Case of Bilateral Thalamic High-Grade Glioma in a Pediatric Patient with LI-Fraumeni Syndrome: Case Presentation and Review of the Literature

Li-Fraumeni syndrome (LFS) is a rare high-penetrance and autosomal-dominant pathological condition caused by the germline mutation of the TP53 gene, predisposing to the development of tumors from pediatric age. We conducted a qualitative systematic review following the ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) framework. A search was made in MEDLINE/Pubmed and MeSH Database using the terms “Li-Fraumeni” AND “pediatric high-grade glioma (HGG)”, identifying six cases of HGGs in pediatric patients with LFS. We added a further case with peculiar features such as no familiar history of LFS, association of embryonal rhabdomyosarcoma and bithalamic HGG, whose immunohistochemical profile was accurately defined by Next Generation Sequencing. Knowledge synthesis and case analysis grounded the discussion about challenges in the management of this pathology in pediatric age

New Neuronal Subtypes With a “Pre-Pancreatic” Signature in the Sea Urchin Stongylocentrotus purpuratus

Neurons and pancreatic endocrine cells have a common physiology and express a similar toolkit of transcription factors during development. To explain these common features, it has been hypothesized that pancreatic cells most likely co-opted a pre-existing gene regulatory program from ancestral neurons. To test this idea, we looked for neurons with a “pre-pancreatic” program in an early-branched deuterostome, the sea urchin. Only vertebrates have a proper pancreas, however, our lab previously found that cells with a pancreatic-like signature are localized within the sea urchin embryonic gut. We also found that the pancreatic transcription factors Xlox/Pdx1 and Brn1/2/4 co-localize in a sub-population of ectodermal cells. Here, we find that the ectodermal SpLox+ SpBrn1/2/4 cells are specified as SpSoxC and SpPtf1a neuronal precursors that become the lateral ganglion and the apical organ neurons. Two of the SpLox+ SpBrn1/2/4 cells also express another pancreatic transcription factor, the LIM-homeodomain gene islet-1. Moreover, we find that SpLox neurons produce the neuropeptide SpANP2, and that SpLox regulates SpANP2 expression. Taken together, our data reveal that there is a subset of sea urchin larval neurons with a gene program that predated pancreatic cells. These findings suggest that pancreatic endocrine cells co-opted a regulatory signature from an ancestral neuron that was already present in an early-branched deuterostome

Pediatric Hodgkin Lymphoma: Predictive value of interim 18F-FDG PET/CT in therapy response assessment

We investigated the prognostic value of interim 18F-FDG PET/CT (PET-2) in pediatric Hodgkin lymphoma (pHL), evaluating both visual and semiquantitative analysis. Thirty pHL patients (age ⠤16) underwent serial 18F-FDG PET/CT: At baseline (PET-0), after 2 cycles of chemotherapy (PET-2) and at the end of first-line chemotherapy (PET-T). PET response assessment was carried out visually according to the Deauville Score (DS), as well as semiquantitatively by using the semiquantitative parameters reduction from PET-0 to PET-2 (ΠΣSUVmax0-2, ΠΣSUVmean0-2). Final clinical response assessment (outcome) at the end of first-line chemotherapy was the criterion standard, considering patients as responders (R) or nonresponders (NR). Disease status was followed identifying patients with absence or relapsed/progression disease (mean follow-up: 24 months, range 3-78). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of visual and semiquantitative assessment were calculated; furthermore, Fisher exact test was performed to evaluate the association between both visual and semiquantitative assessment and outcome at the end of the first-line chemotherapy. The prognostic capability of PET-2 semiquantitative parameters was calculated by ROC analysis and expressed as area under curve (AUC). Finally, progression-free survival (PFS) was analyzed according to PET-2 results based on the 5-point scale and semiquantitative criteria, using the Kaplan-Meier method. Based on the outcome at the end of first-line chemotherapy, 5 of 30 patients were NR, the remnant 25 of 30 were R. Sensitivity, specificity, PPV, NPV, and accuracy of visual analysis were 60%,72%,30%,90%,70%; conversely, sensitivity, specificity, PPV, NPV, and accuracy of semiquantitative assessment were 80%, 92%, 66.7%, 95.8%, 90%. The highest AUC resulted for ΠΣSUVmax0-2 (0.836; cut-off <12.5; sensitivity 80%; specificity 91%). The association between ΠΣSUVmax0-2 and outcome at the end of first-line chemotherapy resulted to have a strong statistical significance (P=0.0026). Both methods demonstrated to influence PFS, even if the semiquantitative assessment allowed a more accurate identification of patients with a high risk of treatment failure (P=0.005). Our preliminary results showed that PET-2 visual assessment, by using Deauville criteria, can be improved by using the semiquantitative analysis. The SUV max reduction (ΠΣSUVmax0-2) evaluation might provide a support for the interpretation of intermediate scores, predicting with good confidence those patients who will have a poor outcome and require alternative therapies

Current role of machine learning and radiogenomics in precision neuro-oncology

In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology

Default-Mode Network Connectivity Changes Correlate with Attention Deficits in ALL Long-Term Survivors Treated with Radio- and/or Chemotherapy

: Whether chemotherapy (ChT) and radiotherapy (RT) determine neurocognitive impairment in acute lymphoblastic leukemia long-term survivors (ALL LTSs) through similar mechanisms affecting the same brain regions is still unknown. We compared neurocognitive alterations, regional brain tissue volumes (by voxel-based morphometry), and functional connectivity of the main default-mode network hubs (by seed-based analysis of resting state functional MRI data), in 13 ALL LTSs treated with RT and ChT (Group A) and 13 treated with ChT only (Group B). Group A performed significantly worse than Group B at the digit span and digit symbol tests (p = 0.023 and 0.013, respectively). Increased connectivity between the medial prefrontal cortex (the main anterior hub of the default-mode network) and the rolandic operculi was present in Group A compared to Group B, along with the absence of significant differences in regional brain tissue volumes. In these regions, the functional connectivity correlated inversely with the speed of processing scores, independent of treatment group. These results suggest that similar mechanisms may be involved in the neurocognitive deficits in ALL LTS patients, regardless of the treatment group. Further studies are needed to clarify whether these changes represent a direct expression of the mechanisms underlying the cognitive deficits or ineffective compensatory phenomena

Neuroimaging of the Most Common Meningitis and Encephalitis of Adults: A Narrative Review

Meningitis is the infection of the meninges, which are connective tissue membranes covering the brain, and it most commonly affects the leptomeninges. Clinically, meningitis may present with fever, neck stiffness, altered mental status, headache, vomiting, and neurological deficits. Encephalitis is an infection of the brain, which usually presents with fever, altered mental status, neurological deficits, and seizure. Meningitis and encephalitis are serious conditions which could also coexist, with high morbidity and mortality, thus requiring prompt diagnosis and treatment. Imaging plays an important role in the clinical management of these conditions, especially Magnetic Resonance Imaging. It is indicated to exclude mimics and evaluate the presence of complications. The aim of this review is to depict imaging findings of the most common meningitis and encephalitis