Machine learning-based differentiation between multiple sclerosis and glioma WHO II°-IV° using O-(2-[18F] fluoroethyl)-L-tyrosine positron emission tomography

The Role of Peripheral Inflammatory Markers and Coagulation Factors in Patients with Central Nervous System (CNS) Immune Disease and Glioma

OBJECTIVE

Gliomas are associated with high rates of disability and mortality, and currently, there is a lack of specific and sensitive biomarkers for diagnosis. The ideal biomarkers should be detected early through noninvasive methods. Our research aims to develop a rapid, convenient, noninvasive diagnostic method for gliomas, as well as for grading and differentiation.

METHOD

We retrospectively collected data from patients who underwent surgery for glioma, trigeminal neuralgia/hemifacial spasmschwannoma, and those diagnosed with multiple sclerosis at our institution from January 2018 to December 2020. Inflammatory markers and coagulation factor levels were collected on admission, and neutrophil count (NLR), (WBC count minus neutrophil count) / lymphocyte count, platelet count / lymphocyte count, lymphocyte count / monocyte count, and albumin count [g/L] + total lymphocyte count × 5 were calculated for patients. Analyze the significance of biomarkers in the diagnosis and grading of gliomas, the diagnosis of MS, and the differential diagnosis of them.

RESULTS

We evaluated 155 healthy individuals, 64 trigeminal neuralgia/hemifacial spasm patients, 47 MS patients, 316 schwannoma patients, and 814 with glioma patients. Compared with healthy controls and MS group, the preoperative levels of NLR, (WBC count minus neutrophil count) / lymphocyte count, D-dimer, Fibrinogen, Antithrobin, and Factor VIII of glioma patients were significantly higher in glioma patients and positively correlated with the grade of glioma. Conversely, 0020 lymphocyte count / Monocyte count and albumin count [g/L] + total lymphocyte count × 5 were significantly lower and negatively correlated with glioma grading. ROC curves confirmed that for the diagnosis of glioma, NLR showed a maximum area under the curve value of 0.8616 (0.8322-0.8910), followed by D-dimer and Antithrombin, with area under the curve values of 0.8205 (0.7601-0.8809) and 0.8455 (0.8153-0.8758), respectively. NLR and d-dimer also showed great sensitivity in the diagnosis of MS and differential diagnosis with gliomas.

CONCLUSIONS

Our study demonstrated that multiple inflammatory markers and coagulation factors could be utilized as biomarkers for the glioma diagnosis, grading, and differential diagnosis of MS. Furthermore, the combination of these markers exhibited high sensitivity and specificity.

Multimodal diagnostics in multiple sclerosis: predicting disability and conversion from relapsing-remitting to secondary progressive disease course - protocol for systematic review and meta-analysis

BACKGROUND

The number of patients diagnosed with multiple sclerosis (MS) has increased significantly over the last decade. The challenge is to identify the transition from relapsing-remitting to secondary progressive MS. Since available methods to examine patients with MS are limited, both the diagnostics and prognostication of disease progression would benefit from the multimodal approach. The latter combines the evidence obtained from disparate radiologic modalities, neurophysiological evaluation, cognitive assessment and molecular diagnostics. In this systematic review we will analyse the advantages of multimodal studies in predicting the risk of conversion to secondary progressive MS.

METHODS AND ANALYSIS

We will use peer-reviewed publications available in Web of Science, Medline/PubMed, Scopus, Embase and CINAHL databases. In vivo studies reporting the predictive value of diagnostic methods will be considered. Selected publications will be processed through Covidence software for automatic deduplication and blind screening. Two reviewers will use a predefined template to extract the data from eligible studies. We will analyse the performance metrics (1) for the classification models reflecting the risk of secondary progression: sensitivity, specificity, accuracy, area under the receiver operating characteristic curve, positive and negative predictive values; (2) for the regression models forecasting disability scores: the ratio of mean absolute error to the range of values. Then, we will create ranking charts representing performance of the algorithms for calculating disability level and MS progression. Finally, we will compare the predictive power of radiological and radiomical correlates of clinical disability and cognitive impairment in patients with MS.

ETHICS AND DISSEMINATION

The study does not require ethical approval because we will analyse publicly available literature. The project results will be published in a peer-review journal and presented at scientific conferences.

PROSPERO REGISTRATION NUMBER

CRD42022354179.

Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma

Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.

Clinical Usefulness of 18 F-FET PET in a Pediatric Patient With Suspected Demyelinating Disease

ABSTRACT

An 11-year-old boy who presented with headache and progressive right-sided weakness exhibited cortical swelling in the parafalcine area of both frontoparietal high convexity and splenium portion of corpus callosum on brain MRI. This suggested the possibility of encephalopathy, but required differential diagnosis from brain tumor. 18 F-FET ( O -(2-[ 18 F]fluoroethyl)- l -tyrosine) PET/CT identified increased uptake along the parafalcine area of the frontoparietal lobes and the splenium portion of the corpus callosum. The relatively low target-to-background ratios were more indicative of inflammatory changes such as demyelinating disease. The patient recovered after empirical steroid and immunoglobulin treatment. Clinically, the patient was diagnosed with acute disseminated encephalomyelitis.

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges

Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.

What Does PET Imaging Bring to Neuro-Oncology in 2022? A Review

Simple Summary

Positron emission tomography (PET) imaging is increasingly used to supplement MRI in the management of patient with brain tumors. In this article, we provide a review of the current place and perspectives of PET imaging for the diagnosis and follow-up of from primary brain tumors such as gliomas, meningiomas and central nervous system lymphomas, as well as brain metastases. Different PET radiotracers targeting different biological processes are used to accurately depict these brain tumors and provide unique metabolic and biologic information. Radiolabeled amino acids such as [¹⁸F]FDOPA or [¹⁸F]FET are used for imaging of gliomas while both [¹⁸F]FDG and amino acids can be used for brain metastases. Meningiomas can be seen with a high contrast using radiolabeled ligands of somatostatin receptors, which they usually carry. Unconventional tracers that allow the study of other biological processes such as cell proliferation, hypoxia, or neo-angiogenesis are currently being studied for brain tumors imaging.

Abstract

PET imaging is being increasingly used to supplement MRI in the clinical management of brain tumors. The main radiotracers implemented in clinical practice include [¹⁸F]FDG, radiolabeled amino acids ([¹¹C]MET, [¹⁸F]FDOPA, [¹⁸F]FET) and [⁶⁸Ga]Ga-DOTA-SSTR, targeting glucose metabolism, L-amino-acid transport and somatostatin receptors expression, respectively. This review aims at addressing the current place and perspectives of brain PET imaging for patients who suffer from primary or secondary brain tumors, at diagnosis and during follow-up. A special focus is given to the following: radiolabeled amino acids PET imaging for tumor characterization and follow-up in gliomas; the role of amino acid PET and [¹⁸F]FDG PET for detecting brain metastases recurrence; [⁶⁸Ga]Ga-DOTA-SSTR PET for guiding treatment in meningioma and particularly before targeted radiotherapy.

Acute Disseminated Encephalomyelitis in FET PET/MR

ABSTRACT

After 3 weeks of daily headache, a 28-year-old, otherwise healthy woman was admitted to the emergency department with a first-time generalized seizure. CT showed a left frontal mass with perifocal edema. Brain MRI raised the suspicion of cerebral lymphoma. Cerebrospinal fluid analysis revealed mononuclear pleocytosis of 14 cells/μL without malignant cells, normal protein levels, and absence of oligoclonal bands. FET PET/MRI of the lesion showed FET characteristics of inflammatory disease, and acute disseminated encephalomyelitis was suggested as diagnosis. Final histopathological results from brain biopsy confirmed acute disseminated encephalomyelitis.

Deep learning identified glioblastoma subtypes based on internal genomic expression ranks

Background

Glioblastoma (GBM) can be divided into subtypes according to their genomic features, including Proneural (PN), Neural (NE), Classical (CL) and Mesenchymal (ME). However, it is a difficult task to unify various genomic expression profiles which were standardized with various procedures from different studies and to manually classify a given GBM sample into a subtype.

Methods

An algorithm was developed to unify the genomic profiles of GBM samples into a standardized normal distribution (SND), based on their internal expression ranks. Deep neural networks (DNN) and convolutional DNN (CDNN) models were trained on original and SND data. In addition, expanded SND data by combining various The Cancer Genome Atlas (TCGA) datasets were used to improve the robustness and generalization capacity of the CDNN models.

Results

The SND data kept unimodal distribution similar to their original data, and also kept the internal expression ranks of all genes for each sample. CDNN models trained on the SND data showed significantly higher accuracy compared to DNN and CDNN models trained on primary expression data. Interestingly, the CDNN models classified the NE subtype with the lowest accuracy in the GBM datasets, expanded datasets and in IDH wide type GBMs, consistent with the recent studies that NE subtype should be excluded. Furthermore, the CDNN models also recognized independent GBM datasets, even with small set of genomic expressions.

Conclusions

The GBM expression profiles can be transformed into unified SND data, which can be used to train CDNN models with high accuracy and generalization capacity. These models suggested NE subtype may be not compatible with the 4 subtypes classification system.

Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model

Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. Materials and Methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401–610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients’ dataset (Siemens and GE scans). Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.