Convolutional neural network to predict IDH mutation status in glioma from chemical exchange saturation transfer imaging at 7 Tesla

Integrating quantitative DCE-MRI parameters and radiomic features for improved IDH mutation prediction in gliomas

Objectives

To develop and validate a multiparametric prognostic model, incorporating dynamic contrast-enhanced (DCE) quantitative parameters and multi-modality radiomic features, for the accurate identification of isocitrate dehydrogenase 1 (IDH1) mutation status from glioma patients.

Methods

A total of 152 glioma patient data with confirmed IDH1 mutation status were retrospectively collected. A segmentation neural network was used to measure MRI quantitative parameters compared with the empirically oriented ROI selection. Radiomic features, extracted from conventional MR images (T1CE, T2W, and ADC), and DCE quantitative parameter images were combined with MRI quantitative parameters in our research to predict IDH1 mutation status. We constructed and analyzed Clinical Models 1-2 (corresponding to manual and automatic MRI quantitative parameters), Radiomic Feature Models 1-3 (corresponding to structural MRI, DCE, and multi-modality respectively), and a Multivariable Combined Model. We tried different usual classifiers and selected logistic regression according to AUC. Fivefold cross-validation was applied for validation.

Results

The Multivariable Combined Model showed the best prediction performance (AUC, 0.915; 95% CI: 0.87, 0.96) in the validation cohort. The Multivariable Combined Model performed better than Clinical Model 1 and Radiomic Feature Model 1 (DeLong all p < 0.05), and Radiomic Feature Model 3 performed better than Radiomic Feature Model 1 (DeLong p < 0.05).

Conclusions

Compared with the conventional MRI Radiomics and Clinical Models, the Multivariable Combined Model, mainly based on DCE quantitative parameters and multi-modality Radiomics features, is the most promising and deserves attention in the current study.

Improving quantification accuracy of a nuclear Overhauser enhancement signal at -1.6 ppm at 4.7 T using a machine learning approach

Objective.A new nuclear Overhauser enhancement (NOE)-mediated saturation transfer MRI signal at -1.6 ppm, potentially from choline phospholipids and termed NOE(-1.6), has been reported in biological tissues at high magnetic fields. This signal shows promise for detecting brain tumors and strokes. However, its proximity to the water peak and low signal-to-noise ratio makes accurate quantification challenging, especially at low fields, due to the difficulty in separating it from direct water saturation and other confounding signals. This study proposes using a machine learning (ML) method to address this challenge.Approach.The ML model was trained on a partially synthetic chemical exchange saturation transfer dataset with a curriculum learning denoising approach. The accuracy of our method in quantifying NOE(-1.6) was validated using tissue-mimicking data from Bloch simulations providing ground truth, with subsequent application to an animal tumor model at 4.7 T. The predictions from the proposed ML method were compared with outcomes from traditional Lorentzian fit and ML models trained on other data types, including measured and fully simulated data.Main results.Our tissue-mimicking validation suggests that our method offers superior accuracy compared to all other methods. The results from animal experiments show that our method, despite variations in training data size or simulation models, produces predictions within a narrower range than the ML method trained on other data types.Significance.The ML method proposed in this work significantly enhances the accuracy and robustness of quantifying NOE(-1.6), thereby expanding the potential for applications of this novel molecular imaging mechanism in low-field environments.

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three-point method, and multiple-pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time.

Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma

Glioblastoma is the most commonly occurring malignant brain tumor, with a high mortality rate despite current treatments. Its classification has evolved over the years to include not only histopathological features but also molecular findings. Given the heterogeneity of glioblastoma, molecular biomarkers for diagnosis have become essential for initiating treatment with current therapies, while new technologies for detecting specific variations using computational tools are being rapidly developed. Advances in molecular genetics have made possible the creation of tailored therapies based on specific molecular targets, with various degrees of success. This review provides an overview of the latest advances in the fields of histopathology and radiogenomics and the use of molecular markers for management of glioblastoma, as well as the development of new therapies targeting the most common molecular markers. Furthermore, we offer a summary of the results of recent preclinical and clinical trials to recognize the current trends of investigation and understand the possible future directions of molecular targeted therapies in glioblastoma.

Advances in diffuse glioma assessment: preoperative and postoperative applications of chemical exchange saturation transfer

Chemical Exchange Saturation Transfer (CEST) is a technique that uses specific off-resonance saturation pulses to pre-saturate targeted substances. This process influences the signal intensity of free water, thereby indirectly providing information about the pre-saturated substance. Among the clinical applications of CEST, Amide Proton Transfer (APT) is currently the most well-established. APT can be utilized for the preoperative grading of gliomas. Tumors with higher APTw signals generally indicate a higher likelihood of malignancy. In predicting preoperative molecular typing, APTw values are typically lower in tumors with favorable molecular phenotypes, such as isocitrate dehydrogenase (IDH) mutations, compared to IDH wild-type tumors. For differential diagnosis, the average APTw values of meningiomas are significantly lower than those of high-grade gliomas. Various APTw measurement indices assist in distinguishing central nervous system lesions with similar imaging features, such as progressive multifocal leukoencephalopathy, central nervous system lymphoma, solitary brain metastases, and glioblastoma. Regarding prognosis, APT effectively differentiates between tumor recurrence and treatment effects, and also possesses predictive capabilities for overall survival (OS) and progression-free survival (PFS).

Evaluating the role of 7-Tesla magnetic resonance imaging in neurosurgery: Trends in literature since clinical approval

BACKGROUND

After approval for clinical use in 2017, early investigations of ultra-high-field abdominal magnetic resonance imaging (MRI) have demonstrated its feasibility as well as diagnostic capabilities in neuroimaging. However, there are no to few systematic reviews covering the entirety of its neurosurgical applications as well as the trends in the literature with regard to the aforementioned application.

AIM

To assess the impact of 7-Tesla MRI (7T MRI) on neurosurgery, focusing on its applications in diagnosis, treatment planning, and postoperative assessment, and to systematically analyze and identify patterns and trends in the existing literature related to the utilization of 7T MRI in neurosurgical contexts.

METHODS

A systematic search of PubMed was conducted for studies published between January 1, 2017, and December 31, 2023, using MeSH terms related to 7T MRI and neurosurgery. The inclusion criteria were: Studies involving patients of all ages, meta-analyses, systematic reviews, and original research. The exclusion criteria were: Pre-prints, studies with insufficient data (e.g., case reports and letters), non-English publications, and studies involving animal subjects. Data synthesis involved standardized extraction forms, and a narrative synthesis was performed.

RESULTS

We identified 219 records from PubMed within our defined period, with no duplicates or exclusions before screening. After screening, 125 articles were excluded for not meeting inclusion criteria, leaving 94 reports. Of these, 2 were irrelevant to neurosurgery and 7 were animal studies, resulting in 85 studies included in our systematic review. Data were categorized by neurosurgical procedures and diseases treated using 7T MRI. We also analyzed publications by country and the number of 7T MRI facilities per country was also presented. Experimental studies were classified into comparison and non-comparison studies based on whether 7T MRI was compared to lower field strengths.

CONCLUSION

7T MRI holds great potential in improving the characterization and understanding of various neurological and psychiatric conditions that may be neurosurgically treated. These include epilepsy, pituitary adenoma, Parkinson's disease, cerebrovascular diseases, trigeminal neuralgia, traumatic head injury, multiple sclerosis, glioma, and psychiatric disorders. Superiority of 7T MRI over lower field strengths was demonstrated in terms of image quality, lesion detection, and tissue characterization. Findings suggest the need for accelerated global distribution of 7T magnetic resonance systems and increased training for radiologists to ensure safe and effective integration into routine clinical practice.

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed.

Artificial intelligence in neurology: opportunities, challenges, and policy implications

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.