Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

O-(2-[18F]-Fluoroethyl)-L-Tyrosine (FET) in Neurooncology: A Review of Experimental Results

In recent years, PET using radiolabelled amino acids has gained considerable interest as an additional tool besides MRI to improve the diagnosis of cerebral gliomas and brain metastases. A very successful tracer in this field is O-(2-[18F]fluoroethyl)-L-tyrosine (FET) which in recent years has replaced short-lived tracers such as [11C]-methyl-L-methionine in many neuro-oncological centers in Western Europe. FET can be produced with high efficiency and distributed in a satellite concept like 2- [18F]fluoro-2-deoxy-D-glucose. Many clinical studies have demonstrated that FET PET provides important diagnostic information regarding the delineation of cerebral gliomas for therapy planning, an improved differentiation of tumor recurrence from treatment-related changes and sensitive treatment monitoring. In parallel, a considerable number of experimental studies have investigated the uptake mechanisms of FET on the cellular level and the behavior of the tracer in various benign lesions in order to clarify the specificity of FET uptake for tumor tissue. Further studies have explored the effects of treatment related tissue alterations on tracer uptake such as surgery, radiation and drug therapy. Finally, the role of blood-brain barrier integrity for FET uptake which presents an important aspect for PET tracers targeting neoplastic lesions in the brain has been investigated in several studies. Based on a literature research regarding experimental FET studies and corresponding clinical applications this article summarizes the knowledge on the uptake behavior of FET, which has been collected in more than 30 experimental studies during the last two decades and discusses the role of these results in the clinical context.

Current trends in the use of O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) in neurooncology

The diagnostic potential of PET using the amino acid analogue O-(2-[¹⁸F]fluoroethyl)-L-tyrosine ([¹⁸F]FET) in brain tumor diagnostics has been proven in many studies during the last two decades and is still the subject of multiple studies every year. In addition to standard magnetic resonance imaging (MRI), positron emission tomography (PET) using [¹⁸F]FET provides important diagnostic data concerning brain tumor delineation, therapy planning, treatment monitoring, and improved differentiation between treatment-related changes and tumor recurrence. The pharmacokinetics, uptake mechanisms and metabolism have been well described in various preclinical studies. The accumulation of [¹⁸F]FET in most benign lesions and healthy brain tissue has been shown to be low, thus providing a high contrast between tumor tissue and benign tissue alterations. Based on logistic advantages of F-18 labelling and convincing clinical results, [¹⁸F]FET has widely replaced short lived amino acid tracers such as L-[¹¹C]methyl-methionine ([¹¹C]MET) in many centers across Western Europe. This review summarizes the basic knowledge on [¹⁸F]FET and its contribution to the care of patients with brain tumors. In particular, recent studies about specificity, possible pitfalls, and the utility of [¹⁸F]FET PET in tumor grading and prognostication regarding the revised WHO classification of brain tumors are addressed.

Neurosarcoidosis presenting with isolated intracranial mass lesion and communicating hydrocephalus

Isolated neurosarcoidosis is a very rare disease, which makes up 5-15% of sarcoidosis cases. Hydrocephalus is a rare clinical feature with a prevalence of 6% among these patients. Considering neurosarcoidosis in the differential diagnosis of a unique parenquimal mass lesion could help in the early identification of this disease. We report the case of a 27-year-old African man who developed with a sole intracranial mass lesion mimicking radiologically a glioma, which finally came out as an isolated neurosarcoidosis. There is a difficulty in diagnosis when isolated neurosarcoidosis appears. In addition, the low prevalence of the disease entails a not standardized medical treatment. Natural outcome is poor even when hydrocephalus is resolved. Multimodal treatments including complete pharmacological treatment do not seem to assure a better outcome in these patients until date.

A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned

The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.