Benign brain tumor
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Understanding Benign Brain Tumors: Detection, Treatment, and Epidemiology
Machine Learning in Detection and Segmentation of Benign Brain Tumors
Machine learning (ML) has shown promise in the detection and segmentation of benign brain tumors, although the field is still in its early stages. A systematic review highlighted that most studies focus on meningioma, vestibular schwannoma, pituitary adenoma, and spinal schwannoma, with segmentation being a common goal. However, the methodologies used in these studies often lack standardization, making it difficult to compare results and assess biases. Only one study used an external test set, raising concerns about overfitting and the generalizability of the models. Stronger adherence to ML best practices is needed to develop models that could be used in clinical settings.
Surgical Resection and Management of Benign Brain Tumors
Surgical resection remains a primary treatment for benign brain tumors, which represent about 40% of primary brain tumors . These tumors include astrocytic neoplasms, oligodendroglial tumors, and mixed glial-neuronal neoplasms, among others. Neurosurgical intervention is often necessary to establish a diagnosis, alleviate symptoms such as mass effect or hydrocephalus, and reduce the risk of malignant transformation . Given the long survival rates of patients with benign tumors, the benefits of surgery must be carefully weighed against potential adverse effects .
Epidemiology and Risk Factors
The incidence of benign brain tumors is approximately 11.52 per 100,000 person-years. Factors that may increase the risk of developing a brain tumor include being male, being white, older age, exposure to radiation, and certain occupational hazards. The only well-established environmental risk factor is ionizing radiation, while the role of radiofrequency electromagnetic fields from cell phone use remains inconclusive.
Molecular Diagnosis and Classification
Recent advances in molecular biology have improved the classification and diagnosis of benign brain tumors. For instance, meningiomas often show chromosomal alterations, including at the NF2 locus, and mutations in genes like TRAF7, KLF4, AKT1, and SMO. The 5th edition of the World Health Organization Classification of Tumors of the Central Nervous System now includes molecular features alongside histology for a more accurate diagnosis. This integrated approach helps in better grading and treatment planning for these tumors.
Conclusion
Benign brain tumors, while less aggressive than their malignant counterparts, still pose significant challenges in terms of detection, treatment, and long-term management. Advances in machine learning, surgical techniques, and molecular diagnostics are paving the way for more effective and personalized treatment strategies. However, more standardized research and adherence to best practices are essential for these advancements to translate into clinical success.
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