| Sumario: | Simple Summary: Gliomas are the most common kind of brain tumor that starts in the brain. Identifying gliomas early and figuring out exactly what kind they are is a challenge. It is also tough to tell how a patient will do over time, such as how well they will respond to treatment or how long they might live. Radiomics is a recent science that uses computer programs and artificial intelligence (AI) to study medical images, like MRIs or CT scans. It looks for patterns or tiny details in these pictures that the human eye might miss. AI helps by quickly and accurately analyzing a huge amount of image data. It can find clues in the pictures that tell doctors more about the tumor, including its type, how serious it is, and how it might react to treatment. This can help doctors choose the best treatment for each patient. If radiomics can be used to learn more about a person's tumor from just an image, there might not be a need for risky surgery to obtain that information. It could also help predict what might happen to the patient so they and their doctors can make better plans. Scientists want to combine radiomics with other tools to make it even more powerful and helpful in the future. Gliomas are the most prevalent and aggressive form of primary brain tumors. The clinical challenge in managing patients with this disease revolves around the difficulty of diagnosis, both at onset and during treatment, and the scarcity of prognostic outcome indicators. Radiomics involves the extraction of quantitative features from medical images with the help of artificial intelligence, positioning it as a promising tool to be integrated into the care of glioma patients. Using data from 52 studies and 12,482 patients over two years, this review explores how radiomics can enhance the initial diagnosis of gliomas, especially helping to differentiate treatment stages that may be difficult for the human eye to do otherwise. Radiomics has also been able to identify patient-specific tumor molecular signatures for targeted treatments without the need for invasive surgical biopsy. Such an approach could lead to earlier interventions and more precise individualized therapies that are tailored to each patient. Additionally, it could be integrated into clinical practice to improve longitudinal diagnosis during treatment and predict tumor recurrence. Finally, radiomics has the potential to predict clinical outcomes, helping both patients and providers set realistic expectations. While this field is continuously evolving, future research should conduct such studies in larger, multi-institutional cohorts to enhance generalizability and applicability in clinical practice and focus on combining radiomics with other modalities to improve its predictive accuracy and clinical utility.
|
|---|