Introduction
Brain tumors are extremely dangerous malignancies owing to the highly intricate anatomy of the brain. Diagnosing them requires skilled expertise backed up by high-quality imaging data. Radiomics refers to extracting information from these imaging studies and simplifying it into mineable data that further assists in establishing the type, grade, severity, and individualized treatment strategy for the brain tumor in question. However, advancing medical technology has made it possible to dive deeper into the characterization of brain tumors by providing invaluable data on genetic information and mutation concerning tumors. This article aims to review how radiogenomics has raised the bar for brain tumor diagnosis.
What Is Radiogenomics?
Radiogenomics is the umbrella term for the combined disciplines of radiomics and gene profiling. When analyzed together, an individual's genetic profile and imaging data can produce a well-thought-out treatment plan considering every clinical consideration. Though it is still in its infancy, the field's use in detecting and managing brain tumors has shown remarkable promise thus far. Every tumor is unique in its characteristics. Radiogenomics aims to provide better clarity on tumor biology, ultimately paving the way for a favorable clinical outcome.
Why Is Radiogenomics Important in the Diagnosis of Brain Tumors?
Radiomics provides a noninvasive method of analyzing tumors through medical imaging techniques like CT (computed tomography) scans or MRI (magnetic resonance imaging) using advanced computer algorithms. Genomics studies individuals' genetic makeup and its role in disease. Radio genomics provides a more comprehensive picture of the tumor by combining imaging features (radiomics) with genetic information and other clinical information. It allows researchers to understand the mechanism behind tumor development better, stratify patients into groups according to the features of their tumors, estimate the prognosis of the patient or how the patient will react to the treatment, and develop individualized treatment plans based on the patient's genetic makeup and particular tumor.
What Are the Steps Involved in a Radiogenomic Approach?
Radiogenomics is a field that combines the extraction of useful clinical data from medical images with the identification of the genetic trait that allows for a detailed understanding of tumor biology. The intended result can only be attained by following an accurate sequence of steps.
- Image Acquisition or Registration: This step requires the original medical images. Positron emission tomography (PET), magnetic resonance imaging (MRI), and computer tomography have all been widely used to help in diagnosis and treatment. These images provide detailed information regarding tumors' functional and anatomical characteristics. To use radiomics to retrieve the data, a clear medical image of the tumor in question must first be obtained. Before attempting to image the tumor, several issues need to be thoroughly evaluated. These include patient movement during image acquisition, the proper operation of the radiography devices, and preventing errors throughout the scanning process.
- Tumor Segmentation: The next stage is to segment and identify the tumor's different compartments. The region of interest (ROI) is focused, and a further approach is determined. ROI is a specific area of the image that contains a tumor. Some tumor components include the necrotic part, the enhancing part, and the edematous periphery, among others. Three approaches are used to segment the region of interest: manually, semi-automatically, and fully automatic.
- Pre-processing of the Acquired Image: This step is extremely important and aims to achieve a standardized intensity and magnetic strength required to obtain a consistent dataset and reproducible results. One of the procedures involved in the pre-processing stage is the skull stripping. When obtaining high-resolution images, the non-brain tissues such as the bone, skin, and eyeballs are also imaged, which may hinder the next step involving analysis. To combat this, skull stripping is performed.
- Radiomic Data Extraction: Now that the image is accurately formed, a thorough radiomic analysis of the segmented tumor is carried out to collect useful data about the tumor related to its semantic features, texture, and shape, as well as any wavelet-based features and any deformities that may be present.
- Feature Selection Determining Prognostic and Survival Rate: The obtained radiometric data is very useful in forecasting the course of treatment and the patient's prognosis or survival rate. When combined with risk-of-bias analysis, statistical observation can help determine the best course of treatment and the probability of a fair prognosis.
- Radiogenomic Application: Lastly, the radiogenic study is incorporated to predict a genotype or identify the biological process responsible for the tumor progression. Examining the gene profile makes it possible to identify the precise gene alterations that are thought to be the primary driver of tumor development.
What Are the Limitations of Radiogenomics in Brain Tumor Diagnosis?
Radiogenomics fulfills an excellent purpose and has established its importance in neuro-oncology. However, certain limitations need to be addressed when implementing the approach. Accurate registration of the picture, preserving the standardized intensity, magnetic field strengths, echo times, and reproducibility of the imaging characteristics are all important for collecting reliable radiogenic data. The pre-processing and segmentation steps must be executed skillfully before radiometric data extraction and radiogenic analysis. When performed manually, tumor segmentation can prove to be extremely labor-intensive. To combat this, semi-automated and automated segmentation is performed. However, the highly inconsistent results across various devices have led to a major setback in obtaining true-to-value radiogenomic analysis. The interobserver and intraobserver variability contributes immensely towards varying degrees of results.
Conclusion
The use of radiogenomic analysis was widely popular for lung cancer cases. However, the discipline of neuro-oncology has recently seen a surge in the use of radiogenomics in the evaluation of CNS (central nervous system) tumors. It is beneficial in forming an accurate diagnosis and identifying the diseased tissue, predicting the outcome and survival rate, and forming an individualized treatment strategy to eradicate the tumor with precision. The use of artificial intelligence (AI) in achieving the desired result is commendable. Radiogenomics is the future, and its ability to produce reliable results can assist in overcoming the best of malignancies easily. The approach's technical deficiencies are still being researched, and this area of study has a bright future owing to advancing medical technology.
