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From pixels to predictions: How does radiomics reveal the hidden features of tumors?
With the continuous advancement of medical imaging technology, radiomics has increasingly become an indispensable part of the cancer diagnosis and treatment process. This emerging technology allows doctors to extract a large number of features from medical images through data feature extraction algorithms. These features are radiological features, which can reveal the characteristics and patterns of tumors, which are often invisible to the naked eye. The hypothesis of radiomics is that unique imaging features between disease forms may be important in predicting clinical outcomes and treatment response, thereby providing valuable information for personalized treatment.
The development of radiomics originally originated from the medical fields of radiology and oncology, but its technology can be applied to any pathological process that can be imaged.
The process of radiomics
Image acquisition
Imaging data usually comes from radiology equipment such as CT, MRI and PET. The raw data generated from these images is used to extract different pixel or voxel features. The extracted features will be stored in a large database for clinicians to easily collaborate and utilize to improve clinical data processing efficiency.
Image segmentation
After the image is saved, it needs to be narrowed down to the key part, that is, the tumor area, which is called the "region of interest". Due to the huge amount of image data, manual segmentation is very time-consuming, so automated segmentation algorithms can only be used to improve efficiency. Automated algorithms need to adhere to standards such as reproducibility, consistency, accuracy and efficiency to ensure correct identification of tumors.
Feature extraction and screening
After image segmentation is completed, various features will be extracted. These radiological features can be classified according to five categories: size, shape, and image intensity histogram. However, due to the large number of features, it is necessary to apply feature screening algorithms to achieve feature selection and reduce redundant information.
Data analysis
After selecting key features, it is crucial to decide how to analyze the data. Integration of clinical and molecular data is critical as it affects the interpretation of analytical results. Data analysis can be performed using supervised or unsupervised methods in order to clearly show the conclusions of the results.
Applications of radiomics
Clinical outcome prediction
Aerts et al. conducted the first large-scale radiomics study in 2014, covering data from more than 1,000 patients, in an attempt to determine the predictive value of features extracted from CT images for patient prognosis. Research shows that radiographic signatures may help predict patient survival and intra-tumor heterogeneity.
In addition, some studies have demonstrated that radiographic features are superior to traditional measures, such as tumor volume and maximum radiographic significance, in predicting treatment response.
Predict the risk of distant metastasis
Radiomics features can also predict the possibility of tumor metastasis. Coroller et al. proposed in 2015 that the radiomic features of CT images can identify patients with a high risk of developing distant metastasis, which can help doctors target individualized treatments. Make judgments about treatment options.
Cancer genetic assessment
Different tumor biological mechanisms may display unique imaging patterns. For example, studies have shown that radiographic signatures are related to genetic makeup, which could help understand the genetic signature of cancer without the need for a biopsy.
Image-guided radiotherapy
The advantage of radiomics is its non-invasive nature, which facilitates continuous monitoring of tumor changes during radiotherapy and provides recommendations for dose escalation in high-risk areas.
Distinguish between true progression and radiation necrosis
After stereotactic radiosurgery for brain metastases, it is often difficult to distinguish treatment effect from true tumor progression. Radiomics has shown promising results and has important applications in distinguishing between the two.
Predict physiological events
Radiomics can also be used to identify challenging physiological events such as brain activity, by analyzing functional MRI images to generate features related to brain activity.
Imaging Genomics
Imaging genomics is an emerging field that uses radiological means to create information that can identify the genome of a tumor, especially cancer, without the need for a biopsy. This technology has proven successful in identifying genomic signatures of a variety of cancers.
Multi-parameter radiomics
Multi-parametric imaging is particularly important in the detection, characterization and diagnosis of many diseases. The recently developed multi-parameter imaging radiomics framework MPRAD can more comprehensively extract image features and improve the effectiveness of clinical applications.
Conclusion
As technology advances, radiomics has unlimited potential in personalized medicine, but further research is still needed to improve consistency and accuracy in different clinical settings. How to further break through the current technical limitations and explore more application potential of radiomics will be a challenge and opportunity for future scientific research?
