What Is Radiomics and How Does AI Enable Its Clinical Application?

By Rasit Dinc

In the era of precision medicine, the quest for more personalized and effective healthcare has led to the emergence of innovative technologies that are reshaping the landscape of diagnostics and treatment. One such groundbreaking field is radiomics, which involves the extraction and analysis of vast amounts of quantitative data from medical images. This data, often imperceptible to the human eye, holds the key to unlocking a deeper understanding of disease characteristics, predicting patient outcomes, and tailoring treatments to individual needs. But how is this wealth of information harnessed and translated into clinical practice? The answer lies in the power of Artificial Intelligence (AI).

What is Radiomics?

At its core, radiomics is the process of converting standard medical images—such as those from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)—into high-dimensional, mineable data [1]. While a radiologist interprets these images to identify visual patterns indicative of disease, radiomics extracts hundreds, or even thousands, of quantitative features from a region of interest. These features, known as radiomic features, go beyond simple metrics like size and volume. They quantify characteristics of the tumor phenotype, such as shape, intensity, and texture, which describe the spatial arrangement and heterogeneity of pixel intensities [2].

This process allows for a more objective and detailed characterization of tissues. For example, radiomics can capture subtle variations in tumor texture that may reflect underlying biological processes like cellularity, necrosis, or angiogenesis—details that are often invisible to the naked eye. The ultimate goal is to create a digital biomarker from imaging data that can be used for clinical decision-making.

The Role of AI in Radiomics

The sheer volume and complexity of data generated through radiomics make manual analysis impossible. This is where AI, particularly machine learning (ML) and deep learning (DL), plays a pivotal role. AI algorithms are adept at identifying complex patterns in large datasets, making them the perfect engine to drive radiomics from a research concept to a clinical tool [3].

The typical AI-powered radiomics workflow includes several key stages:

1. Image Acquisition and Segmentation: The process begins with the acquisition of high-quality medical images. The region of interest (ROI), such as a tumor, is then segmented, either manually by a clinician or automatically using AI-driven segmentation algorithms. Accurate segmentation is crucial as it defines the area from which features will be extracted.

2. Feature Extraction: Once the ROI is defined, a large number of radiomic features are automatically extracted. These features can be categorized into several groups, including shape-based features, first-order statistics (describing the distribution of pixel intensities), and second- and higher-order textural features that capture the spatial relationships between pixels.

3. Feature Selection and Reduction: With hundreds or thousands of features extracted, there is a high risk of overfitting and including redundant or irrelevant information. AI techniques are used to select the most informative and robust features, reducing the dimensionality of the data and improving the performance of the predictive model.

4. Model Building and Validation: The selected features are then used to train an AI model. This model can be designed for various clinical tasks, such as distinguishing between benign and malignant tumors, predicting treatment response, or estimating patient prognosis. The model's performance is rigorously evaluated using independent datasets to ensure its accuracy and generalizability before it can be considered for clinical use [4].

Clinical Applications of AI-Enabled Radiomics

The integration of AI and radiomics is unlocking a wide range of clinical applications, particularly in oncology, where it is poised to revolutionize cancer care.

Diagnosis and Staging

AI-powered radiomics models are being developed to improve the accuracy of cancer diagnosis and staging. For instance, these models can help differentiate between benign and malignant lesions, which can be challenging with conventional imaging alone. In hepatocellular carcinoma (HCC), AI radiomics has shown promise in distinguishing indeterminate liver nodules, assessing pathological grade, and predicting microvascular invasion (MVI), all of which are critical factors for determining the appropriate treatment strategy [5].

Predicting Treatment Response

One of the most exciting applications of radiomics is in predicting how a patient will respond to a specific treatment. By analyzing the radiomic features of a tumor before treatment begins, AI models can predict the likelihood of response to chemotherapy, radiation therapy, or immunotherapy. This allows clinicians to select the most effective treatment for each patient from the outset, avoiding the trial-and-error approach that is often used today. For example, in patients with advanced HCC, radiomics has been used to predict the response to systemic therapies like sorafenib and lenvatinib, as well as to immunotherapy [5].

Prognostication

Radiomics can also provide valuable prognostic information, helping to predict patient outcomes such as survival and risk of recurrence. By identifying radiomic signatures associated with aggressive tumor behavior, clinicians can better stratify patients into different risk groups and tailor follow-up care accordingly. In HCC, radiomics models have demonstrated the ability to predict postoperative recurrence and overall survival with high accuracy [5].

Advancing Precision Medicine

Ultimately, the goal of AI-enabled radiomics is to advance precision medicine. By providing a non-invasive way to characterize tumor biology and predict treatment response, radiomics can help clinicians make more informed and personalized decisions for their patients. This can lead to better patient outcomes, reduced toxicity from ineffective treatments, and more efficient use of healthcare resources.

Challenges and Future Directions

Despite its immense potential, the widespread clinical adoption of AI-enabled radiomics faces several challenges. Standardization of image acquisition protocols, feature extraction methods, and reporting guidelines is crucial to ensure the reproducibility and comparability of studies. The need for large, curated, and diverse datasets for training and validating AI models is another significant hurdle. Furthermore, the "black box" nature of some complex deep learning models can make it difficult for clinicians to understand and trust their predictions, highlighting the need for more interpretable AI.

However, the future of radiomics is bright. Ongoing research is focused on addressing these challenges through the development of standardized frameworks and the creation of large-scale, multi-institutional data-sharing initiatives. The integration of radiomics with other "-omics" data, such as genomics and proteomics (a field known as radiogenomics), promises to provide an even more comprehensive understanding of tumor biology. As these technologies continue to mature and are validated in prospective clinical trials, AI-enabled radiomics is set to become an indispensable tool in the practice of modern medicine.

Conclusion

Radiomics, powered by artificial intelligence, represents a paradigm shift in how we interpret medical images. By transforming images into quantitative data, it provides a deeper, more objective insight into disease that has the potential to revolutionize clinical practice. From improving diagnostic accuracy to predicting treatment response and personalizing patient care, the applications of AI-enabled radiomics are vast and continue to grow. While challenges remain, the ongoing advancements in this field are paving the way for a new era of precision medicine, where treatments are tailored to the individual, leading to better outcomes for patients worldwide.

References

[1] Dercle, L., McGale, J., Sun, S., Marabelle, A., Yeh, R., Deutsch, E., ... & Schwartz, L. H. (2022). Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. Journal for ImmunoTherapy of Cancer, 10(9).

[2] Bo, Z., Song, J., He, Q., Chen, B., Chen, Z., Xie, X., ... & Chen, G. (2024). Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Computers in Biology and Medicine, 173, 108337.

[3] Koçak, B., Durmaz, E. Ş., Ateş, E., & Kılıçkesmez, Ö. (2019). Radiomics with artificial intelligence: a practical guide for beginners. Diagnostic and interventional radiology, 25(6), 485.

[4] Avanzo, M., Stancanello, J., & El Naqa, I. (2020). Machine and deep learning methods for radiomics. Medical Physics, 47(5), e185-e202.

[5] Bo, Z., Song, J., He, Q., Chen, B., Chen, Z., Xie, X., ... & Chen, G. (2024). Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Computers in Biology and Medicine, 173, 108337.