Radiomics — the extraction of high-dimensional quantitative features from medical images — allows the characterization of tissue heterogeneity beyond what can be perceived visually. These features, derived from modalities such as CT, MRI, or PET, have been linked to tumor phenotype, gene expression, and therapeutic response across oncology, neurology, and cardiology studies (Springer, 2024).

Imaging Biomarkers in Practice

Validated imaging biomarkers are redefining how clinicians stratify patients, monitor disease, and predict outcomes. Quantitative features from radiomics pipelines can act as noninvasive surrogates for histopathologic or molecular data, guiding therapy selection and prognosis assessment. For instance, radiomic signatures have shown potential in predicting response to immunotherapy and correlating with tumor-infiltrating lymphocytes in non-small cell lung cancer (ScienceDirect, 2020).

In cardiovascular and neuroimaging applications, biomarkers derived from texture and perfusion patterns are being explored to detect subclinical disease, assess ischemic risk, and evaluate treatment efficacy. The promise lies in moving from population averages toward individualized predictions based on each patient’s unique imaging phenotype.

Radiogenomics and Multi-Omics Integration

The next step in precision imaging is radiogenomics — linking imaging phenotypes with genomic and proteomic data to uncover biologically meaningful correlations. Integrating imaging with multi-omics datasets enables the creation of comprehensive disease models that reflect both spatial and molecular dimensions.

Recent reviews highlight the potential of AI-driven multi-omics integration to refine cancer subtyping, prognostication, and therapeutic decision-making (British Journal of Radiology, 2025) and (ScienceDirect, 2025). Federated approaches and multi-modal AI models are emerging to harmonize these heterogeneous datasets while preserving privacy and reproducibility.

Projects such as NAVIGATOR, a regional imaging biobank integrating multimodal imaging with molecular and clinical data, illustrate how research infrastructure is catching up to these ambitions (European Journal of Radiology, 2025).

From Quantitative Imaging to Clinical Translation

Despite the promise, clinical translation remains the critical frontier. Feature reproducibility, acquisition standardization, and regulatory validation continue to challenge adoption (Insights into Imaging, 2020). However, the increasing presence of quantitative imaging biomarkers in prospective trials, along with support from the Quantitative Imaging Biomarkers Alliance (QIBA) and FDA’s digital health framework, signals that this research is crossing the threshold into practice.

At RSNA 2025, expect sessions emphasizing standardization of radiomics workflows, reproducibility metrics, and AI-assisted integration of multi-omics data. Discussions will likely center on how to validate imaging biomarkers in multi-institutional settings and what infrastructure is required for clinical scalability.

The Role of Teleradiology in Precision Imaging

For teleradiology providers like Vesta, these developments offer both opportunity and responsibility. The same digital infrastructure that enables subspecialty coverage across time zones can support quantitative image analysis, data harmonization, and longitudinal tracking — essential foundations for radiomic and biomarker validation.

By aligning with quantitative imaging standards and collaborating with research institutions, teleradiology networks can help bring precision imaging insights into real-world practice — from oncology to cardiovascular disease management.

Precision imaging is not a distant future — it’s the next evolution of radiology happening now.

At RSNA 2025, Vesta will be on site to explore how radiomics, biomarkers, and AI-driven data integration are redefining what it means to truly “image the individual.”

The post Precision Imaging at RSNA 2025: Radiomics, Biomarkers, and the Era of Multi-Omics Integration first appeared on Vesta Teleradiology.

Thanks to new diagnostic approaches, patients can be grouped according to the biomarkers identified through imaging, providing a deeper understanding of the molecular basis of their disease and the appropriate course of treatment. This has become particularly impactful in oncology.

In recent years, personalized imaging approaches have vastly improved cancer patients’ diagnosis, treatment, and long-term recovery. Treatment response, patient management, and patient outcomes are higher, so more lives are protected and improved thanks to advances in imaging.

Initially, patients receive baseline imaging.

CT radiological imaging can reveal structural changes such as tumor rupture and spinal cord compression. It is one of the first scans performed on patients, and the information is used to diagnose and evaluate cancer-related complications, including malignancy, obstruction, and infection. It can also identify drug-induced changes and inform physicians about the need for medical, surgical, or radiological interventions.

MRI radiological imaging is a valuable tool in the pre-clinical phase of cancer treatment. It can determine characteristics of the tumor’s immune environment and help predict short-term and long-term immunotherapy responses with better accuracy than a CT scan alone. Its most vital component is its ability to show soft tissue anatomy in detail. It is non-invasive and can determine the effectiveness of radiation treatments and other important information, such as cell density and microstructure of the tissue. In addition, the combination of PET/MRI imaging is proving to be even more powerful than MRI alone. PET (Positron Emission Tomography), a molecular imaging technique using radiotracers, identifies tumor characteristics in nuclear imaging. In a single session, the combination of these two tests reveals more information with an even higher level of molecular sensitivity. This cutting-edge technique aids in immunotherapy treatment and is particularly helpful in assessing the progression of advanced cancers.

Then, personalized treatment builds.

While CT and MRI have much to offer, molecular imaging operates on specific biochemical markers. This biological information is not visible to the human eye. The data is considered “high yield” and is being used to inform AI algorithms, which can provide prognostic information for clinical treatment.

Another forerunner in personalized imaging is the revised Response Evaluation in Solid Tumors  (RECIST), a set of rules for measuring tumors based on imaging.  The new guidelines can visualize, characterize, quantify, and measure tumors’ cellular, subcellular, and molecular processes. This non-invasive approach can track the physiological activities of molecules in a tissue or organ, whether they are measurable or non-measurable, clarifying disease progression and informing doctors on treatment.

Radiomics, also known as quantitative image analysis, is another promising personal imaging approach. Using handcrafted radiomics and machine-engineered statistics, it extracts unlimited features, mining for information to predict treatment outcomes after radiotherapy, including segmentation and dose calculation. Radiomics provides a wealth of information, pulling from CTs, MRIs, and PETs, connecting imaging with precision medicine.

Theranostics, the most recent development in nuclear medicine, combines diagnostic imaging with therapy, allowing doctors to visualize and treat based on the same molecule. This groundbreaking approach in cancer care reduces the side effects of traditional therapies while increasing precision and treatment effectiveness. Theranostics, along with molecular and nuclear imaging, are the hallmarks of personalized treatment in oncology.

The field of personalized imaging is growing. While we can anticipate significant diagnostic advances, early detection is key.

Vesta Teleradiology

At Vesta, we understand the critical role that advanced imaging plays in personalized medicine, especially in oncology. As a teleradiology company, we offer specialized diagnostic imaging interpretation services. Our team of expert radiologists is committed to providing timely, accurate reads that help physicians develop tailored treatment plans for their patients. Whether you need subspecialty interpretations or assistance in integrating new imaging technologies into your practice, we’re here to support you in delivering the best patient care possible.

The post Personalized Imaging Approaches and Trends to Watch For first appeared on Vesta Teleradiology.

The fields of science and medicine are always progressing. This progression intends to help both patients and providers.

Today, artificial intelligence (AI) is becoming common as a way to diagnose patients. It provides a more efficient way to collect and store information. The software can even analyze imaging to a high level of accuracy. This helps providers catch a problem that they may have missed before.

AI is a field that is advancing quickly. What progress have we seen in the past couple of years? What programs have we begun to put in place?

What Is Artificial Intelligence?

Artificial intelligence refers to highly advanced computers or computer-controlled robots. These computers are capable of performing incredibly complex tasks. Before, we thought these tasks could only be done by intelligent beings.

These computers are often associated with human characteristics. They seem to be able to reason and learn from past experiences.

How Is Artificial Intelligence Used For Diagnostic Imaging & Radiation?

Using AI in radiology and imaging has been gaining traction in the medical world. We use it largely to store and analyze data, helping physicians to make a prognosis. AI can store and analyze all a patient’s records. It can then make a diagnosis based on those records. The analysis is often far more accurate than what a human counterpart can do.

The use of AI is also helpful because of its storage capability. AI can have large imaging biobanks to hold more images than standard computers.

It also makes the lives of physicians easier by filtering patients by need. It can recommend appropriate diagnostic imaging based on the patient’s current records. It can also sort patients by priority in the case of an emergency.

What Advancements Have Been Made?

AI means to eliminate problems associated with human limitations. Traditional imaging takes a team of technicians. They must take the imaging as well as interpret it. This can be time-consuming. Plus, AI is able to analyze images with far greater accuracy than the human eye.

Radiomics

Radiomics is a tool that performs a deep analysis of tumors down to the molecular level. AI can perform radiomics with far better accuracy than the human eye or brain.

AI can analyze a specific region and extract over 400 elements. It then takes these features and correlates them with other data to form a diagnosis. The AI can analyze features from radiographs, CT, MRI, or PET studies.

Rapid Brain-Imaging AI Software

Hyperfine is the manufacturer of portable MRI machines. They are now creating these machines with new AI intelligence software. They believe that this new software will be able to perform brain scans in under 3 minutes.

AI-Generated Drugs

In 2020, an AI-created drug went to human clinical trials. The drug intends to treat OCD, and was designed entirely by AI. Exscientia is the manufacturer of the drug. They say that it normally takes about 4.5 years to get a new drug to this stage of testing. With AI generation, the drug got to the human clinical trial stage in under 12 months.

Making A Diagnosis

We stated earlier that AI is being used as a way to more efficiently diagnose patients. Still, relying entirely on AI to do this can complicate things and may be unwise.

So, the researchers of MIT’s Computer Science and Artificial Intelligence Lab worked to combat this. They created a machine learning system that analyzes the data and decides whether to diagnose.

If it “feels” it’s unable to make an accurate prediction, it will defer to a medical professional. It even considers whether to defer to an expert based on who in the medical team is available. It will consider each team member’s availability, level of experience, and specialty.

Conclusion  

AI in diagnostic imaging shows promise to truly advance quality of care for patients. We are excited to see more advancements in this arena. In the meantime, we don’t believe any machine can currently replace a trained human eye when it comes to interpretations. At Vesta, we provide US Board Certified radiologists who work to provide accurate preliminary and final interpretations. Learn how we can support your radiology department– contact us today. 

The post How AI is Making an Impact on Radiology and Imaging first appeared on Vesta Teleradiology.