Tag: RSNA 2025

Precision Imaging at RSNA 2025: Radiomics, Biomarkers, and the Era of Multi-Omics Integration

As radiology moves deeper into the era of precision medicine, quantitative imaging is transforming from a promising research tool to a clinical driver of individualized care. The convergence of radiomics, imaging biomarkers, and multi-omics integration represents one of the most exciting frontiers showcased under RSNA 2025’s theme, “Imaging the Individual.”

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.”

 

 

Vesta Teleradiology Heads to RSNA 2025: AI + Expertise = Faster, Smarter Imaging Coverage

 

Every year, the Radiological Society of North America (RSNA) brings together innovators shaping the future of medical imaging. This November 30–December 3, 2025, the Vesta Teleradiology team is proud to join that community at RSNA 2025 in Chicago — showcasing how AI and human expertise combine to deliver faster, smarter imaging coverage for hospitals and imaging centers nationwide.

Meet Vesta at Booth 1346 — South Hall

At Booth 1346, attendees can discover how Vesta helps healthcare facilities overcome some of today’s biggest radiology challenges — from staffing shortages to increasing imaging volumes — without compromising patient care.

Vesta’s solutions are designed to help your organization:

  • ✅ Gain 24/7 radiology coverage without the burnout
  • ✅ Access fellowship-trained subspecialists across all modalities
  • ✅ Deliver faster turnaround times with AI-assisted workflow tools
  • ✅ Scale imaging services without adding staff
  • ✅ Rely on dependable IT services and seamless PACS integration

How Vesta Combines AI + Human Expertise

Teleradiology isn’t just about remote reads — it’s about precision, speed, and collaboration. Vesta’s radiologists use advanced AI-assisted workflow technology to prioritize cases, enhance diagnostic consistency, and streamline communication with hospitals and imaging centers.

AI tools don’t replace radiologists; they empower them. By automating repetitive tasks and highlighting critical findings faster, AI allows Vesta’s board-certified radiologists to focus where their expertise matters most — delivering accurate interpretations and improving patient outcomes around the clock.

Dependable Excellence, Every Time

Since its founding, Vesta has remained committed to providing dependable, high-quality radiology coverage that healthcare organizations can trust. Whether you need overnight support, overflow assistance, or full departmental coverage, Vesta’s network of U.S.-based, fellowship-trained subspecialists ensures that every scan gets the attention it deserves — anytime, anywhere.

Join Us in Chicago

If you’re attending RSNA 2025, we’d love to meet you in person. Stop by Booth 1346 in the South Hall to see how Vesta’s combination of human insight and artificial intelligence is helping healthcare facilities achieve diagnostic excellence — without adding to their workload.

RSNA 2025 — Chicago, IL
 November 30 – December 3, 2025
 VESTARAD.COM

FDA’s 2025 AI Draft Guidance: A Buyer’s Checklist for Imaging Leaders

In January 2025, the U.S. Food and Drug Administration released a draft guidance for AI-enabled medical devices that lays out expectations across the total product life cycle—design, validation, bias mitigation, transparency, documentation, and post-market performance monitoring. For imaging leaders, it’s a clear signal to tighten procurement criteria and operational guardrails before piloting AI in CT, MRI, mammo, ultrasound, or PET.

As teams lock in Q4 budgets and head into RSNA season, the FDA’s AI lifecycle draft (Jan 2025) and the now-final PCCP (Dec 2024) have reset what buyers should expect from AI in imaging—devices, software, and workflows. Vendors are updating claims and governance; this issue distills a practical buyer’s checklist—multisite validation with subgroup results, drift monitoring and version control, clear in-viewer transparency—and how pairing those tools with Vesta’s subspecialty coverage and QA turns promise into measurable gains across CT/MRI/US/mammography.

A practical buyer’s checklist

Use this when evaluating AI for your service lines:

  1. Intended use fit: Verify indications, inputs/outputs, and claims match your pathway and patient mix.
  2. Validation depth: Prefer multisite, diverse datasets; stratified results; pre-specified endpoints; documented data lineage and splits.
  3. Bias mitigation: Demand subgroup performance (sex, age, race/ethnicity when available), scanner/vendor variability analyses, and site-transfer testing.
  4. TPLC plan: Require drift monitoring, retraining triggers, versioning, and how updates are communicated.
  5. Human factors & transparency: Ensure limitations, failure modes, and interpretable outputs are presented in-viewer without slowing reads.
  6. Security & support: Patch cadence, vulnerability disclosure, SOC2/ISO posture, uptime SLAs, and rollback paths for version issues.
  7. Governance: Define metrics owners, review cadence, and thresholds to pause or roll back a model.

Implementation playbook: pilot → scale without disruption

Start with a 60–90 day pilot in one high-impact line (e.g., ED stroke CT or mammography triage) and lock in baselines: median TAT, positive/negative agreement, recall rate, PPV/NPV, and discrepancy rate. Set guardrails—when to auto-triage vs. force human review—and document escalation paths for model failures. Require case-level confidence and structured outputs your radiologists can verify quickly. Stand up a model governance huddle (modality lead, QA, IT security, and your teleradiology partner) that meets biweekly to review drift signals, subgroup performance, and near-misses. Bake in a rollback plan (version pinning) and a quiet-hours change window so updates don’t collide with peak volumes. As results stabilize, scale by cohort (e.g., expand to non-contrast head CT, then CTA) and keep training “micro-bursts” for techs/readers—short videos or checklists in-workflow. Tie vendor SLAs to uptime, support response, and clinical KPIs so the AI program stays accountable to operational value.

Where teleradiology fits

AI only delivers when it’s welded to coverage, quality, and speed. A teleradiology partner should provide:

  • 24/7 subspecialty + surge capacity: Vesta absorbs volume peaks so AI never becomes a bottleneck.
  • QA you can see: We benchmark pre/post-AI performance, add targeted second looks for edge cases, and feed variance data back to your team.
  • Standardized outputs: Structured reports that integrate model outputs with radiologist findings—no black-box surprises.
  • Smooth rollout: Pilot by service line (stroke CT, mammo triage, PE workups), then scale with tracked KPIs (TAT, PPV, recalls).
  • Interoperability & security: Seamless PACS/RIS/EMR integration with strict access controls, audit trails, and support for change-controlled updates.

Bottom line: Pairing AI with Vesta Teleradiology gives you round-the-clock subspecialty reads, measurable QA, and operational breathing room while you pilot and scale responsibly. If you’re mapping your AI roadmap under the FDA’s 2025 draft guidance, we’ll be your coverage and quality backbone—so your clinicians see faster answers and your patients see safer care. Visit vestarad.com to get started.