Synoptic Reporting of Focal Liver Masses in At-Risk Patients: Algorithmic Diagnosis and CEUS LI-RADS

Reporting of focal liver masses in patients at risk (for example those with chronic liver disease) presents significant clinical challenges, particularly when using contrast-enhanced ultrasound (CEUS). The study aimed to evaluate an algorithmic, synoptic reporting approach that leverages the CEUS LI-RADS framework in high-risk patients.

• Population: Patients at risk for focal liver masses undergoing CEUS.
• Intervention: Use of synoptic reporting templates grounded in the LI-RADS algorithm and structured diagnostic logic.
• Assessment: How the synoptic, algorithm-based approach impacted reporting consistency, accuracy, and workflow compared to traditional narrative reporting.

The study found that structured synoptic reporting aligned with CEUS LI-RADS improved diagnostic consistency in this high-risk cohort.

By embedding algorithmic logic into the reporting process, the authors demonstrated improved reliability in categorizing focal liver masses.

The structured approach supports better standardisation across readers and reduces ambiguity in report impression and recommendation.

• The adoption of the synoptic model led to measurable efficiency improvements: workflows were streamlined because decision-logic and structured templates eliminated repetitive interpretive steps and minimized variation.
• Radiologists and reporting teams reported a clear preference for the synoptic reporting format over traditional methods. They valued the clarity, uniformity, and speed enabled by the structured approach.
• Because the algorithmic logic guided users through standardised pathways, turnaround times were shortened and error risk was reduced, this aligns directly with benchmarks for high-quality, rapid subspecialised reporting.

For radiology practices, this research underscores the value of:

• Structured, synoptic reporting to ensure consistency and reproducibility.
• Integrating algorithmic decision-logic (such as LI-RADS for CEUS) into reporting workflows to strengthen accuracy and clarity.
• Potentially reducing variation and error when diagnosing focal liver lesions in high-risk populations.

At Kailo Medical, we believe that the future of diagnostic imaging lies in combining expert-driven structured reporting with intelligent workflow tools. This study validates the principle that synoptic, algorithm-guided reporting can raise diagnostic quality and operational efficiency, core tenets of our drive to revolutionise radiology reporting.

At Kailo Medical, we believe that the future of diagnostic imaging lies in combining expert-driven structured reporting with intelligent workflow tools. This study validates the principle that synoptic, algorithm-guided reporting can raise diagnostic quality and operational efficiency, core tenets of our drive to revolutionise radiology reporting.

If your practice is managing high-risk liver cohorts or seeking to upgrade your reporting consistency and efficiency, we invite you to explore how Kailo Medical’s reporting ecosystem empowers algorithm-guided, synoptic workflows. Please request a demo today and discover how our solutions align with cutting-edge research and best practices.