Explore how RANO 2.0, AI-assisted imaging, and evolving regulatory expectations are changing neuro-oncology clinical trials and imaging workflows.
For years, imaging in neuro-oncology clinical trials has relied on established response assessment criteria and expert radiologist interpretation. While those foundations remain essential, the environment around them is changing rapidly.
Updated response assessment guidelines, growing adoption of AI-assisted imaging, and increasing expectations for real-time data oversight are changing not only how tumors are measured, but also how imaging workflows are designed and managed.
These topics were explored during a recent QMENTA webinar featuring Dr. Aly H. Abayazeed (Neuroradiologist, AI specialist, and Founder of QRadAI), who shared his perspective on how these developments are influencing imaging review in modern clinical trials.
These changes point toward a broader shift: imaging infrastructure is becoming just as important as imaging interpretation.
The introduction of RANO 2.0 is more than an update to existing guidelines. It reflects years of experience with modern therapies and acknowledges that assessing treatment response has become increasingly complex.
One important change is the move away from relying on immediate post-operative scans as the primary comparison point. Instead, post-radiotherapy imaging becomes the new baseline for evaluating disease progression, reducing the uncertainty introduced by surgical changes that can complicate interpretation.
The updated criteria also provide clearer guidance around pseudoprogression, confirmatory imaging, and non-enhancing tumors while bringing together several previously separate RANO frameworks into a more unified approach.
Speaking about the significance of these changes during the discussion, Dr. Abayazeed explained:
"RANO 2.0 is not just a documentation change. It's not a documentation exercise. There is a fundamental shift in how the decision is being made during evaluation of the cases."
The common theme across these updates is straightforward: clinical decisions increasingly depend on understanding how disease evolves over time rather than evaluating individual imaging studies in isolation.
Artificial intelligence is already changing medical imaging, but perhaps not in the way many people expected.
Rather than replacing radiologists, AI is increasingly being used to automate repetitive, time-consuming tasks that improve efficiency while allowing clinical experts to retain oversight.
In neuro-oncology, one of the clearest opportunities is volumetric analysis.
RANO 2.0 formally recognizes three-dimensional volumetric measurements as an acceptable method for assessing tumors alongside traditional two-dimensional measurements. While manual assessment remains the standard in most studies, AI-assisted segmentation and volumetric analysis are becoming practical tools that help improve consistency across longitudinal assessments.
Importantly, these technologies work best when they support—not replace—clinical expertise.
Discussing the role of AI in clinical practice, Dr. Abayazeed noted:
"We still need a radiologist... You need that human in the loop."
This human-in-the-loop model is likely to define AI adoption in neuro-oncology for the foreseeable future, combining automation with expert validation rather than fully autonomous decision-making.
Advances in response assessment and AI inevitably place greater demands on imaging operations.
A volumetric segmentation algorithm is only useful if imaging data is standardized across sites. Longitudinal assessments are only reliable if previous measurements remain accessible throughout the study. AI-generated outputs only become meaningful when they can be reviewed, validated, and traced back through a complete audit trail.
In other words, better measurements depend on better infrastructure.
As imaging workflows become increasingly data-driven, sponsors and imaging core labs will need platforms capable of supporting reader continuity, longitudinal data management, standardized workflows, and complete traceability throughout the trial.
Operational excellence is becoming more important for another reason: regulators increasingly expect continuous visibility into clinical trial data.
Rather than reviewing studies only after database lock, initiatives around real-time clinical trial oversight signal a move toward more continuous governance throughout a study's lifecycle.
This changes the role of imaging systems.
Platforms are no longer simply repositories for storing images. They become part of the quality framework that supports data integrity, auditability, and operational transparency from the first patient visit through the final analysis.
Commenting on this shift in regulatory oversight, Dr. Abayazeed observed:
"The FDA moving from these static milestones to more of a dynamic governance of clinical trials is critical and is the right move."
RANO 2.0, AI-assisted image analysis, and evolving regulatory expectations all point toward the same conclusion.
Neuro-oncology clinical trials are becoming increasingly longitudinal, quantitative, and interconnected.
Technology will continue to improve how tumors are measured. The organizations best positioned for the future will be those that also invest in the infrastructure needed to support those measurements—ensuring imaging data remains connected, traceable, and ready to support confident clinical decisions throughout the entire trial.
RANO 2.0 is an updated response assessment framework for neuro-oncology clinical trials that provides clearer guidance on tumor evaluation, pseudoprogression, confirmatory imaging, non-enhancing tumors, and longitudinal disease assessment.
RANO 2.0 places greater emphasis on longitudinal assessment, post-radiotherapy baseline imaging, volumetric measurement, and consistent review across timepoints, making imaging workflow design and data traceability more important.
AI is not expected to replace radiologists in neuro-oncology clinical trials. Instead, AI-assisted tools are increasingly used to support repetitive tasks such as segmentation and volumetric analysis while keeping expert radiologists in the loop.
AI-assisted imaging depends on standardized data, accessible longitudinal measurements, complete audit trails, and workflows that allow outputs to be reviewed, validated, and traced throughout the clinical trial.
Regulatory expectations are moving toward more continuous oversight of clinical trial data, increasing the need for imaging systems that support data integrity, auditability, operational transparency, and real-time visibility.
Learn how QMENTA supports longitudinal imaging workflows, AI-assisted analysis, central review, and traceable imaging data management across clinical trials.
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