Discover how AI is revolutionizing TSR scoring, enhancing clinical decisions, and shaping the future of healthcare.

The ARABESC project is the result of a collaborative partnership between WSK Medical and IMP Diagnostics. Since 2023, the two companies have been working together to develop an AI driven algorithm for tumor-stroma ratio (TSR) scoring. In Part 1, we explored the clinical relevance of TSR scoring and the potential benefits of integrating AI into this process. In this second part, we address the challenges, ethical considerations, and future prospects of AI in TSR scoring. 

CONCERNS AND LIMITATIONS OF AI IMPLEMENTATION  

 I – What concerns would you have about integrating AI to automatically score TSR? Do you foresee any limitations, such as trust in AI outputs or lack of explainability?  

DM – There are always concerns regarding the implementation of AI tools in healthcare. Some that we can mention are: trust issues (pathologists may hesitate to rely on AI systems’ decision-making, namely when using opaque systems); validation (how to ensure that the AI systems go through rigorous validation to guarantee accuracy and reliability); integration (aligning new AI tools with existing workflows can pose several logistical challenges); over-reliance (there is always the question if excessive dependence on AI may impair critical diagnostic skills).  

 I – How important is transparency and interpretability in AI systems to you when assessing a patient’s pathology? Would you expect a clear explanation of how the AI reaches its TSR score?  

 DM – Transparency and interpretability are very important in clinical practice. For clinical acceptance, AI outputs must be explainable, with visual overlays or other type of explanations, showing how TSR scores were derived. Another option is to have semi-automated solutions, where the pathologists still have most of the control (e.g., for TSR score, the AI tool may allow the pathologist to choose the region of interest to score). These practices strengthen trust and allow the pathologists to validate results against their own expertise.  

 CLINICAL DECISION-MAKING  

I – How do you think AI-based TSR scoring could affect multidisciplinary team discussions and treatment decisions?  

 DO – By providing robust, reproducible TSR data, AI has the potential to improve the precision of prognostic assessments shared in multidisciplinary team meetings. Imagine having a visual map showing the quantification of stroma and tumour percentages in a specific region of interest – instead of the current eyeball estimation of TSR! This can lead to more confident and personalized treatment planning.  

FUTURE  

  I – In your opinion, what is the future of AI in pathology, specifically regarding the quantification of tumour-stroma and other histological features?  

DM – AI tools really have the potential to aid pathologists when it comes to automating and facilitating scoring tasks (TSR and others), which are usually time consuming and tedious. In the future, integrating quantitative analyses (like TSR) with other histological and molecular features can establish comprehensive predictive models for prognosis and treatment response prediction.  

I – What would be your ideal scenario for the integration of AI in your practice—specifically related to TSR—over the next 5-10 years?  

DO – The ideal scenario would firstly involve a seamless integration of the AI tools into the existent pathology workflow (namely LIS and IMS systems), enabling real-time TSR assessment on digitized slides. The system would be explainable to allow the pathologist to validate the results against their own expertise. Also, it would have multiple integrated functions, that would allow for a global assessment of the cases. Additionally, in a perfect world, the results would be easily integrated into the pathology report in the LIS system. On the tool side, it would be important to have continuous learning systems that adapt and improve as they are being exposed to new data, ensuring they remain clinically relevant. Also to have some form of collaborative AI, where pathologists could interact with and refine AI outputs, maintaining their central role in diagnostic decision-making.  

As AI continues to evolve, its role in TSR scoring and broader healthcare applications will undoubtedly grow. By addressing current limitations and ethical concerns, we can unlock its full potential, improving patient outcomes and transforming clinical practice.