A digital pathology approach to increase sustainability in diagnostics

Cancer of Unknown Primary (CUP) remains one of the most enigmatic and challenging entities in oncology, characterized by diagnostic complexity, molecular heterogeneity, and limited therapeutic options. This study leverages advanced digital pathology and genomic profiling techniques to address these challenges, offering innovative strategies for improving diagnosis and treatment. By integrating liquid biopsy-based genetic characterization with state-of-the-art artificial intelligence (AI) algorithms, the research aims to enhance the understanding and clinical management of CUP. A custom target panel was employed to analyse circulating tumour DNA (ctDNA) from liquid biopsy samples, uncovering somatic and germline mutations and calculating tumour mutation burden (TMB) in CUP patients. These findings provide insights into the genetic landscape of CUP, including the identification of potential therapeutic targets and biomarkers for treatment response. In parallel, we applied a CUP site-of-origin predictive approach to CUP diagnostic slides, with the final goal to provide a primary site indication useful for therapy decision. Histopathological data from whole slide images (WSI) were processed using state-of-the-art AI models to infer the tissue of origin. These models demonstrated robust classification accuracy, emphasizing their potential for improving diagnostic workflows while reducing reliance on invasive biopsies and hazardous chemicals. The study also highlights the environmental and operational benefits of digital pathology, including reduced energy consumption, minimized chemical usage, and streamlined data sharing, paving the way for more sustainable diagnostic practices. Despite the promising results, challenges such as the heterogeneity of the data and the need for independent cohorts underline the importance of further research. By combining genomics, AI-driven pathology, and sustainable practices, this research contributes to the evolving paradigm of precision oncology, offering hope for better outcomes in one of the most elusive cancer types.