AI Predictions for Colorectal Cancer: One Step Closer to Efficient Precision Oncology

Large-Scale Evaluation Proves Better Generalization and Data-Efficiency

 The team of researchers developed software that uses the new technology of transformer neural networks throughout the analysis process. They show that their approach substantially improves the performance, generalizability, data efficiency, and interpretability by evaluating it on a large multicentric cohort of over 13,000 patients from 16 cohorts from seven countries (Australia, China, Germany, Israel, Netherlands, UK, USA), part of which was contributed by researchers at the German Cancer Research Center (DKFZ) Heidelberg and the network of the National Centers for Tumor Diseases (NCT). The algorithm trained on the large multicentric cohort achieves a very high sensitivity on resection tissue samples obtained during surgery. Strikingly, even though their model has only been trained on tissue samples from resections, the results can reach also a high performance on biopsy tissue obtained during colonoscopy.

Sophia J. Wagner, the first author of the study, emphasizes that “the applicability to biopsy tissue increases the algorithm’s benefit for the patient when ultimately implemented in clinical routine”.

AI-Based Pre-screening for Biopsies Accelerate Diagnosis

 Because of its high sensitivity on biopsy tissue, the algorithm could serve as a pre-screening tool followed by affirmative testing for cases that received a positive result during AI testing. Applying AI-based biomarker prediction could reduce the testing burden and therefore speed up the step between taking the biopsy and the molecular determination of the genetic risk status, thus enabling an earlier patient treatment with immunotherapy if indicated.