ESTRO 2024 - Abstract Book

S4469

Physics - Machine learning models and clinical applications

ESTRO 2024

Conclusion:

We have successfully developed an AI framework for quantifying the TIL density from digital WSIs and deployed it to investigate the predictive impact of lymphocyte infiltration for rectal cancer. Our approach could be incorporated in the clinical pathway. We found that high pre-treatment TIL density was significantly associated with better DFS and OS. Meanwhile, patients whose tumours had consistently low TIL density experienced the worst outcomes, but the DFS of a subgroup in which the radiotherapy stimulates an immune response was significantly better than the TIL −− patients. We are working on exploring the potential genomic signatures of the sub-cohort by analysing the mRNA and DNA sequencing data.

Keywords: Tumour Microenvironment, Rectal Cancer

References:

[1] ARISTOTLE: a phase III trial comparing standard versus novel chemoradiation treatment (CRT) as pre-operative treatment for magnetic resonance imaging (MRI)-defined locally advanced rectal cancer. https://doi.org/10.1186/ISRCTN09351447.

[2] M. Tan und Q. V. Le, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, arXiv, 2020.