Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy

CONCLUSION: This work illustrates deep learning's potential for assisting pathological response assessment. Spatial heatmaps and patch examples provide intuitive explanations of model predictions, engendering clinical trust and adoption (Code and data will be available at https://github.com/WinnieLaugh/ESCC_Percentage once the paper has been conditionally accepted). Integrating interpretable computational pathology could help enhance the efficiency and consistency of tumor response assessment and empower precise oncology treatment decisions.PMID:38632527 | DOI:10.1186/s12575-024-00234-5

Source: Biological Procedures Online - April 17, 2024 Category: Molecular Biology Authors: Yichen Wang Wenhua Zhang Lijun Chen Jun Xie Xuebin Zheng Yan Jin Qiang Zheng Qianqian Xue Bin Li Chuan He Haiquan Chen Yuan Li Source Type: research