Feature Importance in Predicting Clinical Outcome: Statistics vs. Explainable Artificial Intelligence

At the time of diagnosis for cancer patients, a wide array of data can be gathered, ranging from clinical information to multiple layers of omics data. Determining which of these data are most informative is crucial, not only for advancing biological understanding but also for clinical and economic considerations. This process facilitates the selection of the most significant markers, enhancing patient stratification and informing treatment recommendations. In this paper, we start with 89 features extracted from multiomics and clinical data and aim to identify the most important ones in predicting response to neoadjuvant chemotherapy (NAC) using different explainable Artificial Intelligence (XAI) models and statistics. Our results show that XAI methods consistently recover important features that are missed by statistics and vice versa, hinting towards the need for complementary implementation of these methods. Furthermore, we find that a myriad of features, from mutations to immune infiltration, affect the response to NAC in breast tumors.