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A Roadmap towards Breast Cancer Therapies Supported by Explainable Artificial Intelligence

Authors :
Nicola Amoroso
Domenico Pomarico
Annarita Fanizzi
Vittorio Didonna
Francesco Giotta
Daniele La Forgia
Agnese Latorre
Alfonso Monaco
Ester Pantaleo
Nicole Petruzzellis
Pasquale Tamborra
Alfredo Zito
Vito Lorusso
Roberto Bellotti
Raffaella Massafra
Source :
Applied Sciences, Vol 11, Iss 11, p 4881 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on an adaptive dimensional reduction which (i) outlines the most important clinical features for oncological patients’ profiling and (ii), based on these features, determines the profile, i.e., the cluster a patient belongs to. For these purposes, we collected a cohort of 267 breast cancer patients. The adopted dimensional reduction method determines the relevant subspace where distances among patients are used by a hierarchical clustering procedure to identify the corresponding optimal categories. Our results demonstrate how the molecular subtype is the most important feature for clustering. Then, we assessed the robustness of current therapies and guidelines; our findings show a striking correspondence between available patients’ profiles determined in an unsupervised way and either molecular subtypes or therapies chosen according to guidelines, which guarantees the interpretability characterizing explainable approaches to machine learning techniques. Accordingly, our work suggests the possibility to design data-driven therapies to emphasize the differences observed among the patients.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.955faf334fdf452d8b9d1bb13eab6112
Document Type :
article
Full Text :
https://doi.org/10.3390/app11114881