A Roadmap towards Breast Cancer Therapies Supported by Explainable Artificial Intelligence

Nicola Amoroso,Alfredo Zito,Ester Pantaleo,Nicole Petruzzellis,Francesco Giotta,Alfonso Monaco,Vittorio Didonna,Pasquale Tamborra,Roberto Bellotti,Annarita Fanizzi,Vito Lorusso,Domenico Pomarico,Raffaella Massafra,Agnese Latorre,Daniele La Forgia

doi:10.3390/app11114881

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.

Highlights

The definition of patients’ profiles according to an automated and data-driven procedure represents a cornerstone of personalized medicine [1,2]
For further comparison and to assess the robustness of adaptive dimension reduction (ADR) results we considered embedding techniques which for their popularity can be considered the state of the art: principal component analysis (PCA), Laplacian eigenmap and distinguishing variance embedding (DVE)
Our targeted roadmap towards a XAI being able to identify optimal therapy schemes has to rely on the detection of most informative variables in this purpose, whose role can be verified by the procedure adopted in current guidelines

Summary

Introduction

The definition of patients’ profiles according to an automated and data-driven procedure represents a cornerstone of personalized medicine [1,2]. We investigate if it is possible to identify a quantitative similarity criterion to assess similarities (or dissimilarities) among oncological patients and, quantify to which extent such similarity groups match the current oncological therapies. To this aim, we design an explainable machine learning framework able to support decision making about therapies assigned to breast cancer patients, based on a coherent description of the clinical status. If clusters transparency is somehow easy to obtain, the same consideration does not hold for justification

Methods

Results

Discussion

Conclusion