In the last decades, the growing population of cancer survivors has shifted researchers' focus from primary toward tertiary prevention. Particularly, adolescents and young adults (AYAs) breast cancer (BC) survivors may face long-term outcomes as a result of their treatments, among which cardiovascular diseases (CVDs) are the most life-threatening ones. To plan effective follow-up guidelines for preventing and treating these events, it is essential to disentangle the causal role of cancer treatments in these patients. In this work, we aim to extend the current state of BC treatment guidelines by leveraging on the estimate of the risk of CVDs in AYAs who underwent BC treatments, as provided by a causal Bayesian network. In these regards, we provide counterfactual explanations of a causal query, using real-world data, algorithms and methods from the causal inference domain. We show that while ovarian suppression combined with tamoxifen may be a necessary cause for ischemic heart disease, it is not a sufficient one, i.e., this treatment alone is not enough to cause the disease, other factors must also be present. These findings can contribute to support clinicians in the treatment choice and help in refining treatment strategies and follow-up protocols for AYAs, advancing personalised healthcare in oncology.

Bernasconi, A., Balordi, A., Zanga, A., Cabañas, R. (2024). On Counterfactual Explanations of Cardiovascular Risk in Adolescent and Young Adult Breast Cancer Survivors. In Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024) (pp.211-223). CEUR-WS.

On Counterfactual Explanations of Cardiovascular Risk in Adolescent and Young Adult Breast Cancer Survivors

Bernasconi A.;Zanga A.;
2024

Abstract

In the last decades, the growing population of cancer survivors has shifted researchers' focus from primary toward tertiary prevention. Particularly, adolescents and young adults (AYAs) breast cancer (BC) survivors may face long-term outcomes as a result of their treatments, among which cardiovascular diseases (CVDs) are the most life-threatening ones. To plan effective follow-up guidelines for preventing and treating these events, it is essential to disentangle the causal role of cancer treatments in these patients. In this work, we aim to extend the current state of BC treatment guidelines by leveraging on the estimate of the risk of CVDs in AYAs who underwent BC treatments, as provided by a causal Bayesian network. In these regards, we provide counterfactual explanations of a causal query, using real-world data, algorithms and methods from the causal inference domain. We show that while ovarian suppression combined with tamoxifen may be a necessary cause for ischemic heart disease, it is not a sufficient one, i.e., this treatment alone is not enough to cause the disease, other factors must also be present. These findings can contribute to support clinicians in the treatment choice and help in refining treatment strategies and follow-up protocols for AYAs, advancing personalised healthcare in oncology.
paper
Adolescents; Breast cancer survivors; Cardiovascular diseases; Causal networks; Counterfactual explanations; Treatment guidelines; young adults;
English
3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024) - 27-28 November 2024
2024
Calimeri, F; Dragoni, M; Stella, F
Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024)
2024
3880
211
223
https://ceur-ws.org/Vol-3880/
open
Bernasconi, A., Balordi, A., Zanga, A., Cabañas, R. (2024). On Counterfactual Explanations of Cardiovascular Risk in Adolescent and Young Adult Breast Cancer Survivors. In Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024) (pp.211-223). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/555601
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