Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

SHINER, Audrey, KISS, Alex, SAEDNIA, Khadijeh, JERZAK, Katarzyna J., GANDHI, Sonal, LU, Fang-I, EMMENEGGER, Urban, FLESHNER, Lauren, LAGREE, Andrew, ALERA, Marie Angeli, BIELECKI, Mateusz, LAW, Ethan, LAW, Brianna, KAM, Dylan, KLEIN, Jonathan, PINARD, Christopher J., SHENFIELD, Alex, SADEGHI-NAINI, Ali and TRAN, William T. (2023). Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning. Genes, 14 (9): 1768.

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Abstract

<jats:p>Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	0604 Genetics; 3105 Genetics
Identification Number:	https://doi.org/10.3390/genes14091768
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	08 Sep 2023 14:00
Last Modified:	11 Oct 2023 11:47
URI:	https://shura.shu.ac.uk/id/eprint/32347

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