Enhancing Breast Cancer Recurrence Prediction Across Treatment Scenarios with Weighted Cox Mixtures.

Hasna El Haji, Amara Tariq, Amine Souadka, Nada Sbihi, Felipe Batalini, Mounir Ghogho, Imon Banerjee

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Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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7 authors.

Hasna El HajiMayo Clinic, Department of Radiology, Phoenix, Arizona, USA.

Amara TariqMayo Clinic, Department of Radiology, Phoenix, Arizona, USA.

Amine SouadkaSurgical Oncology Department, National Institute of Oncology, Rabat, Morocco.

Nada SbihiInternational University of Rabat, TICLab, Morocco.

Felipe BataliniMayo Clinic, Department of Oncology, Phoenix, Arizona, USA.

Mounir GhoghoUniversity Mohammed VI Polytechnic, College of Computing, Morocco.

Imon BanerjeeMayo Clinic, Department of Radiology, Phoenix, Arizona, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer treatment involves surgery, radiation, chemotherapy, and endocrine therapy, with recurrence risk depending on treatment execution. We propose a weighted Cox mixtures model that integrates treatment plans and clinical data to estimate recurrence risk. Data from Mayo Clinic (US) and the National Institute of Oncology (Morocco) inform the model. We enhance expectation maximization within the Cox mixtures model using three weighting strategies: Inverse Probability of Treatment Weighting, Adaptive Weights with focal loss, and Prioritizing Subgroups. In the Mayo Clinic cohort, Adaptive Weights improve predictive accuracy (C-index: 0.67-0.88), outperforming the standard Cox model. In the Moroccan cohort, Adaptive Weights also enhance C-index values (0.60-0.71), though with larger confidence intervals. Our findings demonstrate that weighting strategies refine recurrence risk prediction, particularly in imbalanced cohorts. Expanding datasets, especially in underrepresented populations, is crucial for improving model reliability and clinical applicability.

Indexed as

Breast NeoplasmsNeoplasm Recurrence, LocalFemaleHumansPrediction AlgorithmsProportional Hazards ModelsRisk Assessment

Identifiers

PMID41726459

PMCPMC12919416

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