LATIDIA · Investigación

IA multimodal causal para predicción de quimiosensibilidad personalizada

arXiv: 2609.13567v1Tipo de anuncio: nuevo Resumen: La quimioterapia mejora la supervivencia de algunas pacientes con cáncer de mama, pero los médicos no pueden predecir de manera confiable quién. Las pautas actuales se basan en las puntuaciones de recurrencia como un indicador para tr

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arXiv:2609.13567v1 Announce Type: new Abstract: Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who. Current guidelines rely on recurrence scores as a proxy for treatment benefit, which may contribute to the overprescription of chemotherapy. Here we present a causal multi-modal AI model that predicts personalized chemosensitivity using routinely collected pathology and clinical information. We developed our model on a multi-national dataset of 9,141 patients (twelve cohorts, nine countries) and evaluated it on another 1,994 patients (five cohorts, three countries). The model generated treatment-specific recurrence probabilities for each patient, with near-perfect calibration and strong prognostic discrimination across both 5- and 10-year follow-up horizons. Moreover, its