aiqbench analysis
This study demonstrates that integrating structured clinical records with unstructured free-text pathology narratives significantly enhances the accuracy of breast cancer recurrence prediction models. By employing rule-based extraction and conflict reconciliation strategies, the research effectively augments limited data sources to capture fuller clinical contexts. The findings validate multi-modal approaches as a robust method for improving risk assessment in oncology applications.
Multi-Modal Machine Learning for Breast Cancer Recurrence Prediction
arXiv:2606.02892v1 Announce Type: new Abstract: Breast cancer recurrence, a leading cause of long-term mortality among survivors, requires timely and accurate risk assessment to guide follow-up care and treatment planning. Traditional predictive models, often limited to either structured or unstructured data alone, struggle to capture the full clinical context. This study examines the impact of integrating multi-modal clinical data, including treatment records, pathology reports, and clinician notes, on recurrence prediction. By integrating a rule-based regular expression extraction mechanism with a rigorous precedence-based conflict reconciliation strategy, our approach effectively recovers definitive tumor characteristics from free-text pathology narratives to augment structured records. We also benchmark performance against commonly used feature sets from prior breast cancer studies to assess the added value of multi-modal integration. Single-source and multi-modal inputs are evaluated across a range of machine learning models. Results show that multi-modal integration consistently improves predictive accuracy compared to single-modal methods.
Abstract:Breast cancer recurrence, a leading cause of long-term mortality among survivors, requires timely and accurate risk assessment to guide follow-up care and treatment planning. Traditional predictive models, often limited to either structured or unstructured data alone, struggle to capture the full clinical context. This study examines the impact of integrating multi-modal clinical data, including treatment records, pathology reports, and clinician notes, on recurrence prediction. By integrating a rule-based regular expression extraction mechanism with a rigorous precedence-based conflict reconciliation strategy, our approach effectively recovers definitive tumor characteristics from free-text pathology narratives to augment structured records. We also benchmark performance against commonly used feature sets from prior breast cancer studies to assess the added value of multi-modal integration. Single-source and multi-modal inputs are evaluated across a range of machine learning models. Results show that multi-modal integration consistently improves predictive accuracy compared to single-modal methods.
Machine Learning (cs.LG)
arXiv:2606.02892 [cs.LG]
(or arXiv:2606.02892v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.02892 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 236 words
Related coverage
Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Prediction
arXiv · Jul 21, 2026
How to build a cancer vaccine, and whether they will work this time
LessWrong · Jun 8, 2026
Token-Level Cross-Modal Transformer with Contrastive Multi-Task Learning for Breast Cancer Subtype Classification and Survival Prediction
arXiv · Jul 21, 2026
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
arXiv · Oct 9, 2026