Case study: RlapsRisk® BC

Context

Approximately 10% of all eBC patients will relapse after their initial treatment each year.  Breast cancer (BC) is a heterogeneous disease encompassing several subtypes associated to a wide range of prognosis. Risk determination is crucial for treatment decision.

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Methods

Owkin developed RlapsRisk BC, an AI-based tool that assesses the risk of distant relapse at 5 years of ER+/HER2- early invasive (ei)BC patients, post surgery, from HES (hematoxylin-eosin-safran)-stained whole slide images (WSI) and clinical data.

The solution was validated on 2 independent cohorts of 1000+ slides, in a blind single shot fashion.

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Results^{1, 2}

This model accurately discriminate between low and high risk breast cancer patients (ER+/HER2) using digital pathology slides on resection pieces and improves the patients identification compared to clinical scores.

Combining RlapsRisk score and the clinico-pathological factors improved the prognostic discrimination (c-index 0.80) compared to the clinico-pathological factors alone (c-index 0.76).

RlapsRisk BC achieves 76% sensitivity and 76% specificity for post-treatment, time-dependent accuracy at 5 years, outperforming current clinical scores in practice.

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Impact

Optimize clinical development:

• Stratification biomarker to select high value subgroups, improving RCT statistical power
• Inform on the most prognostic variables to increase RCT statistical power

Diagnostic:

• Help oncologists and pathologists to the risk of relapse of eBC patients

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1. Manuscript under review with peer-reviewed journal, and available as a pre-print on bioRxiv.

2. Earlier results of the model published at Garberis IJ, et al. Annals of Oncology (2021): Poster presented at ESMO congress 2021 ; May 9th - 13th 2022; Paris France.

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Learn more about RlapsRisk BC

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