Case study: RlapsRisk® BC

Authors

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Testimonial

Director of Research (Gustave Roussy) ESMO president

“Thanks to the solution we now have a better understanding of the underlying mechanism of highly aggressive tumors and the treatment needs for these patients. Identifying very high-risk patients earlier will enable us to adjust the therapeutic strategy for more favorable patient outcomes.”

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Case study: RlapsRisk® BC

Prediction of patient prognosis from digitized pathology slides in early Breast Cancer (ER+/HER2-)

Breast Cancer

Prognostic biomarkers

Histology Data

Clinical Data

Breast Cancer

Prognostic biomarkers

Histology Data

Clinical Data

Click to enlarge

76%

specificity for post-treatment, time-dependant accuracy at 5 years

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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Testimonial

RlapsRisk® BC can predict the risk of recurrence for ER+/HER2- breast cancer patients at the time of diagnosis. Professor Catherine Guettier, Head of Pathology at Hopital Bicètre Greater Paris University Hospitals - AP-HP, showcases how they’re using digitized slides and CaloPix®, Tribun’s Health IMS system, to support pathologists and oncologists in the future, making more informed treatment decisions and improving patient outcomes.

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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