Evidence map›Paper›PMID 42858729›Full record

ArticleBreast (Edinburgh, Scotland)2026

Belgian validation of the INFLUENCE 3.0 model predicting 5-year locoregional and contralateral breast cancer recurrence.

Aline van der Vorst, Carlijn Grimberg, Marissa C van Maaren, Tom A Hueting, Caroline Weltens, Sabine Siesling, Catharina G M Groothuis-Oudshoorn

Abstract read
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Article in Breast (Edinburgh, Scotland), 2026. 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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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Aline van der VorstDepartment of Radiation Oncology, UZ Leuven, Herestraat 49, Leuven, 3000, Belgium.
Carlijn GrimbergDepartment of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands.
Marissa C van MaarenDepartment of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands. Electronic address: m.c.vanmaaren@utwente.nl.
Tom A HuetingEvidencio Medical Decision Support, Haaksbergen, the Netherlands.
Caroline WeltensDepartment of Radiation Oncology, UZ Leuven, Herestraat 49, Leuven, 3000, Belgium.
Sabine SieslingDepartment of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands.
Catharina G M Groothuis-OudshoornDepartment of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast cancer (BC) surveillance primarily aims to detect locoregional recurrence (LRR) and contralateral breast cancer (CBC), but current strategies are largely one-size-fits-all and not tailored to individual risks. The Dutch INFLUENCE 3.0 prediction model provides personalised 5-year LRR and CBC risks to support shared decision-making on surveillance. In this study, INFLUENCE 3.0 is externally validated in a Belgian population.

methodsAll female BC patients curatively treated between 2000 and 2018 at the University Hospitals of Leuven were included. Calibration was assessed using the Integrated Calibration Index (ICI), E50, E90 metrics, and calibration plots. Discrimination was evaluated by calculating the Area Under the Curve (AUC) for each year, based on cumulative events up to that year.

resultsThe final Leuven cohort included 8548 patients, of whom 7549 were not treated with neoadjuvant systemic therapy (non-NST) and 999 were (NST). Calibration metrics were generally acceptable, with ICI and E50 values below 0.01 in most subgroups. Calibration plots showed overestimation of predicted risks, particularly at lower predicted risk levels. Discrimination for LRR risk was good to very good (AUC 0.72-0.86 in the non-NST group; >0.82 in the NST group). For CBC risk, discrimination was poor in the non-NST group (AUC ≈ 0.5) and moderate-to-very good in the NST group (AUC 0.68-0.88).

conclusionINFLUENCE 3.0 showed acceptable calibration and discrimination for LRR risk, and although CBC risk discrimination in the non-NST group was poor. The overall risk estimates - primarily the LRR risks - may still help in guiding tailored surveillance schemes.

Indexed as

Breast cancerContralateral breast cancerExternal validationPredictionRecurrence

Identifiers

PMID42858729
PMCPMC13675742

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.