Advanced analysis of multiparametric volumetric ultrafast ultrasound: a novel approach for non-invasive breast cancer diagnosis

This project aims to enhance non-invasive breast cancer diagnosis by integrating machine learning with advanced ultrasound techniques to create predictive models for tumor characteristics, reducing reliance on biopsies.

Subsidie
€ 1.499.498
2025

Projectdetails

Introduction

Tumour development follows a diversity of complementary biological pathways, including the modification of the tissue structure and vascularization, which are not currently captured by imaging techniques in the clinic. Breast cancer patients undergo a series of imaging sessions using complementary modalities, including ionizing mammographies.

Challenges in Current Diagnosis

In many cases, this imaging is not precise enough for a diagnosis, so that tissue samples in the form of biopsies are used to further characterize the tumour and determine the appropriate treatment. Beyond the pain and stress associated with biopsies, this complex process is costly and time-consuming, and delays the time to diagnosis.

Advantages of Ultrasound Imaging

Ultrasound B-mode imaging is largely used in the diagnostic process of breast cancer, in part because it is low cost, portable, and largely available, as well as non-ionizing and non-invasive.

Innovative Techniques

Our laboratory, Institute Physics for Medicine Paris, has recently developed several quantitative techniques allowing for the measurement of:

  • Tissue stiffness
  • Fiber organization
  • Vascular mapping

All of these are relevant to tumour development.

Proposed Approach

In this project, I propose a new approach to diagnosing breast cancer non-invasively by applying machine learning analysis to rich volumetric multiparametric maps of complementary tumour aspects, obtained using these innovative ultrafast ultrasound techniques.

Technological Integration

The project will tackle the technological challenge of integrating these techniques into a common acquisition and analysis framework. This will include:

  1. The collection of a large clinical dataset
  2. The development and validation of a predictive malignancy model informing on tumour characteristics for the diagnosis.

Impact on Society

This approach will open the door to fully virtual biopsies, impacting society on a large scale in terms of cost, diagnostic efficacy, and patient comfort.

Financiële details & Tijdlijn

Financiële details

Subsidiebedrag€ 1.499.498
Totale projectbegroting€ 1.499.498

Tijdlijn

Startdatum1-1-2025
Einddatum31-12-2029
Subsidiejaar2025

Partners & Locaties

Projectpartners

  • INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALEpenvoerder

Land(en)

France

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