A Trial on the Use of Point-of-care Ultrasound in the Assessment of Breast Symptoms

NCT ID: NCT06932133

Last Updated: 2025-04-29

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 600 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-04-07
• Study Completion Date: 2026-08-31

Brief Summary

The high cost of diagnostic equipment, limited expertise, and inadequate infrastructure are major barriers to early breast cancer diagnosis in low- and middle-income countries. Point-of-care ultrasound (POCUS) offers a relatively low-cost, portable solution that, when combined with artificial intelligence (AI)-driven image analysis, has the potential to significantly expand access to breast assessment in these settings. The purpose of this study is to evaluate the performance of POCUS for women with focal breast symptoms and to assess the performance of AI to analyze POCUS images. The study will be divided in two parts: a prospective interventional study and a retrospective multicase multireader study.

Detailed Description

In this trial we want to understand if the use of POCUS is non-inferior to Standard of Care (SoC) and if the combination of POCUS AI can reach non-inferior performance to that of breast radiologists. There is a need for breast diagnostic tools in underserved countries since late-stage diagnosis is a major cause of the high breast-cancer mortality in low-and middle-income countries. Showing that POCUS can be sufficient for an assessment of focal breast symptoms can provide evidence for a broader use. Also, enabling automated interpretation using AI can add to the value of this low-cost and accessible solution. The first part of the trial is a prospective open-label accuracy study with paired design. The intervention of POCUS as a targeted diagnostic method for women with focal breast complaints will be compared with SoC. We will also be able to compare POCUS with the individual components of SoC (mammography and standard ultrasound) and retrospectively with POCUS AI. The second part of the trial is a single-blinded retrospective paired mulitcase multireader study. In this part we can directly assess POCUS and POCUS AI without the influence of mammography and benchmark to a larger group of radiologists and in addition compare with standard ultrasound

Conditions

Breast Cancer

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Intervention

Group Type: EXPERIMENTAL

Point-of-care ultrasound

Intervention Type: DIAGNOSTIC_TEST

Point-of-care ultrasound will be performed on symptomatic breast patients. The images will be analysed by AI

Interventions

Point-of-care ultrasound

Point-of-care ultrasound will be performed on symptomatic breast patients. The images will be analysed by AI

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Women (≥18 years of age) referred to diagnostic imaging with a suspicion on malignancy

Exclusion Criteria

• Individuals unable to comprehend the study information due to language barriers or cognitive impairments.

• Minimum Eligible Age: 18 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Region Skane

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Kristina Lång, MD PhD

Role: PRINCIPAL_INVESTIGATOR

Lund University, Unilabs Mammography

Locations

Unilabs Mammography Unit, Skane University Hospital

Malmo, , Sweden

Site Status: RECRUITING

Countries

Sweden

Central Contacts

Kristina Lång, MD PhD

Role: CONTACT

kristina.lang@med.lu.se

+46771407710

Facility Contacts

Kristina Lång, MD PhD

Role: primary

kristina.lang@med.lu.se

+46771407710

References

Karlsson J, Arvidsson I, Sahlin F, Astrom K, Overgaard NC, Lang K, Heyden A. Breast cancer classification in point-of-care ultrasound imaging-the impact of training data. J Med Imaging (Bellingham). 2025 Jan;12(1):014502. doi: 10.1117/1.JMI.12.1.014502. Epub 2025 Jan 17.

Reference Type: BACKGROUND

PMID: 39830074 (View on PubMed)

Karlsson, J, Wodrich, M, Overgaard, NC, Sahlin, F, Lång, K, Heyden, A & Arvidsson, I 2025, Towards Out-of-Distribution Detection for Breast Cancer Classification in Point-of-Care Ultrasound Imaging. in, Pattern Recognition - 27th International Conference, ICPR 2024, Proceedings, Part XIII. Lecture Notes in Computer Science

Reference Type: BACKGROUND

Wodrich, M, Karlsson, J, Lång, K & Arvidsson, I 2025, Trustworthiness for Deep Learning Based Breast Cancer Detection Using Point-of-Care Ultrasound Imaging in Low-Resource Settings. in Medical Information Computing: MICCAI Meets Africa Workshop, https://doi.org/10.1007/978-3-031-79103-1_5.

Reference Type: BACKGROUND

Related Links

https://portal.research.lu.se/en/projects/b54ebe52-259e-4562-98d0-16eef09c0674

Official webpage of Lund University research portal and the webpage of the PI

Other Identifiers

CIV-ID 24-10-049596

Identifier Type: -

Identifier Source: org_study_id