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

Breast Point-of-Care Ultrasound Examination Trial

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Breast Cancer
• Intervention / Treatment: Diagnostic test: Point-of-care ultrasound

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

• Study Type: Interventional
• Enrollment (Estimated): 600
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Sweden
  • Malmo, Sweden, 20502
    • Unilabs Mammography Unit, Skane University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Intervention	Diagnostic test: Point-of-care ultrasound; Point-of-care ultrasound will be performed on symptomatic breast patients. The images will be analysed by AI

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The area under the receiver operating characteristic curve (AUC) for the intervention, compared to that of the comparator	The AUC of POCUS compared to SoC; The AUC of AI compared to average radiologists on POCUS	From the last enrolled participant to the end of one-year follow up

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The performance of POCUS and POCUS AI	The sensitivity, specificity, PPV, and NPV of POCUS compared to SoC (Part 1); The sensitivity, specificity, PPV, and NPV of POCUS compared to standard ultrasound (Part 1); The sensitivity, specificity, PPV, and NPV of POCUS compared to mammography (Part 1); The AUC of POCUS AI compared to SoC (Part 1); The AUC, sensitivity, and specificity of POCUS in relation to tumor type, stage, breast density, previous history of breast cancer, age, and demographics (Part 1 and Part 2); The AUC, sensitivity and specificity of POCUS AI in relation to tumor type, stage, breast density, previous history of breast cancer, age, and demographics (Part 1 and Part 2); The sensitivity, specificity, PPV, and NPV of average radiologists on POCUS compared to standard ultrasound (Part 2); The sensitivity and specificity of POCUS AI at different operating thresholds (Part 2); The balanced accuracy of POCUS AI compared to average radiologists (Part 2); The AUC, sensitivity, and specificity of POCUS AI	From the last enrolled participants to the end of one year follow up

Collaborators and Investigators

• Sponsor: Region Skane
• Investigators: Principal Investigator: Kristina Lång, MD PhD, Lund University, Unilabs Mammography

Publications and helpful links

General Publications

• 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.
• 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
• 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.

Helpful Links

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

Study record dates

Study Major Dates

• Study Start (Actual): April 7, 2025
• Primary Completion (Actual): February 12, 2026
• Study Completion (Estimated): February 12, 2027

Study Registration Dates

• First Submitted: April 10, 2025
• First Submitted That Met QC Criteria: April 10, 2025
• First Posted (Actual): April 17, 2025

Study Record Updates

• Last Update Posted (Actual): March 30, 2026
• Last Update Submitted That Met QC Criteria: March 27, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: artificial intelligence, point-of-care ultrasound
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Skin Diseases; Breast Diseases; Skin and Connective Tissue Diseases; Breast Neoplasms
• Other Study ID Numbers: CIV-ID 24-10-049596

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: De-identified data will be made available upon reasonable request, with investigator support and a signed data access agreement. A proposal should be submitted to be reviewed by the study steering committee. Individual data are not publicly available due to data protection regulations. The Clinical Investigator Plan is available online at https://portal.research.lu.se/sv/projects/breast-point-of-care-examination-trial
• IPD Sharing Time Frame: Supporting information will be available from study start and up to 10 years after study completion. IPD will be available at the publication of results, if conditions are met.
• IPD Sharing Access Criteria: See plan description
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: Yes