Clinical trial NCT06932133: A Trial on the Use of Point-of-care Ultrasound in the Assessment of Breast Symptoms
Official title: Breast Point-of-Care Ultrasound Examination Trial
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Breast Cancer
Keywords: artificial intelligence, point-of-care ultrasound
Lead sponsor: Region Skane
Sponsor class: OTHER
First posted: 2025-04-17
Enrollment: 600
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

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

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.

Primary outcomes:
measure: The area under the receiver operating characteristic curve (AUC) for the intervention, compared to that of the comparator
timeFrame: From the last enrolled participant to the end of one-year follow up
Secondary outcomes:
measure: The performance of POCUS and POCUS AI
timeFrame: From the last enrolled participants to the end of one year follow up
Other outcomes:
Locations:
city: Malmö
country: Sweden
facility: Unilabs Mammography Unit, Skane University Hospital
Study officials:
affiliation: Lund University, Unilabs Mammography
name: Kristina Lång, MD PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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
citation: 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.