Densitas highlights study in Breast Cancer Research

AuntMinnie.com staff writers

Feb 20, 2018

Medical software developer Densitas is directing attention to a recent review that validated the clinical use of its automated breast-density measurement software to help stratify patients based on their risk for breast cancer.

The findings were published online February 5 in Breast Cancer Research.

Researchers from the University of Manchester in the U.K. reviewed five different methods for assessing breast density in 366 women participating in the Predicting Risk of Cancer at Screening (PROCAS) study. They found that both the Densitas software and the VolparaDensity algorithm (Volpara Solutions) could be used practically in breast cancer risk models due to the strong association of these density measurements with the risk of breast cancer.

Visual and semiautomated density assessments also show strong relationships with breast cancer risk, but the researchers judged these approaches to be impractical in population-based screening, Densitas said.

The company also said that several scientific and poster presentations at ECR 2018 in March will further confirm the clinical validation of its breast density software.

(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.

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Images in a 70-year-old asymptomatic woman with a prior excisional biopsy in the left breast yielding lobular carcinoma in situ 3 years earlier. Screening contrast-enhanced mammography (CEM) was performed, which was considered a prevalence screen as the woman had no prior breast MRI in the past 3 years and no prior CEM. (A) Low-energy (26–30 kVp) mediolateral oblique (MLO) and (B) craniocaudal (CC) views show surgical clips but no abnormality. (C) Recombined MLO and (D) CC images show an enhancing mass in the medial left breast (arrows). Biopsy revealed invasive lobular carcinoma. Size was 0.2 cm; tumor grade II/III; negative lymph nodes; and estrogen receptor–positive, progesterone receptor–positive, and human epidermal growth factor receptor 2–negative.

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