Volpara to launch mammo decision support at RSNA 2018

AuntMinnie.com staff writers

Nov 15, 2018

Breast imaging software developer Volpara Solutions plans to launch real-time decision-support software for mammography at the RSNA 2018 meeting later this month.

The software, called VolparaLive, automatically analyzes patient positioning and breast compression to help radiologic technologists acquire consistent, high-quality mammograms, according to the company. Volpara believes that use of the software will result in fewer patient recalls for technical reasons, as technologists will have actionable data on images before the patient leaves the room.

The company also plans to show improvements to its VolparaEnterprise software at RSNA 2018 for the first time. Volpara will show a new reporting tool for periodic image quality reviews that further streamlines compliance with the U.S. Food and Drug Administration's Enhancing Quality Using the Inspection Program (EQUIP) initiative. Users will be able to generate a list of exams based on the VolparaEnterprise quality assessment for periodic image quality reviews and sort them by interpreting physicians and technologists.

VolparaEnterprise also now features vPGMI, a new image positioning quality standard. Patient positioning is assessed using the categories of perfect, good, moderate, and inadequate (PGMI).

(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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A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.

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