1 2 A I & M A M M O G R A P H Y Identifying cancers in symptomatic younger women DBT shows superiority Report: Cynthia E. Keen Digital breast tomosynthesis (DBT) increases detection of breast can- cer in symptomatic women under the age of 60, especially in dense breasts. A large, multi-institution- al study conducted in the United Kingdom to compare the sensitivity of full-field digital mammography (FFDM), DBT, and FFDM plus DBT supports findings of two similar published studies, both conduct- ed in China within the same time frame. Much research has been carried out to confirm the increased cancer detection capabilities of DBT tech- nology as a breast cancer screening tool, but very little has been under- taken to evaluate and quantify the accuracy of DBT in symptomatic patients. The rigorous double-blind ret- rospective study, led by research- ers at the University of Dundee in Scotland, utilised images acquired from a different mammography sys- tem than that used by the Chinese researchers. Because DBT technol- ogy differs significantly, findings of a single-vendor study do not necessarily apply when other equip- ment is used. This new study pub- lished online in the British Journal of Radiology complements the research in China with similar data for a European population. The participants included 300 women with symptoms or signs of breast cancer representing a 20% or greater likelihood of malignancy fol- lowing a clinical examination. They had both FFDM and DBT at five UK hospitals’ specialist breast centres within a five-year period, starting in 2011. All sites used MAMMOMAT Inspiration units from Siemens Healthineers. Images were acquired at all sites. The study cohort includ- ed all 152 women recruited who were diagnosed with cancer, plus a randomised selection of half of the 209 recruited women with benign ma in situ (DCIS) represented only 1% of the 157 cancerous breasts. Twelve breast imagers experi- enced with Siemens DBT indepen- dently analysed a batch of 50 cases for each category, for 150 read- ings, two readers per batch. Each batch had similar distributions of different aged patients, and can- cerous, benign, and normal cases. To replicate real-life practice condi- tions, they were provided with the patient’s age and clinical records. The MAMMOMAT Inspiration from Siemens Healthineers is equipped with 50˚ Wide-Angle Tomosynthesis and offers high-quality results using up to 30% less dose in FFDM, with MoodLight and OpComp personalised compression to improve patient experience. lesions and half of the 76 women with normal breasts. The mean age was 47 years, but some participants were as young as 24 years. None of the patients had breast implants, as DBT had not yet been approved for use. Masses, at 89%, were the dominant radiologi- cal feature in malignant breasts, and 85% were unifocal tumours. The majority of cancers were Grade 3 or 2 invasive ductal tumours (42% and 33% respectively) and invasive lobu- lar carcinoma (12%). Ductal carcino- Case 1. A symptomatic woman was screened with both FFDM and wide-angle digital breast tomosynthesis. Imaging revealed an 8 mm spiculated mass that was more visible on wide-angle digital breast tomosynthesis as compared to FFDM. Biopsy samples from this lesion demonstrated an invasive ductal carcinoma on histopathology. The imagers made their interpre- tations using FFDM images only, DBT images only, and FFDM+DBT images, marking regions of interest, measuring lesions they identified, and describing abnormalities. They scored suspicious lesions on a 1-to- 5 scale (normal to malignant). For FFDM images, the readers catego- rised breast density using a BI-RADS density 1-4 category score, and used an on-screen 0-100 mm visual ana- logue scale to assign an area-based percentage mammographic density. The percentage of volumetric breast density was also assessed using Volpara Data Manager software (Volpara Solutions, Wellington, NZ). Andy Evans MD, Professor of Breast Imaging at the University of Dundee and honorary consultant radiologist in NHS Tayside, and co- authors, compared the 1,800 case findings with the diagnosis of each patient, based on clinical exam, medical imaging, and histopathol- ogy. Readers identified 260 unifocal malignant lesions on DBT images compared to 214 on FFDM. Highest sensitivity The FFDM+DBT combined exams had the highest sensitivity in detect- ing breast cancer, at 97%, followed by 89.1% for DBT alone, and 86.6% for FFDM alone. In the densest breast category, FFDM+DBT identi- fied 10.3% more cancers than FFDM alone, but DBT alone was most accurate for breasts in the third BI-RADS breast density category. Overall specificity was highest for DBT alone (84.6%), followed by FFDM (81.4%), and then FFDM+DBT (79.6%). The researchers attribute the lower FFDM+DBT reader results as a function of needing to inter- pret two exams instead of just one. Tumour measurement accuracy was comparable for all three groups. Based on their findings, the authors recommend that DBT be used for symptomatic younger women with known very dense After leading the mammography train- ing service at King’s College Hospital in London, since 2009 Patsy Whelehan has researched clinical breast imaging in Dundee alongside Professor Andy Evans. As well as radiology research, she has a strong interest in patients’ mammogra- phy experience. Clinically, she exemplifies the extended role opportunities available to radiographers in the UK, whereby with rigorous additional training radiographers can undertake advanced practice, such as reporting mammograms and performing breast biopsies. ‘With breasts, or as a second-line exam following a negative FFDM. And, in view of increasingly available contrast-enhanced digital mammog- raphy (CEDM), they also recom- mend that the performance of DBT be evaluated in clinical studies with this breast exam. respect to additional research, the Dundee team is cur- rently conducting a UK multicentric study called CONTEST, comparing the diagnostic accuracy of CEDM with FFDM, DBT and MRI,’ lead author Patsy Whelehan, a research- er at the University of Dundee School of Medicine and a consultant radiographer at NHS Tayside, told European Hospital. ‘The study start- ed as single centre in 2018, but we now have several centres on board and recruitment is ongoing. Another major current UK-wide project from the Dundee team is MEDICI, which aims to assess whether a reduc- tion in mammographic density in women taking adjuvant endocrine therapy following breast cancer sur- gery is associated with a lower risk of recurrence and/or a lower risk of dying.’ Malignancy detection as good as radiologists AI potential in breast imaging efficiency The detection and classification of breast cancer as well as all aspects of the breast imaging workflow chain could be much easier with AI models. K s a m o T © The contribution of Artificial intelli- gence (AI) has great potential in breast imaging efficiency, Professor Linda Moy MD told attendees at the 2021 Society of Breast Imaging/American College of Radiology (SBI/ACR) Breast Imaging Symposium this April. AI models for breast imaging have focused mainly on the diagnostic classification and detec- tion of breast cancer. However, AI appli- cations for workflow optimisation can provide support for interpretation tasks and increase overall operational efficien- cies in all aspects of the breast imaging workflow chain. ‘There are currently numerous AI applications in development that have the potential to benefit breast imaging, from test ordering to report communication,’ said Moy, who is professor of radiol- ogy at New York University’s Grossman School of Medicine. ‘At a time when there’s a shortage of breast imagers, there’s increasing patient volume, and with increased utilisation of digital breast tomosynthesis (DBT) and breast MRI, an increasing number of images in an exam. Radiologists can benefit from intelligent, automated, time-efficient tools to perform or expedite repetitive tasks. These can help increase efficiency for breast imagers.’ The workflow chain in imaging includes multiple segments, starting with an order for the patient. This needs to be reviewed for imaging appropriateness and the protocol to be used, sched- uled, performed, managed, reported with results communicated, and billed. A staff member is needed at each step to inte- grate information and accurately pass the information along to the next step. AI can potentially address every aspect of the workflow, Moy believes, ‘enhancing practice workflow efficiency, reducing variability, and improving quality.’ ‘Most attention for AI in breast imaging goes to pixel-based image analysis, with a focus on narrow AI tasks including detection, classification, segmentation, prepopulat- ing a report, prediction of breast can- cer risk, and treatment response predic- tion,’ she explained. ‘Less attention is being paid to radiology workflow, but I think the greatest potential for AI lies in making these back-end processes more efficient. The current limitation and dif- More than just MRI accessories www.allmri.com EUROPEAN HOSPITAL Vol 30 Issue 2/21