Organizational impact of systemic implementation of digital breast tomosynthesis as a primary test for breast cancer screening in Italy.

PURPOSE: We present a comprehensive investigation into the organizational, social, and ethical impact of implementing digital breast tomosynthesis (DBT) as a primary test for breast cancer screening in Italy. The analyses aimed to assess the feasibility of DBT specifically for all women aged 45-74, women aged 45-49 only, or those with dense breasts only. METHODS: Questions were framed according to the European Network of Health Technology Assessment (EuNetHTA) Screening Core Model to produce evidence for the resources, equity, acceptability, and feasibility domains of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) decision framework. The study integrated evidence from the literature, the MAITA DBT trials, and Italian pilot programs. Structured interviews, surveys, and systematic reviews were conducted to gather data on organizational impact, acceptability among women, reading and acquisition times, and the technical requirements of DBT in screening. RESULTS: Implementing DBT could significantly affect the screening program, primarily due to increased reading times and the need for additional human resources (radiologists and radiographers). Participation rates in DBT screening were similar, if not better, to those observed with standard digital mammography, indicating good acceptability among women. The study also highlighted the necessity for specific training for radiographers. The interviewed key persons unanimously considered feasible tailored screening strategies based on breast density or age, but they require effective communication with the target population. CONCLUSIONS: An increase in radiologists' and radiographers' workload limits the feasibility of DBT screening. Tailored screening strategies may maximize the benefits of DBT while mitigating potential challenges.

Budget impact analysis of introducing digital breast tomosynthesis in breast cancer screening in Italy.

PURPOSE: This study quantifies the impact on budget and cost per health benefit of implementing digital breast tomosynthesis (DBT) in place of digital mammography (DM) for breast cancer screening among asymptomatic women in Italy. METHODS: A budget impact analysis and a cost consequence analysis were conducted using parameters from the MAITA project and literature. The study considered four scenarios for DBT implementation, i.e., DBT for all women, DBT for women aged 45-49 years, DBT based on breast density (BI-RADS C + D or D only), and compared these to the current DM screening. Healthcare provider's perspective was adopted, including screening, diagnosis, and cancer treatment costs. RESULTS: Introducing DBT for all women would increase overall screening costs by 20%. Targeting DBT to women aged 45-49 years or with dense breasts would result in smaller cost increases (3.2% for age-based and 1.4-10.7% for density-based scenarios). The cost per avoided interval cancer was significantly higher when DBT was applied to all women compared to targeted approaches. The cost per gained early-detected cancer slightly increases in targeted approaches, while the assumptions on the clinical significance and overdiagnosis of cancers detected by DBT and not by DM have a strong impact. CONCLUSIONS: Implementing DBT as a primary breast cancer test in screening programs in Italy would lead to a substantial increase in costs. Tailoring DBT use to women aged 45-49 or with dense breasts could enhance the feasibility and sustainability of the intervention. Further research is needed to clarify the impact of DBT on overdiagnosis and the long-term outcomes.

European validation of an image-derived AI-based short-term risk model for individualized breast cancer screening-a nested case-control study.

Background: Image-derived artificial intelligence (AI)-based risk models for breast cancer have shown high discriminatory performances compared with clinical risk models based on family history and lifestyle factors. However, little is known about their generalizability across European screening settings. We therefore investigated the discriminatory performances of an AI-based risk model in European screening settings. Methods: Using four European screening populations in three countries (Italy, Spain, Germany) screened between 2009 and 2020 for women aged 45-69, we performed a nested case-control study to assess the predictive performance of an AI-based risk model. In total, 739 women with incident breast cancers were included together with 7812 controls matched on year of study-entry. Mammographic features (density, microcalcifications, masses, left-right breast asymmetries of these features) were extracted using AI from negative digital mammograms at study-entry. Two-year absolute risks of breast cancer were predicted and assessed after two years of follow-up. Adjusted risk stratification performance metrics were reported per clinical guidelines. Findings: The overall adjusted Area Under the receiver operating characteristic Curve (aAUC) of the AI risk model was 0.72 (95% CI 0.70-0.75) for breast cancers developed in four screening populations. In the 6.2% [529/8551] of women at high risk using the National Institute of Health and Care Excellence (NICE) guidelines thresholds, cancers were more likely diagnosed after 2 years follow-up, risk-ratio (RR) 6.7 (95% CI 5.6-8.0), compared with the 69% [5907/8551] of women classified at general risk by the model. Similar risk-ratios were observed across levels of mammographic density. Interpretation: The AI risk model showed generalizable discriminatory performances across European populations and, predicted ∼30% of clinically relevant stage 2 and higher breast cancers in ∼6% of high-risk women who were sent home with a negative mammogram. Similar results were seen in women with fatty and dense breasts. Funding: Swedish Research Council.

Physical and dosimetric characterisation of different Contrast-Enhanced digital mammographic systems: A multicentric study.

PURPOSE: Contrast-enhanced digital mammography (CEDM) is a relatively new imaging technique recombining low- and high-energy mammograms to emphasise iodine contrast. This work aims to perform a multicentric physical and dosimetric characterisation of four state-of-the-art CEDM systems. METHODS: We evaluated tube output, half-value-layer (HVL) for low- and high-energy and average glandular dose (AGD) in a wide range of equivalent breast thicknesses. CIRS phantom 022 was used to estimate the overall performance of a CEDM examination in the subtracted image in terms of the iodine difference signal (S). To calculate dosimetric impact of CEDM examination, we collected 4542 acquisitions on patients. RESULTS: Even if CEDM acquisition strategies differ, all the systems presented a linear behaviour between S and iodine concentration. The curve fit slopes expressed in PV/mg/cm2 were in the range [92-97] for Fujifilm, [31-32] for GE Healthcare, [35-36] for Hologic, and [114-130] for IMS. Dosimetric data from patients were matched with AGD values calculated using equivalent PMMA thicknesses. Fujifilm exhibited the lowest values, while GE Healthcare showed the highest. CONCLUSION: The subtracted image showed the ability of all the systems to give important information about the linearity of the signal with the iodine concentrations. All the patient-collected doses were under the AGD EUREF 2D Acceptable limit, except for patients with thicknesses ≤35 mm belonging to GE Healthcare and Hologic, which were slightly over. This work demonstrates the importance of testing each CEDM system to know how it performs regarding dose and the relationship between PV and iodine concentration.

Comparing accuracy of tomosynthesis plus digital mammography or synthetic 2D mammography in breast cancer screening: baseline results of the MAITA RCT consortium.

AIM: The analyses here reported aim to compare the screening performance of digital tomosynthesis (DBT) versus mammography (DM). METHODS: MAITA is a consortium of four Italian trials, REtomo, Proteus, Impeto, and MAITA trial. The trials adopted a two-arm randomised design comparing DBT plus DM (REtomo and Proteus) or synthetic-2D (Impeto and MAITA trial) versus DM; multiple vendors were included. Women aged 45 to 69 years were individually randomised to one round of DBT or DM. FINDINGS: From March 2014 to February 2022, 50,856 and 63,295 women were randomised to the DBT and DM arm, respectively. In the DBT arm, 6656 women were screened with DBT plus synthetic-2D. Recall was higher in the DBT arm (5·84% versus 4·96%), with differences between centres. With DBT, 0·8/1000 (95% CI 0·3 to 1·3) more women received surgical treatment for a benign lesion. The detection rate was 51% higher with DBT, ie. 2·6/1000 (95% CI 1·7 to 3·6) more cancers detected, with a similar relative increase for invasive cancers and ductal carcinoma in situ. The results were similar below and over the age of 50, at first and subsequent rounds, and with DBT plus DM and DBT plus synthetic-2D. No learning curve was appreciable. Detection of cancers >= 20 mm, with 2 or more positive lymph nodes, grade III, HER2-positive, or triple-negative was similar in the two arms. INTERPRETATION: Results from MAITA confirm that DBT is superior to DM for the detection of cancers, with a possible increase in recall rate. DBT performance in screening should be assessed locally while waiting for long-term follow-up results on the impact of advanced cancer incidence.