Dense-Breast Action Kit

Advanced Detection Cheatsheet

Compare technologies that boost cancer detection rates in dense breast tissue

43.3%¹ of women have dense breasts

2-4x² higher cancer risk

45-54%³ of radiologists report burnout

Technology Comparison Matrix

Technology	Key Advantage	Dense Breast Performance	Clinical Impact
Fuji ASPIRE Cristalle 3D Tomo + CEM	• 65,536 grayscale levels⁴ • ISR reconstruction • 4-second acquisition⁴ • Integrated CEM capability	• Superior tissue differentiation • 30% fewer callbacks⁴ • Comfort Paddle™ technology • 25% faster exams⁴ • CEM: High-80s to mid-90s% sensitivity⁵	• Single-system solution • Same-visit problem solving • Reduced patient anxiety
SOFIA 3D ABUS (Automated Breast Ultrasound)	• Whole breast coverage • Operator independent • No radiation	• ~2.5× more cancers (additional 3.4 / 1,000)⁶ • Ideal for category D • Reproducible results	• Same-day supplemental • CPT 76641 billable⁷ • No compression needed
Transpara + Volpara AI	• 7M mammograms analyzed⁸ • Temporal comparison⁸ • Objective density⁹ • Works with ALL major systems¹⁰	• 92% AUC maintained¹¹ • 81.9% sensitivity¹¹ • 45% interval cancer detection⁸	• 44% efficiency gain¹² • Optimized callback rates • Automated compliance

💡 Quick Decision Guide:

Primary Screening: Fuji ASPIRE Cristalle 3D for all patients
Dense Categories C/D: Add SOFIA ABUS for supplemental screening
Problem Solving: Fuji ASPIRE Cristalle CEM for immediate answers
Workflow Optimization: Transpara + Volpara AI for all exams
High Accuracy: SOFIA ABUS provides superior dense tissue visualization¹³

Citations:

¹ FDA. Important Information Final Rule to Amend MQSA. 2024.

² McCormack VA, et al. Breast density and parenchymal patterns as markers of breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1159-69.

³ Kang K, et al. Burnout Among Radiologists: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol. 2024;222(2).

⁴ Fujifilm Healthcare. ASPIRE Cristalle Technical Specifications (Manufacturer specification). 2024.

⁵ Jochelson MS, et al. Contrast-enhanced Mammography: State of the Art. Radiology. 2019;299(1):36-48.

⁶ Brem RF, et al. Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue. Radiology. 2015;274(3):663-73.

⁷ American Medical Association. CPT Code 76641. 2024.

⁸ ScreenPoint Medical. Transpara Clinical Evidence Portfolio (Manufacturer specification). 2024.

⁹ Volpara Health. TruDensity Technical Documentation. 2024.

¹⁰ ScreenPoint Medical. System Compatibility Documentation. 2024.

¹¹ Rodriguez-Ruiz A, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography. Radiology. 2019;290(2):310-318.

¹² Lång K, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI). Lancet Oncol. 2023;24(8):936-944.

¹³ SOFIA Product Information (Manufacturer specification). 2024.

FDA-Ready Compliance Roadmap

Step-by-step checklists for the breast density rule effective September 10, 2024

⚠️ CRITICAL: The FDA's final rule requires EXACT standardized language in all patient notifications. This language cannot be altered. Rule effective September 10, 2024¹.

⚖️ MALPRACTICE LIABILITY CONSIDERATIONS:

Documentation is Defense: Failure to notify patients of dense breast tissue can result in litigation exposure
Standard of Care: Courts increasingly expect compliance with FDA notification requirements
Audit Trail Required: Maintain records of when and how patients were notified
AI Systems Provide Protection: Automated, objective density assessment creates defensible documentation
Missing Notifications = Liability: Inconsistent manual assessments increase malpractice risk

Federal Requirements Checklist

Documentation Requirements

Written policy for density assessment and notification

Standardized notification templates (federal + state)

Tracking log showing notification dates

Evidence of 30-day compliance²

Staff training documentation

Quality assurance program for consistency

Legal-defensible audit trail system

System Integration

MIS configured for automated density reporting

EMR integration for notification delivery

Audit trail capability

Backup notification method (mail/portal)

Spanish language notifications available

Objective AI density assessment integration

FDA-Mandated Patient Notification Language

Required Verbatim Text for Dense Breast Patients:³

"Breast tissue can be either dense or not dense. Dense tissue makes it harder to find breast cancer on a mammogram and also raises the risk of developing breast cancer. Your breast tissue is dense. In some people with dense tissue, other imaging tests in addition to a mammogram may help find cancers. Talk to your healthcare provider about breast density, risks for breast cancer, and your individual situation."

Timeline Requirements

30² days to notify patients

7² days for urgent findings

8th⁴ grade reading level

Citations:

¹ FDA. Important Information Final Rule to Amend MQSA. 2024.

² ProAssurance. Breast Density Notification Updated Requirements. 2024.

³ DenseBreast-info.org. FDA National Dense Breast Reporting. 2024.

⁴ FDA Guidance Document. Patient Lay Summaries. 2024.

AI ROI Blueprint

Calculate your AI workflow efficiency gains and callback reduction savings

Results are estimates based on industry averages and will vary by facility.

Mammography Callback Reduction Calculator

Calculate Your Callback Reduction ROI with AI-Enhanced Imaging

Quantify operational savings from reduced callbacks using Transpara AI + Volpara + SOFIA ABUS technology stack

📊 Technology Performance Evidence:

Transpara AI: 15-20% callback reduction in clinical studies¹
Volpara Density: 8-10% workflow optimization through objective assessment²
SOFIA ABUS: 10-12% managed callbacks through supplemental screening³
Combined Stack: 20-25% total callback reduction potential⁴
Works with ALL major mammography systems - vendor agnostic solution

Annual mammography volume:

Total screening + diagnostic exams per year

Current callback rate (%):

Industry average: 10-12%⁵

Average revenue per mammogram:

Medicare: $165-185, Commercial: $250-350

Radiologist hourly rate:

Range: $250-400

Technologist hourly rate:

National average: $45/hour

Technology selection:

Annual Callback Reduction Impact

Annual callbacks: 0

Callbacks avoided: 0

Lost revenue recovered: $0

Operational cost savings: $0

Total annual savings: $0

Note: Callback costs vary from $361-852 per episode depending on complexity.⁶ This calculator uses conservative estimates.

AI Workflow Efficiency Calculator

Calculate Your AI Investment Benefits & Workforce Impact

Based on the landmark MASAI trial with 80,033 women⁷

Annual mammography volume:

Average reading time per exam (minutes):

Current callback rate (%):

For additional callback savings calculation

Radiologist hourly rate:

Annual AI cost (optional):

Leave blank to see gross benefits only

Your AI Investment Analysis

Proven Efficiency Gains

0 Hours

Radiologist time made available annually

44% faster reading per exam: 0 minutes saved per exam

Capacity increase: 0% more volume with same staff

Callback reduction benefit: $0 additional savings

Quality of life improvement: Radiologists can leave on time, reduce weekend reads

Citations:

¹ Rodriguez-Ruiz A, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography. Radiology. 2019;290(2):310-318.

² Volpara Health Technologies. Clinical Evidence Portfolio for Density Assessment and Workflow Optimization (Manufacturer data). 2024.

³ Brem RF, et al. Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue. Radiology. 2015;274(3):663-73.

⁴ Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms. Insights Imaging. 2023;14(1):13.

⁵ Lehman CD, et al. National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. Radiology. 2017;283(1):49-58.

⁶ Ong MS, Mandl KD. National expenditure for false-positive mammograms and breast cancer overdiagnoses estimated at $4 billion a year. Health Aff (Millwood). 2015;34(4):576-83.

⁷ Lång K, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI). Lancet Oncol. 2023;24(8):936-944.

Fast-Track Workflow Playbook

Proven pathways that reduce anxiety and deliver answers faster

48%¹ report improved experience with same-day results

96.7%¹ would recommend same-day results

66% vs 56%² adherence with same-day access

Workflow Transformation

Traditional Workflow

Initial screening mammogram

Wait 2-7 days for results

Callback for additional views

Possible ultrasound (another visit)

Potential MRI referral

Total: 3-4 visits over 2-4 weeks

Fast-Track Workflow

Fuji 16-bit screening + AI triage

Immediate density notification

Same-visit ABUS if indicated

CEM for problem solving if needed

Results within 24-48 hours

Total: 1-2 visits, faster answers

Implementation Protocol

Morning Preparation

Review scheduled patients for density history

Ensure ABUS slots available for dense patients

Confirm radiologist availability for priority reads

Prepare density notification materials

During Screening

Perform Fuji ASPIRE Cristalle 3D mammography

AI triage with Transpara scoring (1-10)

Volpara density assessment (objective measurement)

Immediate radiologist notification for scores 8-10

Decision Points

Transpara 1-7 + Non-dense: Complete, annual follow-up

Transpara 1-7 + Dense C: Discuss supplemental options

Transpara 1-7 + Dense D: Recommend immediate ABUS

Transpara 8-10 any density: Priority diagnostic workup

BI-RADS 0: Consider same-day or next-day CEM

💡 Implementation Timeline Note:

Technology implementation timelines vary significantly by facility size, existing infrastructure, and vendor. Typical ranges:

AI Software Integration: 2-4 weeks
ABUS Installation: 4-8 weeks
Full Workflow Optimization: 2-3 months
Staff Training & Adoption: Ongoing

Citations:

¹ Shah BA, Mirchandani A, Abrol S. Impact of same day screening mammogram results on women's satisfaction and overall breast cancer screening experience: a quality improvement survey analysis. BMC Womens Health. 2022;22(1):338.

² Gur D, et al. Impact of same-day screening mammography availability: results of a controlled clinical trial. Arch Intern Med. 1999;159(4):393-8.