Mammography Screening With Artificial Intelligence (MASAI)

Study Overview
=================
Brief Summary
-----------------
The purpose of this randomized controlled trial is to assess whether AI can improve the efficacy of mammography screening, by adapting single and double reading based on AI derived cancer-risk scores and to use AI as a decision support in the screen reading, compared with conventional mammography screening (double reading without AI).

Detailed Description
-----------------
European guidelines recommend that mammography exams in breast cancer screening are read by two breast radiologists to ensure a high sensitivity. Double reading is, however, resource demanding and still results in missed cancers. Computer-aided detection based on AI has been shown to have similar accuracy as an average breast radiologist. AI can be used as decision support by highlighting suspicious findings in the image as well as a means to triage screen exams according to risk of malignancy. Eligible women will be randomized (1:1) to the intervention (AI-integrated mammography screening) or control arm (conventional mammography screening). In the intervention arm, exams will be analysed with AI and triaged into two groups based on risk of malignancy. Low risk exams will be single read and high risk exams will be double read. The high risk group will contain appx. 10% of the screening population. Within the high-risk group, exams with the highest 1% risk will by default be recalled by the readers with the exception of obvious false positives. AI risk scores and Computer-Aided Detection (CAD)-marks of suspicious calcifications and masses are provided to the reader(s). In the control arm, screen exams are double read without AI (standard of care). Considering the interplay of number of interval cancers and workload, the study will be considered successful if the interval-cancer rate in the intervention arm is not more than 20% larger than in the control arm. If the interval-cancer rate is statistically and clinically significantly lower in the intervention arm than in the control arm, AI-integrated mammography screening will be considered superior to conventional mammography screening.

Official Title
-----------------
A Randomized, Single-blinded, Controlled Trial on the Efficacy of Mammography Screening With Artificial Intelligence - the MASAI Study

Conditions
-----------------
Breast Cancer

Intervention / Treatment
-----------------
* Other: AI screening modality
* Other: Conventional screening modality


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: Women eligible for population-based mammography screening. Exclusion Criteria: None.

Ages Eligible for Study
-----------------
Minimum Age: 40 Years
Maximum Age: 74 Years

Sexes Eligible for Study
-----------------
Female

Accepts Healthy Volunteers
-----------------
Accepts Healthy Volunteers

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Screening
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Intervention arm<br>AI-integrated mammography screening | Other: AI screening modality<br>* Screen exam will be analysed with an AI system (Transpara, ScreenPoint, Nijmegen, The Netherlands) that assigns exams with a cancer-risk score from 1 to 10, as well as presenting CAD-marks at suspicious findings. Exams with risk score 1-9 will be single read and exam with score 10 will be double read. Risk scores and CAD-marks are provided to the reader(s). The reader(s) will decide whether to recall the woman for work-up or not (as per standard of care). In addition, exams with the highest 1% risk will by default be recalled with the exception of obvious false positives.<br>|
| Experimental: Control arm<br>Conventional mammography screening (standard of care) | Other: Conventional screening modality<br>* Screen exams will be read by two radiologists without the support of AI.<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Interval-cancer rate | Women with interval cancer per 1000 screens | 43 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Cancer-detection rate | Women with screen-detected cancer per 1000 screens | 15 months | 
| Recall rate | Number of recalls per 1000 screens | 15 months | 
| False-positive rate | Women with false positive per 1000 screens | 15 months | 
| Positive Predictive Value-1 | Women with cancer for all recalls | 15 months | 
| Sensitivity and specificity | True and false-positive rate | 43 months | 
| Cancer detection per cancer type | Screen detection of cancer in relation to cancer type, size and stage | 19 months | 
| Tumour biology of interval cancers | Characterization of interval cancers per type, size and stage | 43 months | 
| Screen-reading workload | Number of screen-readings and number of consensus meetings | 19 months | 
| Incremental cost-effectiveness ratio | The incremental cost-effectiveness ratio for AI-integrated mammography screening versus standard of care | 43 months | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Mammography Screening, Artificial Intelligence