A HIPAA-compliant fully-crossed fully-randomized multi-reader multi-case (MRMC) study was performed with 18 radiologists, reading a series of wide-angle DBT exams twice, with and without AI support.
This study included 360 cases: 110 biopsy-proven cancer cases, 104 benign cases (proven by biopsy or at least 6-month follow-up), and 146 randomly selected negative cases (at least 1-year follow-up). Cases were collected from a dataset of a previous, IRB-approved, clinical trial registered with protocol number NCT01373671 [19, 20]. Data was collected between May 2011 and February 2014 from seven US clinical sites, representative of women undergoing screening and diagnostic DBT exams in the USA. The mean age was 56.3 ± 9.8 (standard deviation) years. Each case consists of a bilateral two-view (cranio-caudal/mediolateral oblique CC/MLO) DBT exam acquired using standard exposure settings with a Mammomat Inspiration (Siemens Healthineers)) and reconstructed with the latest algorithm (EMPIRE), also generating the corresponding synthetic mammography (SM) images . The DBT system has a wide 50° scanning angle. This data was not used for the development of the AI support system.
Case selection protocol
The case selection was aimed at obtaining a challenging and representative set for the observer evaluation. Exclusion criteria were as follows: breast implants, sub-optimal quality (judged by a radiologist and a radiographer with respectively 14 and 38 years of breast imaging experience), missing image data, or missing truth data. After exclusion and performing a power analysis , to achieve a power of at least 0.8 (80%) to test the primary hypothesis of the study, we target to select 110 negative cases, 65 benign cases, and 65 malignant cases. Negative and benign cases were randomly selected to avoid selection bias. The aim for the malignant case selection was to include all cases categorized as “subtle”, and as many “moderately subtle” cases as available while including at least a random selection of five “obvious” cases. To reach the targeted sample size of 65 malignant cases, a subtlety score (1, “subtle”; 2, “moderately subtle”; 3, “obvious”) was independently determined by three breast radiologists (respectively 14, 39, and 5 years mammography experience; 5, 5, and 5 years DBT experience), with the third acting as an arbiter in case of disagreement.
For every case, per breast, the reference standard based on pathology and imaging reports was available in electronic format and reviewed by the radiologists participating in the case selection process (not participating in the observer study), including location and radiological characterization of cancers, location of benign lesions, or confirmed normal status.
AI support system
The AI support system used during the observer evaluation was Transpara™ 1.6.0, (ScreenPoint Medical BV). This system is based on deep convolutional neural networks [23, 24] and automatically detects lesions suspicious of breast cancer in 2D and DBT mammograms from different vendors. The results are shown to radiologists in two distinct ways:
A score from 1 to 10, indicating the increasing likelihood that a visible cancer is present at the mammogram. In a screening setting, approximately 10% of mammograms are assigned each score.
The most suspicious findings are marked and a scored with the level of suspicion (LOS) for cancer (1–100).
The system has been validated for 2D mammograms in previously performed clinical studies with independent datasets [10, 11, 15]. It has been trained and tested using a proprietary database containing over 1,000,000 2D mammography and DBT images (over 20,000 with cancer), acquired with machines from five different mammography vendors at a dozen institutions (academic and private health centers) across 10 countries in Europe, America, and Asia.
Each selected DBT mammogram was processed by the AI system. The results of this analysis were shown during the observer evaluation. Radiologists could concurrently use the AI system with or without the corresponding SM and interactive navigation support. Interactive navigation support consists of automatic access to the DBT plane where the AI algorithm detected abnormalities, with a single click on a mark shown on the SM.
Sessions and training
The observer evaluation consisted of two parts. Exams were read twice, with and without AI support, separated by a wash-out period of at least 4 weeks. The case order and the availability of AI support were randomized for each radiologist.
During the evaluation of the cases with the AI support available, two reading protocols were tested. Half of the radiologists (readers 1–9) read the exams with access to the corresponding SM and interactive navigation support, while the other half (readers 10–18) read exams without these functionalities, showing AI findings only in the 3D DBT stack.
To get familiar with the AI system and workstation before participating in the study, all radiologists were trained by evaluating a set of 50 DBT exams (not included in the study). Radiologists were blinded to patient history and any other information not visible in the included DBT imaging exams.
The radiologists used a reading workstation for DBT exams and a 12MP DBT-certified diagnostic color display (Coronis Uniti, Barco) calibrated to the DICOM Grayscale Standard Display Function. The workstation tracked the reader actions in the interface with timestamps.
For every case, radiologists were instructed to the following:
Mark the 3D location of findings in every view visible
Assign a LOS to each finding
Provide a BI-RADS category (1, 2, 3, 4a, 4b, 4c, or 5) per breast.
All radiologists were American Board of Radiology–certified, qualified to interpret mammograms under the Mammography Quality Standard Act (MQSA) and active in reading DBT exams in clinical practice. Half of the readers devoted less than 75% of their professional time to breast imaging for the last 3 years while the other half devoted more time. The median experience with MQSA qualification of the readers was 9 years (range 2–23 years) and the median volume of 2D or DBT mammograms read per year was 4200 (1000-18,000). All the readers were at the time of the study reading DBT exams in clinical practice.
Endpoints and statistical analysis
The primary hypothesis was that radiologists’ average breast-level area under the receiver operating characteristic (ROC) curve (AUC) for detection of malignancies in DBT using AI reading support is superior to reading unaided. This was tested against the null hypothesis: radiologists’ average breast-level AUC with AI support being equivalent to their average AUC unaided. p < 0.05 indicated a statistically significant difference between both reading conditions.
AUC superiority analysis was performed using the statistical package developed by Tabata et al , using the Obuchowski and Rockette method adapted to consider clustered data when calculating reader by modality covariances [26, 27].
ROC curves were built using the LOS assigned to each breast. Standard errors (SE) and 95% confidence intervals (CI) were computed.
If the primary hypothesis was met, four secondary hypotheses were evaluated in a (hierarchical) fixed sequence to control type I error rate at significance level alpha = 0.05.
Radiologists’ average reading time per DBT exam using AI support is superior to (shorter than) the average reading time per DBT exam unaided.
Average reading times per DBT exam were compared between reading conditions by using a generalized linear mixed-effects (GLME) model, taking repeated measures by multiple readers into account .
Radiologists’ average sensitivity reading DBT exams with AI support is non-inferior/superior compared to reading DBT exams unaided, at a pre-specified non-inferiority margin delta of 0.05.
Radiologists’ average specificity reading DBT exams with AI support is non-inferior/superior compared to reading DBT exams unaided, at a pre-specified non-inferiority margin delta of 0.05.
The analysis was performed following the analysis described in the primary hypothesis for AUC comparisons, formatting the input data accordingly. A breast was considered positive if the radiologist assigned a BI-RADS score ≥ 3.
The stand-alone AI system AUC is non-inferior to the radiologists’ average breast-level AUC reading DBT exams unaided at a pre-specified non-inferiority margin delta of 0.05.
The public domain iMRMC software (version 4.0.3, Division of Imaging, Diagnostics, and Software Reliability, OSEL/CDRH/FDA) was used, which can also handle single reader data (the AI system) .