Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis

Abstract

Purpose: To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT.

Materials and methods: In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010-2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1-100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics.

Results: The highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18).

Conclusion: Almost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.Keywords: Computer-aided Diagnosis, Mammography, Breast, Diagnosis, Classification, Application DomainClinical trial registration no. NCT01091545© RSNA, 2021.

Keywords: Application Domain; Breast; Classification; Computer-aided Diagnosis; Diagnosis; Mammography.

Conflict of interest statement

Disclosures of Conflicts of Interest: V.D. institution received governmental funding for clinical research, AIDA/VINNOVA, and The Swedish Cancer Society; ScreenPoint Medical provided the software and technical support under a research agreement, no financial support was received. I.A. disclosed no relevant relationships. K.L. disclosed no relevant relationships. A.T. disclosed no relevant relationships. S.Z. institution received speaker's fees from Siemens Healthcare; issued US patent no PCT/EP2014/057372. M.D. issued US patent no PCT/EP2014/057372.

2021 by the Radiological Society of North America, Inc.

Figures

Graphical abstract

Figure 1:

Overview of the study population, including exclusions, recalls, and cancers. Digital mammography (DM) and digital breast tomosynthesis (DBT) cancer detection is based on double reading with consensus. AI = artificial intelligence, MBTST = Malmӧ Breast Tomosynthesis Screening Trial.

Figure 2:

Distribution of examination artificial intelligence (AI) risk score for the whole population and the cancers. AI scores are based on analysis of digital mammography (DM) examinations. DM and digital breast tomosynthesis (DBT) screening cancer detection is based on double reading with consensus. Interval cancers were those diagnosed during the 18- to 24-month follow-up period.

Figure 3:

Receiver operating characteristic (ROC) curves for artificial intelligence (AI) cancer detection at digital mammography (DM) with ground truth (GT) defined by DM double-reading screening-detected cancers, DM plus digital breast tomosynthesis (DBT) double-reading screening-detected cancers, and cancers detected at DM plus DBT double-reading screening or diagnosed as interval cancers (ICs) during 18- to 24-month follow-up. Area under the ROC curve (AUC) with 95% CIs. Operating points of radiologist DM double reading with consensus with corresponding ground truths are shown for comparison.

Figure 4:

Artificial intelligence (AI) scores (left) for all findings of the AI system analysis of digital mammography examinations and (right) for the findings corresponding to cancer lesions.

Figure 5:

Example of an invasive ductal carcinoma (circles) detected with artificial intelligence (AI) on digital mammogram (DM), but otherwise detected only at digital breast tomosynthesis (DBT). (A–D) Mediolateral oblique and craniocaudal DM images with AI system lesion scores and (E, F) DBT images for comparison.