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Portrait of Magnus Dustler. Photo

Magnus Dustler

Researcher

Portrait of Magnus Dustler. Photo

Artificial intelligence detection of missed cancers at digital mammography that were detected at digital breast tomosynthesis

Author

  • Victor Dahlblom
  • Ingvar Andersson
  • Kristina Lång
  • Anders Tingberg
  • Sophia Zackrisson
  • Magnus Dustler

Summary, in English

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.

Department/s

  • Radiology Diagnostics, Malmö
  • LUCC: Lund University Cancer Centre
  • Medical Radiation Physics, Malmö
  • EpiHealth: Epidemiology for Health

Publishing year

2021-11

Language

English

Publication/Series

Radiology: Artificial Intelligence

Volume

3

Issue

6

Document type

Journal article

Publisher

Radiological Society of North America Inc.

Topic

  • Radiology, Nuclear Medicine and Medical Imaging

Status

Published

Research group

  • Radiology Diagnostics, Malmö
  • Medical Radiation Physics, Malmö

ISBN/ISSN/Other

  • ISSN: 2638-6100