OBJECTIVES: Evaluation of the degree of concordance between an artificial intelligence (AI) program and radiologists in assessing malignant lesions in screening mammograms. METHODS: The study population consisted of all consecutive cases of screening-detected histopathologically confirmed breast cancer in females who had undergone mammography at the NU Hospital Group (Region Västra Götaland, Sweden) in 2018 to 2019. Data were retrospectively collected from the AI program (lesion risk score in percent and overall malignancy risk score ranging from 1 to 10) and from medical records (independent assessments by two radiologists). Ethical approval was obtained. RESULTS: Altogether, 120 females with screening-detected histopathologically confirmed breast cancer were included in this study. The AI program assigned the highest overall malignancy risk score 10 to 86% of the mammograms. Five cases (4%) were assigned an overall malignancy risk score ≤5. Lack of consensus between the two radiologists involved in the initial assessment was associated with lower overall malignancy risk scores (p = 0,002). CONCLUSION: The AI program detected a majority of the cancerous lesions in the mammograms. The investigated version of the program has, however, limited use as an aid for radiologists, due to the pre-calibrated risk distribution and its tendency to miss the same lesions as the radiologists. A potential future use for the program, aimed at reducing radiologists' workload, might be to preselect and exclude low-risk mammograms. Although, depending on cut-off score, a small percentage of the malignant lesions can be missed using this procedure, which thus requires a thorough risk-benefit analysis. ADVANCES IN KNOWLEDGE: This study conducts an independent evaluation of an AI program's detection capacity under screening-like conditions which has not previously been done for this program.
OBJECTIVES: Evaluation of the degree of concordance between an artificial intelligence (AI) program and radiologists in assessing malignant lesions in screening mammograms. METHODS: The study population consisted of all consecutive cases of screening-detected histopathologically confirmed breast cancer in females who had undergone mammography at the NU Hospital Group (Region Västra Götaland, Sweden) in 2018 to 2019. Data were retrospectively collected from the AI program (lesion risk score in percent and overall malignancy risk score ranging from 1 to 10) and from medical records (independent assessments by two radiologists). Ethical approval was obtained. RESULTS: Altogether, 120 females with screening-detected histopathologically confirmed breast cancer were included in this study. The AI program assigned the highest overall malignancy risk score 10 to 86% of the mammograms. Five cases (4%) were assigned an overall malignancy risk score ≤5. Lack of consensus between the two radiologists involved in the initial assessment was associated with lower overall malignancy risk scores (p = 0,002). CONCLUSION: The AI program detected a majority of the cancerous lesions in the mammograms. The investigated version of the program has, however, limited use as an aid for radiologists, due to the pre-calibrated risk distribution and its tendency to miss the same lesions as the radiologists. A potential future use for the program, aimed at reducing radiologists' workload, might be to preselect and exclude low-risk mammograms. Although, depending on cut-off score, a small percentage of the malignant lesions can be missed using this procedure, which thus requires a thorough risk-benefit analysis. ADVANCES IN KNOWLEDGE: This study conducts an independent evaluation of an AI program's detection capacity under screening-like conditions which has not previously been done for this program.
Authors: Alejandro Rodriguez-Ruiz; Kristina Lång; Albert Gubern-Merida; Mireille Broeders; Gisella Gennaro; Paola Clauser; Thomas H Helbich; Margarita Chevalier; Tao Tan; Thomas Mertelmeier; Matthew G Wallis; Ingvar Andersson; Sophia Zackrisson; Ritse M Mann; Ioannis Sechopoulos Journal: J Natl Cancer Inst Date: 2019-09-01 Impact factor: 13.506
Authors: Karin Dembrower; Erik Wåhlin; Yue Liu; Mattie Salim; Kevin Smith; Peter Lindholm; Martin Eklund; Fredrik Strand Journal: Lancet Digit Health Date: 2020-09
Authors: Alejandro Rodríguez-Ruiz; Elizabeth Krupinski; Jan-Jurre Mordang; Kathy Schilling; Sylvia H Heywang-Köbrunner; Ioannis Sechopoulos; Ritse M Mann Journal: Radiology Date: 2018-11-20 Impact factor: 11.105
Authors: Mattie Salim; Erik Wåhlin; Karin Dembrower; Edward Azavedo; Theodoros Foukakis; Yue Liu; Kevin Smith; Martin Eklund; Fredrik Strand Journal: JAMA Oncol Date: 2020-10-01 Impact factor: 31.777
Authors: Alejandro Rodriguez-Ruiz; Kristina Lång; Albert Gubern-Merida; Jonas Teuwen; Mireille Broeders; Gisella Gennaro; Paola Clauser; Thomas H Helbich; Margarita Chevalier; Thomas Mertelmeier; Matthew G Wallis; Ingvar Andersson; Sophia Zackrisson; Ioannis Sechopoulos; Ritse M Mann Journal: Eur Radiol Date: 2019-04-16 Impact factor: 5.315
Authors: Scott Mayer McKinney; Marcin Sieniek; Varun Godbole; Jonathan Godwin; Natasha Antropova; Hutan Ashrafian; Trevor Back; Mary Chesus; Greg S Corrado; Ara Darzi; Mozziyar Etemadi; Florencia Garcia-Vicente; Fiona J Gilbert; Mark Halling-Brown; Demis Hassabis; Sunny Jansen; Alan Karthikesalingam; Christopher J Kelly; Dominic King; Joseph R Ledsam; David Melnick; Hormuz Mostofi; Lily Peng; Joshua Jay Reicher; Bernardino Romera-Paredes; Richard Sidebottom; Mustafa Suleyman; Daniel Tse; Kenneth C Young; Jeffrey De Fauw; Shravya Shetty Journal: Nature Date: 2020-01-01 Impact factor: 49.962