The role of an artificial intelligence software in clinical senology: a mammography multi-reader study.
Accuracy
Artificial intelligence
Breast cancer
Clinical senology
Digital mammography
Second reader
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
11 Dec 2023
11 Dec 2023
Historique:
received:
29
05
2023
accepted:
07
11
2023
medline:
12
12
2023
pubmed:
12
12
2023
entrez:
11
12
2023
Statut:
aheadofprint
Résumé
To evaluate the diagnostic role of a dedicated AI software in detecting anomalous breast findings on mammography and tomosynthesis images in the clinical setting, stand-alone and as aid of four readers. A total of 210 patients with complete clinical and radiologic records were retrospectively analyzed. Pathology was used as the reference standard for patients undergoing surgery or biopsy, and a 1-year follow-up was used to confirm no change in the remaining patients. The image evaluation was performed by four readers with different levels of experience (a junior and three senior breast radiologists) using a 5-point Likert scale moving from 1 (definitively no cancer) to 5 (definitively cancer). The positivity of mammograms was assessed on the presence of any breast lesion (masses, architectural distortions, asymmetries, calcifications), including malignant and benign ones. A multi-reader multi-case analysis was performed. A p value < 0.05 was considered statistically significant. The stand-alone AI system achieved an accuracy of 71% (69% sensitivity and 73% specificity), which is overall lower than the value achieved by readers without AI. However, with the aid of AI, a significant increase of accuracy (p value = 0.004) and specificity (p value = 0.04) was achieved for the less experienced radiologist and a senior one. The use of AI software as a second reader for breast lesions assessment could play a crucial role in the clinical setting, by increasing sensitivity and specificity, especially for less experienced radiologists.
Identifiants
pubmed: 38082194
doi: 10.1007/s11547-023-01751-1
pii: 10.1007/s11547-023-01751-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. Italian Society of Medical Radiology.
Références
Dembrower K, Wåhlin E, Liu Y, Salim M, Smith K, Lindholm P, Eklund M (2020) Fredrik Strand “Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. Lancet Digital Health 2:e468–e474
doi: 10.1016/S2589-7500(20)30185-0
pubmed: 33328114
Geras KJ, Mann RM, Moy L (2019) Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology 293:246–259
doi: 10.1148/radiol.2019182627
pubmed: 31549948
Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
doi: 10.1016/j.media.2017.07.005
pubmed: 28778026
Le EPV, Wang Y, Huang Y, Hickman S, Gilbert FJ (2019) Artificial intelligence in breast imaging. Clin Radiol 74:357–366
doi: 10.1016/j.crad.2019.02.006
pubmed: 30898381
Chan H-P, Samala RK, Hadjiiski LM (2020) CAD and AI for breast cancer-recent development and challenges. Br J Radiol 93:20190580
doi: 10.1259/bjr.20190580
pubmed: 31742424
Bazzocchi M, Mazzarella F, Del Frate C, Girometti R, Zuiani C (2007) CAD Systems for mammography: a real opportunity? A review of the literature. Radiol med 112:329–353
doi: 10.1007/s11547-007-0145-5
pubmed: 17440698
Gur D, Sumkin JH (2006) CAD in screening mammography, AJR Women’s Imaging Commentary. AJR 187:1474
Azavedo E, Zackrisson S, Mejarè I, Arnlind MH (2012) Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review. BMC Med Imag 12:22
doi: 10.1186/1471-2342-12-22
Dembrower K, Wåhlin E, Liu Y et al (2020) Effect of artificial intelligence based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. Lancet Digit Health 2:e468–e474
doi: 10.1016/S2589-7500(20)30185-0
pubmed: 33328114
Kyono T, Gilbert FJ, van der Schaar M (2020) Improving workflow efficiency for mammography using machine learning. J Am Coll Radiol 17:56–63
doi: 10.1016/j.jacr.2019.05.012
pubmed: 31153798
Raya-Povedano JL, Romero-Martín S, Elías-Cabot E, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M (2021) AI-based strategies to reduce workload in breast cancer screening with mammography and tomosynthesis: a retrospective evaluation. Radiology 1:203555
Yala A, Schuster T, Miles R, Barzilay R, Lehman C (2019) A Deep learning model to triage screening mammograms: a simulation study. Radiology 293:38–46
doi: 10.1148/radiol.2019182908
pubmed: 31385754
Rodríguez-Ruiz Al et al (2019) Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 290(2):305–314
doi: 10.1148/radiol.2018181371
pubmed: 30457482
Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Tan T, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Mann RM, Sechopoulos I (2019) Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI J Natl Cancer Inst 111(9):djy222
doi: 10.1093/jnci/djy222
Lång K, Dustler M, Dahlblom V, Åkesson A, Andersson I, Zackrisson S (2021) Identifying normal mammograms in a large screening population using artificial intelligence. Europ Radiol 31:1687–1692
doi: 10.1007/s00330-020-07165-1
Leibig C, Brehmer M, Bunk S, Byng D, Pinker K, Umutlu L (2022) Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis. Lancet Digit Health 4:e507–e519
doi: 10.1016/S2589-7500(22)00070-X
pubmed: 35750400
pmcid: 9839981
Schaffter T, Buist DS, Lee CI et al (2020) Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open 3:e200265
doi: 10.1001/jamanetworkopen.2020.0265
pubmed: 32119094
pmcid: 7052735
Wu N, Phang J, Park J et al (2019) Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transact Med Imag 39:1184–1194
doi: 10.1109/TMI.2019.2945514
Balta C, Rodriguez-Ruiz A, Mieskes C, Karssemeijer N, Heywang-Köbrunner S (2020) Going from double to single reading for screening exams labeled as likely normal by AI: What is the impact?: SPIE 11513 15th International Workshop on Breast Imaging (IWBI2020); May 22, (115130D)
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577(2):89
doi: 10.1038/s41586-019-1799-6
pubmed: 31894144
Shoshan Y, Bakalo R, Gilboa-Solomon F, Ratner V, Barkan E, Ozery-Flato M, Amit M, Khapun D, Ambinder EB, Oluyemi ET, Panigrahi B, DiCarlo PA, Rosen-Zvi M, Mullen LA (2022) Artificial intelligence for reducing workload in breast cancer screening with digital breast tomosynthesis. Radiology 303:69–77
doi: 10.1148/radiol.211105
pubmed: 35040677
Taylor-Philips S, Freeman K (2022) Artificial intelligence to complement rather than replace radiologists in breast screening. The Lancet Digit Health 4(7):E478–E479
doi: 10.1016/S2589-7500(22)00094-2
Vicini S et al (2022) A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol Med (Torino) 127(8):819–836
doi: 10.1007/s11547-022-01512-6
pubmed: 35771379
Houssami N, Kirkpatrick-Jones G, Noguchi N, Lee CI (2019) Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice”e. Expert Rev Med Dev 16:351–362
doi: 10.1080/17434440.2019.1610387
Lehman CD, Arao RF, Sprague BL et al (2017) National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology 283:49–58
doi: 10.1148/radiol.2016161174
pubmed: 27918707
American College of Radiology (2013) Breast imaging reporting and data system, 5th ed. Reston: American College of Radiology
Eng J. ROC analysis: web-based calculator for ROC curves. Baltimore: Johns Hopkins University [updated 2022 February 17]. Available from: http://www.jrocfit.org
McKinney SM, Sieniek M, Godbole V et al (2020) International evaluation of an AI system for breast cancer screening. Nature 577:89–94
doi: 10.1038/s41586-019-1799-6
pubmed: 31894144
Kim H-E, Kim HH, Han B-K, Kim KH, Han K, Nam H, Lee EH, Kim E-K (2020) Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective multi-reader study. Lancet Digit Health 2:138–148
doi: 10.1016/S2589-7500(20)30003-0
Guermazi A, Tannoury C, Kompel AJ, Murakami AM et al (2021) Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology 000:1–10