Improving Breast Cancer Detection Accuracy of Mammography with the                     Concurrent Use of an Artificial Intelligence Tool

Pacilè, Serena; Lopez, January; Chone, Pauline; Bertinotti, Thomas; Grouin, Jean Marie; Fillard, Pierre

doi:10.1148/ryai.2020190208

Cited by 100 publications

(79 citation statements)

References 28 publications

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“…Because of this, CNNs should not be viewed as the sole basis for reporting, but rather as additional tools that can speed up and facilitate the radiologist’s work and possibly reduce interrater disagreement. Recently, Pacilè et al investigated the impact of concurrent use of an AI algorithm on the diagnostic performance of radiologists reading mammograms and observed a positive effect on interrater agreement and reading times for cases with low suspicion for malignancy as classified by the CNN [ 46 ]. Our results suggest that the CNN we used can especially improve the radiologist’s efficiency for studies that do not show any relevant pathologies, although our study design did not focus on the actual performance of a radiologist using the software.…”

Section: Discussionmentioning

confidence: 99%

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

et al. 2021

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Our objective was to evaluate the diagnostic performance of a convolutional neural network (CNN) trained on multiple MR imaging features of the lumbar spine, to detect a variety of different degenerative changes of the lumbar spine. One hundred and forty-six consecutive patients underwent routine clinical MRI of the lumbar spine including T2-weighted imaging and were retrospectively analyzed using a CNN for detection and labeling of vertebrae, disc segments, as well as presence of disc herniation, disc bulging, spinal canal stenosis, nerve root compression, and spondylolisthesis. The assessment of a radiologist served as the diagnostic reference standard. We assessed the CNN’s diagnostic accuracy and consistency using confusion matrices and McNemar’s test. In our data, 77 disc herniations (thereof 46 further classified as extrusions), 133 disc bulgings, 35 spinal canal stenoses, 59 nerve root compressions, and 20 segments with spondylolisthesis were present in a total of 888 lumbar spine segments. The CNN yielded a perfect accuracy score for intervertebral disc detection and labeling (100%), and moderate to high diagnostic accuracy for the detection of disc herniations (87%; 95% CI: 0.84, 0.89), extrusions (86%; 95% CI: 0.84, 0.89), bulgings (76%; 95% CI: 0.73, 0.78), spinal canal stenoses (98%; 95% CI: 0.97, 0.99), nerve root compressions (91%; 95% CI: 0.89, 0.92), and spondylolisthesis (87.61%; 95% CI: 85.26, 89.21), respectively. Our data suggest that automatic diagnosis of multiple different degenerative changes of the lumbar spine is feasible using a single comprehensive CNN. The CNN provides high diagnostic accuracy for intervertebral disc labeling and detection of clinically relevant degenerative changes such as spinal canal stenosis and disc extrusion of the lumbar spine.

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Section: Discussionmentioning

confidence: 99%

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

et al. 2021

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“…35 The other two anonymised AI systems detected fewer stage 62 63 and US (11% false positive rate). 64 Retrospective test accuracy studies: Salim et al, 35 Schaffter et al, 36 and McKinney et al 29 Enriched test set multiple reader multiple case laboratory studies: Pacilè et al, 30 Watanabe et al, 37 Rodriguez-Ruiz et al 33 (Rodriguez-Ruiz 2019a in figure), Lotter 2021, 28 and Rodriguez-Ruiz et al 32 (Rodriguez-Ruiz 2019b in figure ) 2 or higher invasive cancers (58.3% and 60.8%) than the radiologists. In an enriched test set multiple reader multiple case laboratory study, a standalone in-house AI model (DeepHealth Inc.) detected more invasive cancer (+12.7%, 95% confidence interval 8.5 to 16.5) and more ductal carcinoma in situ (+16.3%, 95% confidence interval 10.9 to 22.2) than the average single reader.…”

Section: Cancer Typementioning

confidence: 99%

Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

Freeman

Geppert

Stinton

et al. 2021

BMJ

196

118

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Objective To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. Design Systematic review of test accuracy studies. Data sources Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. Eligibility criteria Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women’s digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. Study selection and synthesis Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. Results Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were less accurate than a single radiologist, and all were less accurate than consensus of two or more radiologists. Five smaller studies (1086 women, 520 cancers) at high risk of bias and low generalisability to the clinical context reported that all five evaluated AI systems (as standalone to replace radiologist or as a reader aid) were more accurate than a single radiologist reading a test set in the laboratory. In three studies, AI used for triage screened out 53%, 45%, and 50% of women at low risk but also 10%, 4%, and 0% of cancers detected by radiologists. Conclusions Current evidence for AI does not yet allow judgement of its accuracy in breast cancer screening programmes, and it is unclear where on the clinical pathway AI might be of most benefit. AI systems are not sufficiently specific to replace radiologist double reading in screening programmes. Promising results in smaller studies are not replicated in larger studies. Prospective studies are required to measure the effect of AI in clinical practice. Such studies will require clear stopping rules to ensure that AI does not reduce programme specificity. Study registration Protocol registered as PROSPERO CRD42020213590.

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“…A 2020 study published in Radiology: Artificial Intelligence has contributed to growing research on the use of algorithms based on deep learning to augment digital mammography 2 . In that study, investigators reported on the use of an artificial intelligence (AI) system designed to identify regions of the breast that look suspicious for cancer on 2‐dimensional digital mammograms and to assess their likelihood of being malignant.…”

Section: Artificial Intelligence and Digital Mammographymentioning

confidence: 99%

Trio of mammography studies demonstrate enhanced screening effectiveness

Nierengarten¹

2021

Cancer

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Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool

Cited by 100 publications

References 28 publications

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

Trio of mammography studies demonstrate enhanced screening effectiveness

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