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Selecting Women for...
Selecting Women for Supplemental Breast Imaging using AI Biomarkers of Cancer Signs, Masking, and Risk
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- Liu, Yue (author)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab
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- Sorkhei, Moein (author)
- KTH,Science for Life Laboratory, SciLifeLab,Beräkningsvetenskap och beräkningsteknik (CST)
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- Dembrower, Karin (author)
- Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden; Department of Radiology, Capio Sankt Gorans Hospital, Stockholm, Sweden
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- Azizpour, Hossein, 1985- (author)
- KTH,Robotik, perception och lärande, RPL
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- Strand, Fredrik (author)
- Department of Pathology and Oncology, Karolinska Institute, Stockholm, Sweden; Breast Radiology, Karolinska University Hospital, Stockholm, Sweden
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- Smith, Kevin, 1975- (author)
- KTH,Science for Life Laboratory, SciLifeLab,Beräkningsvetenskap och beräkningsteknik (CST)
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(creator_code:org_t)
- 2023
- English.
- Related links:
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https://urn.kb.se/re...
Abstract
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- Background: Traditional mammographic density aids in determining the need for supplemental imagingby MRI or ultrasound. However, AI image analysis, considering more subtle and complex image features,may enable a more effective identification of women requiring supplemental imaging.Purpose: To assess if AISmartDensity, an AI-based score considering cancer signs, masking, and risk,surpasses traditional mammographic density in identifying women for supplemental imaging after negativescreening mammography.Methods: This retrospective study included randomly selected breast cancer patients and healthy controlsat Karolinska University Hospital between 2008 and 2015. Bootstrapping simulated a 0.2% interval cancerrate. We included previous exams for diagnosed women and all exams for controls. AISmartDensity hadbeen developed using random mammograms from a population non-overlapping with the current studypopulation. We evaluated AISmartDensity to, based on negative screening mammograms, identify womenwith interval cancer and next-round screen-detected cancer. It was compared to age and density models, withsensitivity and PPV calculated for women with the top 8% scores, mimicking the proportion of BIRADS“extremely dense” category. Statistical significance was determined using the Student’s t-test.Results: The study involved 2043 women, 258 with breast cancer diagnosed within 3 years of a negativemammogram, and 1785 healthy controls. Diagnosed women had a median age of 57 years (IQR 16) versus53 years (IQR 15) for controls (p < .001). At the 92nd percentile, AISmartDenstiy identified 87 (33.67%)future cancers with PPV 1.68%, whereas mammographic density identified 34 (13.18%) with PPV 0.66%(p < .001). AISmartDensity identified 32% interval and 36% next-round cancers, versus mammographicdensity’s 16% and 10%. The combined mammographic density and age model yielded an AUC of 0.60,significantly lower than AISmartDensity’s 0.73 (p < .001).Conclusions: AISmartDensity, integrating cancer signs, masking, and risk, more effectively identifiedwomen for additional breast imaging than traditional age and density models.
Publication and Content Type
- vet (subject category)
- ovr (subject category)
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