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Park EK, Lee H, Kim M, Kim T, Kim J, Kim KH, Kooi T, Chang Y, Ryu S. Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction. Diagnostics (Basel) 2024; 14:1212. [PMID: 38928628 PMCID: PMC11202482 DOI: 10.3390/diagnostics14121212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.
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Affiliation(s)
- Eun Kyung Park
- Department of Radiology, We Comfortable Clinic, Seoul 07327, Republic of Korea
| | - Hyeonsoo Lee
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Minjeong Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Taesoo Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Junha Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Ki Hwan Kim
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Thijs Kooi
- Lunit Inc., Seoul 06241, Republic of Korea; (H.L.); (M.K.); (T.K.); (J.K.); (K.H.K.); (T.K.)
| | - Yoosoo Chang
- Center of Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea; (Y.C.); (S.R.)
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea
| | - Seungho Ryu
- Center of Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea; (Y.C.); (S.R.)
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Republic of Korea
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Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, Ock CY, Kim S. Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. J Breast Cancer 2023; 26:405-435. [PMID: 37926067 PMCID: PMC10625863 DOI: 10.4048/jbc.2023.26.e45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/25/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023] Open
Abstract
Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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Eriksson M, Destounis S, Czene K, Zeiberg A, Day R, Conant EF, Schilling K, Hall P. A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care. Sci Transl Med 2022; 14:eabn3971. [PMID: 35544593 DOI: 10.1126/scitranslmed.abn3971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Screening with digital breast tomosynthesis (DBT) improves breast cancer detection and reduces false positives. However, currently, no breast cancer risk model takes advantage of the additional information generated by DBT imaging for breast cancer risk prediction. We developed and internally validated a DBT-based short-term risk model for predicting future late-stage and interval breast cancers after negative screening exams. We included the available 805 incident breast cancers and a random sample of 5173 healthy women matched on year of study entry in a nested case-control study from 154,200 multiethnic women, aged 35 to 74, attending DBT screening in the United States between 2014 and 2019. A relative risk model was trained using elastic net logistic regression and nested cross-validation to estimate risks for using imaging features and age. An absolute risk model was developed using derived risks and U.S. incidence and competing mortality rates. Absolute risks, discrimination performance, and risk stratification were estimated in the left-out validation set. The discrimination performance of 1-year risk was 0.82 (95% CI, 0.79 to 0.85) with good calibration (P = 0.7). Using the U.S. Preventive Service Task Force guidelines, 14% of the women were at high risk, 19.6 times higher compared to general risk. In this high-risk group, 76% of stage II and III cancers and 59% of stage 0 cancers were observed (P < 0.01). Using mammographic features generated from DBT screens, our image-based risk prediction model could guide radiologists in selecting women for clinical care, potentially leading to earlier detection and improved prognoses.
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Affiliation(s)
- Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska institutet, SE-171 77 Stockholm, Sweden
| | | | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska institutet, SE-171 77 Stockholm, Sweden
| | - Andrew Zeiberg
- Radiology Associates of Burlington County, Hainesport, NJ 08036, USA
| | - Robert Day
- Zwanger-Pesiri Radiology, Lindenhurst, NY 11757, USA
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska institutet, SE-171 77 Stockholm, Sweden.,Department of Oncology, Södersjukhuset University Hospital, Stockholm SE-118 61, Sweden
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Eriksson M, Czene K, Strand F, Zackrisson S, Lindholm P, Lång K, Förnvik D, Sartor H, Mavaddat N, Easton D, Hall P. Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening. Radiology 2020; 297:327-333. [PMID: 32897160 DOI: 10.1148/radiol.2020201620] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background Mammography screening reduces breast cancer mortality, but a proportion of breast cancers are missed and are detected at later stages or develop during between-screening intervals. Purpose To develop a risk model based on negative mammograms that identifies women likely to be diagnosed with breast cancer before or at the next screening examination. Materials and Methods This study was based on the prospective screening cohort Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA), 2011-2017. An image-based risk model was developed by using the Stratus method and computer-aided detection mammographic features (density, masses, microcalcifications), differences in the left and right breasts, and age. The lifestyle extended model included menopausal status, family history of breast cancer, body mass index, hormone replacement therapy, and use of tobacco and alcohol. The genetic extended model included a polygenic risk score with 313 single nucleotide polymorphisms. Age-adjusted relative risks and tumor subtype specific risks were estimated by using logistic regression, and absolute risks were calculated. Results Of 70 877 participants in the KARMA cohort, 974 incident cancers were sampled from 9376 healthy women (mean age, 54 years ± 10 [standard deviation]). The area under the receiver operating characteristic curve (AUC) for the image-based model was 0.73 (95% confidence interval [CI]: 0.71, 0.74). The AUCs for the lifestyle and genetic extended models were 0.74 (95% CI: 0.72, 0.75) and 0.77 (95% CI: 0.75, 0.79), respectively. There was a relative eightfold difference in risk between women at high risk and those at general risk. High-risk women were more likely to be diagnosed with stage II cancers and with tumors 20 mm or larger and were less likely to have stage I and estrogen receptor-positive tumors. The image-based model was validated in three external cohorts. Conclusion By combining three mammographic features, differences in the left and right breasts, and optionally lifestyle factors and family history and a polygenic risk score, the model identified women at high likelihood of being diagnosed with breast cancer within 2 years of a negative screening examination and in possible need of supplemental screening. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Mikael Eriksson
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Kamila Czene
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Fredrik Strand
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Sophia Zackrisson
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Peter Lindholm
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Kristina Lång
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Daniel Förnvik
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Hanna Sartor
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Nasim Mavaddat
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Doug Easton
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
| | - Per Hall
- From the Department of Medical Epidemiology and Biostatistics (M.E., K.C., P.H.) and Department of Oncology-Pathology (F.S.), Karolinska Institutet, Nobelsv 12A, Stockholm 171 77, Sweden; Department of Breast Radiology, Karolinska University Hospital, Stockholm, Sweden (F.S.); Department of Diagnostic Radiology, Lund University, Skåne University Hospital Malmö, Sweden (S.Z., K.L., D.F., H.S.); Department of Thoracic Radiology, Imaging and Physiology and Department of Physiology and Pharmacology, Karolinska Hospital, Stockholm, Sweden (P.L.); Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care (N.M., D.E.) and Department of Oncology (D.E.), University of Cambridge, Cambridge, England; and Department of Oncology, Södersjukhuset, Stockholm, Sweden (P.H.)
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