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Lee SE, Han K, Rho M, Kim EK. Artificial intelligence-based computer-aided diagnosis abnormality score trends in the serial mammography of patients with breast cancer. Eur J Radiol 2024; 178:111626. [PMID: 39024665 DOI: 10.1016/j.ejrad.2024.111626] [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: 04/01/2024] [Revised: 07/08/2024] [Accepted: 07/12/2024] [Indexed: 07/20/2024]
Abstract
PURPOSE To explore the abnormality score trends of artificial intelligence-based computer-aided diagnosis (AI-CAD) in the serial mammography of patients until a final diagnosis of breast cancer. METHOD From 2015 to 2019, 126 breast cancer patients who had at least two previous mammograms obtained from 2008 up to cancer diagnosis were included. AI-CAD was retrospectively applied to 487 previous mammograms and all the abnormality scores calculated by AI-CAD were obtained. The contralateral breast of each affected breast was defined as the control group. We divided all mammograms by 6-month intervals from cancer diagnosis in reverse chronological order. The random coefficient model was used to estimate whether the chronological trend of AI-CAD abnormality scores differed between cancer and normal breasts. Subgroup analyses were performed according to mammographic visibility, invasiveness and molecular subtype of the invasive cancer. RESULTS Mean period from initial examination to cancer diagnosis was 6.0 years (range 1.7-10.7 years). The abnormality scores of breasts diagnosed with cancer showed a significantly increasing trend during the previous examination period (slope 0.6 per 6 months, p for the slope < 0.001), while the contralateral normal breast showed no trend (slope 0.03, p = 0.776). The difference in slope between the cancerous and contralateral breasts was significant (p < 0.001). For mammography-visible cancers, the abnormality scores in cancerous breasts showed a significant increasing trend (slope 0.8, p < 0.001), while for mammography-occult cancers, the trend was not significant (slope 0.1, p = 0.6). For invasive cancers, the slope of the abnormality scores showed a significant increasing trend (slope 1.4, p = 0.002), unlike ductal carcinoma in situ (DCIS) which showed no significant trend. There was no significant difference in the slope of abnormality scores among the subtypes of invasive cancers (p = 0.418). CONCLUSION Breasts diagnosed with cancer showed an increase in AI-CAD abnormality scores in previous serial mammograms, suggesting that AI-CAD could be useful for early detection of breast cancer.
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Affiliation(s)
- Si Eun Lee
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiologic Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Miribi Rho
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eun-Kyung Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
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Lowry KP, Zuiderveld CC. Artificial Intelligence for Breast Cancer Risk Assessment. Radiol Clin North Am 2024; 62:619-625. [PMID: 38777538 DOI: 10.1016/j.rcl.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Breast cancer risk prediction models based on common clinical risk factors are used to identify women eligible for high-risk screening and prevention. Unfortunately, these models have only modest discriminatory accuracy with disparities in performance in underrepresented race and ethnicity groups. The field of artificial intelligence (AI) and deep learning are rapidly advancing the field of breast cancer risk prediction with the development of mammography-based AI breast cancer risk models. Early studies suggest mammography-based AI risk models may perform better than traditional risk factor-based models with more equitable performance.
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Affiliation(s)
- Kathryn P Lowry
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA; Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Biroš M, Kvak D, Dandár J, Hrubý R, Janů E, Atakhanova A, Al-antari MA. Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study. Diagnostics (Basel) 2024; 14:1117. [PMID: 38893643 PMCID: PMC11172127 DOI: 10.3390/diagnostics14111117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/20/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen's Kappa (κ) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.
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Affiliation(s)
- Marek Biroš
- Carebot, Ltd., 128 00 Prague, Czech Republic; (M.B.); (J.D.); (R.H.); (A.A.)
| | - Daniel Kvak
- Carebot, Ltd., 128 00 Prague, Czech Republic; (M.B.); (J.D.); (R.H.); (A.A.)
- Department of Simulation Medicine, Faculty of Medicine, Masaryk University, 625 00 Brno, Czech Republic
| | - Jakub Dandár
- Carebot, Ltd., 128 00 Prague, Czech Republic; (M.B.); (J.D.); (R.H.); (A.A.)
| | - Robert Hrubý
- Carebot, Ltd., 128 00 Prague, Czech Republic; (M.B.); (J.D.); (R.H.); (A.A.)
| | - Eva Janů
- Department of Radiology, Masaryk Memorial Cancer Institute, 602 00 Brno, Czech Republic
| | - Anora Atakhanova
- Carebot, Ltd., 128 00 Prague, Czech Republic; (M.B.); (J.D.); (R.H.); (A.A.)
| | - Mugahed A. Al-antari
- Department of Artificial Intelligence and Data Science, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea;
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Eriksson M, Román M, Gräwingholt A, Castells X, Nitrosi A, Pattacini P, Heywang-Köbrunner S, Rossi PG. European validation of an image-derived AI-based short-term risk model for individualized breast cancer screening-a nested case-control study. THE LANCET REGIONAL HEALTH. EUROPE 2024; 37:100798. [PMID: 38362558 PMCID: PMC10866984 DOI: 10.1016/j.lanepe.2023.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 02/17/2024]
Abstract
Background Image-derived artificial intelligence (AI)-based risk models for breast cancer have shown high discriminatory performances compared with clinical risk models based on family history and lifestyle factors. However, little is known about their generalizability across European screening settings. We therefore investigated the discriminatory performances of an AI-based risk model in European screening settings. Methods Using four European screening populations in three countries (Italy, Spain, Germany) screened between 2009 and 2020 for women aged 45-69, we performed a nested case-control study to assess the predictive performance of an AI-based risk model. In total, 739 women with incident breast cancers were included together with 7812 controls matched on year of study-entry. Mammographic features (density, microcalcifications, masses, left-right breast asymmetries of these features) were extracted using AI from negative digital mammograms at study-entry. Two-year absolute risks of breast cancer were predicted and assessed after two years of follow-up. Adjusted risk stratification performance metrics were reported per clinical guidelines. Findings The overall adjusted Area Under the receiver operating characteristic Curve (aAUC) of the AI risk model was 0.72 (95% CI 0.70-0.75) for breast cancers developed in four screening populations. In the 6.2% [529/8551] of women at high risk using the National Institute of Health and Care Excellence (NICE) guidelines thresholds, cancers were more likely diagnosed after 2 years follow-up, risk-ratio (RR) 6.7 (95% CI 5.6-8.0), compared with the 69% [5907/8551] of women classified at general risk by the model. Similar risk-ratios were observed across levels of mammographic density. Interpretation The AI risk model showed generalizable discriminatory performances across European populations and, predicted ∼30% of clinically relevant stage 2 and higher breast cancers in ∼6% of high-risk women who were sent home with a negative mammogram. Similar results were seen in women with fatty and dense breasts. Funding Swedish Research Council.
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Affiliation(s)
- Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Public Health and Primary Care, University of Cambridge, UK
| | - Marta Román
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | | | - Xavier Castells
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Andrea Nitrosi
- Azienda Unitá Sanitaria Locale-IRCCS di Reggio Emilia, Reggia Emilia, Italy
| | | | | | - Paolo G. Rossi
- Azienda Unitá Sanitaria Locale-IRCCS di Reggio Emilia, Reggia Emilia, Italy
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Schopf CM, Ramwala OA, Lowry KP, Hofvind S, Marinovich ML, Houssami N, Elmore JG, Dontchos BN, Lee JM, Lee CI. Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review. J Am Coll Radiol 2024; 21:319-328. [PMID: 37949155 PMCID: PMC10926179 DOI: 10.1016/j.jacr.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/05/2023] [Accepted: 10/05/2023] [Indexed: 11/12/2023]
Abstract
PURPOSE To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction. MATERIALS AND METHODS A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC). RESULTS Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement. CONCLUSIONS Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.
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Affiliation(s)
- Cody M Schopf
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Ojas A Ramwala
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Kathryn P Lowry
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Solveig Hofvind
- Section Head of Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - M Luke Marinovich
- The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
| | - Nehmat Houssami
- The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia; National Breast Cancer Foundation Chair in Breast Cancer Prevention at the University of Sydney and Coeditor of The Breast
| | - Joann G Elmore
- David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, California; Director of UCLA's National Clinician Scholars Program and Editor-in-Chief of Adult Primary Care at Up-To-Date. https://twitter.com/JoannElmoreMD
| | - Brian N Dontchos
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Clinical Director of Breast Imaging at Fred Hutchinson Cancer Center
| | - Janie M Lee
- Section Chief of Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Director of Breast Imaging at Fred Hutchinson Cancer Center
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and Department of Health Systems & Population Health, University of Washington School of Public Health, Seattle, WA; Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington and Deputy Editor of Journal of the American College of Radiology.
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Okunola HL, Shuryak I, Repin M, Wu HC, Santella RM, Terry MB, Turner HC, Brenner DJ. Improved prediction of breast cancer risk based on phenotypic DNA damage repair capacity in peripheral blood B cells. RESEARCH SQUARE 2023:rs.3.rs-3093360. [PMID: 37461559 PMCID: PMC10350237 DOI: 10.21203/rs.3.rs-3093360/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Background Standard Breast Cancer (BC) risk prediction models based only on epidemiologic factors generally have quite poor performance, and there have been a number of risk scores proposed to improve them, such as AI-based mammographic information, polygenic risk scores and pathogenic variants. Even with these additions BC risk prediction performance is still at best moderate. In that decreased DNA repair capacity (DRC) is a major risk factor for development of cancer, we investigated the potential to improve BC risk prediction models by including a measured phenotypic DRC assay. Methods Using blood samples from the Breast Cancer Family Registry we assessed the performance of phenotypic markers of DRC in 46 matched pairs of individuals, one from each pair with BC (with blood drawn before BC diagnosis) and the other from controls matched by age and time since blood draw. We assessed DRC in thawed cryopreserved peripheral blood mononuclear cells (PBMCs) by measuring γ-H2AX yields (a marker for DNA double-strand breaks) at multiple times from 1 to 20 hrs after a radiation challenge. The studies were performed using surface markers to discriminate between different PBMC subtypes. Results The parameter F res , the residual damage signal in PBMC B cells at 20 hrs post challenge, was the strongest predictor of breast cancer with an AUC (Area Under receiver-operator Curve) of 0.89 [95% Confidence Interval: 0.84-0.93] and a BC status prediction accuracy of 0.80. To illustrate the combined use of a phenotypic predictor with standard BC predictors, we combined F res in B cells with age at blood draw, and found that the combination resulted in significantly greater BC predictive power (AUC of 0.97 [95% CI: 0.94-0.99]), an increase of 13 percentage points over age alone. Conclusions If replicated in larger studies, these results suggest that inclusion of a fingerstick-based phenotypic DRC blood test has the potential to markedly improve BC risk prediction.
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Affiliation(s)
| | | | | | - Hui-Chen Wu
- Columbia University Mailman School of Public Health
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