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Orsini L, Czene K, Humphreys K. Random effects models of tumour growth for investigating interval breast cancer. Stat Med 2024; 43:2957-2971. [PMID: 38747450 DOI: 10.1002/sim.10105] [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: 09/28/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/19/2024]
Abstract
In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.
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Affiliation(s)
- Letizia Orsini
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden
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Salim M, Liu Y, Sorkhei M, Ntoula D, Foukakis T, Fredriksson I, Wang Y, Eklund M, Azizpour H, Smith K, Strand F. AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial. Nat Med 2024:10.1038/s41591-024-03093-5. [PMID: 38977914 DOI: 10.1038/s41591-024-03093-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 05/23/2024] [Indexed: 07/10/2024]
Abstract
Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594 .
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Affiliation(s)
- Mattie Salim
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Yue Liu
- School of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
| | - Moein Sorkhei
- School of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
| | - Dimitra Ntoula
- Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Theodoros Foukakis
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Irma Fredriksson
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Yanlu Wang
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Hossein Azizpour
- Division of Robotics, Perception, and Learning, Karolinska Institutet, Stockholm, Sweden
| | - Kevin Smith
- School of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
| | - Fredrik Strand
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
- Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden.
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3
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Çelik L, Aribal E. The efficacy of artificial intelligence (AI) in detecting interval cancers in the national screening program of a middle-income country. Clin Radiol 2024; 79:e885-e891. [PMID: 38649312 DOI: 10.1016/j.crad.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/14/2024] [Accepted: 03/21/2024] [Indexed: 04/25/2024]
Abstract
AIM We aimed to investigate the efficiency and accuracy of an artificial intelligence (AI) algorithm for detecting interval cancers in a middle-income country's national screening program. MATERIAL AND METHODS A total of 2,129,486 mammograms reported as BIRADS 1 and 2 were matched with the national cancer registry for interval cancers (IC). The IC group consisted of 442 cases, of which 36 were excluded due to having mammograms incompatible with the AI system. A control group of 446 women with two negative consequent mammograms was defined as time-proven normal and constituted the normal group. The cancer risk scores of both groups were determined from 1 to 10 with the AI system. The sensitivity and specificity values of the AI system were defined in terms of IC detection. The IC group was divided into subgroups with six-month intervals according to their time from screening to diagnosis: 0-6 months, 6-12 months, 12-18 months, and 18-24 months. The diagnostic performance of the AI system for all patients was evaluated using receiver operating characteristics (ROC) curve analysis. The diagnostic performance of the AI system for major and minor findings that expert readers determined was re-evaluated. RESULTS AI labeled 53% of ICs with the highest score of 10. The sensitivity of AI in detecting ICs was 53.7% and 38.5% at specificities of 90% and 95%, respectively. Area under the curve (AUC) of AI in detecting major signs was 0.93 (95% CI: 0.90-0.95) with a sensitivity of 81.6% and 72.4% at specificities of 90% and 95%, respectively (95% CI: 0.73-0.88 and 95% CI: 0.60-0.82 respectively) and minor signs was 0.87 (95% CI: 0.87-0.92) with a sensitivity of 70% and 53% at a specificity of 90% and 95%, respectively (95% CI: 0.65-0.82 and 95% CI: 0.52-0.71 respectively). In subgroup analysis for time to diagnosis, the AUC value of the AI system was higher in the 0-6 month period than in later periods. CONCLUSION This study showed the potential of AI in detecting ICs in initial mammograms and reducing human errors and undetected cancers.
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Affiliation(s)
- L Çelik
- Maltepe University Hospital, Feyzullah cad 39, Maltepe, 34843, Istanbul, Turkey.
| | - E Aribal
- Acibadem University, School of Medicine, 34752, Istanbul, Turkey; Acibadem Altunizade Hospital, Tophanelioglu cad 13, Altunizade, 34662, Istanbul, Turkey.
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Ellis S, Gomes S, Trumble M, Halling-Brown MD, Young KC, Chaudhry NS, Harris P, Warren LM. Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort. Radiol Artif Intell 2024; 6:e230431. [PMID: 38775671 DOI: 10.1148/ryai.230431] [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: 07/11/2024]
Abstract
Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191 488 risk-negative examinations, training (n = 89 285), validation (n = 2106), and test (n = 39 351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities, or across age groups. Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, United Kingdom-specific dataset. Keywords: Deep Learning, Artificial Intelligence, Breast Cancer, Screening, Risk Prediction Supplemental material is available for this article. ©RSNA, 2024.
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Affiliation(s)
- Sam Ellis
- From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England
| | - Sandra Gomes
- From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England
| | - Matthew Trumble
- From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England
| | - Mark D Halling-Brown
- From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England
| | - Kenneth C Young
- From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England
| | - Nouman S Chaudhry
- From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England
| | - Peter Harris
- From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England
| | - Lucy M Warren
- From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England
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Ray KM. Interval Cancers in Understanding Screening Outcomes. Radiol Clin North Am 2024; 62:559-569. [PMID: 38777533 DOI: 10.1016/j.rcl.2023.12.012] [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
Interval breast cancers are not detected at routine screening and are diagnosed in the interval between screening examinations. A variety of factors contribute to interval cancers, including patient and tumor characteristics as well as the screening technique and frequency. The interval cancer rate is an important metric by which the effectiveness of screening may be assessed and may serve as a surrogate for mortality benefit.
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Affiliation(s)
- Kimberly M Ray
- Department of Radiology and Biomedical Sciences, University of California, San Francisco, UCSF Medical Center, 1825 4th Street, L3185, Box 4034, San Francisco, CA 94107, USA.
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Waugh J, Evans J, Miocevic M, Lockie D, Aminzadeh P, Lynch A, Bell RJ. Performance of artificial intelligence in 7533 consecutive prevalent screening mammograms from the BreastScreen Australia program. Eur Radiol 2024; 34:3947-3957. [PMID: 37955669 PMCID: PMC11166844 DOI: 10.1007/s00330-023-10396-7] [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: 06/15/2023] [Revised: 08/15/2023] [Accepted: 09/05/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES To assess the performance of an artificial intelligence (AI) algorithm in the Australian mammography screening program which routinely uses two independent readers with arbitration of discordant results. METHODS A total of 7533 prevalent round mammograms from 2017 were available for analysis. The AI program classified mammograms into deciles on the basis of breast cancer (BC) risk. BC diagnoses, including invasive BC (IBC) and ductal carcinoma in situ (DCIS), included those from the prevalent round, interval cancers, and cancers identified in the subsequent screening round two years later. Performance was assessed by sensitivity, specificity, positive and negative predictive values, and the proportion of women recalled by the radiologists and identified as higher risk by AI. RESULTS Radiologists identified 54 women with IBC and 13 with DCIS with a recall rate of 9.7%. In contrast, 51 of 54 of the IBCs and 12/13 cases of DCIS were within the higher AI score group (score 10), a recall equivalent of 10.6% (a difference of 0.9% (CI -0.03 to 1.89%, p = 0.06). When IBCs were identified in the 2017 round, interval cancers classified as false negatives or with minimal signs in 2017, and cancers from the 2019 round were combined, the radiologists identified 54/67 and 59/67 were in the highest risk AI category (sensitivity 80.6% and 88.06 % respectively, a difference that was not different statistically). CONCLUSIONS As the performance of AI was comparable to that of expert radiologists, future AI roles in screening could include replacing one reader and supporting arbitration, reducing workload and false positive results. CLINICAL RELEVANCE STATEMENT AI analysis of consecutive prevalent screening mammograms from the Australian BreastScreen program demonstrated the algorithm's ability to match the cancer detection of experienced radiologists, additionally identifying five interval cancers (false negatives), and the majority of the false positive recalls. KEY POINTS • The AI program was almost as sensitive as the radiologists in terms of identifying prevalent lesions (51/54 for invasive breast cancer, 63/67 when including ductal carcinoma in situ). • If selected interval cancers and cancers identified in the subsequent screening round were included, the AI program identified more cancers than the radiologists (59/67 compared with 54/67, sensitivity 88.06 % and 80.6% respectively p = 0.24). • The high negative predictive value of a score of 1-9 would indicate a role for AI as a triage tool to reduce the recall rate (specifically false positives).
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Affiliation(s)
- John Waugh
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia.
| | - Jill Evans
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Miranda Miocevic
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Darren Lockie
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Parisa Aminzadeh
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Anne Lynch
- Monash BreastScreen, Monash Cancer Centre, Moorabbin Hospital, 823-865 Centre Road, Bentleigh East, Victoria, 3165, Australia
| | - Robin J Bell
- Women's Health Research Program, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
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7
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Holen ÅS, Martiniussen MA, Bergan MB, Moshina N, Hovda T, Hofvind S. Women's attitudes and perspectives on the use of artificial intelligence in the assessment of screening mammograms. Eur J Radiol 2024; 175:111431. [PMID: 38520804 DOI: 10.1016/j.ejrad.2024.111431] [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] [Received: 01/12/2024] [Revised: 02/26/2024] [Accepted: 03/15/2024] [Indexed: 03/25/2024]
Abstract
PURPOSE To investigate attitudes and perspectives on the use of artificial intelligence (AI) in the assessment of screening mammograms among women invited to BreastScreen Norway. METHOD An anonymous survey was sent to all women invited to BreastScreen Norway during the study period, October 10, 2022, to December 25, 2022 (n = 84,543). Questions were answered on a 10-point Likert scale and as multiple-choice, addressing knowledge of AI, willingness to participate in AI studies, information needs, confidence in AI results and AI assisted reading strategies, and thoughts on concerns and benefits of AI in mammography screening. Analyses were performed using χ2 and logistic regression tests. RESULTS General knowledge of AI was reported as extensive by 11.0% of the 8,355 respondents. Respondents were willing to participate in studies using AI either for decision support (64.0%) or triaging (54.9%). Being informed about use of AI-assisted image assessment was considered important, and a reading strategy of AI in combination with one radiologist preferred. Having extensive knowledge of AI was associated with willingness to participate in AI studies (decision support; odds ratio [OR]: 5.1, 95% confidence interval [CI]: 4.1-6.4, and triaging; OR: 3.4, 95% CI: 2.8-4.0) and trust in AI's independent assessment (OR: 6.8, 95% CI: 5.7, 8.3). CONCLUSIONS Women invited to BreastScreen Norway had a positive attitude towards the use of AI in image assessment, given that human readers are still involved. Targeted information and increased public knowledge of AI could help achieve high participation in AI studies and successful implementation of AI in mammography screening.
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Affiliation(s)
- Åsne Sørlien Holen
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
| | - Marit Almenning Martiniussen
- Department of Radiology, Østfold Hospital Trust, Kalnes, Norway; University of Oslo, Institute of Clinical Medicine, Oslo, Norway.
| | - Marie Burns Bergan
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
| | - Nataliia Moshina
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
| | - Tone Hovda
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway.
| | - Solveig Hofvind
- Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway; Department of Health and Care Sciences, UiT, The Artic University of Norway, Tromsø, Norway.
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8
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Mills C, Sud A, Everall A, Chubb D, Lawrence SED, Kinnersley B, Cornish AJ, Bentham R, Houlston RS. Genetic landscape of interval and screen detected breast cancer. NPJ Precis Oncol 2024; 8:122. [PMID: 38806682 PMCID: PMC11133314 DOI: 10.1038/s41698-024-00618-6] [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: 02/14/2024] [Accepted: 05/17/2024] [Indexed: 05/30/2024] Open
Abstract
Interval breast cancers (IBCs) are cancers diagnosed between screening episodes. Understanding the biological differences between IBCs and screen-detected breast-cancers (SDBCs) has the potential to improve mammographic screening and patient management. We analysed and compared the genomic landscape of 288 IBCs and 473 SDBCs by whole genome sequencing of paired tumour-normal patient samples collected as part of the UK 100,000 Genomes Project. Compared to SDBCs, IBCs were more likely to be lobular, higher grade, and triple negative. A more aggressive clinical phenotype was reflected in IBCs displaying features of genomic instability including a higher mutation rate and number of chromosomal structural abnormalities, defective homologous recombination and TP53 mutations. We did not however, find evidence to indicate that IBCs are associated with a significantly different immune response. While IBCs do not represent a unique molecular class of invasive breast cancer they exhibit a more aggressive phenotype, which is likely to be a consequence of the timing of tumour initiation. This information is relevant both with respect to treatment as well as informing the screening interval for mammography.
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Affiliation(s)
- Charlie Mills
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Amit Sud
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Centre of Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Andrew Everall
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Daniel Chubb
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Samuel E D Lawrence
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Ben Kinnersley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
- University College London Cancer Institute, University College London, London, UK
| | - Alex J Cornish
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK
| | - Robert Bentham
- University College London Cancer Institute, University College London, London, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, Surrey, SM2 5NG, UK.
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9
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Siegel SD, Zhang Y, Lynch SM, Rowland J, Curriero FC. A Novel Approach for Conducting a Catchment Area Analysis of Breast Cancer by Age and Stage for a Community Cancer Center. Cancer Epidemiol Biomarkers Prev 2024; 33:646-653. [PMID: 38451180 PMCID: PMC11062816 DOI: 10.1158/1055-9965.epi-23-1125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/07/2023] [Accepted: 03/05/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND The U.S. Preventive Services Task Force recently issued an updated draft recommendation statement to initiate breast cancer screening at age 40, reflecting well-documented disparities in breast cancer-related mortality that disproportionately impact younger Black women. This study applied a novel approach to identify hotspots of breast cancer diagnosed before age 50 and/or at an advanced stage to improve breast cancer detection within these communities. METHODS Cancer registry data for 3,497 women with invasive breast cancer diagnosed or treated between 2012 and 2020 at the Helen F. Graham Cancer Center and Research Institute (HFGCCRI) and who resided in the HFGCCRI catchment area, defined as New Castle County, Delaware, were geocoded and analyzed with spatial intensity. Standardized incidence ratios stratified by age and race were calculated for each hotspot. RESULTS Four hotspots were identified, two for breast cancer diagnosed before age 50, one for advanced breast cancer, and one for advanced breast cancer diagnosed before age 50. Younger Black women were overrepresented in these hotspots relative to the full-catchment area. CONCLUSIONS The novel use of spatial methods to analyze a community cancer center catchment area identified geographic areas with higher rates of breast cancer with poor prognostic factors and evidence that these areas made an outsized contribution to racial disparities in breast cancer. IMPACT Identifying and prioritizing hotspot breast cancer communities for community outreach and engagement activities designed to improve breast cancer detection have the potential to reduce the overall burden of breast cancer and narrow racial disparities in breast cancer.
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Affiliation(s)
- Scott D. Siegel
- Cawley Center for Translational Cancer Research, Helen F. Graham Cancer Center & Research Institute, ChristianaCare, Newark, DE, United States
| | - Yuchen Zhang
- Cawley Center for Translational Cancer Research, Helen F. Graham Cancer Center & Research Institute, ChristianaCare, Newark, DE, United States
- Center for Strategic Information Management, ChristianaCare, Newark, DE, United States
| | - Shannon M. Lynch
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, United States
| | - Jennifer Rowland
- Department of Radiology, Breast Imaging Section, Helen F. Graham Cancer Center & Research Institute, ChristianaCare, Newark, DE, United States
| | - Frank C. Curriero
- Johns Hopkins Spatial Science for Public Health Center, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health
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10
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Nykänen A, Sudah M, Masarwah A, Vanninen R, Okuma H. Radiological features of screening-detected and interval breast cancers and subsequent survival in Eastern Finnish women. Sci Rep 2024; 14:10001. [PMID: 38693256 PMCID: PMC11063164 DOI: 10.1038/s41598-024-60740-0] [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: 08/22/2023] [Accepted: 04/26/2024] [Indexed: 05/03/2024] Open
Abstract
Interval breast cancers are diagnosed between scheduled screenings and differ in many respects from screening-detected cancers. Studies comparing the survival of patients with interval and screening-detected cancers have reported differing results. The aim of this study was to investigate the radiological and histopathological features and growth rates of screening-detected and interval breast cancers and subsequent survival. This retrospective study included 942 female patients aged 50-69 years with breast cancers treated and followed-up at Kuopio University Hospital between January 2010 and December 2016. The screening-detected and interval cancers were classified as true, minimal-signs, missed, or occult. The radiological features were assessed on mammograms by one of two specialist breast radiologists with over 15 years of experience. A χ2 test was used to examine the association between radiological and pathological variables; an unpaired t test was used to compare the growth rates of missed and minimal-signs cancers; and the Kaplan-Meier estimator was used to examine survival after screening-detected and interval cancers. Sixty occult cancers were excluded, so a total of 882 women (mean age 60.4 ± 5.5 years) were included, in whom 581 had screening-detected cancers and 301 interval cancers. Disease-specific survival, overall survival and disease-free survival were all worse after interval cancer than after screening-detected cancer (p < 0.001), with a mean follow-up period of 8.2 years. There were no statistically significant differences in survival between the subgroups of screening-detected or interval cancers. Missed interval cancers had faster growth rates (0.47% ± 0.77%/day) than missed screening-detected cancers (0.21% ± 0.11%/day). Most cancers (77.2%) occurred in low-density breasts (< 25%). The most common lesion types were masses (73.9%) and calcifications (13.4%), whereas distortions (1.8%) and asymmetries (1.7%) were the least common. Survival was worse after interval cancers than after screening-detected cancers, attributed to their more-aggressive histopathological characteristics, more nodal and distant metastases, and faster growth rates.
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Affiliation(s)
- Aki Nykänen
- Department of Clinical Radiology, Diagnostic Imaging Centre, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland.
- School of Medicine, Institute of Clinical Medicine, University of Eastern Finland, Yliopistonranta 1, 70210, Kuopio, Finland.
| | - Mazen Sudah
- Department of Clinical Radiology, Diagnostic Imaging Centre, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
| | - Amro Masarwah
- Department of Clinical Radiology, Diagnostic Imaging Centre, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
| | - Ritva Vanninen
- Department of Clinical Radiology, Diagnostic Imaging Centre, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
- School of Medicine, Institute of Clinical Medicine, University of Eastern Finland, Yliopistonranta 1, 70210, Kuopio, Finland
- Cancer Center of Eastern Finland, University of Eastern Finland, Yliopistonranta 1, 70210, Kuopio, Finland
| | - Hidemi Okuma
- Department of Clinical Radiology, Diagnostic Imaging Centre, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
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11
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Song H, Tran TXM, Kim S, Park B. Risk Factors and Mortality Among Women With Interval Breast Cancer vs Screen-Detected Breast Cancer. JAMA Netw Open 2024; 7:e2411927. [PMID: 38767918 PMCID: PMC11107304 DOI: 10.1001/jamanetworkopen.2024.11927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 03/17/2024] [Indexed: 05/22/2024] Open
Abstract
Importance The risk factors for interval breast cancer (IBC) compared with those for screen-detected breast cancer (SBC) and their association with mortality outcomes have not yet been evaluated among Korean women. Objective To evaluate risk factors associated with IBC and survival among Korean women with IBC compared with those with SBC. Design, Setting, and Participants This retrospective cohort study used data from the Korean National Health Insurance Service Database. Women who participated in a national mammographic breast cancer screening program between January 1, 2009, and December 31, 2012, were included. Mortality outcomes were calculated from the date of breast cancer diagnosis to the date of death or December 31, 2020. Data were analyzed from March 1 to June 30, 2023. Exposure Breast cancer diagnosed within 6 to 24 months after a negative screening result (ie, IBC) or within 6 months after a positive screening result (ie, SBC). Main Outcomes and Measures Risk factors and survival rates for IBC and SBC. Results This study included 8702 women with IBC (mean [SD] age, 53.3 [8.6] years) and 9492 women with SBC (mean [SD] age, 54.1 [9.0] years). Compared with SBC, the probability of IBC decreased as mammographic density increased. Lower body mass index, menopausal status, hormone replacement therapy (HRT) use, and lack of family history of breast cancer were associated with a higher likelihood of IBC. When stratified by detection time, younger age at breast cancer diagnosis and family history of breast cancer were associated with an increased likelihood of IBC diagnosed at 6 to 12 months but a decreased likelihood of IBC diagnosed at 12 to 24 months. Overall mortality of IBC was comparable with SBC, but total mortality and cancer-related mortality of IBC diagnosed between 6 and 12 months was higher than that of SBC. Conclusions and Relevance The findings of this cohort study suggest that breast density, obesity, and HRT use were associated with IBC compared with SBC. These findings also suggest that higher supplemental breast ultrasound use among Korean women, especially those with dense breasts, could be attributed to a lower incidence of IBC among women with dense breasts compared with women with SBC, due to greater detection. Finally, overall mortality of IBC was comparable with that of SBC.
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Affiliation(s)
- Huiyeon Song
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Thi Xuan Mai Tran
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Soyeoun Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Boyoung Park
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
- Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea
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12
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Bahl M, Do S. Artificial Intelligence for Breast Cancer Screening: Trade-offs between Sensitivity and Specificity. Radiol Artif Intell 2024; 6:e240184. [PMID: 38717286 PMCID: PMC11140497 DOI: 10.1148/ryai.240184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 05/26/2024]
Affiliation(s)
- Manisha Bahl
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA 02114
| | - Synho Do
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA 02114
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13
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Barnes I, Garcia-Closas M, Gathani T, Sweetland S, Floud S, Reeves GK. A comparative analysis of risk factor associations with interval and screen-detected breast cancers: A large UK prospective study. Int J Cancer 2024. [PMID: 38669116 DOI: 10.1002/ijc.34968] [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: 09/09/2023] [Revised: 02/28/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024]
Abstract
The associations of certain factors, such as age and menopausal hormone therapy, with breast cancer risk are known to differ for interval and screen-detected cancers. However, the extent to which associations of other established breast cancer risk factors differ by mode of detection is unclear. We investigated associations of a wide range of risk factors using data from a large UK cohort with linkage to the National Health Service Breast Screening Programme, cancer registration, and other health records. We used Cox regression to estimate adjusted relative risks (RRs) and 95% confidence intervals (CIs) for associations between risk factors and breast cancer risk. A total of 9421 screen-detected and 5166 interval cancers were diagnosed in 517,555 women who were followed for an average of 9.72 years. We observed the following differences in risk factor associations by mode of detection: greater body mass index (BMI) was associated with a smaller increased risk of interval (RR per 5 unit increase 1.07, 95% CI 1.03-1.11) than screen-detected cancer (RR 1.27, 1.23-1.30); having a first-degree family history was associated with a greater increased risk of interval (RR 1.81, 1.68-1.95) than screen-detected cancer (RR 1.52, 1.43-1.61); and having had previous breast surgery was associated with a greater increased risk of interval (RR 1.85, 1.72-1.99) than screen-detected cancer (RR 1.34, 1.26-1.42). As these differences in associations were relatively unchanged after adjustment for tumour grade, and are in line with the effects of these factors on mammographic density, they are likely to reflect the effects of these risk factors on screening sensitivity.
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Affiliation(s)
- Isobel Barnes
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Toral Gathani
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Siân Sweetland
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sarah Floud
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Gillian K Reeves
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Cerekci E, Alis D, Denizoglu N, Camurdan O, Ege Seker M, Ozer C, Hansu MY, Tanyel T, Oksuz I, Karaarslan E. Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis. Eur J Radiol 2024; 173:111356. [PMID: 38364587 DOI: 10.1016/j.ejrad.2024.111356] [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] [Received: 08/23/2023] [Revised: 12/10/2023] [Accepted: 02/02/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Explainable Artificial Intelligence (XAI) is prominent in the diagnostics of opaque deep learning (DL) models, especially in medical imaging. Saliency methods are commonly used, yet there's a lack of quantitative evidence regarding their performance. OBJECTIVES To quantitatively evaluate the performance of widely utilized saliency XAI methods in the task of breast cancer detection on mammograms. METHODS Three radiologists drew ground-truth boxes on a balanced mammogram dataset of women (n = 1496 cancer-positive and negative scans) from three centers. A modified, pre-trained DL model was employed for breast cancer detection, using MLO and CC images. Saliency XAI methods, including Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, and Eigen-CAM, were evaluated. We utilized the Pointing Game to assess these methods, determining if the maximum value of a saliency map aligned with the bounding boxes, representing the ratio of correctly identified lesions among all cancer patients, with a value ranging from 0 to 1. RESULTS The development sample included 2,244 women (75%), with the remaining 748 women (25%) in the testing set for unbiased XAI evaluation. The model's recall, precision, accuracy, and F1-Score in identifying cancer in the testing set were 69%, 88%, 80%, and 0.77, respectively. The Pointing Game Scores for Grad-CAM, Grad-CAM++, and Eigen-CAM were 0.41, 0.30, and 0.35 in women with cancer and marginally increased to 0.41, 0.31, and 0.36 when considering only true-positive samples. CONCLUSIONS While saliency-based methods provide some degree of explainability, they frequently fall short in delineating how DL models arrive at decisions in a considerable number of instances.
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Affiliation(s)
- Esma Cerekci
- Sisli Hamidiye Etfal Training and Research Hospital, Department of Radiology, Istanbul, Turkey.
| | - Deniz Alis
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Department of Radiology, Istanbul, Turkey.
| | - Nurper Denizoglu
- Acibadem Healthcare Group, Department of Radiology, Istanbul, Turkey.
| | - Ozden Camurdan
- Acibadem Healthcare Group, Department of Radiology, Istanbul, Turkey.
| | - Mustafa Ege Seker
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey.
| | - Caner Ozer
- Istanbul Technical University, Department of Computer Engineering, Istanbul, Turkey.
| | - Muhammed Yusuf Hansu
- Istanbul Technical University, Department of Electronics and Communication Engineering, Istanbul, Turkey.
| | - Toygar Tanyel
- Istanbul Technical University, Department of Biomedical Engineering, Istanbul, Turkey.
| | - Ilkay Oksuz
- Istanbul Technical University, Department of Computer Engineering, Istanbul, Turkey.
| | - Ercan Karaarslan
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Department of Radiology, Istanbul, Turkey
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15
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Bergan MB, Larsen M, Moshina N, Bartsch H, Koch HW, Aase HS, Satybaldinov Z, Haldorsen IHS, Lee CI, Hofvind S. AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway. Eur Radiol 2024:10.1007/s00330-024-10681-z. [PMID: 38528136 DOI: 10.1007/s00330-024-10681-z] [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: 09/25/2023] [Revised: 01/19/2024] [Accepted: 02/10/2024] [Indexed: 03/27/2024]
Abstract
OBJECTIVE To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. MATERIALS AND METHOD We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.
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Affiliation(s)
- Marie Burns Bergan
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Nataliia Moshina
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Hauke Bartsch
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
| | - Henrik Wethe Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | | | - Zhanbolat Satybaldinov
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
| | - Ingfrid Helene Salvesen Haldorsen
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, USA
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
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16
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Ashoor M, Khorshidi A. Improving signal-to-noise ratio by maximal convolution of longitudinal and transverse magnetization components in MRI: application to the breast cancer detection. Med Biol Eng Comput 2024; 62:941-954. [PMID: 38100039 DOI: 10.1007/s11517-023-02994-w] [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] [Received: 07/28/2023] [Accepted: 12/07/2023] [Indexed: 02/22/2024]
Abstract
PURPOSE The extraction of information from images provided by medical imaging systems may be employed to obtain the specific objectives in the various fields. The quantity of signal to noise ratio (SNR) plays a crucial role in displaying the image details. The higher the SNR value, the more the information is available. METHODS In this study, a new function has been formulated using the appropriate suggestions on convolutional combination of the longitudinal and transverse magnetization components related to the relaxation times of T1 and T2 in MRI, where by introducing the distinct index on the maximum value of this function, the new maps are constructed toward the best SNR. Proposed functions were analytically simulated using Matlab software and evaluated with respect to various relaxation times. This proposed method can be applied to any medical images. For instance, the T1- and T2-weighted images of the breast indicated in the reference [35] were selected for modelling and construction of the full width at x maximum (FWxM) map at the different values of x-parameter from 0.01 to 0.955 at 0.035 and 0.015 intervals. The range of x-parameter is between zero and one. To determine the maximum value of the derived SNR, these intervals have been first chosen arbitrarily. However, the smaller this interval, the more precise the value of the x-parameter at which the signal to noise is maximum. RESULTS The results showed that at an index value of x = 0.325, the new map of FWxM (0.325) will be constructed with a maximum derived SNR of 22.7 compared to the SNR values of T1- and T2-maps by 14.53 and 17.47, respectively. CONCLUSION By convolving two orthogonal magnetization vectors, the qualified images with higher new SNR were created, which included the image with the best SNR. In other words, to optimize the adoption of MRI technique and enable the possibility of wider use, an optimal and cost-effective examination has been suggested. Our proposal aims to shorten the MRI examination to further reduce interpretation times while maintaining primary sensitivity. SIGNIFICANCE Our findings may help to quantitatively identify the primary sources of each type of solid and sequential cancer.
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Affiliation(s)
- Mansour Ashoor
- Radiation Applications Research School, Nuclear Science and Technology Research Institute, Tehran, Iran
| | - Abdollah Khorshidi
- Radiation Applications Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
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17
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Rodriguez J, Grassmann F, Xiao Q, Eriksson M, Mao X, Bajalica-Lagercrantz S, Hall P, Czene K. Investigation of Genetic Alterations Associated With Interval Breast Cancer. JAMA Oncol 2024; 10:372-379. [PMID: 38270937 PMCID: PMC10811589 DOI: 10.1001/jamaoncol.2023.6287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/16/2023] [Indexed: 01/26/2024]
Abstract
Importance Breast cancers (BCs) diagnosed between 2 screening examinations are called interval cancers (ICs), and they have worse clinicopathological characteristics and poorer prognosis than screen-detected cancers (SDCs). However, the association of rare germline genetic variants with IC have not been studied. Objective To evaluate whether rare germline deleterious protein-truncating variants (PTVs) can be applied to discriminate between IC and SDC while considering mammographic density. Design, Setting, and Participants This population-based genetic association study was based on women aged 40 to 76 years who were attending mammographic screening in Sweden. All women with a diagnosis of BC between January 2001 and January 2016 were included, together with age-matched controls. Patients with BC were followed up for survival until 2021. Statistical analysis was performed from September 2021 to December 2022. Exposure Germline PTVs in 34 BC susceptibility genes as analyzed by targeted sequencing. Main Outcomes and Measures Odds ratios (ORs) were used to compare IC with SDC using logistic regression. Hazard ratios were used to investigate BC-specific survival using Cox regression. Results All 4121 patients with BC (IC, n = 1229; SDC, n = 2892) were female, with a mean (SD) age of 55.5 (7.1) years. There were 5631 age-matched controls. The PTVs of the ATM, BRCA1, BRCA2, CHEK2, and PALB2 genes were more common in patients with IC compared with SDC (OR, 1.48; 95% CI, 1.06-2.05). This association was primarily influenced by BRCA1/2 and PALB2 variants. A family history of BC together with PTVs of any of these genes synergistically increased the probability of receiving a diagnosis of IC rather than SDC (OR, 3.95; 95% CI, 1.97-7.92). Furthermore, 10-year BC-specific survival revealed that if a patient received a diagnosis of an IC, carriers of PTVs in any of these 5 genes had significantly worse survival compared with patients not carrying any of them (hazard ratio, 2.04; 95% CI, 1.06-3.92). All of these associations were further pronounced in a subset of patients with IC who had a low mammographic density at prior screening examination. Conclusions and Relevance The results of this study may be helpful in future optimizations of screening programs that aim to lower mortality as well as the clinical treatment of patients with BC.
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Affiliation(s)
- Juan Rodriguez
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Health and Medical University, Potsdam, Germany
| | - Qingyang Xiao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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18
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Majid SZ, Senapati GM, Lacson R, Chikarmane SA, Giess CS. Imaging characteristics of interval cancers detected on Full Field Digital Mammography (FFDM) versus Digital Breast Tomosynthesis (DBT). Clin Imaging 2024; 107:110063. [PMID: 38232642 DOI: 10.1016/j.clinimag.2023.110063] [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] [Received: 09/14/2023] [Revised: 10/26/2023] [Accepted: 12/10/2023] [Indexed: 01/19/2024]
Abstract
OBJECTIVE To compare imaging features of interval cancers detected in patients screened with full field digital mammography (FFDM) versus digital breast tomosynthesis (DBT). MATERIALS/METHODS This retrospective observational study consisted of female patients undergoing screening DM or FFDM at an academic medical center and two outpatient imaging facilities between January 2012 and June 2017. A natural language processing algorithm queried breast imaging reports for breast density and BI-RADS category. This was cross-referenced to an institutional breast cancer registry to identify interval cancers. Retrospective consensus review of the cases was done to categorize imaging features of interval cancers on FFDM vs DBT. RESULTS The rate of interval cancers was comparable in patients screened with FFDM (30/39793) and DBT (29/32180) (p = 0.58). There was no significant difference in the rate, histopathology, or imaging features of interval cancers in patients screened with FFDM versus DBT. The most common mammographic features on diagnostic imaging across both groups was the presence of a mass (13/47). Almost equally common was negative diagnostic mammogram with mass detected only on ultrasound (11/47). The rate of interval cancers detected by high-risk surveillance breast MRI was increased in patients who previously had screening with DBT relative to those who had screening with FFDM (p = 0.0419). CONCLUSION There is no significant difference in rate of detection, histopathology, or imaging features of interval cancers in patients screened with FFDM versus DBT. However, across both cohorts, the most common features on diagnostic mammogram were either the presence of a mass or a negative mammogram.
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Affiliation(s)
- Sana Z Majid
- Brigham and Women's Hospital, Boston, MA 02115, United States of America.
| | - Gunjan M Senapati
- Brigham and Women's Hospital, Boston, MA 02115, United States of America
| | - Ronilda Lacson
- Brigham and Women's Hospital, Boston, MA 02115, United States of America
| | - Sona A Chikarmane
- Brigham and Women's Hospital, Boston, MA 02115, United States of America
| | - Catherine S Giess
- Brigham and Women's Hospital, Boston, MA 02115, United States of America
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19
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Koch HW, Larsen M, Bartsch H, Martiniussen MA, Styr BM, Fagerheim S, Haldorsen IHS, Hofvind S. How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway. Eur Radiol 2024:10.1007/s00330-024-10662-2. [PMID: 38396248 DOI: 10.1007/s00330-024-10662-2] [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: 06/27/2023] [Revised: 01/18/2024] [Accepted: 01/28/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. METHODS In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. RESULTS All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. CONCLUSION AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. CLINICAL RELEVANCE STATEMENT In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. KEY POINTS • All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.
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Affiliation(s)
- Henrik Wethe Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Hauke Bartsch
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Marit Almenning Martiniussen
- Department of Radiology, Østfold Hospital Trust, Kalnes, Norway
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | | | - Siri Fagerheim
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
| | - Ingfrid Helene Salvesen Haldorsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
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Seker ME, Koyluoglu YO, Ozaydin AN, Gurdal SO, Ozcinar B, Cabioglu N, Ozmen V, Aribal E. Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program. Eur Radiol 2024:10.1007/s00330-024-10661-3. [PMID: 38388718 DOI: 10.1007/s00330-024-10661-3] [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: 11/20/2023] [Revised: 01/18/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVES We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. MATERIALS AND METHODS The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden's index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. RESULTS The AI software achieved an AUC of 89.6% (86.1-93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. CONCLUSION This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. CLINICAL RELEVANCE STATEMENT The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. KEY POINTS • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading.
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Affiliation(s)
- Mustafa Ege Seker
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey
| | - Yilmaz Onat Koyluoglu
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey
| | | | | | - Beyza Ozcinar
- Istanbul University, School of Medicine, Istanbul, Turkey
| | | | - Vahit Ozmen
- Istanbul University, School of Medicine, Istanbul, Turkey
| | - Erkin Aribal
- Department of Radiology, Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, Turkey.
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21
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Buschmann L, Wellmann I, Bonberg N, Wellmann J, Hense HW, Karch A, Minnerup H. Isolating the effect of confounding from the observed survival benefit of screening participants - a methodological approach illustrated by data from the German mammography screening programme. BMC Med 2024; 22:43. [PMID: 38287392 PMCID: PMC10826012 DOI: 10.1186/s12916-024-03258-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 01/15/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Mammography screening programmes (MSP) aim to reduce breast cancer mortality by shifting diagnoses to earlier stages. However, it is difficult to evaluate the effectiveness of current MSP because analyses can only rely on observational data, comparing women who participate in screening with women who do not. These comparisons are subject to several biases: one of the most important is self-selection into the MSP, which introduces confounding and is difficult to control for. Here, we propose an approach to quantify confounding based on breast cancer survival analyses using readily available routine data sources. METHODS Using data from the Cancer Registry of North Rhine-Westphalia, Germany, we estimate the relative contribution of confounding to the observed survival benefit of participants of the German MSP. This is accomplished by comparing non-participants, participants with screen-detected and participants with interval breast cancers for the endpoints "death from breast cancer" and "death from all causes other than breast cancer" - the latter being assumed to be unrelated to any MSP effect. By using different contrasts, we eliminate the effects of stage shift, lead and length time bias. The association of breast cancer detection mode with survival is analysed using Cox models in 68,230 women, aged 50-69 years, with breast cancer diagnosed in 2006-2014 and followed up until 2018. RESULTS The hazard of dying from breast cancer was lower in participants with screen-detected cancer than in non-participants (HR = 0.21, 95% CI: 0.20-0.22), but biased by lead and length time bias, and confounding. When comparing participants with interval cancers and non-participants, the survival advantage was considerably smaller (HR = 0.62, 95% CI: 0.58-0.66), due to the elimination of stage shift and lead time bias. Finally, considering only mortality from causes other than breast cancer in the latter comparison, length time bias was minimised, but a survival advantage was still present (HR = 0.63, 95% CI: 0.56-0.70), which we attribute to confounding. CONCLUSIONS This study shows that, in addition to stage shift, lead and length time bias, confounding is an essential component when comparing the survival of MSP participants and non-participants. We further show that the confounding effect can be quantified without explicit knowledge of potential confounders by using a negative control outcome.
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Affiliation(s)
- Laura Buschmann
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
| | - Ina Wellmann
- Cancer Registry of North Rhine-Westphalia, Bochum, Germany
| | - Nadine Bonberg
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Jürgen Wellmann
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Hans-Werner Hense
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Heike Minnerup
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
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22
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Pulido-Carmona C, Romero-Martín S, Raya-Povedano JL, Cara-García M, Font-Ugalde P, Elías-Cabot E, Pedrosa-Garriguet M, Álvarez-Benito M. Interval cancer in the Córdoba Breast Tomosynthesis Screening Trial (CBTST): comparison of digital breast tomosynthesis plus digital mammography to digital mammography alone. Eur Radiol 2024:10.1007/s00330-023-10546-x. [PMID: 38177619 DOI: 10.1007/s00330-023-10546-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 11/17/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024]
Abstract
PURPOSE This work aims to compare the interval cancer rate and interval cancer characteristics between women screened with digital breast tomosynthesis (DBT) + digital mammography (DM) and those screened with DM alone. METHODS The interval cancer rate and interval cancer characteristics of the study population included in the Córdoba Breast Tomosynthesis Screening Trial (CBTST) were compared to a contemporary control population screened with DM. The tumour characteristics of screen-detected and interval cancers were also compared. Contingency tables were used to compare interval cancer rates. The chi-square test and Fisher's exact test were used to compare the qualitative characteristics of the cancers whereas Student's t test and the Mann-Whitney U test were used to analyse quantitative features. RESULTS A total of 16,068 screening exams with DBT + DM were conducted within the CBTST (mean age 57.59 ± 5.9 [SD]) between January 2015 and December 2016 (study population). In parallel, 23,787 women (mean age 58.89 ± 5.9 standard deviation [SD]) were screened with DM (control population). The interval cancer rate was lower in the study population than in the control population (15 [0.93‰; 95% confidence interval (CI): 0.73, 1.14] vs 43 [1.8‰; 95% CI: 1.58, 2.04] respectively; p = 0.045). The difference in rate was more marked in women with dense breasts (0.95‰ in the study population vs 3.17‰ in the control population; p = 0.031). Interval cancers were smaller in the study population than in the control population (p = 0.031). CONCLUSIONS The interval cancer rate was lower in women screened with DBT + DM compared to those screened with DM alone. These differences were more pronounced in women with dense breasts. CLINICAL RELEVANCE STATEMENT Women screened using tomosynthesis and digital mammography had a lower rate of interval cancer than women screened with digital mammography, with the greatest difference in the interval cancer rate observed in women with dense breasts. KEY POINTS • The interval cancer rate was lower in the study population (digital breast tomosynthesis [DBT] + digital mammography [DM]) than in the control population (DM). • The difference in interval cancer rates was more pronounced in women with dense breasts. • Interval cancers were smaller in the study population (DBT + DM) than in the control population (DM).
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Affiliation(s)
- Cristina Pulido-Carmona
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain.
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Avenida Menéndez Pidal s/n, 14004, Córdoba, Spain.
- University of Córdoba, Córdoba, Spain.
| | - Sara Romero-Martín
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Avenida Menéndez Pidal s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
| | - José Luis Raya-Povedano
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Avenida Menéndez Pidal s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
| | - María Cara-García
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Avenida Menéndez Pidal s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
| | - Pilar Font-Ugalde
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- University of Córdoba, Córdoba, Spain
- Rheumatology Department, Reina Sofía University Hospital, Avenida Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - Esperanza Elías-Cabot
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Avenida Menéndez Pidal s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
| | - Margarita Pedrosa-Garriguet
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Avenida Menéndez Pidal s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
| | - Marina Álvarez-Benito
- Maimónides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Breast Cancer Unit, Department of Diagnostic Radiology, Reina Sofía University Hospital, Avenida Menéndez Pidal s/n, 14004, Córdoba, Spain
- University of Córdoba, Córdoba, Spain
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23
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Elhakim MT, Stougaard SW, Graumann O, Nielsen M, Lång K, Gerke O, Larsen LB, Rasmussen BSB. Breast cancer detection accuracy of AI in an entire screening population: a retrospective, multicentre study. Cancer Imaging 2023; 23:127. [PMID: 38124111 PMCID: PMC10731688 DOI: 10.1186/s40644-023-00643-x] [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: 05/24/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish screening population. METHODS We retrieved a consecutive screening cohort from the Region of Southern Denmark including all participating women between Aug 4, 2014, and August 15, 2018. Screening mammograms were processed by a commercial AI system and detection accuracy was evaluated in two scenarios, Standalone AI and AI-integrated screening replacing first reader, with first reader and double reading with arbitration (combined reading) as comparators, respectively. Two AI-score cut-off points were applied by matching at mean first reader sensitivity (AIsens) and specificity (AIspec). Reference standard was histopathology-proven breast cancer or cancer-free follow-up within 24 months. Coprimary endpoints were sensitivity and specificity, and secondary endpoints were positive predictive value (PPV), negative predictive value (NPV), recall rate, and arbitration rate. Accuracy estimates were calculated using McNemar's test or exact binomial test. RESULTS Out of 272,008 screening mammograms from 158,732 women, 257,671 (94.7%) with adequate image data were included in the final analyses. Sensitivity and specificity were 63.7% (95% CI 61.6%-65.8%) and 97.8% (97.7-97.8%) for first reader, and 73.9% (72.0-75.8%) and 97.9% (97.9-98.0%) for combined reading, respectively. Standalone AIsens showed a lower specificity (-1.3%) and PPV (-6.1%), and a higher recall rate (+ 1.3%) compared to first reader (p < 0.0001 for all), while Standalone AIspec had a lower sensitivity (-5.1%; p < 0.0001), PPV (-1.3%; p = 0.01) and NPV (-0.04%; p = 0.0002). Compared to combined reading, Integrated AIsens achieved higher sensitivity (+ 2.3%; p = 0.0004), but lower specificity (-0.6%) and PPV (-3.9%) as well as higher recall rate (+ 0.6%) and arbitration rate (+ 2.2%; p < 0.0001 for all). Integrated AIspec showed no significant difference in any outcome measures apart from a slightly higher arbitration rate (p < 0.0001). Subgroup analyses showed higher detection of interval cancers by Standalone AI and Integrated AI at both thresholds (p < 0.0001 for all) with a varying composition of detected cancers across multiple subgroups of tumour characteristics. CONCLUSIONS Replacing first reader in double reading with an AI could be feasible but choosing an appropriate AI threshold is crucial to maintaining cancer detection accuracy and workload.
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Affiliation(s)
- Mohammad Talal Elhakim
- Department of Radiology, Odense University Hospital, Kløvervaenget 47, Entrance 27, Ground floor, 5000, Odense C, Denmark.
- Department of Clinical Research, University of Southern Denmark, Kløvervaenget 10, Entrance 112, 2nd floor, 5000, Odense C, Denmark.
| | - Sarah Wordenskjold Stougaard
- Department of Clinical Research, University of Southern Denmark, Kløvervaenget 10, Entrance 112, 2nd floor, 5000, Odense C, Denmark
| | - Ole Graumann
- Department of Clinical Research, University of Southern Denmark, Kløvervaenget 10, Entrance 112, 2nd floor, 5000, Odense C, Denmark
- Department of Radiology, Aarhus University Hospital, Palle Juul-Jensens Blvd. 99, 8200, Aarhus N, Denmark
- Department of Clinical Research, Aarhus University, Palle Juul-Jensens Blvd. 99, 8200, Aarhus N, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100, København Ø, Denmark
| | - Kristina Lång
- Department of Translational Medicine, Lund University, Inga Maria Nilssons gata 47, SE-20502, Malmö, Sweden
- Unilabs Mammography Unit, Skåne University Hospital, Jan Waldenströms gata 22, SE-20502, Malmö, Sweden
| | - Oke Gerke
- Department of Clinical Research, University of Southern Denmark, Kløvervaenget 10, Entrance 112, 2nd floor, 5000, Odense C, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Kløvervaenget 47, Entrance 44, 5000, Odense C, Denmark
| | - Lisbet Brønsro Larsen
- Department of Radiology, Odense University Hospital, Kløvervaenget 47, Entrance 27, Ground floor, 5000, Odense C, Denmark
| | - Benjamin Schnack Brandt Rasmussen
- Department of Radiology, Odense University Hospital, Kløvervaenget 47, Entrance 27, Ground floor, 5000, Odense C, Denmark
- Department of Clinical Research, University of Southern Denmark, Kløvervaenget 10, Entrance 112, 2nd floor, 5000, Odense C, Denmark
- CAI-X - Centre for Clinical Artificial Intelligence, Odense University Hospital, Kløvervaenget 8C, Entrance 102, 5000, Odense C, Denmark
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24
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Şendur HN, Şendur AB. The Distinction Between Interval and Missed Breast Cancer Requires Re-evaluation of Prior Imaging. Acad Radiol 2023; 30:3166. [PMID: 37858504 DOI: 10.1016/j.acra.2023.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 02/11/2023] [Accepted: 09/18/2023] [Indexed: 10/21/2023]
Affiliation(s)
- Halit Nahit Şendur
- Gazi University Faculty of Medicine, Department of Radiology, Mevlana Bulvarı No:29 06560, Yenimahalle, Ankara, Turkey (H.N.Ş.).
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25
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Duijm LEM, Strobbe LJA, van Breest Smallenburg V, Op de Coul-Froger CL, Setz-Pels W, Vreuls W, van Beek HC, van Bommel RMG, Voogd AC. Trends in the pre-operative diagnosis and surgical management of axillary lymph node metastases in women with screen-detected breast cancer. Breast 2023; 72:103593. [PMID: 37890215 PMCID: PMC10624574 DOI: 10.1016/j.breast.2023.103593] [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] [Received: 08/02/2023] [Revised: 10/14/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
AIM The aim of the current study was to investigate time-trends in pre-operative diagnosis and surgical treatment of axillary lymph node metastases in breast cancers detected at screening mammography. METHODS We included all women who underwent screening mammography in the South of the Netherlands between 2005 and 2020. During a follow-up period of at least two years, data on clinical radiological examinations, biopsy procedures and surgical interventions were obtained. The 15 years of inclusion were divided into five cohorts of three years each. RESULTS Of the 4049 women with invasive breast cancer, 22.1 % (896/4049) had axillary lymph node metastasis at pathology (ALN+). Percutaneous axillary biopsy was performed in 39.6 % (355/896) of these women, with the proportions of fine needle aspiration biopsy (FNAB) decreasing from 97.6 % (40/41) in 2005-2007 to 41.6 % (37/89) in 2017-2019 and core needle biopsy (CNB) rising from 2.4 % (1/41) in 2005-2007 to 58.4 % (52/89) in 2017-2019 (P < 0.001). Sensitivity of FNAB and CNB was comparable (77.4 % (188/243, 95%CI = 71%-82 %) versus 82.4 % (103/125), 95%CI = 74%-88 %) (P = 0.26). Pre-operative confirmation of ALN + by percutaneous biopsy ranged from 27.3 % (56/205) in 2011-2013 to 39.0 % (80/205) in 2017-2019, with no significant trend changes over time (P = 0.103). The proportion of ALN + women who underwent axillary lymph node dissection (ALND) decreased from 96.0 % (97/101) in 2005-2007 to 16.6 % (34/205) in 2017-2019 (P < 0.001). CONCLUSION Pre-operative confirmation of axillary lymph node metastasis by ultrasound-guided biopsy did not rise despite the increased use of CNB at the expense of less invasive FNAB. A significant reduction in ALND was observed through the years.
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Affiliation(s)
- Lucien E M Duijm
- Department of Radiology, Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532, SZ, Nijmegen, the Netherlands
| | - Luc J A Strobbe
- Department of Surgical Oncology, Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532, SZ, Nijmegen, the Netherlands
| | | | | | - Wikke Setz-Pels
- Department of Radiology, Catharina Hospital, Michelangelolaan 2, 5623, EJ, Eindhoven, the Netherlands
| | - Willem Vreuls
- Department of Pathology, Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532, SZ, Nijmegen, the Netherlands
| | - Hermen C van Beek
- Department of Radiology, Maxima Medical Center, De Run 4600, 5504, DB Veldhoven, the Netherlands
| | - Rob M G van Bommel
- Department of Radiology, St Anna Hospital, Bogardeind 2, 5664, EH, Geldrop, the Netherlands
| | - Adri C Voogd
- Department of Epidemiology, Maastricht University, PO Box 616, 6200, MD, Maastricht, the Netherlands.
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26
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Ng AY, Oberije CJG, Ambrózay É, Szabó E, Serfőző O, Karpati E, Fox G, Glocker B, Morris EA, Forrai G, Kecskemethy PD. Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer. Nat Med 2023; 29:3044-3049. [PMID: 37973948 PMCID: PMC10719086 DOI: 10.1038/s41591-023-02625-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/04/2023] [Indexed: 11/19/2023]
Abstract
Artificial intelligence (AI) has the potential to improve breast cancer screening; however, prospective evidence of the safe implementation of AI into real clinical practice is limited. A commercially available AI system was implemented as an additional reader to standard double reading to flag cases for further arbitration review among screened women. Performance was assessed prospectively in three phases: a single-center pilot rollout, a wider multicenter pilot rollout and a full live rollout. The results showed that, compared to double reading, implementing the AI-assisted additional-reader process could achieve 0.7-1.6 additional cancer detection per 1,000 cases, with 0.16-0.30% additional recalls, 0-0.23% unnecessary recalls and a 0.1-1.9% increase in positive predictive value (PPV) after 7-11% additional human reads of AI-flagged cases (equating to 4-6% additional overall reading workload). The majority of cancerous cases detected by the AI-assisted additional-reader process were invasive (83.3%) and small-sized (≤10 mm, 47.0%). This evaluation suggests that using AI as an additional reader can improve the early detection of breast cancer with relevant prognostic features, with minimal to no unnecessary recalls. Although the AI-assisted additional-reader workflow requires additional reads, the higher PPV suggests that it can increase screening effectiveness.
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Affiliation(s)
- Annie Y Ng
- Kheiron Medical Technologies, London, UK.
| | | | | | | | | | | | | | - Ben Glocker
- Kheiron Medical Technologies, London, UK
- Department of Computing, Imperial College London, London, UK
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27
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Eijkelboom AH, de Munck L, Larsen M, Bijlsma MJ, Tjan-Heijnen VCG, van Gils CH, Broeders MJM, Nygård JF, Lobbes MBI, Helsper CW, Pijnappel RM, Strobbe LJA, Wesseling J, Hofvind S, Siesling S. Impact of the COVID-19 pandemic on breast cancer incidence and tumor stage in the Netherlands and Norway: A population-based study. Cancer Epidemiol 2023; 87:102481. [PMID: 37897970 DOI: 10.1016/j.canep.2023.102481] [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] [Received: 07/17/2023] [Revised: 10/05/2023] [Accepted: 10/19/2023] [Indexed: 10/30/2023]
Abstract
BACKGROUND Comparing the impact of the COVID-19 pandemic on the incidence of newly diagnosed breast tumors and their tumor stage between the Netherlands and Norway will help us understand the effect of differences in governmental and social reactions towards the pandemic. METHODS Women newly diagnosed with breast cancer in 2017-2021 were selected from the Netherlands Cancer Registry and the Cancer Registry of Norway. The crude breast cancer incidence rate (tumors per 100,000 women) during the first (March-September 2020), second (October 2020-April 2021), and Delta COVID-19 wave (May-December 2021) was compared with the incidence rate in the corresponding periods in 2017, 2018, and 2019. Incidence rates were stratified by age group, method of detection, and clinical tumor stage. RESULTS During the first wave breast cancer incidence declined to a larger extent in the Netherlands than in Norway (27.7% vs. 17.2% decrease, respectively). In both countries, incidence decreased in women eligible for screening. In the Netherlands, incidence also decreased in women not eligible for screening. During the second wave an increase in the incidence of stage IV tumors in women aged 50-69 years was seen in the Netherlands. During the Delta wave an increase in overall incidence and incidence of stage I tumors was seen in Norway. CONCLUSION Alterations in breast cancer incidence and tumor stage seem related to a combined effect of the suspension of the screening program, health care avoidance due to the severity of the pandemic, and other unknown factors.
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Affiliation(s)
- Anouk H Eijkelboom
- Department of Health Technology and Services Research, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, the Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Godebaldkwartier 419, 3511 DT Utrecht, the Netherlands.
| | - Linda de Munck
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Godebaldkwartier 419, 3511 DT Utrecht, the Netherlands
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway
| | - Maarten J Bijlsma
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Godebaldkwartier 419, 3511 DT Utrecht, the Netherlands; PharmacoTherapy, -Epidemiology and -Economics, Groningen Research Institute of Pharmacy, University of Groningen, P.O. Box 196, 9700 AD Groningen, the Netherlands
| | - Vivianne C G Tjan-Heijnen
- Department of Medical Oncology, School for Oncology and Reproduction (GROW), Maastricht University Medical Centre, P. Debyelaan 25, 6229 HX Maastricht, the Netherlands
| | - Carla H van Gils
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG Utrecht, the Netherlands
| | - Mireille J M Broeders
- Department for Health Evidence, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands; Dutch Expert Centre for Screening, Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands
| | - Jan F Nygård
- Department of Register Informatics, Cancer Registry Norway, P.O. Box 5313, 0304 Oslo, Norway
| | - Marc B I Lobbes
- Department of Medical Imaging, Zuyderland Medical Center Sittard-Geleen, Dr. H. van der Hoffplein 1, 6162 BG Sittard-Geleen, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, 6229 HX Maastricht, the Netherlands; School for Oncology and Reproduction (GROW), Maastricht University Medical Centre, Universiteitssingel 40, 6220 ER, Maastricht, the Netherlands
| | - Charles W Helsper
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CG, Utrecht, the Netherlands
| | - Ruud M Pijnappel
- Dutch Expert Centre for Screening, Wijchenseweg 101, 6538 SW, Nijmegen, the Netherlands; Department of Radiology, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - Luc J A Strobbe
- Department of Surgical Oncology, Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532 SZ, Nijmegen, the Netherlands
| | - Jelle Wesseling
- Divisions of Diagnostic Oncology and Molecular Pathology, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands; Department of Pathology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Health and Care Sciences, UiT The Arctic University of Norway, P.O. 6050, 9037 Tromsø, Norway
| | - Sabine Siesling
- Department of Health Technology and Services Research, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, the Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Godebaldkwartier 419, 3511 DT Utrecht, the Netherlands
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Eftekharian M, Nodehi A, Enayatifar R. ML-DSTnet: A Novel Hybrid Model for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning and Dempster-Shafer Theory. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:7510419. [PMID: 37954096 PMCID: PMC10635746 DOI: 10.1155/2023/7510419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/18/2022] [Accepted: 04/25/2023] [Indexed: 11/14/2023]
Abstract
Medical intelligence detection systems have changed with the help of artificial intelligence and have also faced challenges. Breast cancer diagnosis and classification are part of this medical intelligence system. Early detection can lead to an increase in treatment options. On the other hand, uncertainty is a case that has always been with the decision-maker. The system's parameters cannot be accurately estimated, and the wrong decision is made. To solve this problem, we have proposed a method in this article that reduces the ignorance of the problem with the help of Dempster-Shafer theory so that we can make a better decision. This research on the MIAS dataset, based on image processing machine learning and Dempster-Shafer mathematical theory, tries to improve the diagnosis and classification of benign, malignant masses. We first determine the results of the diagnosis of mass type with MLP by using the texture feature and CNN. We combine the results of the two classifications with Dempster-Shafer theory and improve its accuracy. The obtained results show that the proposed approach has better performance than others based on evaluation criteria such as accuracy of 99.10%, sensitivity of 98.4%, and specificity of 100%.
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Affiliation(s)
- Mohsen Eftekharian
- Department of Computer Engineering, Gorgan Branch, Islamic Azad University, Gorgan, Iran
| | - Ali Nodehi
- Department of Computer Engineering, Gorgan Branch, Islamic Azad University, Gorgan, Iran
| | - Rasul Enayatifar
- Department of Computer Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
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29
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Mehta TS. Leveraging Differences in AI and Human "Vision" to Improve Breast Cancer Detection. Radiology 2023; 309:e232520. [PMID: 37847134 DOI: 10.1148/radiol.232520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Affiliation(s)
- Tejas S Mehta
- From the Department of Radiology, UMass Memorial Medical Center, UMass Chan Medical School, 55 Lake Ave N, Worcester, MA 01655
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30
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Arıbal E. Future of Breast Radiology. Eur J Breast Health 2023; 19:262-266. [PMID: 37795010 PMCID: PMC10546805 DOI: 10.4274/ejbh.galenos.2023.2023-8-3] [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: 08/22/2023] [Accepted: 09/15/2023] [Indexed: 10/06/2023]
Abstract
The landscape of breast imaging has transformed significantly since mammography's introduction in the 1960s, accelerated by ultrasound and imageguided biopsies in the 1990s. The emergence of magnetic resonance imaging (MRI) in the 2000s added a valuable dimension to advanced imaging. Multimodality and multiparametric imaging have firmly established breast radiology's pivotal role in managing breast disorders. A shift from conventional to digital radiology emerged in the late 20th and early 21st centuries, enabling advanced techniques like digital breast tomosynthesis, contrast-enhanced mammography, and artificial intelligence (AI) integration. AI's impending integration into breast radiology may enhance diagnostics and workflows. It involves computer-aided diagnosis (CAD) algorithms, workflow support algorithms, and data processing algorithms. CAD systems, developed since the 1980s, optimize cancer detection rates by addressing false positives and negatives. Radiologists' roles will evolve into specialized clinicians collaborating with AI for efficient patient care and utilizing advanced techniques with multiparametric imaging and radiomics. Wearable technologies, non-contrast MRI, and innovative modalities like photoacoustic imaging show potential to enhance diagnostics. Imaging-guided therapy, notably cryotherapy, and theranostics, gains traction. Theranostics, integrating therapy and diagnostics, holds potential for precise treatment. Advanced imaging, AI, and novel therapies will revolutionize breast radiology, offering refined diagnostics and personalized treatments. Personalized screening, AI's role, and imaging-guided therapies will shape the future of breast radiology.
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Affiliation(s)
- Erkin Arıbal
- Acıbadem University Faculty of Medicine, Department of Radiology, İstanbul, Turkey
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31
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Kim H, Choi JS, Kim K, Ko ES, Ko EY, Han BK. Effect of artificial intelligence-based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study. Eur Radiol 2023; 33:7186-7198. [PMID: 37188881 DOI: 10.1007/s00330-023-09692-z] [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] [Received: 06/21/2022] [Revised: 02/21/2023] [Accepted: 03/09/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE To investigate whether artificial intelligence-based computer-aided diagnosis (AI-CAD) can improve radiologists' performance when used to support radiologists' interpretation of digital mammography (DM) in breast cancer screening. METHODS A retrospective database search identified 3158 asymptomatic Korean women who consecutively underwent screening DM between January and December 2019 without AI-CAD support, and screening DM between February and July 2020 with image interpretation aided by AI-CAD in a tertiary referral hospital using single reading. Propensity score matching was used to match the DM with AI-CAD group in a 1:1 ratio with the DM without AI-CAD group according to age, breast density, experience level of the interpreting radiologist, and screening round. Performance measures were compared with the McNemar test and generalized estimating equations. RESULTS A total of 1579 women who underwent DM with AI-CAD were matched with 1579 women who underwent DM without AI-CAD. Radiologists showed higher specificity (96% [1500 of 1563] vs 91.6% [1430 of 1561]; p < 0.001) and lower abnormal interpretation rates (AIR) (4.9% [77 of 1579] vs 9.2% [145 of 1579]; p < 0.001) with AI-CAD than without. There was no significant difference in the cancer detection rate (CDR) (AI-CAD vs no AI-CAD, 8.9 vs 8.9 per 1000 examinations; p = 0.999), sensitivity (87.5% vs 77.8%; p = 0.999), and positive predictive value for biopsy (PPV3) (35.0% vs 35.0%; p = 0.999) according to AI-CAD support. CONCLUSIONS AI-CAD increases the specificity for radiologists without decreasing sensitivity as a supportive tool in the single reading of DM for breast cancer screening. CLINICAL RELEVANCE STATEMENT This study shows that AI-CAD could improve the specificity of radiologists' DM interpretation in the single reading system without decreasing sensitivity, suggesting that it can benefit patients by reducing false positive and recall rates. KEY POINTS • In this retrospective-matched cohort study (DM without AI-CAD vs DM with AI-CAD), radiologists showed higher specificity and lower AIR when AI-CAD was used to support decision-making in DM screening. • CDR, sensitivity, and PPV for biopsy did not differ with and without AI-CAD support.
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Affiliation(s)
- Haejung Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Ji Soo Choi
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.
| | - Kyunga Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Eun Sook Ko
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Eun Young Ko
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Boo-Kyung Han
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
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32
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Larsen M, Olstad CF, Koch HW, Martiniussen MA, Hoff SR, Lund-Hanssen H, Solli HS, Mikalsen KØ, Auensen S, Nygård J, Lång K, Chen Y, Hofvind S. AI Risk Score on Screening Mammograms Preceding Breast Cancer Diagnosis. Radiology 2023; 309:e230989. [PMID: 37847135 DOI: 10.1148/radiol.230989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
Background Few studies have evaluated the role of artificial intelligence (AI) in prior screening mammography. Purpose To examine AI risk scores assigned to screening mammography in women who were later diagnosed with breast cancer. Materials and Methods Image data and screening information of examinations performed from January 2004 to December 2019 as part of BreastScreen Norway were used in this retrospective study. Prior screening examinations from women who were later diagnosed with cancer were assigned an AI risk score by a commercially available AI system (scores of 1-7, low risk of malignancy; 8-9, intermediate risk; and 10, high risk of malignancy). Mammographic features of the cancers based on the AI score were also assessed. The association between AI score and mammographic features was tested with a bivariate test. Results A total of 2787 prior screening examinations from 1602 women (mean age, 59 years ± 5.1 [SD]) with screen-detected (n = 1016) or interval (n = 586) cancers showed an AI risk score of 10 for 389 (38.3%) and 231 (39.4%) cancers, respectively, on the mammograms in the screening round prior to diagnosis. Among the screen-detected cancers with AI scores available two screening rounds (4 years) before diagnosis, 23.0% (122 of 531) had a score of 10. Mammographic features were associated with AI score for invasive screen-detected cancers (P < .001). Density with calcifications was registered for 13.6% (43 of 317) of screen-detected cases with a score of 10 and 4.6% (15 of 322) for those with a score of 1-7. Conclusion More than one in three cases of screen-detected and interval cancers had the highest AI risk score at prior screening, suggesting that the use of AI in mammography screening may lead to earlier detection of breast cancers. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Mehta in this issue.
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Affiliation(s)
- Marthe Larsen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Camilla F Olstad
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Henrik W Koch
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Marit A Martiniussen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Solveig R Hoff
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Håkon Lund-Hanssen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Helene S Solli
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Karl Øyvind Mikalsen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Steinar Auensen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Jan Nygård
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Kristina Lång
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Yan Chen
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
| | - Solveig Hofvind
- From the Section for Breast Cancer Screening (M.L., C.F.O., S.H.) and Department of Register Informatics (S.A., J.N.), Cancer Registry of Norway, P.O. Box 5313, 0304 Oslo, Norway; Department of Radiology, Stavanger University Hospital, Stavanger, Norway (H.W.K.); Faculty of Health Sciences, University of Stavanger, Stavanger, Norway (H.W.K.); Department of Radiology, Østfold Hospital Trust, Kalnes, Norway (M.A.M.); Institute of Clinical Medicine, University of Oslo, Oslo, Norway (M.A.M.); Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway (S.R.H.); Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, National University for Science and Technology, Trondheim, Norway (S.R.H.); Department of Radiology and Nuclear Medicine, St Olavs University Hospital, Trondheim, Norway (H.L.H.); Department of Radiology, Hospital of Southern Norway, Kristiansand, Norway (H.S.S.); SPKI-The Norwegian Centre for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway (K.Ø.M.); Department of Clinical Medicine (K.Ø.M.) and Health and Care Sciences (S.H.), Faculty of Health Sciences, UiT-The Arctic University of Norway, Tromsø, Norway; Department of Translational Medicine, Diagnostic Radiology, Lund University, Lund, Sweden (K.L.); Unilabs Mammography Unit, Skåne University Hospital, Malmø, Sweden (K.L.); School of Medicine, University of Nottingham, Clinical Science Building, Nottingham City Hospital, Nottingham, United Kingdom (Y.C.)
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Wu S, Liang D, Shi J, Li D, Liu Y, Hao Y, Shi M, Du X, He Y. Evaluation of a population-based breast cancer screening in North China. J Cancer Res Clin Oncol 2023; 149:10119-10130. [PMID: 37266660 PMCID: PMC10423103 DOI: 10.1007/s00432-023-04905-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/20/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND Despite mammography-based screening for breast cancer has been conducted in many countries, there are still little data on participation and diagnostic yield in population-based breast cancer screening in China. METHODS We enrolled 151,973 eligible women from four cities in Hebei Province within the period 2013-2021 and followed up until December 31, 2021. Participants aged 40-74 who assessed as high risk were invited to undergo breast ultrasound and mammography examination. Overall and group-specific participation rates were calculated. Multivariable analyses were used to estimate the factors associated with participation rates. The diagnostic yield of both screening and no screening groups was calculated. We further analyzed the stage distribution and molecular subtype of breast cancer cases by different modes of cancer detection. RESULTS A total of 42,547 participants were evaluated to be high risk of breast cancer. Among them, 23,009 subjects undertook screening services, with participation rate of 54.08%. Multivariable logistic regression model showed that aged 45-64, high education level, postmenopausal, current smoking, alcohol consumption, family history of breast cancer, and benign breast disease were associated with increased participation of screening. After median follow-up of 3.79 years, there were 456 breast cancer diagnoses of which 65 were screen-detected breast cancers (SBCs), 27 were interval breast cancers (IBCs), 68 were no screening cancers, and 296 were cancers detected outside the screening program. Among them, 92 participants in the screening group (0.40%) and 364 in the non-screening group (0.28%) had breast cancer detected, which resulted in an odds ratio of 1.42 (95% CI 1.13-1.78; P = 0.003). We observed a higher detection rate of breast cancer in the screening group, with ORs of 2.42 (95% CI 1.72-3.41) for early stage (stages 0-I) and 2.12 (95% CI 1.26-3.54) for luminal A subtype. SBCs had higher proportion of early stage (71.93%) and luminal A subtype (47.22%) than other groups. CONCLUSIONS The significant differences in breast cancer diagnosis between the screening and non-screening group imply an urgent need for increased breast cancer awareness and early detection in China.
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Affiliation(s)
- Siqi Wu
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China
| | - Di Liang
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China
| | - Jin Shi
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China
| | - Daojuan Li
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China
| | - Yanyu Liu
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China
| | - Yahui Hao
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China
| | - Miaomiao Shi
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China
| | - Xinyu Du
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China
| | - Yutong He
- Cancer Institute, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital, No. 12 Jian Kang Road, Changan District, Shijiazhuang, 050011, Hebei, China.
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Lång K, Josefsson V, Larsson AM, Larsson S, Högberg C, Sartor H, Hofvind S, Andersson I, Rosso A. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol 2023; 24:936-944. [PMID: 37541274 DOI: 10.1016/s1470-2045(23)00298-x] [Citation(s) in RCA: 78] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/07/2023] [Accepted: 06/21/2023] [Indexed: 08/06/2023]
Abstract
BACKGROUND Retrospective studies have shown promising results using artificial intelligence (AI) to improve mammography screening accuracy and reduce screen-reading workload; however, to our knowledge, a randomised trial has not yet been conducted. We aimed to assess the clinical safety of an AI-supported screen-reading protocol compared with standard screen reading by radiologists following mammography. METHODS In this randomised, controlled, population-based trial, women aged 40-80 years eligible for mammography screening (including general screening with 1·5-2-year intervals and annual screening for those with moderate hereditary risk of breast cancer or a history of breast cancer) at four screening sites in Sweden were informed about the study as part of the screening invitation. Those who did not opt out were randomly allocated (1:1) to AI-supported screening (intervention group) or standard double reading without AI (control group). Screening examinations were automatically randomised by the Picture Archive and Communications System with a pseudo-random number generator after image acquisition. The participants and the radiographers acquiring the screening examinations, but not the radiologists reading the screening examinations, were masked to study group allocation. The AI system (Transpara version 1.7.0) provided an examination-based malignancy risk score on a 10-level scale that was used to triage screening examinations to single reading (score 1-9) or double reading (score 10), with AI risk scores (for all examinations) and computer-aided detection marks (for examinations with risk score 8-10) available to the radiologists doing the screen reading. Here we report the prespecified clinical safety analysis, to be done after 80 000 women were enrolled, to assess the secondary outcome measures of early screening performance (cancer detection rate, recall rate, false positive rate, positive predictive value [PPV] of recall, and type of cancer detected [invasive or in situ]) and screen-reading workload. Analyses were done in the modified intention-to-treat population (ie, all women randomly assigned to a group with one complete screening examination, excluding women recalled due to enlarged lymph nodes diagnosed with lymphoma). The lowest acceptable limit for safety in the intervention group was a cancer detection rate of more than 3 per 1000 participants screened. The trial is registered with ClinicalTrials.gov, NCT04838756, and is closed to accrual; follow-up is ongoing to assess the primary endpoint of the trial, interval cancer rate. FINDINGS Between April 12, 2021, and July 28, 2022, 80 033 women were randomly assigned to AI-supported screening (n=40 003) or double reading without AI (n=40 030). 13 women were excluded from the analysis. The median age was 54·0 years (IQR 46·7-63·9). Race and ethnicity data were not collected. AI-supported screening among 39 996 participants resulted in 244 screen-detected cancers, 861 recalls, and a total of 46 345 screen readings. Standard screening among 40 024 participants resulted in 203 screen-detected cancers, 817 recalls, and a total of 83 231 screen readings. Cancer detection rates were 6·1 (95% CI 5·4-6·9) per 1000 screened participants in the intervention group, above the lowest acceptable limit for safety, and 5·1 (4·4-5·8) per 1000 in the control group-a ratio of 1·2 (95% CI 1·0-1·5; p=0·052). Recall rates were 2·2% (95% CI 2·0-2·3) in the intervention group and 2·0% (1·9-2·2) in the control group. The false positive rate was 1·5% (95% CI 1·4-1·7) in both groups. The PPV of recall was 28·3% (95% CI 25·3-31·5) in the intervention group and 24·8% (21·9-28·0) in the control group. In the intervention group, 184 (75%) of 244 cancers detected were invasive and 60 (25%) were in situ; in the control group, 165 (81%) of 203 cancers were invasive and 38 (19%) were in situ. The screen-reading workload was reduced by 44·3% using AI. INTERPRETATION AI-supported mammography screening resulted in a similar cancer detection rate compared with standard double reading, with a substantially lower screen-reading workload, indicating that the use of AI in mammography screening is safe. The trial was thus not halted and the primary endpoint of interval cancer rate will be assessed in 100 000 enrolled participants after 2-years of follow up. FUNDING Swedish Cancer Society, Confederation of Regional Cancer Centres, and the Swedish governmental funding for clinical research (ALF).
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Affiliation(s)
- Kristina Lång
- Division of Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden; Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden.
| | - Viktoria Josefsson
- Division of Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden; Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden
| | - Anna-Maria Larsson
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Stefan Larsson
- Department of Technology and Society, Lund University, Lund, Sweden
| | | | - Hanna Sartor
- Division of Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden; Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway; Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway
| | - Ingvar Andersson
- Division of Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden; Unilabs Mammography Unit, Skåne University Hospital, Malmö, Sweden
| | - Aldana Rosso
- Division of Diagnostic Radiology, Department of Translational Medicine, Lund University, Malmö, Sweden
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Hovda T, Larsen M, Romundstad L, Sahlberg KK, Hofvind S. Breast cancer missed at screening; hindsight or mistakes? Eur J Radiol 2023; 165:110913. [PMID: 37311339 DOI: 10.1016/j.ejrad.2023.110913] [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: 02/04/2023] [Revised: 04/01/2023] [Accepted: 05/31/2023] [Indexed: 06/15/2023]
Abstract
PURPOSE To investigate radiologists' interpretation scores of screening mammograms prior to diagnosis of screen-detected and interval breast cancers retrospectively classified as missed or true negative. METHODS We included data on radiologists' interpretation scores at screening prior to diagnosis for 1223 screen-detected and 1007 interval cancer cases classified as missed or true negative in an informed consensus-based review. All prior screening examinations were independently scored 1-5 by two radiologists; score 1 by both was considered concordant negative, score ≥ 2 by one radiologist discordant, and score ≥ 2 by both concordant positive. We analyzed associations between interpretation, review categories, mammographic features and histopathological findings using descriptive statistics and logistic regression. RESULTS Among screen-detected cancers, 31% of missed and 10% of true negative cancers had discordant or concordant positive interpretation at prior screening. The corresponding percentages for interval cancer were 21% and 8%. Age-adjusted odds ratio (OR) and 95% confidence interval (CI) for missed screen-detected cancer was 3.8 (95% CI: 2.6-5.4) after discordant and 5.5 (95% CI: 3.2-9.5) after concordant positive interpretation, using concordant negative as reference. Corresponding ORs for missed interval cancer were 3.0 (95% CI: 2.0-4.5) for discordant and 6.3 (95% CI: 2.3-17.5) for concordant positive interpretation. Asymmetry was the dominating mammographic feature at prior screening for all, except concordant positive screen-detected cancers where a mass dominated. Histopathological characteristics did not vary statistically with interpretation. CONCLUSIONS Most cancers were interpreted negatively at screening prior to diagnosis. Increased risk for missed screen-detected or interval cancer was observed after positive interpretation at prior screening.
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Affiliation(s)
- Tone Hovda
- Department of Radiology, Vestre Viken Hospital Trust, PO Box 800, 3004 Drammen, Norway.
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - Linda Romundstad
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway
| | - Kristine Kleivi Sahlberg
- Department of Research and Innovation, Vestre Viken Hospital Trust, Drammen, Norway; Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway; Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway
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Koch HW, Larsen M, Bartsch H, Kurz KD, Hofvind S. Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases. Eur Radiol 2023; 33:3735-3743. [PMID: 36917260 PMCID: PMC10121532 DOI: 10.1007/s00330-023-09461-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 12/13/2022] [Accepted: 01/24/2023] [Indexed: 03/16/2023]
Abstract
OBJECTIVES To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. METHODS In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density. RESULTS A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. CONCLUSIONS The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. KEY POINTS • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.
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Affiliation(s)
- Henrik Wethe Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - Hauke Bartsch
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathinka Dæhli Kurz
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, The University of Stavanger, Stavanger, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway.
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Qenam BA, Li T, Ekpo E, Frazer H, Brennan PC. Test-set training improves the detection rates of invasive cancer in screening mammography. Clin Radiol 2023; 78:e260-e267. [PMID: 36646529 DOI: 10.1016/j.crad.2022.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/23/2022] [Accepted: 11/27/2022] [Indexed: 12/24/2022]
Abstract
AIM To investigate if mammographic test-set participation affects routine breast cancer screening performance. MATERIALS AND METHODS Clinical audit data between 2008 and 2018 were collected for 35 breast screen readers who participated in the BreastScreen Reader Assessment Strategy (BREAST) and 22 readers with no history of test-set participation. For BREAST readers, the annual audit data were divided according to the year they completed their first test set, and the same years were used randomly to align and divide the data of non-BREAST readers into pre- and post-test set periods. Multiple audit parameters were inspected retrospectively for the two cohorts to identify how their reading performance has evolved in screening mammography. RESULTS Investigating 2 calendar years before and after test-set participation, BREAST and non-BREAST readers recalled lower rates of women in the latter period (p=0.03 and p=0.02, respectively). They also improved their positive predictive value (PPV; p=0.01 and p=0.02, respectively). BREAST readers additionally improved their detection rates of invasive cancer (p=0.02) and all cancers (p=0.01). In an extended 3-year comparison, similar improvements occurred in the recall rate for BREAST (p=0.02) and non-BREAST readers (p=0.02) and in PPV (p=0.001, 0.01, respectively); however, improvements in detection rates also occurred exclusively in BREAST readers' performance for invasive cancer (p=0.04), DCIS (p=0.05), and all cancers (p=0.02); however, significant improvements in detection did not involve <15 mm invasive cancers in both periods. Meanwhile, non-BREAST readers demonstrated a decrease in sensitivity (p=0.02). CONCLUSION Participation in test sets is linked to over-time improvements in most audit-measured cancer detection rates.
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Affiliation(s)
- B A Qenam
- Medical Image Optimisation and Perception Research Group (MIOPeG), Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia; Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia.
| | - T Li
- Medical Image Optimisation and Perception Research Group (MIOPeG), Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia; The Daffodil Centre, The University of Sydney, a Joint Venture with Cancer Council NSW, Australia; Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - E Ekpo
- Medical Image Optimisation and Perception Research Group (MIOPeG), Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia; Orange Radiology, Laboratories and Research Centre, Calabar 540281, Nigeria
| | - H Frazer
- Screening and Assessment Service, St Vincent's BreastScreen, 1st Floor Healy Wing, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia
| | - P C Brennan
- Medical Image Optimisation and Perception Research Group (MIOPeG), Medical Imaging Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia
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Marinovich ML, Wylie E, Lotter W, Lund H, Waddell A, Madeley C, Pereira G, Houssami N. Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection. EBioMedicine 2023; 90:104498. [PMID: 36863255 PMCID: PMC9996220 DOI: 10.1016/j.ebiom.2023.104498] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/27/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has been proposed to reduce false-positive screens, increase cancer detection rates (CDRs), and address resourcing challenges faced by breast screening programs. We compared the accuracy of AI versus radiologists in real-world population breast cancer screening, and estimated potential impacts on CDR, recall and workload for simulated AI-radiologist reading. METHODS External validation of a commercially-available AI algorithm in a retrospective cohort of 108,970 consecutive mammograms from a population-based screening program, with ascertained outcomes (including interval cancers by registry linkage). Area under the ROC curve (AUC), sensitivity and specificity for AI were compared with radiologists who interpreted the screens in practice. CDR and recall were estimated for simulated AI-radiologist reading (with arbitration) and compared with program metrics. FINDINGS The AUC for AI was 0.83 compared with 0.93 for radiologists. At a prospective threshold, sensitivity for AI (0.67; 95% CI: 0.64-0.70) was comparable to radiologists (0.68; 95% CI: 0.66-0.71) with lower specificity (0.81 [95% CI: 0.81-0.81] versus 0.97 [95% CI: 0.97-0.97]). Recall rate for AI-radiologist reading (3.14%) was significantly lower than for the BSWA program (3.38%) (-0.25%; 95% CI: -0.31 to -0.18; P < 0.001). CDR was also lower (6.37 versus 6.97 per 1000) (-0.61; 95% CI: -0.77 to -0.44; P < 0.001); however, AI detected interval cancers that were not found by radiologists (0.72 per 1000; 95% CI: 0.57-0.90). AI-radiologist reading increased arbitration but decreased overall screen-reading volume by 41.4% (95% CI: 41.2-41.6). INTERPRETATION Replacement of one radiologist by AI (with arbitration) resulted in lower recall and overall screen-reading volume. There was a small reduction in CDR for AI-radiologist reading. AI detected interval cases that were not identified by radiologists, suggesting potentially higher CDR if radiologists were unblinded to AI findings. These results indicate AI's potential role as a screen-reader of mammograms, but prospective trials are required to determine whether CDR could improve if AI detection was actioned in double-reading with arbitration. FUNDING National Breast Cancer Foundation (NBCF), National Health and Medical Research Council (NHMRC).
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Affiliation(s)
- M Luke Marinovich
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia; Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia.
| | | | - William Lotter
- Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Helen Lund
- BreastScreen WA, Perth, Western Australia, Australia
| | | | | | - Gavin Pereira
- Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Nehmat Houssami
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia; Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
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Alsharif WM. The utilization of artificial intelligence applications to improve breast cancer detection and prognosis. Saudi Med J 2023; 44:119-127. [PMID: 36773967 PMCID: PMC9987701 DOI: 10.15537/smj.2023.44.2.20220611] [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: 02/13/2023] Open
Abstract
Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.
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Affiliation(s)
- Walaa M. Alsharif
- From the Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Al Madinah Al Munawwarah; and from the Society of Artificial Intelligence in Healthcare, Riyadh, Kingdom of Saudi Arabia.
- Address correspondence and reprint request to: Dr. Walaa M. Alsharif, Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Al Madinah Al Munawwarah, Kingdom of Saudi Arabia. E-mail: ORCID ID: https//:orcid.org/0000-0001-7607-3255
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Pandya T, Liu Z, Dolan H, Hersch J, Brennan M, Houssami N, Nickel B. Australian Women's Responses to Breast Density Information: A Content Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1596. [PMID: 36674351 PMCID: PMC9861812 DOI: 10.3390/ijerph20021596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
Breast density (BD) is an independent risk factor for breast cancer and reduces mammographic sensitivity. This study explored women's responses and intentions if notified that they had dense breasts. METHODS Content analysis was used to assess responses from a written questionnaire undertaken in conjunction with focus groups on BD involving 78 Australian women aged 40-74. RESULTS Half the women reported that they would feel a little anxious if notified they had dense breasts, while 29.5% would not feel anxious. The most common theme (29.5%) related to anxiety was the psychosocial impact of the possibility of developing cancer, and women believed that being better informed could help with anxiety (26.9%). When asked what they would do if notified of having dense breasts, the most common response was to consult their doctor for information/advice (38.5%), followed by considering supplemental screening (23%). Consequently, when asked directly, 65.4% were interested in undergoing supplemental screening, while others (10.3%) said they "wouldn't worry about it too much". DISCUSSION These findings have important implications for health systems with population-based breast screening programs that are currently considering widespread BD notification in terms of the impact on women, health services and primary care.
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Affiliation(s)
- Tanvi Pandya
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Zixuan Liu
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Hankiz Dolan
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Jolyn Hersch
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Meagan Brennan
- Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2145, Australia
- The National School of Medicine, The University of Notre Dame Australia, Sydney, NSW 2007, Australia
| | - Nehmat Houssami
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- The Daffodil Centre, The University of Sydney, a Joint Venture with Cancer Council NSW, Sydney, NSW 2006, Australia
| | - Brooke Nickel
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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Breast cancer: emerging principles of metastasis, adjuvant and neoadjuvant treatment from cancer registry data. J Cancer Res Clin Oncol 2023; 149:721-735. [PMID: 36538148 PMCID: PMC9931789 DOI: 10.1007/s00432-022-04369-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 09/17/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Growing primary breast cancers (PT) can initiate local recurrences (LR), regional lymph nodes (pLN) and distant metastases (MET). Components of these progressions are initiation, frequency, growth duration, and survival. These characteristics describe principles which proposed molecular concepts and hypotheses must align with. METHODS In a population-based retrospective modeling approach using data from the Munich Cancer Registry key steps and factors associated with metastasis were identified and quantified. Analysis of 66.800 patient datasets over four time periods since 1978, reliable evidence is obtained even in small subgroups. Together with results of clinical trials on prevention and adjuvant treatment (AT) principles for the MET process and AT are derived. RESULTS The median growth periods for PT/MET/LR/pLN comes to 12.5/8.8/5/3.5 years, respectively. Even if 30% of METs only appear after 10 years, a pre-diagnosis MET initiation principle not a delayed one should be true. The growth times of PTs and METs vary by a factor of 10 or more but their ratio is robust at about 1.4. Principles of AT are 50% PT eradication, the selective and partial eradication of bone and lung METs. This cannot be improved by extending the duration of the previously known ATs. CONCLUSION A paradigm of ten principles for the MET process and ATs is derived from real world data and clinical trials indicates that there is no rationale for the long-term application of endocrine ATs, risk of PTs by hormone replacement therapies, or cascading initiation of METs. The principles show limits and opportunities for innovation also through alternative interpretations of well-known studies. The outlined MET process should be generalizable to all solid tumors.
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Farber R, Houssami N, Barnes I, McGeechan K, Barratt A, Bell KJL. Considerations for Evaluating the Introduction of New Cancer Screening Technology: Use of Interval Cancers to Assess Potential Benefits and Harms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14647. [PMID: 36429373 PMCID: PMC9691207 DOI: 10.3390/ijerph192214647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
This framework focuses on the importance of the consideration of the downstream intermediate and long-term health outcomes when a change to a screening program is introduced. The authors present a methodology for utilising the relationship between screen-detected and interval cancer rates to infer the benefits and harms associated with a change to the program. A review of the previous use of these measures in the literature is presented. The framework presents other aspects to consider when utilizing this methodology, and builds upon an existing framework that helps researchers, clinicians, and policy makers to consider the impacts of changes to screening programs on health outcomes. It is hoped that this research will inform future evaluative studies to assess the benefits and harms of changes to screening programs.
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Affiliation(s)
- Rachel Farber
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
| | - Nehmat Houssami
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
- The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney 2006, Australia
| | - Isabelle Barnes
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
- Centre for Women’s Health Research, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan 2308, Australia
- Australian Longitudinal Study on Women’s Health, The University of Newcastle, Callaghan 2308, Australia
| | - Kevin McGeechan
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
| | - Alexandra Barratt
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
| | - Katy J. L. Bell
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia
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Nickel B, Copp T, Li T, Dolan H, Brennan M, Verde A, Vaccaro L, McCaffery K, Houssami N. A systematic assessment of online international breast density information. Breast 2022; 65:23-31. [PMID: 35763979 PMCID: PMC9240362 DOI: 10.1016/j.breast.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 12/02/2022] Open
Abstract
Background Breast density has become a topic of international discussion due to its associated risk of breast cancer. As online is often a primary source of women's health information it is therefore essential that breast density information it is understandable, accurate and reflects the best available evidence. This study aimed to systematically assess online international breast density information including recommendations to women. Methods Searches were conducted from five different English-speaking country-specific Google locations. Relevant breast density information was extracted from the identified websites. Readability was assessed using the SHeLL Editor, and understandability and actionability using the Patient Education Materials Assessment Tool (PEMAT). A content analysis of specific recommendations to women was also conducted. Results Forty-two eligible websites were identified and systematically assessed. The included informational content varied across websites. The average grade reading level across all websites was 12.4 (range 8.9–15.4). The mean understandability was 69.9% and the mean actionability was 40.1%, with 18/42 and 39/42 websites respectively scoring lower than adequate (70%). Thirty-six (85.7%) of the websites had breast density-related recommendation to women, with ‘talk to your doctor’ (n = 33, 78.6%) the most common. Conclusions Online information about breast density varies widely and is not generally presented in a way that women can easily understand and act on, therefore greatly reducing the ability for informed decision-making. International organisations and groups disseminating breast density information need to ensure that women are presented with health literacy-sensitive and balanced information, and be aware of the impact that recommendations may have on practice. First study to systematically assess online international breast density information. Information across the websites varies widely. Readability, understandability and actionability are low and poor. The most common recommendation to women is to ‘talk to your doctor’. More health literacy-sensitive online information about breast density is needed internationally.
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Affiliation(s)
- Brooke Nickel
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia; Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
| | - Tessa Copp
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia; Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Tong Li
- The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia
| | - Hankiz Dolan
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Meagan Brennan
- The University of Notre Dame Australia, School of Medicine Sydney, Sydney, Australia; Westmead Breast Cancer Institute, Westmead Hospital, Sydney, Australia
| | - Angela Verde
- Breast Cancer Network Australia, Melbourne, Australia
| | - Lisa Vaccaro
- Health Consumers New South Wales, Sydney, Australia; Discipline of Behavioural and Social Sciences in Health, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Kirsten McCaffery
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia; Sydney Health Literacy Lab, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Nehmat Houssami
- Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia; The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia
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Nickel B, Dolan H, Houssami N, Cvejic E, Brennan M, Hersch J, Dorrington M, Verde A, Vaccaro L, McCaffery K. Factors associated with women's supplemental screening intentions following dense breast notification in an online randomised experimental study. J Med Screen 2022; 30:92-95. [PMID: 36071630 DOI: 10.1177/09691413221125320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Controversy surrounding recommendations for supplemental screening (ultrasound and magnetic resonance screening) in women with dense breasts exists, as the long-term benefits from these additional modalities may not outweigh the harms. This study aimed to examine factors associated with supplemental screening intentions following a hypothetical breast density notification in a population of women who have not been routinely notified. Australian women of breast screening age participated in an online randomised experimental study where they were presented with one of two breast density notifications (with or without health literacy-sensitive information) and asked their screening intentions. After adjusting for covariates in multivariable analyses, women in both groups (n = 940) who indicated higher levels of breast cancer worry, had private health insurance, had a family history of breast cancer, and had a greater number of times previously attending mammography screening had higher intentions for supplemental screening. Understanding women's supplemental screening intentions following notification of dense breasts has important implications for health systems with breast screening considering the impacts of widespread notification. Personal, clinical and psychological factors should be considered when discussing both the benefits and harms of supplemental screening with women with dense breasts.
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Affiliation(s)
- Brooke Nickel
- Wiser Healthcare, School of Public Health, 4334University of Sydney, Sydney, Australia.,Sydney Health Literacy Lab, School of Public Health, University of Sydney, Sydney, Australia
| | - Hankiz Dolan
- Wiser Healthcare, School of Public Health, 4334University of Sydney, Sydney, Australia.,Sydney Health Literacy Lab, School of Public Health, University of Sydney, Sydney, Australia
| | - Nehmat Houssami
- Wiser Healthcare, School of Public Health, 4334University of Sydney, Sydney, Australia.,The Daffodil Centre, University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia
| | - Erin Cvejic
- Wiser Healthcare, School of Public Health, 4334University of Sydney, Sydney, Australia.,Sydney Health Literacy Lab, School of Public Health, University of Sydney, Sydney, Australia
| | - Meagan Brennan
- School of Medicine Sydney, 3431University of Notre Dame Australia, Sydney, Australia.,Westmead Breast Cancer Institute, Westmead Hospital, Sydney, Australia
| | - Jolyn Hersch
- Wiser Healthcare, School of Public Health, 4334University of Sydney, Sydney, Australia.,Sydney Health Literacy Lab, School of Public Health, University of Sydney, Sydney, Australia
| | | | - Angela Verde
- 104351Breast Cancer Network Australia, Melbourne, Australia
| | - Lisa Vaccaro
- 430854Health Consumers New South Wales, Sydney, Australia.,Discipline of Behavioural and Social Sciences in Health, School of Health Sciences, University of Sydney, Sydney, Australia
| | - Kirsten McCaffery
- Wiser Healthcare, School of Public Health, 4334University of Sydney, Sydney, Australia.,Sydney Health Literacy Lab, School of Public Health, University of Sydney, Sydney, Australia
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Wang J, Greuter MJ, Zheng S, van Veldhuizen DW, Vermeulen KM, Wang Y, Lu W, de Bock GH. Assessment of the Benefits and Cost-Effectiveness of Population-Based Breast Cancer Screening in Urban China: A Model-Based Analysis. Int J Health Policy Manag 2022; 11:1658-1667. [PMID: 34273933 PMCID: PMC9808213 DOI: 10.34172/ijhpm.2021.62] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 05/30/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND To decrease the burden of breast cancer (BC), the Chinese government recently introduced biennial mammography screening for women aged 45-70 years. In this study, we assess the effectiveness and cost-effectiveness of implementing this programme in urban China using a micro-simulation model. METHODS The 'Simulation Model on radiation Risk and breast cancer Screening' (SiMRiSc) was applied, with parameters updated based on available data for the Chinese population. The base scenario was biennial mammography screening for women aged 45-70 years, and this was compared to a reference population with no screening. Seven alternative scenarios were then simulated by varying the screening intervals and participant ages. This analysis was conducted from a societal perspective. The discounted incremental cost-effectiveness ratio (ICER) was compared to a threshold of triple the gross domestic product (GDP) per life years gained (LYG), which was 30 785 USD/LYG. Univariate sensitivity analyses were conducted to evaluate model robustness. In addition, a budget impact analysis was performed by comparing biennial screening with no screening at a time horizon of 10 years. RESULTS Compared with no screening, the base scenario was cost-effective in urban China, giving a discounted average cost-effectiveness ratio (ACER) of 17 309 USD/LYG. The model was most sensitive to the cost of mammography per screen, followed by mean size of self-detected tumours, mammographic breast density and the cumulative lifetime risk of BC. The efficient frontier showed that at a threshold of 30 785 USD/LYG, the base scenario was the optimal scenario with a discounted ICER of 25 261 USD/LYG. Over 10 years, screening would incur a net cost of almost 38.1 million USD for a city with 1 million citizens. CONCLUSION Compared to no screening, biennial mammography screening for women aged from 45-70 is cost-effective in urban China.
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Affiliation(s)
- Jing Wang
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marcel J.W. Greuter
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Robotics and Mechatronics (RaM) Group, Faculty of Electrical Engineering Mathematics and Computer Science, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Senshuang Zheng
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Daniëlle W.A. van Veldhuizen
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Karin M. Vermeulen
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Yuan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Collaborative Innovation Center of Chronic Disease Prevention and Control, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Wenli Lu
- Department of Epidemiology and Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Collaborative Innovation Center of Chronic Disease Prevention and Control, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Geertruida H. de Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147080] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In 2020, according to the publications of both the Global Cancer Observatory (GCO) and the World Health Organization (WHO), breast cancer (BC) represents one of the highest prevalent cancers in women worldwide. Almost 47% of the world’s 100,000 people are diagnosed with breast cancer, among females. Moreover, BC prevails among 38.8% of Egyptian women having cancer. Current deep learning developments have shown the common usage of deep convolutional neural networks (CNNs) for analyzing medical images. Unlike the randomly initialized ones, pre-trained natural image database (ImageNet)-based CNN models may become successfully fine-tuned to obtain improved findings. To conduct the automatic detection of BC by the CBIS-DDSM dataset, a CNN model, namely CoroNet, is proposed. It relies on the Xception architecture, which has been pre-trained on the ImageNet dataset and has been fully trained on whole-image BC according to mammograms. The convolutional design method is used in this paper, since it performs better than the other methods. On the prepared dataset, CoroNet was trained and tested. Experiments show that in a four-class classification, it may attain an overall accuracy of 94.92% (benign mass vs. malignant mass) and (benign calcification vs. malignant calcification). CoroNet has a classification accuracy of 88.67% for the two-class cases (calcifications and masses). The paper concluded that there are promising outcomes that could be improved because more training data are available.
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Body Mass Index Is Inversely Associated with Risk of Postmenopausal Interval Breast Cancer: Results from the Women’s Health Initiative. Cancers (Basel) 2022; 14:cancers14133228. [PMID: 35804998 PMCID: PMC9264843 DOI: 10.3390/cancers14133228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Interval breast cancer refers to cancer diagnosed after a negative screening mammogram and before the next scheduled screening mammogram. Interval breast cancer has worse prognosis than screening-detected cancer. Body mass index (BMI) influences the accuracy of mammography and overall postmenopausal breast cancer risk, yet how is obesity associated with postmenopausal interval breast cancer incidence is unclear. The current study included cancer-free postmenopausal women aged 50–79 years at enrollment in the Women’s Health Initiative who were diagnosed with breast cancer during follow-up. Analyses include 324 interval breast cancer cases diagnosed within one year after the participant’s last negative screening mammogram and 1969 screening-detected breast cancer patients. Obesity (BMI ≥ 30 kg/m2) was measured at baseline. Associations between obesity and incidence of interval cancer were determined by sequential logistic regression analyses. In multivariable-adjusted models, obesity was inversely associated with interval breast cancer risk [OR (95% CI) = 0.65 (0.46, 0.92)]. The inverse association persisted after excluding women diagnosed within 2 years [OR (95% CI) = 0.60 (0.42, 0.87)] or 4 years [OR (95% CI) = 0.56 (0.37, 0.86)] of enrollment, suggesting consistency of the association regardless of screening practices prior to trial entry. These findings warrant confirmation in studies with body composition measures.
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Friedewald SM, Grimm LJ. Digital Breast Tomosynthesis and Detection of Interval Invasive and Advanced Breast Cancers. JAMA 2022; 327:2198-2200. [PMID: 35699719 DOI: 10.1001/jama.2021.25018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Sarah M Friedewald
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Lynn Sage Comprehensive Breast Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Lars J Grimm
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina
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The Impact of Dense Breasts on the Stage of Breast Cancer at Diagnosis: A Review and Options for Supplemental Screening. Curr Oncol 2022; 29:3595-3636. [PMID: 35621681 PMCID: PMC9140155 DOI: 10.3390/curroncol29050291] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of breast cancer screening is to find cancers early to reduce mortality and to allow successful treatment with less aggressive therapy. Mammography is the gold standard for breast cancer screening. Its efficacy in reducing mortality from breast cancer was proven in randomized controlled trials (RCTs) conducted from the early 1960s to the mid 1990s. Panels that recommend breast cancer screening guidelines have traditionally relied on the old RCTs, which did not include considerations of breast density, race/ethnicity, current hormone therapy, and other risk factors. Women do not all benefit equally from mammography. Mortality reduction is significantly lower in women with dense breasts because normal dense tissue can mask cancers on mammograms. Moreover, women with dense breasts are known to be at increased risk. To provide equity, breast cancer screening guidelines should be created with the goal of maximizing mortality reduction and allowing less aggressive therapy, which may include decreasing the interval between screening mammograms and recommending consideration of supplemental screening for women with dense breasts. This review will address the issue of dense breasts and the impact on the stage of breast cancer at the time of diagnosis, and discuss options for supplemental screening.
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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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