| 1. |
- Axelsson, Rebecca, et al.
(författare)
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Simultaneous digital breast tomosynthesis and mechanical imaging in women recalled from screening - A preliminary analysis
- 2022
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Ingår i: 16th International Workshop on Breast Imaging, IWBI 2022. - : SPIE. - 0277-786X .- 1996-756X. - 9781510655843 ; 12286
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Konferensbidrag (refereegranskat)abstract
- We have developed a method for simultaneous tomosynthesis and mechanical imaging, called DBTMI. Mechanical imaging measures the stress distribution over the compressed breast surface. Malignant tissue is usually stiffer than benign, which results in higher stress on the compressed breast and enables to distinguish malignant from benign findings. By combining tomosynthesis and mechanical imaging, we could improve cancer detection accuracy by reducing the number of false positive findings. In this study we have analysed clinical DBTMI data, collected from 52 women from an ongoing pilot study at the Skåne University Hospital, Malmö, Sweden. We measured the range of the average stress over the breast surface, the range of average stress over the location of suspected lesions, and the normalized stress over the lesion location. Preliminary results show that the range of stress over the breast surface was 1.23-5.84 kPa, the range over the lesion location 2.10-10.10 kPa, and the normalized stress 1.12-2.44 over the lesion location. Overall, the local stress over malignant lesions was higher than the average stress over the entire breast surface. This is the first step investigating criteria to distinguish between malignant and benign findings based upon clinical DBTMI data.
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| 2. |
- Dahlblom, Victor, et al.
(författare)
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Artificial intelligence detection of missed cancers at digital mammography that were detected at digital breast tomosynthesis
- 2021
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Ingår i: Radiology: Artificial Intelligence. - : Radiological Society of North America (RSNA). - 2638-6100. ; 3:6
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT. Materials and Methods: In this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010–2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1–100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics. Results: The highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18). Conclusion: Almost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.
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| 3. |
- Dahlblom, Victor, et al.
(författare)
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Breast cancer screening with digital breast tomosynthesis : comparison of different reading strategies implementing artificial intelligence
- 2023
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 33:5, s. 3754-3765
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: Digital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.METHODS: An AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.RESULTS: By excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected-26% (25/95) more than DM screening (p < 0.001)-while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689).CONCLUSION: AI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach.KEY POINTS: • Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload. • Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader. • Retrospective study based on paired mammography and digital breast tomosynthesis screening data.
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| 4. |
- Dahlblom, Victor, et al.
(författare)
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Correspondence between areas causing recall in breast cancer screening and artificial intelligence findings
- 2022
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Ingår i: 16th International Workshop on Breast Imaging, IWBI 2022. - : SPIE. - 0277-786X .- 1996-756X. - 9781510655843 ; 12286
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Konferensbidrag (refereegranskat)abstract
- False positive recall is a major issue in breast cancer screening and the introduction of artificial intelligence (AI) might affect which women who are unnecessarily recalled. We have investigated how an AI system works on false positive recalls at screening and compared with radiologist findings. Two-view digital mammography (DM) examinations from 656 recalled women (136 with screening detected cancer), were analysed with a commercial AI system. The AI findings were matched with the areas on the images causing the recalls. The agreement was studied both at the examination level and for individual findings. Scores were compared between true positive and false positive recalls. ROC analysis was used to study the AI-system's ability to distinguish between true and false positive recalls. It was also studied how the AI system performed on cases where there were discordant readings. AI identified the same areas as radiologists in 80% of the cases recalled on DM. For true positives both the proportion of matching areas and AI scores were higher than for false positive recalls. The AI system also had a relatively large AUC (0.83) for differentiating between false positive recalls and cancers. Further, the AI system identified most of the findings leading to recall in cases where only one of the readers had marked the case for discussion. There is a relatively large agreement between the AI system and radiologists. The AI system scores the false positives lower than true positives. AI complements a single reader in a way similar to a second reader.
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| 5. |
- Dahlblom, Victor
(författare)
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Improving breast cancer screening with artificial intelligence
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Introduction: The current standard method for breast cancer screening is digital mammography (DM). Digital breast tomosynthesis (DBT) can detect more cancers but is more resource-demanding, not the least due to a more time-consuming reading, which hinders the implementation in screening. Artificial intelligence (AI) might open possibilities to overcome this, but different potential ways of using AI need to be tested using representative screening data. To facilitate the testing and further development of AI, it is necessary to collect and organise more data in a research-friendly form.Aim: To create a breast imaging research database and explore different ways of using AI to improve breast cancer screening.Methods: All DM and DBT examinations performed in Malmö, Sweden during 2004–2020 were collected and combined with other relevant information in a research database. A subset consisting of 14 848 women had been examined with paired DM and DBT as part of the Malmö Breast Tomosynthesis Screening Trial (MBTST). This cohort was used to test different ways of using an AI cancer-detection system, which scores examinations based on cancer risk. It was studied whether the AI system could be used on DM to exclude normal cases from human reading, detect additional cancers on DM that radiologists only detected on DBT, or add DBT in selected high-gain cases. Further, it was studied how the AI system can be utilised to reduce the workload of DBT screening.Results: A research database was created that contained 449 000 examinations from 103 000 women, performed during a time span of 17 years. This includes 9 250 cancers in 7 371 women. It was found that the tested AI system can be used on DM to exclude 19% of examinations from human reading without missing any cancers and that AI can detect 44% of DBT-only detected cancers using only DM. Further, adding DBT for the 10% of the women with the highest AI risk score can detect 25% more cancers than DM screening. For DBT screening, the AI system can reduce the reading workload to the level of DM screening, either by replacing the second reader in a double reader setup or by discarding half of examinations from reading, thus focusing double reading on the half with the highest risk.Discussion: The results indicate that AI can be used to improve the performance and efficiency of breast cancer screening in several ways, including making it possible to use DBT in screening without demanding more resources. The research database can facilitate larger retrospective studies on these and other subjects. However, before clinical implementation, prospective studies would also be necessary, where e.g. the interaction between radiologists and AI can be investigated.
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| 6. |
- Dahlblom, Victor, et al.
(författare)
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Malmö Breast ImaginG database: objectives and development
- 2023
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Ingår i: Journal of Medical Imaging. - 2329-4302. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- PurposeWe describe the design and implementation of the Malmö Breast ImaginG (M-BIG) database, which will support research projects investigating various aspects of current and future breast cancer screening programs. Specifically, M-BIG will provide clinical data to: 1. investigate the effect of breast cancer screening on breast cancer prognosis and mortality; 2. develop and validate the use of artificial intelligence and machine learning in breast image interpretation; and 3. develop and validate image-based radiological breast cancer risk profiles.ApproachThe M-BIG database is intended to include a wide range of digital mammography (DM) and digital breast tomosynthesis (DBT) examinations performed on women at the Mammography Clinic in Malmö, Sweden, from the introduction of DM in 2004 through 2020. Subjects may be included multiple times and for diverse reasons. The image data are linked to extensive clinical, diagnostic, and demographic data from several registries.ResultsTo date, the database contains a total of 451,054 examinations from 104,791 women. During the inclusion period, 95,258 unique women were screened. A total of 19,968 examinations were performed using DBT, whereas the rest used DM.ConclusionsWe describe the design and implementation of the M-BIG database as a representative and accessible medical image database linked to various types of medical data. Work is ongoing to add features and curate the existing data.
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| 7. |
- Dahlblom, Victor, et al.
(författare)
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Personalised breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence
- 2020
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Ingår i: 15th International Workshop on Breast Imaging, IWBI 2020. - : SPIE. - 0277-786X .- 1996-756X. - 9781510638310 ; 11513
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Konferensbidrag (refereegranskat)abstract
- Breast cancer screening is predominantly performed using digital mammography (DM), but higher sensitivity has been demonstrated with digital breast tomosynthesis (DBT). A partial DBT screening in selected groups with a clear benefit from DBT might be more feasible than a full implementation, and using artificial intelligence (AI) to select women for DBT might be a possibility. This study used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately read DM and DBT. We retrospectively analysed DM examinations (n=14768) with a breast cancer detection software and used the provided risk score (1-10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. If using a threshold of 9.0, 25 (26 %) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61 % would be detected, with only 1797 (12 %) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, while the false positive recalls would be increased with 58 (21 %). Using DBT only for selected high gain cases could be an alternative to a complete DBT screening. AI could be used for analysing DM to identify high gain cases, where DBT can be added during the same visit. There might be logistical challenges and further studies in a prospective setting are necessary.
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| 8. |
- Dahlblom, Victor, et al.
(författare)
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Personalized breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence
- 2023
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Ingår i: Journal of Medical Imaging. - 2329-4302. ; 10:Suppl 2
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: Breast cancer screening is predominantly performed using digital mammography (DM), but digital breast tomosynthesis (DBT) has higher sensitivity. DBT demands more resources than DM, and it might be more feasible to reserve DBT for women with a clear benefit from the technique. We explore if artificial intelligence (AI) can select women who would benefit from DBT imaging.APPROACH: We used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately double read DM and DBT. We retrospectively analyzed DM examinations (n=14768) with a breast cancer detection system and used the provided risk score (1 to 10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives.RESULTS: If using a threshold of 9.0, 25 (26%) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61% would be detected, with only 1797 (12%) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, whereas the false-positive recalls would be increased with 58 (21%).CONCLUSION: Using DBT only for selected high gain cases could be an alternative to complete DBT screening. AI can analyze initial DM images to identify high gain cases where DBT can be added during the same visit. There might be logistical challenges, and further studies in a prospective setting are necessary.
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| 9. |
- Dustler, Magnus, et al.
(författare)
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The effect of breast density on the performance of deep learning-based breast cancer detection methods for mammography
- 2020
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Ingår i: 15th International Workshop on Breast Imaging, IWBI 2020. - : SPIE. - 1996-756X .- 0277-786X. - 9781510638310 ; 11513
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Konferensbidrag (refereegranskat)abstract
- Mammographic sensitivity in breasts with higher density has been questioned. Higher breast density is also linked to an increased risk for breast cancer. Even though digital breast tomosynthesis (DBT) offers an attractive solution, for varied reasons it has not yet been widely adopted in screening. An alternative could be to boost the performance of standard mammography by using computer-aided detection based on deep learning, but it remains to be proven how such methods are affected by density. A deep-learning based computer-aided detection program was used to score the suspicion of cancer on a scale of 1 to 10. A set of 13838 mammography screening exams were used. All cases had BIRADS density values available. The set included 2304 exams (11 cancers) in BIRADS 1, 5310 (51 cancers) in BIRADS 2, 4844 (73 cancers) in BIRADS 3 and 1223 (22 cancers) in BIRADS 4. A Kruskal-Wallis analysis of variance showed no statistically significant differences between the cancer risk scores of the density categories for cases diagnosed with cancer (P=0.9225). An identical analysis for cases without cancer, showed significant differences between the density categories (P<0.0001). The results suggest that the risk categorization of the deep-learning software is not affected by density, as though some density categories receive higher risk assessments in general, this does not hold for cancer cases, which show uniformly high risk values despite density. This shows the potential for deep-learning to improve screening sensitivity even for women with high density breasts.
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| 10. |
- Lång, Kristina, et al.
(författare)
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Identifying normal mammograms in a large screening population using artificial intelligence
- 2020
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084.
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. Methods: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning–based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI). Results: If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3–19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1–8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0–54.0) exams, including 7 (10.3%; 95% CI 3.1–17.5) cancers and 52 (27.8%; 95% CI 21.4–34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible. Conclusions: The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency. Key Points: • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.
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