| 1. |
- Mavaddat, Nasim, et al.
(författare)
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Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants
- 2015
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 1460-2105 .- 0027-8874. ; 107:5, s. 036-036
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Tidskriftsartikel (refereegranskat)abstract
- Background: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. Methods: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. Results: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. Conclusions: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.
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| 2. |
- Boita, Joana, et al.
(författare)
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Development and content validity evaluation of a candidate instrument to assess image quality in digital mammography : A mixed-method study
- 2021
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Ingår i: European Journal of Radiology. - : Elsevier BV. - 0720-048X. ; 134
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To develop a candidate instrument to assess image quality in digital mammography, by identifying clinically relevant features in images that are affected by lower image quality. Methods: Interviews with fifteen expert breast radiologists from five countries were conducted and analysed by using adapted directed content analysis. During these interviews, 45 mammographic cases, containing 44 lesions (30 cancers, 14 benign findings), and 5 normal cases, were shown with varying image quality. The interviews were performed to identify the structures from breast tissue and lesions relevant for image interpretation, and to investigate how image quality affected the visibility of those structures. The interview findings were used to develop tentative items, which were evaluated in terms of wording, understandability, and ambiguity with expert breast radiologists. The relevance of the tentative items was evaluated using the content validity index (CVI) and modified kappa index (k*). Results: Twelve content areas, representing the content of image quality in digital mammography, emerged from the interviews and were converted into 29 tentative items. Fourteen of these items demonstrated excellent CVI ≥ 0.78 (k* > 0.74), one showed good CVI < 0.78 (0.60 ≤ k* ≤ 0.74), while fourteen were of fair or poor CVI < 0.78 (k* ≤ 0.59). In total, nine items were deleted and five were revised or combined resulting in 18 items. Conclusions: By following a mixed-method methodology, a candidate instrument was developed that may be used to characterise the clinically-relevant impact that image quality variations can have on digital mammography.
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| 3. |
- Boita, Joana, et al.
(författare)
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How does image quality affect radiologists’ perceived ability for image interpretation and lesion detection in digital mammography?
- 2021
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 31:7, s. 5335-5343
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To study how radiologists’ perceived ability to interpret digital mammography (DM) images is affected by decreases in image quality. Methods: One view from 45 DM cases (including 30 cancers) was degraded to six levels each of two acquisition-related issues (lower spatial resolution and increased quantum noise) and three post-processing-related issues (lower and higher contrast and increased correlated noise) seen during clinical evaluation of DM systems. The images were shown to fifteen breast screening radiologists from five countries. Aware of lesion location, the radiologists selected the most-degraded mammogram (indexed from 1 (reference) to 7 (most degraded)) they still felt was acceptable for interpretation. The median selected index, per degradation type, was calculated separately for calcification and soft tissue (including normal) cases. Using the two-sided, non-parametric Mann-Whitney test, the median indices for each case and degradation type were compared. Results: Radiologists were not tolerant to increases (medians: 1.5 (calcifications) and 2 (soft tissue)) or decreases (median: 2, for both types) in contrast, but were more tolerant to correlated noise (median: 3, for both types). Increases in quantum noise were tolerated more for calcifications than for soft tissue cases (medians: 3 vs. 4, p = 0.02). Spatial resolution losses were considered less acceptable for calcification detection than for soft tissue cases (medians: 3.5 vs. 5, p = 0.001). Conclusions: Perceived ability of radiologists for image interpretation in DM was affected not only by image acquisition-related issues but also by image post-processing issues, and some of those issues affected calcification cases more than soft tissue cases. Key Points: • Lower spatial resolution and increased quantum noise affected the radiologists’ perceived ability to interpret calcification cases more than soft tissue lesion or normal cases. • Post-acquisition image processing-related effects, not only image acquisition-related effects, also impact the perceived ability of radiologists to interpret images and detect lesions. • In addition to current practices, post-acquisition image processing-related effects need to also be considered during the testing and evaluation of digital mammography systems.
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| 4. |
- Boita, Joana, et al.
(författare)
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Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis
- 2021
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Ingår i: European Journal of Radiology. - : Elsevier BV. - 1872-7727 .- 0720-048X. ; 139
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo validate a candidate instrument, to be used by different professionals to assess image quality in digital mammography (DM), against detection performance results.MethodsA receiver operating characteristics (ROC) study was conducted to assess the detection performance in DM images with four different image quality levels due to different quality issues. Fourteen expert breast radiologists from five countries assessed a set of 80 DM cases, containing 60 lesions (40 cancers, 20 benign findings) and 20 normal cases. A visual grading analysis (VGA) study using a previously-described candidate instrument was conducted to evaluate a subset of 25 of the images used in the ROC study. Eight radiologists that had participated in the ROC study, and seven expert breast-imaging physicists, evaluated this subset. The VGA score (VGAS) and the ROC and visual grading characteristics (VGC) areas under the curve (AUCROC and AUCVGC) were compared.ResultsNo large differences in image quality among the four levels were detected by either ROC or VGA studies. However, the ranking of the four levels was consistent: level 1 (partial AUCROC: 0.070, VGAS: 6.77) performed better than levels 2 (0.066, 6.15), 3 (0.061, 5.82), and 4 (0.062, 5.37). Similarity between radiologists’ and physicists’ assessments was found (average VGAS difference of 10 %).ConclusionsThe results from the candidate instrument were found to correlate with those from ROC analysis, when used by either observer group. Therefore, it may be used by different professionals, such as radiologists, radiographers, and physicists, to assess clinically-relevant image quality variations in DM.
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| 5. |
- 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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| 6. |
- Bakic, Predrag R., et al.
(författare)
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Evaluation of a flat fielding method for simultaneous DBT and MI acquisition
- 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
- We are developing a prototype system for simultaneous digital breast tomosynthesis (DBT) and mechanical imaging (MI). MI maps the local pressure distribution during clinical exams, to distinguish breast abnormalities from the normal tissue. Both DBT alone, and MI when combined with digital mammography, have demonstrated the ability to reduce false positives; however, the benefit of combining DBT with MI has not been investigated. A practical limitation in simultaneous DBT and MI is the presence of the MI sensor in DBT images. Metallic elements of the sensor generate noticeable artifacts, which may interfere with clinical analysis. Previously, we shown that the sensor artifacts can be reduced by flat fielding, which combines projections of the sensor acquired with and without the breast. In this paper we evaluate the flat fielding by assessing artifact reduction and visibility of breast abnormalities. Images of a physical anthropomorphic breast phantom were acquired using a clinical wide-angle DBT system. Visual evaluation was performed by experienced medical physicists. Image quality descriptors were calculated in images with and without flat fielding. To evaluate the visibility of abnormalities we estimated the full width at half maximum (FWHM) for calcifications modeled in the phantom. Our preliminary results suggest a substantial reduction of artifacts by flat fielding (on average 83%). Few noticeable artifacts remain near the breast edge, in the reconstructed image with the sensor in focus. We observed a 17% reduction in the FWHM. Future work would include a detailed assessment, and method optimization using virtual trials as a design aid.
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| 7. |
- Bejnö, Anna, et al.
(författare)
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Artificial intelligence together with mechanical imaging in mammography
- 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
- Artificial intelligence (AI) applications are increasingly seeing use in breast imaging, particularly to assist in or automate the reading of mammograms. Another novel technique is mechanical imaging (MI) which estimates the relative stiffness of suspicious breast abnormalities by measuring the distribution of pressure on the compressed breast. This study investigates the feasibility of combining AI and MI information in breast imaging to provide further diagnostic information. Forty-six women recalled from screening were included in the analysis. Mammograms with findings scored on a suspiciousness scale by an AI tool, and corresponding pressure distributions were collected for each woman. The cases were divided into three groups by diagnosis; biopsy-proven cancer, biopsy-proven benign and non-biopsied, very likely benign. For all three groups, the relative increase of pressure at the location of the finding marked most suspicious by the AI software was recorded. A significant correlation between the relative pressure increase at the AI finding and the AI score was established in the group with cancer (p=0.043), but neither group of healthy women showed such a correlation. This study suggests that AI and MI indicate independent markers for breast cancer. The combination of these two methods has the potential to increase the accuracy of mammography screening, but further research is needed.
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| 8. |
- Bick, Ulrich, et al.
(författare)
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Image-guided breast biopsy and localisation : recommendations for information to women and referring physicians by the European Society of Breast Imaging
- 2020
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Ingår i: Insights into Imaging. - : Springer Science and Business Media LLC. - 1869-4101. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- We summarise here the information to be provided to women and referring physicians about percutaneous breast biopsy and lesion localisation under imaging guidance. After explaining why a preoperative diagnosis with a percutaneous biopsy is preferred to surgical biopsy, we illustrate the criteria used by radiologists for choosing the most appropriate combination of device type for sampling and imaging technique for guidance. Then, we describe the commonly used devices, from fine-needle sampling to tissue biopsy with larger needles, namely core needle biopsy and vacuum-assisted biopsy, and how mammography, digital breast tomosynthesis, ultrasound, or magnetic resonance imaging work for targeting the lesion for sampling or localisation. The differences among the techniques available for localisation (carbon marking, metallic wire, radiotracer injection, radioactive seed, and magnetic seed localisation) are illustrated. Type and rate of possible complications are described and the issue of concomitant antiplatelet or anticoagulant therapy is also addressed. The importance of pathological-radiological correlation is highlighted: when evaluating the results of any needle sampling, the radiologist must check the concordance between the cytology/pathology report of the sample and the radiological appearance of the biopsied lesion. We recommend that special attention is paid to a proper and tactful approach when communicating to the woman the need for tissue sampling as well as the possibility of cancer diagnosis, repeat tissue sampling, and or even surgery when tissue sampling shows a lesion with uncertain malignant potential (also referred to as “high-risk” or B3 lesions). Finally, seven frequently asked questions are answered.
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| 9. |
- Boll, Måns, et al.
(författare)
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Evaluation of 3D printed contrast detail phantoms for mammography quality assurance
- 2022
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Ingår i: 16th International Workshop on Breast Imaging, IWBI 2022. - : SPIE. - 1996-756X .- 0277-786X. - 9781510655843 ; 12286
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Konferensbidrag (refereegranskat)abstract
- Objects created by 3D printers are increasingly used in various medical applications. Today, affordable 3D printers, using Fused Deposition Modeling are widely available. In this project, a commercially available 3D printer was used to replicate a conventional radiographic contrast detail phantom. Printing materials were selected by comparing their x-ray attenuation properties. Two replicas were printed using polylactic acid, with different filling patterns. The printed phantoms were imaged by a clinical mammography system, using automatic exposure control. Phantom images were visually and quantitively compared to images of the corresponding conventional contrast detail phantom. Visual scoring of the contrast detail elements was performed by a medical physics student. Contrast-to-noise ratio (CNR) was calculated for each phantom element. The diameter and thickness of the smallest visible phantom object were 0.44 mm and 0.09 mm, respectively, for both filling patterns. For the conventional phantom, the diameter and thickness of the smallest visible object were 0.31 mm and 0.09 mm. Visual inspection of printed phantoms revealed some linear artefacts. These artefacts were however not visible on mammographic projections. Quantitively, average CNR of printed phantom objects followed the same trend with an increase of average CNR with increasing disk height. However, there is a limitation of detail objects with disk diameters below 1.25 mm, caused by the available nozzle size. Based upon the encouraging results, future work will explore the use of different materials and smaller nozzle diameters.
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| 10. |
- 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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| 11. |
- 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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| 12. |
- 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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| 13. |
- Ohashi, Akane, et al.
(författare)
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Prediction of Ki-67 expression of breast cancer with a multiparametric model using MRI parameters from ultrafast DCE-MRI and DWI
- 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
- The purpose of this study is to investigate the prediction of Ki-67 expression of breast cancers using MRI parameters from ultrafast (UF) DCE-MRI, DWI, T2WI, and the lesion size. Breast MRI was performed with a 3T scanner using dedicated breast coils. UF DCE-MRI was obtained using Compressed Sensing-VIBE (prototype sequence). As a kinetic parameter of UF DCE-MRI, maximum slope (MS) was defined as percentage relative enhancement (%/s), and time to enhance (TTE) was defined as the time interval between the aorta and lesion enhancement. The apparent diffusion coefficient (ADC) was derived from DWI. Two radiologists measured each MR parameter, and inter-rater agreement was evaluated. Univariate and multivariate logistic regression analyses were perfomed to predict low Ki-67 (< 14%) and high Ki-67 (≥ 14%) expression using MS, TTE, ADC, T2-signal intensity (SI), and lesion size. The significant parameters (p-values of < 0.05) were selected for the prediction model, and the diagnostic performance of the model was evaluated using ROC curve analysis. A total of 191 invasive carcinomas defined as mass lesions were included (72 low Ki-67/ 119 high Ki-67 lesions). The inter-rater agreements of all parameters were excellent. After univariate and multivariate logistic regression analysis, ADC and lesion size remained significant parameters. Using these significant parameters, the multi-parametric prediction model yielded an AUC of 0.77 (95%CI of 0.70-0.84) (sensitivity 72.3%, specificity 76.4%, and PPV 83.5%, and NPV 62.5%). DWI parameter (ADC) may be more valuable than UF DCE-MRI parameters (MS, TTE) to predict high Ki-67 in mass-shaped invasive breast carcinoma.
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| 14. |
- Tomic, Hanna, et al.
(författare)
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Tumor growth rate estimations in a breast cancer screening population
- 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
- Tumor growth rate estimations can provide useful information about tumor progression and aggressiveness. Understanding the breast cancer progression and aggressiveness could aid with personalized screening/follow-up, treatment options, and prognosis. This paper reports a preliminary estimation of the tumor volume doubling time (TVDT) for cancers detected during the Malmö Breast Tomosynthesis Screening Trial (MBTST). The trial included 14 848 women in whom 139 cancers were detected. Out of those, 101 spiculated or circumscribed masses, had prior images available, making them suitable for tumor growth evaluation. In the preliminary analysis of images from 30 women, tumor size was measured in mammograms from MBTST and prior images. The analyzed cases were selected among women with visible tumors in two consecutive screening exams. The tumor size was measured in two orthogonal directions. The average of the two measurements was used in the analysis. The mean time and the corresponding standard deviation (SD) between the two consecutive mammograms were 744 ± 73 days. The mean TVDT and SD were 637 ± 428 days (range 159-2373 days). Future work will include the analysis of a larger number of women and a stratification of TVDT related to screening intervals.
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| 15. |
- Torlegård, B., et al.
(författare)
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Identifying and modelling clinical subpopulations from the Malmö breast tomosynthesis screening trial
- 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
- Virtual Clinical Trials (VCT) are an effective tool to evaluate the performance of novel imaging systems using computer simulations. VCT results depend on the selection of virtual patient populations. In the case of breast imaging, virtual patients should be matched to a desired clinical population in terms of selected anatomical or demographic descriptors. We are developing a virtual population of women who participated in the Malmö Breast Tomosynthesis Screening Trial (MBTST). We have used clinical values of the compressed breast thickness and volumetric breast density to develop a multidimensional distribution of women in MBTST. Breast density and thickness values were obtained from anonymized, previously collected tomosynthesis images of 14,746 women. In this paper, we compare several approaches to identify clinical subpopulations and select virtual patients that represent various groups of clinical subjects. We performed two methods to identify clinical subpopulations by clustering clinical data using the K-means algorithm or woman's age. The obtained clusters have been explored and compared using the silhouette mean. The K-means algorithm yielded grouping of MBTST data into two clusters; however, that grouping was, shown to be suboptimal by the silhouette analysis. The agebased clustering showed significant overlap in terms of breast thickness and density. We also compared two approaches to select sets of representative phantoms. Our analysis has emphasized benefits and limitations of different clustering methods. The preferred method depends on the specific task that should be addressed using VCTs. Simulation of representative phantoms is ongoing. Potential correlations with pathological findings and/or parenchymal properties will be investigated.
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