Deep learning in breast cancer screening

Breast cancer is the most common cancer form among women worldwide and the incidence is rising. When mammography was introduced in the 1980s, mortality rates decreased by 30% to 40%. Today all women in Sweden between 40 to 74 years are invited to screening every 18 to 24 months. All women attending screening are examined with mammography, using two views, the mediolateral oblique (MLO) view and the craniocaudal (CC) view, producing four images in total. The screening process is the same for all women and based purely on age, and not on other risk factors for developing breast cancer. Although the introduction of population-based breast cancer screening is a great success, there are still problems with interval cancer (IC) and large screen detected cancers (SDC), which are connected to an increased morbidity and mortality. To have a good prognosis, it is important to detect a breast cancer early while it has not spread to the lymph nodes, which usually means that the primary tumor is small. To improve this, we need to individualize the screening program, and be flexible on screening intervals and modalities depending on the individual breast cancer risk and mammographic sensitivity. In Sweden, at present, the only modality in the screening process is mammography, which is excellent for a majority of women but not for all. The major lack of breast radiologists is another problem that is pressing and important to address. As their expertise is in such demand, it is important to use their time as efficiently as possible. This means that they should primarily spend time on difficult cases and less time on easily assessed mammograms and healthy women. One challenge is to determine which women are at high risk of being diagnosed with aggressive breast cancer, to delineate the low-risk group, and to take care of these different groups of women appropriately. In studies II to IV we have analysed how we can address these challenges by using deep learning techniques. In study I, we described the cohort from which the study populations for study II to IV were derived (as well as study populations in other publications from our research group). This cohort was called the Cohort of Screen Aged Women (CSAW) and contains all 499,807 women invited to breast cancer screening within the Stockholm County between 2008 to 2015. We also described the future potentials of the dataset, as well as the case control subset of annotated breast tumors and healthy mammograms. This study was presented orally at the annual meeting of the Radiological Society of North America in 2019. In study II, we analysed how a deep learning risk score (DLrisk score) performs compared with breast density measurements for predicting future breast cancer risk. We found that the odds ratios (OR) and areas under the receiver operating characteristic curve (AUC) were higher for age-adjusted DLrisk score than for dense area and percentage density. The numbers for DLrisk score were: OR 1.56, AUC, 0.65; dense area: OR 1.31, AUC 0.60, percent density: OR 1.18, AUC, 0.57; with P < .001 for differences between all AUCs). Also, the false-negative rates, in terms of missed future cancer, was lower for the DLrisk score: 31%, 36%, and 39% respectively. This difference was most distinct for more aggressive cancers. In study III, we analyzed the potential cancer yield when using a commercial deep learning software for triaging screening examinations into two work streams – a ‘no radiologist’ work stream and an ‘enhanced assessment’ work stream, depending on the output score of the AI tumor detection algorithm. We found that the deep learning algorithm was able to independently declare 60% of all mammograms with the lowest scores as “healthy” without missing any cancer. In the enhanced assessment work stream when including the top 5% of women with the highest AI scores, the potential additional cancer detection rate was 53 (27%) of 200 subsequent IC, and 121 (35%) of 347 next-round screen-detected cancers. In study IV, we analyzed different principles for choosing the threshold for the continuous abnormality score when introducing a deep learning algorithm for assessment of mammograms in a clinical prospective breast cancer screening study. The deep learning algorithm was supposed to act as a third independent reader making binary decisions in a double-reading environment (ScreenTrust CAD). We found that the choice of abnormality threshold will have important consequences. If the aim is to have the algorithm work at the same sensitivity as a single radiologist, a marked increase in abnormal assessments must be accepted (abnormal interpretation rate 12.6%). If the aim is to have the combined readers work at the same sensitivity as before, a lower sensitivity of AI compared to radiologists is the consequence (abnormal interpretation rate 7.0%). This study was presented as a poster at the annual meeting of the Radiological Society of North America in 2021. In conclusion, we have addressed some challenges and possibilities by using deep learning techniques to make breast cancer screening programs more individual and efficient. Given the limitations of retrospective studies, there is a now a need for prospective clinical studies of deep learning in mammography screening

ASSESSMENT AND PREDICTION OF BREAST CANCER OUTCOME IN SUBGROUPS OF PATIENTS

Ph.DDOCTOR OF PHILOSOPH

Information Fusion of Magnetic Resonance Images and Mammographic Scans for Improved Diagnostic Management of Breast Cancer

Medical imaging is critical to non-invasive diagnosis and treatment of a wide spectrum of medical conditions. However, different modalities of medical imaging employ/apply di erent contrast mechanisms and, consequently, provide different depictions of bodily anatomy. As a result, there is a frequent problem where the same pathology can be detected by one type of medical imaging while being missed by others. This problem brings forward the importance of the development of image processing tools for integrating the information provided by different imaging modalities via the process of information fusion. One particularly important example of clinical application of such tools is in the diagnostic management of breast cancer, which is a prevailing cause of cancer-related mortality in women. Currently, the diagnosis of breast cancer relies mainly on X-ray mammography and Magnetic Resonance Imaging (MRI), which are both important throughout different stages of detection, localization, and treatment of the disease. The sensitivity of mammography, however, is known to be limited in the case of relatively dense breasts, while contrast enhanced MRI tends to yield frequent 'false alarms' due to its high sensitivity. Given this situation, it is critical to find reliable ways of fusing the mammography and MRI scans in order to improve the sensitivity of the former while boosting the specificity of the latter. Unfortunately, fusing the above types of medical images is known to be a difficult computational problem. Indeed, while MRI scans are usually volumetric (i.e., 3-D), digital mammograms are always planar (2-D). Moreover, mammograms are invariably acquired under the force of compression paddles, thus making the breast anatomy undergo sizeable deformations. In the case of MRI, on the other hand, the breast is rarely constrained and imaged in a pendulous state. Finally, X-ray mammography and MRI exploit two completely di erent physical mechanisms, which produce distinct diagnostic contrasts which are related in a non-trivial way. Under such conditions, the success of information fusion depends on one's ability to establish spatial correspondences between mammograms and their related MRI volumes in a cross-modal cross-dimensional (CMCD) setting in the presence of spatial deformations (+SD). Solving the problem of information fusion in the CMCD+SD setting is a very challenging analytical/computational problem, still in need of efficient solutions. In the literature, there is a lack of a generic and consistent solution to the problem of fusing mammograms and breast MRIs and using their complementary information. Most of the existing MRI to mammogram registration techniques are based on a biomechanical approach which builds a speci c model for each patient to simulate the effect of mammographic compression. The biomechanical model is not optimal as it ignores the common characteristics of breast deformation across different cases. Breast deformation is essentially the planarization of a 3-D volume between two paddles, which is common in all patients. Regardless of the size, shape, or internal con guration of the breast tissue, one can predict the major part of the deformation only by considering the geometry of the breast tissue. In contrast with complex standard methods relying on patient-speci c biomechanical modeling, we developed a new and relatively simple approach to estimate the deformation and nd the correspondences. We consider the total deformation to consist of two components: a large-magnitude global deformation due to mammographic compression and a residual deformation of relatively smaller amplitude. We propose a much simpler way of predicting the global deformation which compares favorably to FEM in terms of its accuracy. The residual deformation, on the other hand, is recovered in a variational framework using an elastic transformation model. The proposed algorithm provides us with a computational pipeline that takes breast MRIs and mammograms as inputs and returns the spatial transformation which establishes the correspondences between them. This spatial transformation can be applied in different applications, e.g., producing 'MRI-enhanced' mammograms (which is capable of improving the quality of surgical care) and correlating between different types of mammograms. We investigate the performance of our proposed pipeline on the application of enhancing mammograms by means of MRIs and we have shown improvements over the state of the art

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Breast cancer risk associated with changes in mammographic density.

PhD ThesisBreast cancer is the most common cancer in the UK, and mammographic density (‘density’) is one of its strongest known risk factors. At present, most research focuses on static measures of density to determine population effects. The central hypothesis of this thesis is that repeated measures of density are more valuable for personalised breast cancer prevention. This hypothesis was tested through the following research. Study-I investigated within-women associations between body mass index (BMI) and density, to assess whether density (visual/Cumulus/volumetric ‘Stepwedge’) acts as a mediator for breast cancer risk reduction during a premenopausal weight-loss intervention (n=65). Study-II evaluated the benefit of using a woman’s longitudinal history of (BI-RADS) density to improve breast cancer risk estimation (n=132,439). Study-III was a Cochrane systematic review investigating the association between endocrine therapy-induced density reduction and breast cancer risk and mortality. Studies-IV and V (n=575) evaluated visually-assessed density reduction with prophylactic anastrozole during the International Breast Cancer Intervention Study-II, and its use as a biomarker for concurrent breast cancer risk reduction, respectively. In Study-I, change in BMI was associated with change in breast fat but not dense tissue, negating density reduction as a biomarker for risk reduction with weight-loss. In Study-II, longitudinal density provided approximately a quarter more statistical information than most recent density and improved discriminatory accuracy. Study-III found evidence that density reduction may be a biomarker for reduction in risk and mortality with tamoxifen, but the level of evidence was limited by some study quality issues. Study-IV indicated that preventive anastrozole might marginally reduce density, but statistical significance was not obtained. In Study-V, sample size was too small to draw definitive conclusions. Overall, changes in density were useful for the study of breast cancer risk and should be considered for personalised breast cancer prevention strategies

The Impact of Access to Cancer Care on Adjuvant Endocrine Therapy Use Among Breast Cancer Survivors in Appalachia.

OBJECTIVES: The Appalachia region experiences excess cancer mortality and a lack of access to cancer care resources. There is limited research examining adjuvant treatment use disparities in this region. This study aims to explore adjuvant endocrine therapy (AET) utilization in Appalachia, and delineate the effects of access to cancer on AET use. METHODS: Female breast cancer patients were identified in cancer registries from the Appalachian counties in four states (KY, NC, OH, and PA) and linked to 2006-2008 Medicare claims data. We included patients with invasive, non-metastatic, hormone-receptor-positive breast cancer and assessed the prevalence of receiving guideline-recommended AET. We then assessed AET adherence among those who received guideline-recommended AET using the Medication Possession Ratio (MPR), and determined non-persistence, defined as exceeding a 60-day medication gap. We also used survival analyses to examine the influences of AET adherence and persistence on overall survival. RESULTS: Only 450 of the 946 eligible patients (47.6%) received guideline-recommended AET, which was significantly associated with shorter travel time to receive care, dual Medicare and Medicaid eligibility, being unmarried (vs. married), and living in Pennsylvania (vs. Ohio). The non-adherence rate was about 31% and non-persistence rate was 30% over an average follow-up period of 421 days. Tamoxifen, relative to aromatase inhibitors, was associated with higher odds of adherence (Odds Ratio = 2.82, p < 0.001) and a lower risk of non-persistence (Hazard Ratio = 0.40, p < 0.001). Side effects like pain may be an important factor leading to non-adherence and early discontinuation. Non-adherence to and non-persistence with AET were associated with higher risks of all-cause mortality. CONCLUSIONS: In Appalachia, geographic and socioeconomic factors such as travel time to receive care and healthcare plan type are important elements that could contribute to disparities in access to adjuvant treatment, while treatment choice and medication-related factors may exert strong influences on AET use behaviors.PhDSocial and Administrative SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111366/1/tanxi_1.pd