Feature density as an uncertainty estimator method in the binary classification mammography images task for a supervised deep learning model

Labeled medical datasets may include a limited number of observations for each class, while unlabeled datasets may include observations from patients with pathologies other than those observed in the labeled dataset. This negatively influences the performance of the prediction algorithms. Including out-of-distribution data in the unlabeled dataset can lead to varying degrees of performance degradation, or even improvement, by using a distance to measure how out-of-distribution a piece of data is. This work aims to propose an approach that allows estimating the predictive uncertainty of supervised algorithms, improving the behaviour when atypical samples are presented to the distribution of the dataset. In particular, we have used this approach to mammograms X-ray images applied to binary classification tasks. The proposal makes use of Feature Density, which consists of estimating the density of features from the calculation of a histogram. The obtained results report slight differences when different neural network architectures and uncertainty estimators are usedUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

The Role Of Tissue Sound Speed As A Surrogate Marker Of Breast Density

Breast density is one of the strongest predictors of breast cancer risk as women with the densest breasts have a three- to five-fold increase in risk compared to women with the least dense breasts. Breast density is currently measured by using mammography, the current gold standard for breast imaging. There are many shortcomings to using mammography to measure breast density, including the use of ionizing radiation. Ultrasound tomography (UST) does not use ionizing radiation and can create tomographic breast sound speed images. These sound speed images are useful because breast density is proportional to sound speed. The purpose of this work was to assess the ability of UST to measure breast density and its ability to measure changes in breast density over short periods of time. A cohort of 251 patients was examined using both UST and mammography. Many different associations were found between the UST density measurement, the volume averaged sound speed, and the mammographic percent density. Additional associations were found between many other UST and mammographic imaging characteristics. UST density was found to correlate with various patient characteristics in a similar manner to mammographic density. Additionally, UST was used to examine the effects of tamoxifen on breast density. Tamoxifen has been shown to reduce mammographic density and breast cancer risk for some women. Preliminary data for 52 patients has shown promising results so far. UST density has decreased for approximately a similar percentage of patients as has been measured for mammographic density. These changes have been measured over short time frames that could not be achieved using mammography. These results show that UST\u27s ability to measure breast density is consistent with mammography, the current standard of care. UST has the potential to become a safe and effective device that can be used to reliably assess breast density and serial changes in breast density

A review of mammographic positioning image quality criteria for the craniocaudal projection

Detection of breast cancer is reliant on optimal breast positioning and the production of quality images. Two projections, the mediolateral (MLO) and craniocaudal (CC), are routinely performed. Determination of successful positioning and inclusion of all breast tissue is achieved through meeting stated image quality criteria. For the CC view, current image quality criteria are inconsistent. Absence of reliable anatomical markers, other than the nipple, further contribute to difficulties in assessing the quality of CC views. The aim of this paper was to explore published international quality standards to identify and find the origin of any CC positioning criteria which might provide for quantitative assessment. The pectoralis major (pectoral) muscle was identified as a key posterior anatomical structure to establish optimum breast tissue inclusion on mammographic projections. It forms the first two of the three main CC metrics that are frequently reported 1. visualisation of the pectoral muscle, 2. measurement of the posterior nipple line (PNL) and 3. depiction of retroglandular fat. This literature review explores the origin of the three metrics, and discusses three key publications, spanning 1992 to 1994, on which subsequent image quality standards have been based. The evidence base to support published CC metrics is sometimes not specified and more often the same set of publications are cited, most often without critical evaluation. To conclude, there remains uncertainty if the metrics explored for the CC view support objective evaluation and reproducibility to confirm optimal breast positioning and quality images

Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134976/1/mp8512.pd

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