[Digital Breast Tomosynthesis Quality Control Manual Overview].

Recently, mammography systems equipped with digital breast tomosynthesis (DBT) have become widely used in Japan. Therefore, it is urgently necessary to establish a quality control method for DBTs. So far, we have been studying acceptance tests for DBTs with reference to EUREF. In 2020, IEC 61223-3-6 was published, which provides not only acceptance tests but also constancy test methods. Therefore, we conducted data collection using DBTs sold in Japan and examined the feasibility of conducting constancy tests. Although there were some items that were difficult to implement in each device, we were able to confirm quality control items that could be implemented in many devices. In addition, we were able to confirm routine tests that enable rapid evaluation. Based on these results, we have developed a "Digital Breast Tomosynthesis Quality Control Manual". In this paper, we report an overview of the manual and the results of routine tests.

[Studies on the Effectiveness of High-luminance Monitors in Mammography Softcopy Interpretation].

PURPOSE: Recently, monitors with maximum luminance exceeding 2000 cd/m2 (high-luminance monitor) have been used for diagnostic mammography. In this study, we examined the visibility of high-luminance monitors by converting luminance meter measurements into the just noticeable difference (JND) Index. The ambient light was also examined at the same time. METHOD: The high-luminance monitor is a 21.3-inch IPS monochrome monitor with a maximum luminance of 3000 cd/m2. Experiments were conducted with a minimum luminance of 0.6 cd/m2 and a maximum luminance of 500, 850, and 1200 cd/m2. The luminance ratio was set to 1 : 2000 and the maximum luminance was changed to 500, 1000, and 2000 cd/m2. The ambient light was varied to 8.7, 36.1, 61.3, and 129.6 lx. The Japan Radiological Society recommended luminance values for each stage of phantom and Grayscale Standard Display Function curves were measured. RESULT: The JND increased as the maximum luminance was increased for both the case with the same minimum luminance and the case with the same luminance ratio, and visibility was improved. CONCLUSION: In both the case of the same minimum luminance and the case of the same luminance ratio, the JND was found to increase as the maximum luminance was increased. The results suggest that high-luminance monitors may improve visibility and allow for higher ambient light settings. Furthermore, the degree of eye fatigue needs to be verified.

[Examination of the Quality Control Items for Digital Breast Tomosynthesis System in Japan].

Mammography equipment attached to the digital breast tomosynthesis (DBT) system is widespread in Japan. However, there are no guidelines for quality control methods for DBT in Japan. Therefore, it is necessary to rapidly establish a performance evaluation procedure and a quality control procedure for DBT. In this study, we conducted basic experiments using DBTs of five companies (Canon Medical, Fujifilm Medical, GE Healthcare, Hologic, Siemens) already sold in Japan and examined feasible common items. We aimed to establish a quality control method for DBT in Japan. The measurement was performed based on the European Reference Organisation for Quality Assured Breast Screening and Diagnostic Services (EUREF) breast tomosynthesis quality control protocol, version 1.03. In this study, we tried to measure 18 items in DBT. We examined whether the 18 items could be measured using each device; it is not an evaluation of device performance based on the measured values. There were some management items that were difficult to implement due to the specifications of DBT, such as devices that required pressure on DBT operation, problems due to the shape of bucky, and devices that did not have stationary mode. There were also problems with measurement data; for example, devices could not retrieve projection data and reconstruction data. This study clarified points to be considered for establishing common quality control items. In the future, we will carefully refer to the recently published IEC 61223-3-6, consider international harmonization, and establish DBT guidelines customized for the Japanese market.

A deep learning-based automated diagnostic system for classifying mammographic lesions.

BACKGROUND: Screening mammography has led to reduced breast cancer-specific mortality and is recommended worldwide. However, the resultant doctors' workload of reading mammographic scans needs to be addressed. Although computer-aided detection (CAD) systems have been developed to support readers, the findings are conflicting regarding whether traditional CAD systems improve reading performance. Rapid progress in the artificial intelligence (AI) field has led to the advent of newer CAD systems using deep learning-based algorithms which have the potential to reach human performance levels. Those systems, however, have been developed using mammography images mainly from women in western countries. Because Asian women characteristically have higher-density breasts, it is uncertain whether those AI systems can apply to Japanese women. In this study, we will construct a deep learning-based CAD system trained using mammography images from a large number of Japanese women with high quality reading. METHODS: We will collect digital mammography images taken for screening or diagnostic purposes at multiple institutions in Japan. A total of 15,000 images, consisting of 5000 images with breast cancer and 10,000 images with benign lesions, will be collected. At least 1000 images of normal breasts will also be collected for use as reference data. With these data, we will construct a deep learning-based AI system to detect breast cancer on mammograms. The primary endpoint will be the sensitivity and specificity of the AI system with the test image set. DISCUSSION: When the ability of AI reading is shown to be on a par with that of human reading, images of normal breasts or benign lesions that do not have to be read by a human can be selected by AI beforehand. Our AI might work well in Asian women who have similar breast density, size, and shape to those of Japanese women. TRIAL REGISTRATION: UMIN, trial number UMIN000039009. Registered 26 December 2019, https://www.umin.ac.jp/ctr/.

[Selection of Radiomic Features for the Classification of Triple-negative Breast Cancer Based on Radiogenomics].

Subtype classification of breast cancer by analyzing the gene expression profile of cancer cells is becoming a standard procedure. Breast cancer subtype classification is more useful than the conventional method because the characteristics of subtype classification is directly connected with the treatment method. However, genetic testing is invasive, and a part of cancer cells may not represent the overall nature of the cancer. In the computer-aided diagnosis (CAD) scheme for differentiation of triple-negative breast cancer (TNBC) by estimating the genetic properties of cancer based on Radiogenomics, principal component analysis (PCA) and least absolute shrinkage and selection operator (Lasso) were used for reducing the dimension of radiomic features, and we compared usefulness of both. We collected 81 magnetic resonance (MR) images, which included 30 TNBC and 51 others, from the public database. From the MR slice images, we selected the slice containing the largest area of the cancer and manually marked the cancer region. We subsequently calculated 294 radiomic features in the cancer region, and reduced the dimension of radiomic features. Finally, linear discriminant analysis, with the dimensionally compressed 10 image features, was used for distinguishing between TNBC and others. Area under the curve (AUC) was 0.60 when we used PCA, whereas AUC was 0.70 when we used Lasso (p=0.0058). Therefore, Lasso is useful for the determination of radiomic features in Radiogenomics.

Computerized quantitative evaluation of mammographic accreditation phantom images.

PURPOSE: The objective was to develop and investigate an automated scoring scheme of the American College of Radiology (ACR) mammographic accreditation phantom (RMI 156, Middleton, WI) images. METHODS: The developed method consisted of background subtraction, determination of region of interest, classification of fiber and mass objects by Mahalanobis distance, detection of specks by template matching, and rule-based scoring. Fifty-one phantom images were collected from 51 facilities for this study (one facility provided one image). A medical physicist and two radiologic technologists also scored the images. The human and computerized scores were compared. RESULTS: In terms of meeting the ACR's criteria, the accuracies of the developed method for computerized evaluation of fiber, mass, and speck were 90%, 80%, and 98%, respectively. Contingency table analysis revealed significant association between observer and computer scores for microcalcifications (p<5%) but not for masses and fibers. CONCLUSIONS: The developed method may achieve a stable assessment of visibility for test objects in mammographic accreditation phantom image in whether the phantom image meets the ACR's criteria in the evaluation test, although there is room left for improvement in the approach for fiber and mass objects.

[A comparison between physicians' interpretation and a CAD system's cancer detection by using a mammogram database in a physicians' self-learning course].

We have been developing automated detection algorithms for masses and clustered microcalcifications in a mammography computer-aided diagnosis (CAD) system. In this study, we investigated the potential of our CAD system by comparing 579 physicians' interpretation results with that of the CAD system's cancer detection for 100 mammograms (21 malignant and 29 benign cases) employed in a physicians' self-learning course. As a result, our CAD system detected 7 out of 8 malignant lesions whose physicians' averaged sensitivity was less than 60%. Although the average of physicians' sensitivities were 76% (about 16 cases), the CAD system's detection rate was 90% (19 cases). Sensitivity was raised up to 97% if the physicians' interpretation and the CAD system's detection result were treated in a matter of logical OR. Thus, it was raised the possibility that even the less-experienced physicians would diagnose with a higher sensitivity by using the computer output as a guide effectively.