AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions.

Although the value of adding AI as a surrogate second reader in various scenarios has been investigated, it is unknown whether implementing an AI tool within double reading practice would capture additional subtle cancers missed by both radiologists who independently assessed the mammograms. This paper assesses the effectiveness of two state-of-the-art Artificial Intelligence (AI) models in detecting retrospectively-identified missed cancers within a screening program employing double reading practices. The study also explores the agreement between AI and radiologists in locating the lesions, considering various levels of concordance among the radiologists in locating the lesions. The Globally-aware Multiple Instance Classifier (GMIC) and Global-Local Activation Maps (GLAM) models were fine-tuned for our dataset. We evaluated the sensitivity of both models on missed cancers retrospectively identified by a panel of three radiologists who reviewed prior examinations of 729 cancer cases detected in a screening program with double reading practice. Two of these experts annotated the lesions, and based on their concordance levels, cases were categorized as 'almost perfect,' 'substantial,' 'moderate,' and 'poor.' We employed Similarity or Histogram Intersection (SIM) and Kullback-Leibler Divergence (KLD) metrics to compare saliency maps of malignant cases from the AI model with annotations from radiologists in each category. In total, 24.82% of cancers were labeled as "missed." The performance of GMIC and GLAM on the missed cancer cases was 82.98% and 79.79%, respectively, while for the true screen-detected cancers, the performances were 89.54% and 87.25%, respectively (p-values for the difference in sensitivity < 0.05). As anticipated, SIM and KLD from saliency maps were best in 'almost perfect,' followed by 'substantial,' 'moderate,' and 'poor.' Both GMIC and GLAM (p-values < 0.05) exhibited greater sensitivity at higher concordance. Even in a screening program with independent double reading, adding AI could potentially identify missed cancers. However, the challenging-to-locate lesions for radiologists impose a similar challenge for AI.

Designing a breast support device for phase contrast tomographic imaging: getting ready for a clinical trial.

OBJECTIVE: To design a device that can support the breast during phase-contrast tomography, and characterise its fit parameterisation and comfort rating. METHODS: 27 participants were recruited to trial a system for breast support during simulated phase contrast imaging, including being positioned on a prone imaging table while wearing the device. Participants underwent a photogrammetry analysis to establish the geometric parameterisations. All participants trialled a single-cup design while 14 participants also trialled a double-cup with suction holder and all completed a series of questionnaires to understand subjective comfort. RESULTS: Photogrammetry revealed significant positive correlations between bra cup volume and measured prone volume (p < 0.001), and between "best fit" single-cup holder volume and measured prone volume (p < 0.005). Both holders were suitable devices in terms of subjective comfort and immobilisation while stationary. However, some re-engineering to allow for quick, easy fitting in future trials where rotation through the radiation beam will occur is necessary. Light suction was well-tolerated when required. CONCLUSION: All participants indicated the table and breast support devices were comfortable, and they would continue in the trial. ADVANCES IN KNOWLEDGE: Phase contrast tomography is an emerging breast imaging modality and clinical trials are commencing internationally. This paper describes the biomedical engineering designs, in parallel with optimal imaging, that are necessary to measure breast volume so that adequate breast support can be achieved. Breast support devices have implications for comfort, motion correction and maximising breast tissue visualisation.

Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection.

The information captured by the gist signal, which refers to radiologists' first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captured by radiologists (experiment 1), or computer algorithms (experiment 2) based on detailed mammogram inspection. In the first experiment, 19 radiologists assessed a case set twice, once based on a half-second image presentation (i.e., gist signal) and once in the usual viewing condition. Their performances in two viewing conditions were compared using repeated measure correlation (rm-corr). The cancer cases (19 cases × 19 readers) exhibited non-significant trend with rm-corr = 0.012 (p = 0.82, CI: -0.09, 0.12). For normal cases (41 cases × 19 readers), a weak correlation of rm-corr = 0.238 (p < 0.001, CI: 0.17, 0.30) was found. In the second experiment, we combined the abnormality score from a state-of-the-art deep learning-based tool (DL) with the radiological gist signal using a support vector machine (SVM). To obtain the gist signal, 53 radiologists assessed images based on half-second image presentation. The SVM performance for each radiologist and an average reader, whose gist responses were the mean abnormality scores given by all 53 readers to each image was assessed using leave-one-out cross-validation. For the average reader, the AUC for gist, DL, and the SVM, were 0.76 (CI: 0.62-0.86), 0.79 (CI: 0.63-0.89), and 0.88 (CI: 0.79-0.94). For all readers with a gist AUC significantly better than chance-level, the SVM outperformed DL. The gist signal provided malignancy evidence with no or weak associations with the information captured by humans in normal radiologic reporting, which involves detailed mammogram inspection. Adding gist signal to a state-of-the-art deep learning-based tool improved its performance for the breast cancer detection.

Effect of x-ray energy on the radiological image quality in propagation-based phase-contrast computed tomography of the breast.

Purpose: Breast cancer is the most common cancer in women in developing and developed countries and is responsible for 15% of women's cancer deaths worldwide. Conventional absorption-based breast imaging techniques lack sufficient contrast for comprehensive diagnosis. Propagation-based phase-contrast computed tomography (PB-CT) is a developing technique that exploits a more contrast-sensitive property of x-rays: x-ray refraction. X-ray absorption, refraction, and contrast-to-noise in the corresponding images depend on the x-ray energy used, for the same/fixed radiation dose. The aim of this paper is to explore the relationship between x-ray energy and radiological image quality in PB-CT imaging. Approach: Thirty-nine mastectomy samples were scanned at the imaging and medical beamline at the Australian Synchrotron. Samples were scanned at various x-ray energies of 26, 28, 30, 32, 34, and 60 keV using a Hamamatsu Flat Panel detector at the same object-to-detector distance of 6 m and mean glandular dose of 4 mGy. A total of 132 image sets were produced for analysis. Seven observers rated PB-CT images against absorption-based CT (AB-CT) images of the same samples on a five-point scale. A visual grading characteristics (VGC) study was used to determine the difference in image quality. Results: PB-CT images produced at 28, 30, 32, and 34 keV x-ray energies demonstrated statistically significant higher image quality than reference AB-CT images. The optimum x-ray energy, 30 keV, displayed the largest area under the curve ( AUC VGC ) of 0.754 ( p = 0.009 ). This was followed by 32 keV ( AUC VGC = 0.731 , p ≤ 0.001 ), 34 keV ( AUC VGC = 0.723 , p ≤ 0.001 ), and 28 keV ( AUC VGC = 0.654 , p = 0.015 ). Conclusions: An optimum energy range (around 30 keV) in the PB-CT technique allows for higher image quality at a dose comparable to conventional mammographic techniques. This results in improved radiological image quality compared with conventional techniques, which may ultimately lead to higher diagnostic efficacy and a reduction in breast cancer mortalities.

Reading High Breast Density Mammograms: Differences in Diagnostic Performance between Radiologists from Hong Kong SAR/Guangdong Province in China and Australia.

BACKGROUND: Variations in the performance of radiologists reading mammographic images are well reported, but key parameters explaining such variations in different countries are not fully explored. The main aim of this study is to investigate performances of Chinese (Hong Kong SAR and Guangdong Province) and Australian radiologists in interpreting dense breast mammographic images. METHODS: A test set, contained 60 mammographic examinations with high breast density, was used to assess radiologists' performance. Twelve Chinese and thirteen Australian radiologists read all the cases independently and were asked to identify all lesions and provide a grade from 1 to 5 to each lesion. Case sensitivity, specificity, lesion sensitivity, AUC and JAFROC were used to assess radiologists' performances. Demographic information and reading experience were also collected from the readers. Performance scores were compared between the two populations and the relationships between performance scores and their reading experience were discovered. RESULTS: For radiologists who were less than 40-year-old, lesion sensitivity, AUC and JAFROC were significantly lower in Chinese radiologists than those in Australian (52.10% vs 71.45%, p=0.043; 0.76 vs 0.84, p=0.031; 0.59 vs 0.72, p=0.045; respectively). Australian radiologists with less than 10 years of reading experience had higher AUC and JAFROC scores compared with their Chinese counterparts (0.83 vs 0.76, p=0.039; 0.70 vs 0.56, p=0.020, respectively). CONCLUSIONS: We found that younger Australian radiologists performed better at reading dense breast cases which is likely to be linked to intensive fellowship training, immersion in a screening program and exposure to the benefits of a performance-measuring education tool.

X-Ray Phase-Contrast Technology in Breast Imaging: Principles, Options, and Clinical Application.

OBJECTIVE: The purpose of this article is to review different x-ray phase-contrast breast imaging techniques and their potential application in clinical settings. CONCLUSION: Phase-contrast imaging depicts not only the absorption contrast but also the refraction contrast of the transmitted x-ray beam. Early data suggest that this new modality may overcome some of the diagnostic limitations associated with current clinically available mammography systems and that it has potential for improving breast cancer detection.

Social networks and expertise development for Australian breast radiologists.

BACKGROUND: In this study, we explore the nexus between social networks and expertise development of Australian breast radiologists. Background literature has shown that a lack of appropriate social networks and interaction among certain professional group(s) may be an obstacle for knowledge acquisition, information flow and expertise sharing. To date there have not been any systematic studies investigating how social networks and expertise development are interconnected and whether this leads to improved performance for breast radiologists. METHODS: This study explores the value of social networks in building expertise alongside with other constructs of performance for the Australian radiology workforce using semi-structured in-depth interviews with 17 breast radiologists. RESULTS: The findings from this study emphasise the influences of knowledge transfer and learning through social networks and interactions as well as knowledge acquisition and development through experience and feedback. The results also show that accessibility to learning resources and a variety of timely feedback on performance through the information and communication technologies (ICT) is likely to facilitate improved performance and build social support. CONCLUSIONS: We argue that radiologists' and, in particular, breast radiologists' work performance, needs to be explored not only through individual numerical characteristics but also by analysing the social context and peer support networks in which they operate and we identify multidisciplinary care as a core entity of social learning.

A systems life cycle approach to managing the radiology profession: an Australian perspective.

OBJECTIVE: Although the medical system has expanded considerably over the past two decades in almost all countries, so too has the demand for health care. The radiology specialisation may be an early system indicator, being especially sensitive to changes in supply and demand in both rural and urban environments. The question is whether the new policies of increasing the number of radiologists can be a proper long-term solution for the imbalance of workforce supply and demand or not. METHODS: Using system dynamics modelling, we present our integrated descriptive models for the supply and demand of Australian radiologists to find the actual gap. Followed by this, we pose a prescriptive model for the supply in order to lessen the identified imbalance between supply and demand. Our system dynamics models compare the demand and supply of Australian radiologists over 40 years between 2010 and 2050. RESULTS: The descriptive model shows that even if the radiology training program grows at a higher rate than the medical training growth rate and its own historical growth, the system will never be able to meet demand. The prescriptive model also indicates that although changing some influential factors (e.g the intake rate) reduces the level of imbalance, the system will still stay unstable during the study period. CONCLUSION: We posit that Australia may need to design a new system of radiology provision to meet future demands for high-quality medical radiation services. We also suggest some strategies, such as greater development of radiographers' role, are critical for enabling sustainable change over time. What is known about the topic? Long-term workforce planning for medical services at the national level has been very challenging for policy makers of the 21st century. The current demographic imbalance in the supply and demand of the Australian radiologist workforce makes it difficult to plan the effects of extra inflow of radiology students over time.