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Background: Running online training in mammography interpretation poses a challenge to radiologists and reporting radiographers due to the large size of digital mammograms in DICOM format and limited bandwidth capabilities of the users for image transmission. This study aims to compare image quality between the full-quality with minimal compressed JPEG and DICOM format of mammograms on a diagnostic monitor through the evaluation of radiologists and radiographers., Methods: Twelve participants including six radiologists and six radiographers participated as observers in this study. The observers viewed 60 2D digital mammography screening cases (22 cancer and 38 normal cases) in DICOM and minimal compressed JPEG formats on a 5MP diagnostic monitor. A 5-point Likert scale was provided for observers to compare the quality of mammograms between the two formats, with text anchors indicating to one image being significantly better, slightly better or of equal quality in terms of technical and diagnostic aspects. Nonparametric descriptive statistics were used to evaluate the ratings of radiologists and radiographers in different characteristics of mammograms of two image formats., Results: The DICOM and JPEG images were statistically equivalent through ratings from radiographers in brightness, contrast, dynamic range, sharpness, no significant distortion, no significant noise, and background homogeneity in all mammograms. Similarly, most radiologists rated DICOM and JPEG images clinically and statistically equivalent with respect to difficulty of interpretation, brightness, contrast, dynamic range, sharpness, the appearance of Cooper's ligaments, visibility of subtle microcalcifications, visibility of structures at the margins of the breast. Normal cases were marginally favored by radiologists in DICOM format (ranging from 0.4% to 5.3%) while cancer cases in JPEG (ranging from 0.8% to 7.6%) received slightly higher rating., Conclusions: Findings showed that baseline full-quality with minimal compression JPEG was equivalent to the DICOM format of full-field digital mammograms which suggests that this type of JPEG could be used for online training and education in radiology., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Provision of online and remote specialist education and general continued professional education in medicine is a growing field. For radiology specifically, the ability to access web-based platforms that house high resolution medical images, and the high fidelity of simulated activities is increasingly growing due to positive changes in technology. This study investigates the differences in providing a self-directed specialist radiology education system in two modes: at clinics and in-person workshops. 335 Australian radiologists completed 562 readings of mammogram test sets through the web-based interactive BREAST platform with 325 at conference workshops and 237 at their workplaces. They engaged with test sets with each comprising of 60 mammogram cases (20 cancer and 40 normal). Radiologists marked the location of any cancers and had their performance measured via 5 metrics of diagnostic accuracy. Results show that the location of engagement with BREAST did not yield any significant difference in the performances of all radiologists and the same radiologists between two reading modes (P > 0.05). Radiologists who read screening mammograms for BreastScreen Australia performed better when they completed the test sets at designated workshops (P < 0.05), as was also the case for radiologists who read > 100 cases per week (P < 0.05). In contrast, radiologists who read less mammograms frequently recorded better performances in specificity and JAFROC at clinics (P < 0.05). Findings show that remotely accessed online education for specialised training and core skills building in radiology can provide a similar learning opportunity for breast radiologists when compared to on-site dedicated workshops at scientific meetings. For readers with high volumes of mammograms, a workshop setting may provide a superior experience while clinic setting is more helpful to less experienced readers., (© 2022. The Author(s).)

Objectives: This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods: Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results: Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion: The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike. What is known about the topic? Artificial intelligence (AI) holds promise in providing computer-aided detection in health care, however, current research suggests that standalone AI applications in clinical practice fall short of matching the accuracy of a single radiologist. What does this paper add? The study showed a significant preference among clinicians for using AI as a supplementary tool, serving as a second-reader. Such an integrated approach, where AI aids in flagging suspicious areas on mammograms or offers automatic classification, reflects the ideal cooperation between breast screening readers and AI systems. What are the implications for practitioners? These insights shed light on clinicians' familiarity with and expectations of AI tools that can boost the effectiveness of breast screening programs. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explored the diagnostic efficacy of radiologists when reading screening mammograms in the absence of previous images (NP), and with prior images obtained from the same (SP) and different vendors (DP). There were 612 radiologists reading 9 mammogram test sets (361-normal and 179-cancer) with 245 cases having prior images from the same vendor and 129 from different vendors. Radiologists obtained 12.8% and 10.3% higher sensitivity in NP and DP than SP. The ROC AUC for NP and DP were also significantly higher than SP. The odds ratio of true positive for NP and DP was 1.6 and 1.5, respectively, relative to SP cases. Radiologists were more likely to detect architectural distortion (OR = 3.2) and calcifications (OR = 2.85) in DP than SP. The findings suggest exploring a mixed reading strategy in viewing cases with prior mammograms acquired from the same and different manufacturers to enhance the diagnostic accuracy in the digital era. Background: This study aims to investigate the diagnostic efficacy of radiologists when reading screening mammograms in the absence of previous images, and with the presence of prior images from the same and different vendors. Methods: 612 radiologists' readings across 9 test sets, consisting of 540 screening mammograms (361-normal and 179-cancer) with 245 cases having prior images obtained from same vendor as current images, 129 from a different vendor and 166 cases having no prior images, were retrospectively analysed. True positive (sensitivity), true negative (specificity) and area under ROC curve (AUC) values of radiologists were calculated for three groups of cases (without prior images (NP), with prior images from same vendor (SP), and with prior images from different vendor (DP)). Logistic regression was used to estimate the odds ratio (OR) of true positive, true negative and true cancer localization among case groups with different levels of breast density and lesion characteristics. Results: Radiologists obtained 12.8% and 10.3% higher sensitivity in NP and DP than SP (0.803-and-0.785 vs. 0.712; p < 0.0001). Specificity in NP and DP cases were 4.8% and 2.0% lower than SP cases (0.749 and 0.771 vs. 0.787). The AUC values for NP and DP were significantly higher than SP cases across different levels of breast density (0.814-and-0.820 vs. 0.782; p < 0.0001). The odds ratio (OR) of true positive for NP relative to SP was 1.6 (p < 0.0001) and DP relative to SP was 1.5 (p < 0.0001). Radiologists were more like to detect architectural distortion in DP than SP cases (OR = 3.2; p < 0.0001), whilst the OR for abnormal calcifications was 2.85 (p < 0.0001). Conclusions: Cases without previous mammograms or with prior mammograms obtained from different vendors were more likely to benefit radiologists in cancer detection, whilst prior mammograms undertaken from the same vendor were more useful for radiologists in evaluating normal cases. [ABSTRACT FROM AUTHOR]

Aim: Breast cancer incidence is rapidly increasing throughout South East Asia, highlighting the need for high‐quality early diagnosis and treatment. This study aims to investigate the efficacy of mammography detection in Hanoi and Ho Chi Minh City (HCMC), Vietnam, using data from Australian radiologists as a benchmark; factors that influence performance will be highlighted. Methods: A total of 53, 35 and 52 clinicians from Australia, HCMC and Hanoi, respectively, examined and diagnosed a test set of 60 mammograms, 20 of which contained cancers. Each clinician completed an accompanying questionnaire establishing demographic and experiential characteristics. The performance metrics of specificity, sensitivity, area under the receiver operating characteristic curve (AUC), location sensitivity and Jackknife free‐response ROC (JAFROC) figure of merit were used to evaluate clinicians performance. Mann‐Whitney and Kruskal‐Wallis statistical methods were employed to establish significance. Results: Vietnamese radiologists demonstrated significantly lower sensitivity, AUC, lesion sensitivity and JAFROC scores compared to Australian radiologists. There was no difference in performance between clinicians from Hanoi and HCMC. However, certain performance features (older and more experienced clinicians compared with their younger, less experienced counterparts, readers who read more compared with fewer mammograms per week, clinicians with greater radiological experience and clinicians that completed a fellowship) demonstrated significantly better performances. Conclusions: The significant difference in diagnostic efficacy of mammograms between Vietnam and Australia identifies the need for improvements in breast radiology training, management and practice. Cost‐effective solutions are available that can improve the reading efficacy of clinicians, and consequently health outcomes for Vietnamese women. [ABSTRACT FROM AUTHOR]

Aims: To explore radiologist characteristics and case features associated with diagnostic performances in cancer detection on mammograms in a South East Asian population. Methods: Fifty‐three radiologists reported 60 mammographic examinations which consisted of 40 normal and 20 cancer‐containing cases at the BREAST workshops. Radiologists were asked to examine each mammogram using the BIRADS on diagnostic monitors. Differences in reader characteristics and case features between correct and incorrect decisions were assessed separately for cancer and normal cases. Univariate and multivariate logistic regressions were applied to generate odds ratios (OR) for significant factors related to correct decisions. Results: Radiologists who spent ≥10 hours/week reporting mammograms had a higher possibility of detecting cancer lesions (OR = 1.6; P = 0.01). A higher rate of accuracy in reporting negative cases was associated with female radiologists (OR = 1.4; P = 0.002), radiologists who read ≤20 mammograms per week (OR = 1.5; P < 0.0001), had completed training course (OR = 1.7; P < 0.0001) or wore eyeglasses (OR = 1.4; P = 0.01). Cancer cases with breast density >50% (OR = 2.1; P < 0.0001), having abnormal lesions ≥9 mm (OR = 1.8; P < 0.0001), or displaying calcifications, a discrete mass or nonspecific density (OR = 1.6; P < 0.0001) were recorded with a higher detection rate by radiologists than other cases. Lesions located on the right breasts (OR = 1.8; P < 0.0001) or found in the lower inner, upper outer or mixed locations (OR = 2.7; P < 0.0001) were also recorded with a better diagnostic possibility compared with other lesions. Conclusion: This work identified key features related to diagnostic accuracy of breast cancer on mammograms in a nonscreening population, which is helpful for developing appropriate strategies to improve breast cancer detectability of radiologists. [ABSTRACT FROM AUTHOR]