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Purpose: This study explores the attributes of true positive and false positive rates in screening mammogram test sets assessed by breast screening radiologists in order to identify the combined impact of prior images, breast density and lesion features with experience factors linked to diagnostic performance., Methods: 869 radiologists' first-time readings across nine mammogram BREAST test sets with 361 normal and 179 cancer mammograms were collected between 2014 and 2023. Participants viewed digital mammograms on diagnostic monitors and localized abnormal lesions. The performances of readers in normal and cancer cases were compared with the ground truth and analyzed in four quartiles of breast density, lesion types and the availability of prior images using Man-Whitney U and Kruskal Wallis tests. The general linear model was applied to determine independent and significant covariates that affected the true positives and false positives. The correlation of the readers' experience with the performances in different case and lesion features was explored using Spearman test., Results: The combination of lesion appearance and the availability of prior images had a significant impact on false positive results (P=0.033). The model that included lesion appearance, breast density, and no prior image status significantly influenced case true positives of readers (P=0.026). Meanwhile, the model considering only lesion appearance and breast density (P=0.002) had a significant effect on lesion true positives. There was a positive correlation observed between the number of cases read per week and readers' performances, including TP rates and lesion sensitivity across various lesion types (P<0.05). Radiologists reading over 100 cases weekly achieved 80 % true positive rate for architectural distortion, asymmetric density, and masses, while this threshold increased to over 150 cases for calcifications. Detecting mixed lesion types required reading more than 60 cases weekly. Radiologists with over 5 years' experience achieved 70-75 % accuracy in localizing cancer lesions., Conclusion: Findings highlight the significant combined impact of breast density, prior image availability, lesion characteristics, and breast screening readers' experience on breast cancer detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Aims: To establish the impact that access to prior mammograms has on radiologists' performance and the influence of radiologists' characteristics and breast density on their subsequent performance., Methods: Eight participants independently interpreted 72 digital screening mammograms in two reading sessions using the Royal Australian and New Zealand College of Radiologist's classification. In the first reading session, participants were given access to current and prior mammograms. In the second reading session six months later, participants only had access to the current mammograms. Radiologists' specificity, sensitivity, lesion sensitivity, Receiver Operating Characteristic (ROC) curve, and Jacknife Alternative Free-response ROC (JAFROC) were calculated. A Paired T-test was used to compare readings with and without prior mammograms, and to assess if breast density influenced participants performance. Independent Sample T-test was used to compare performance across radiologists' characteristics. A relative risk analysis was conducted to assess the probability of false positives and false negatives when prior mammograms were available., Results: Access to prior mammograms improved specificity in dense and non-dense breasts (p≤0.01) and reduced false positives (p = 0.01) but had no effect on sensitivity (p = 0.37), lesion sensitivity (p = 0.67), ROC (p = 0.16), and JAFROC (p = 0.24). Prior mammogram also reduced the probability of false positives (RR = 0.38; 95%CI:0.26-0.57, p<0.0001) without affecting the false negative rate (RR = 1.14; 95%CI:0.88-1.49, p = 0.30). The impact of prior mammograms on performance was not influenced by breast density or radiologists' characteristics., Conclusions: Access to prior mammograms improves radiologists' specificity and reduces false positives without affecting sensitivity and the false negative rate regardless of radiologists' characteristics and breast density., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Rationale and Objectives: Integrating learning spacing in medicine has shown promise in enhancing knowledge retention and diagnostic proficiency. While studies demonstrate the effectiveness of spaced learning in various fields, limited research exists on its application in radiological training. This study aims to investigate the impact of intervals in spaced training on radiologists' and trainees' diagnostic performance via mammogram test sets., Methods: 54 radiologists and 101 radiology trainees completed 207 and 458 first-time readings of 9 mammogram test sets between 2019 and 2023. Each test set comprised of 60 mammograms (20 cancer and 40 normal), sourced retrospectively from BreastScreen Australia. Each radiologist evaluated mammograms using the BIRADS lexicon. Readers' performance was compared with truth data and evaluated in terms of specificity, case sensitivity, lesion sensitivity, ROC AUC and JAFROC FOM. The progress of readers' performances in following test sets after the first one was analyzed using Wilcoxon Signed Rank test. The association of participants' performances and the intervals among test sets' completions was investigated using Pearson's test., Results: Significant positive correlations were found between intervals and radiologists' improvement in specificity and JAFROC FOM (P < 0.05). The separation of 4 to 10 days showed the most improvement among radiologists across all metrics, while intervals exceeding 90 days related to highest increase in case sensitivity (5.15%), lesion sensitivity (6.55%), ROC AUC (3.05%) and JAFROC FOM (6.3%). Trainees completing test sets in one day showed positive correlations with their ROC AUC (R=0.45; P = 0.008) and JAFROC FOM (R=0.43; P = 0.02), while those taking a longer time to complete showed negative impacts on case sensitivity (P = 0.009) and ROC AUC (P = 0.02). Remarkable progress in trainees was found in case sensitivity (6.15%), lesion sensitivity (11.6%), ROC AUC (3.5%) and JAFROC FOM (4.35%) with test set intervals of 31-90 days., Conclusions: Radiologists demonstrated superior performance when the training test sets were spaced over longer intervals, whereas trainees exhibited proficiency with shorter time separations. By optimizing the spacing of reviewing and practicing radiological concepts, mammogram readers can bolster memory retention and diagnostic decision-making skills., Competing Interests: Declaration of Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Competing Interests All authors have no conflicts of interest related to the work described in the study., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

G protein-coupled receptors (GPCRs) are the largest group of membrane receptors in the central nervous system and one of the key proteins for signal transduction between cells. Currently, many drugs available on the market act via GPCRs and these receptors remain attractive targets for the treatment of brain disorders, including alcohol use disorder (AUD). Here, we describe the most recent literature, with a primary focus on the past 5 years, on GPCR targets with the potential for reducing behaviours associated with excessive alcohol intake. Specifically, we focus on preclinical evidence of compounds with attractive pharmacological profiles and potential for future clinical investigation for the treatment of AUD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.

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., (© 2024. The Author(s).)

This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms ( n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences ( p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.

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).)

Objective: The study aims to evaluate the diagnostic efficacy of radiologists and radiology trainees in digital breast tomosynthesis (DBT) alone vs DBT plus synthesized view (SV) for an understanding of the adequacy of DBT images to identify cancer lesions., Methods: Fifty-five observers (30 radiologists and 25 radiology trainees) participated in reading a set of 35 cases (15 cancer) with 28 readers reading DBT and 27 readers reading DBT plus SV. Two groups of readers had similar experience in interpreting mammograms. The performances of participants in each reading mode were compared with the ground truth and calculated in term of specificity, sensitivity, and ROC AUC. The cancer detection rate in various levels of breast density, lesion types and lesion sizes between 'DBT' and 'DBT + SV' were also analyzed. The difference in diagnostic accuracy of readers between two reading modes was assessed using Man-Whitney U test. p < 0.05 indicated a significant result., Results: There was no significant difference in specificity (0.67- vs -0.65; p = 0.69), sensitivity (0.77- vs -0.71; p = 0.09), ROC AUC (0.77- vs -0.73; p = 0.19) of radiologists reading DBT plus SV compared with radiologists reading DBT. Similar result was found in radiology trainees with no significant difference in specificity (0.70- vs -0.63; p = 0.29), sensitivity (0.44- vs -0.55; p = 0.19), ROC AUC (0.59- vs -0.62; p = 0.60) between two reading modes. Radiologists and trainees obtained similar results in two reading modes for cancer detection rate with different levels of breast density, cancer types and sizes of lesions ( p > 0.05)., Conclusion: Findings show that the diagnostic performances of radiologists and radiology trainees in DBT alone and DBT plus SV were equivalent in identifying cancer and normal cases., Advances in Knowledge: DBT alone had equivalent diagnostic accuracy as DBT plus SV which could imply the consideration of using DBT as a sole modality without SV.

Purpose: This study aims to investigate the diagnostic performances of Australian and Shanghai-based Chinese radiologists in reading full-field digital mammogram (FFDM) and digital breast tomosynthesis (DBT) with different levels of breast density., Approach: Eighty-two Australian radiologists interpreted a 60-case FFDM set, and 29 radiologists also reported a 35-case DBT set. Sixty Shanghai radiologists read the same FFDM set, and 32 radiologists read the DBT set. The diagnostic performances of Australian and Shanghai radiologists were assessed using truth data (cancer cases were biopsy proven) and compared overall in specificity, case sensitivity, lesion sensitivity, receiver operating characteristics (ROC) area under the curve, and jack-knife free-response receiver operating characteristics (JAFROC) figure of merit, and they were stratified by case characteristics using the Mann-Whitney U test. The Spearman rank test was used to explore the association between radiologists' performances and their work experience in mammogram interpretation., Results: There were significantly higher performances of Australian radiologists compared with Shanghai radiologists in low breast density for case sensitivity, lesion sensitivity, ROC, and JAFROC in the FFDM set ( P < 0.0001 ); in high breast density, Shanghai radiologists' performances in lesion sensitivity and JAFROC were also lower than Australian radiologists ( P < 0.0001 ). In the DBT test set, Australian radiologists performed better than Shanghai radiologists in cancer detection in both low and high breast density. The work experience of Australian radiologists was positively linked to their diagnostic performances, whereas this association was not statistically significant in Shanghai radiologists., Conclusion: There were significant variations in reading performances between Australian and Shanghai radiologists in FFDM and DBT across different levels of breast density, lesion types, and lesion sizes. An effective training initiative tailored to suit local readers is essential to enhancing the diagnostic accuracy of Shanghai radiologists., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

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.

The development of screening mammography over 30 years has remarkedly reduced breast cancer-associated mortality by 20%-30% through detection of small cancer lesions at early stages. Yet breast screening programmes may function differently in each nation depending on the incidence rate, national legislation, local health infrastructure and training opportunities including feedback on performance. Mammography has been the frontline breast cancer screening tool for several decades; however, it is estimated that there are 15% to 35% of cancers missed on screening which are owing to perceptual and decision-making errors by radiologists and other readers. Furthermore, mammography screening is not available in all countries and the increased speed in the number of new breast cancer cases among less developed countries exceeds that of the developed world in recent decades. Studies conducted through the BreastScreen Reader Assessment Strategy (BREAST) training tools for breast screening readers have documented benchmarking and significant variation in diagnostic performances in screening mammogram test sets in different countries. The performance of the radiologists from less well-established breast screening countries such as China, Mongolia and Vietnam were significant lower in detecting early-stage cancers than radiologists from developed countries such as Australia, USA, Singapore, Italy. Differences in breast features and cancer presentations, discrepancies in the level of experiences in reading screening mammograms, the availability of high-quality national breast screening program and breast image interpretation training courses between developed and less developed countries are likely to have impact on the variation of readers' performances. Hence dedicated education training programs with the ability to tailor to different reader cohorts and different population presentations are suggested to ameliorate challenges in exposure to a range of cancer cases and improve the interpretation skills of local radiologists. Findings from this review provide a good understanding of the radiologist' performances and their improvement using the education interventions, primarily the BREAST program, which has been deployed in a large range of developing and developed countries in the last decade. Self-testing and immediate feedback loops have been shown to have important implications for benchmarking and improving the diagnostic accuracy in radiology worldwide for better breast cancer control., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Trieu, Mello-Thoms, Barron and Lewis.)

Much of young people's fertility health knowledge has been limited to avoiding sexually transmitted infections and preventing pregnancy thus lacking what is necessary to support future overall health as well as reproductive goals. This study assesses university students' knowledge related to fertility health factors to verify consistencies and discrepancies in fertility health knowledge, with a sub-assessment focusing on men's knowledge. The Fertility Health Knowledge survey was delivered to 17,189 students at three American universities. Twenty percent or 546 of the 2,692 participants were male. The 30-question survey addresses knowledge of modifiable and non-modifiable risk factors on fertility health in men and women, and four questions regarding fertility intentions. Across all 30 questions, 63% of female responses were correct and 61% of male responses were correct. For 10 questions, less than 70% of males and females answered correctly, with men answering correctly more often than females for six of the questions. Males exhibited more knowledge regarding male fertility. Knowledge of fertility health was consistently limited, regardless of site or demographics. Men demonstrated improved overall fertility health knowledge and more knowledge regarding male factors. There are still considerable gaps in knowledge of modifiable risk factors that may impact fertility health and future overall health. Fertility health promotion through education should be comprehensive and widely available in secondary schools, colleges, and universities. As well, increased education regarding fertility health in primary care settings should become the norm-with male inclusion as a standard of their care.