Background:Breast cancer remains the most common cancer among women globally. Mammography is a key diagnostic modality; however, interpretation is increasingly challenged by rising imaging volumes, a global shortage of breast radiologists, and variability in reader experience. Artificial intelligence (AI) has been proposed as a potential adjunct to address these issues, particularly in settings with high breast density, such as Asian populations. This study aimed to evaluate the impact of AI assistance on mammographic diagnostic performance among resident and consultant radiologists in Singapore. Objective:To assess whether AI assistance improves diagnostic accuracy in mammographic breast cancer detection across radiologists with varying levels of experience. Methods:A multi-reader, multi-case study was conducted at the National University Hospital, Singapore, from May to August 2023. De-identified digital mammograms from 500 women (250 with cancer and 250 normal or benign) were interpreted by 17 radiologists (4 consultants, 4 senior residents, and 9 junior residents). Each radiologist read all cases over 2 reading sessions: one without AI assistance and another with AI assistance, separated by a 1-month washout period. The AI system (FxMammo) provided heatmaps and malignancy risk scores to support decision-making. Area under the curve of the receiver operating characteristic (AUROC) was used to evaluate diagnostic performance. Results:Among the 500 cases, 250 were malignant and 250 were non-malignant. Of the malignant cases, 16%(80/500) were ductal carcinoma in situ and 84%(420/500) were invasive cancers. Among non-malignant cases, 69.2%(346/500) were normal, 17.6%(88) benign, and 3.6%(18/500) possibly benign but stable on follow-up. Masses (54.4%, 272) and calcifications (10.8%, 54/500) were the most common findings in cancer cases. A majority of both malignant (66.8%, 334/500) and non-malignant (68%, 340/500) cases had heterogeneously or extremely dense breasts (BIRADS [Breast Imaging Reporting and Data System] categories C and D). The AI model achieved an AUROC of 0.93 (95% CI 0.91-0.95), slightly higher than consultant radiologists (AUROC 0.90, 95% CI 0.89-0.92; P=.21). With AI assistance, AUROC improved among junior residents (from 0.84 to 0.86; P=.38) and senior residents (from 0.85 to 0.88; P=.13), with senior residents approaching consultant-level performance (AUROC difference 0.02; P=.051). Diagnostic gains with AI were greatest in women with dense breasts and among less experienced radiologists. AI also improved inter-reader agreement and time efficiency, particularly in benign or normal cases. Conclusions:This is the first study in Asia to evaluate AI assistance in mammography interpretation by radiologists of varying experience. AI significantly improved diagnostic performance and efficiency among residents, helping to narrow the experience-performance gap without compromising specificity. These findings suggest a role for AI in enhancing diagnostic consistency, improving workflow, and supporting training. Integration into clinical and educational settings may offer scalable benefits, though careful attention to threshold calibration, feedback loops, and real-world validation remains essential. Further studies in routine screening settings are needed to confirm generalizability and cost-effectiveness.

Introduction: Breast cancer is the most common type of cancer in women globally and mammograms are a primary method for diagnosing it. The challenge of interpreting mammograms is exacerbated by a shortage of dedicated breast radiologists and the lengthy training required to cultivate such expertise. This study is designed to investigate the performance of AI assistance for resident radiologists compared to consultant radiologists and to conduct a cost analysis of implementing AI in a diagnostic setting. Methods: A multi-reader multi-case study was conducted at the National University Hospital of Singapore from May to August 2023. De-identified mammograms from 550 women formed the dataset. Seventeen radiologists read mammograms for six weeks without and with AI assistance, with a one-month washout period in between. FxMammo AI software provided heatmaps over areas of suspicion and malignancy risk scores. Diagnostic performance between resident and consultant radiologists were compared with and without AI assistance. Cost analysis was performed. Findings: A total of 550 women were included. 261 had breast cancer whilst 289 had normal or benign breast lesions. Majority had dense breasts (66.9%). The diagnostic performance of AI standalone versus consultant radiologists was comparable AUROC of 0.93 (95% CI 0.90-0.95) versus 0.90 (95% CI 0.89-0.92) (p=0.97). With AI assistance, both junior and senior residents showed improvement in diagnostic performance AUROC 0.84 to 0.86 (95% CI 0.85- 0.87), p=0.001, and AUROC 0.85 to 0.89 (95% CI 0.87- 0.90) respectively, p<0.001). With AI assistance, the AUROC of senior residents was comparable to consultant radiologists with difference in AUROC of 0.02 (95% CI -0.001 – 0.04, p=0.055). Time savings were observed, particularly in non-malignant cases, with potential annual cost savings of SGD $2,732,104 - $2,766,193. Interpretation: The AI performance was comparable to consultant radiologists in a study population with high proportion of dense breasts. The findings suggest that AI can bridge gaps in expertise between senior resident radiologists and consultant radiologists. Funding: This study was supported by Temasek Foundation and Ministry of Health Singapore through the CHI START UP ENTERPRISE LINK (CHISEL) programme organized by Centre for Healthcare Innovation in 2022, NUHS Clinician Scientist Program 2.0 (NCSP 2.0) under Department of Surgery, National University Hospital, and Breast Cancer Screening Prevention Programme (NUHSRO/2020/121/BCSPP/LOA) under Yong Loo Lin School of Medicine, National University of Singapore. The grant was used to fund the manpower required for the project. The study design, data collection, data analysis and interpretation were conducted by the study team independently.Declaration of Interest: Mengling Fang, As a faculty member at NUHS and a co-founder of FathomX, I have a financial and professional interest in the success and adoption of FathomX's AI technology in the field of radiology. This interest may be perceived as a competing interest, as the outcomes of the research could have implications for the acceptance and utilization of AI technology in radiology. All other have nothing to declare.Ethical Approval: The research protocol received approval from the National Health Group Institutional Review Boards on 16 December 2022 (Reference Number 2022/00843).

INTRODUCTION:The Royal Australian and New Zealand College of Radiologists (RANZCR) established a working group to explore how the college should engage with the future development of structured radiology reporting in our region, particularly in the context of a broader move to digital healthcare. Phase 1 of the project surveyed college members and affiliated interest groups about how they are using structured reporting currently and might like it to evolve.METHODS:Member and interest group questionnaires were based on previously published studies and posted to the Survey Monkey platform. Responses were analysed descriptively.RESULTS:There were 114 members and 58 affiliated group responses. There is clearest support for RANZCR developing guidelines around structured report quality, for improvements in report content, particularly tailoring to clinical context and study parameters, and for improved integration of structured reporting and RIS/PACS systems.CONCLUSIONS:Phase 2 of the structured reporting working group project will aim to develop guidelines for structured report quality and processes through which RANZCR can implement them.

The Breast JournalVolume 26, Issue 11 p. 2260-2262 BREAST IMAGES An elusive diagnosis of IgG4-related sclerosing mastitis Amos H. S. Tan MBBS, Corresponding Author Amos H. S. Tan MBBS Amos_hs_tan@nuhs.edu.sg orcid.org/0000-0002-1729-803X Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore Correspondence Amos H. S. Tan, Department of Diagnostic Imaging, National University Hospital, 1E Kent Ridge Rd, Singapore 119228. Email: Amos_hs_tan@nuhs.edu.sgSearch for more papers by this authorSwee Tian Quek MBBS, FRCR, FAMS, Swee Tian Quek MBBS, FRCR, FAMS orcid.org/0000-0001-7561-7492 Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorPavel Singh MBBS, FRCR, M.Med, Pavel Singh MBBS, FRCR, M.Med orcid.org/0000-0003-1109-0876 Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorThomas Choudary Putti MBBS, MD, Thomas Choudary Putti MBBS, MD orcid.org/0000-0001-8791-7257 Department of Pathology, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorPremilla Gopinathan Pillay MBBS, FRCR, Premilla Gopinathan Pillay MBBS, FRCR Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorFelicity Jane Pool MBChB, FRANZCR, Felicity Jane Pool MBChB, FRANZCR orcid.org/0000-0002-3575-8853 Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorPooja Jagmohan MBBS, MD, FRCR, Pooja Jagmohan MBBS, MD, FRCR orcid.org/0000-0001-8491-0375 Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this author Amos H. S. Tan MBBS, Corresponding Author Amos H. S. Tan MBBS Amos_hs_tan@nuhs.edu.sg orcid.org/0000-0002-1729-803X Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore Correspondence Amos H. S. Tan, Department of Diagnostic Imaging, National University Hospital, 1E Kent Ridge Rd, Singapore 119228. Email: Amos_hs_tan@nuhs.edu.sgSearch for more papers by this authorSwee Tian Quek MBBS, FRCR, FAMS, Swee Tian Quek MBBS, FRCR, FAMS orcid.org/0000-0001-7561-7492 Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorPavel Singh MBBS, FRCR, M.Med, Pavel Singh MBBS, FRCR, M.Med orcid.org/0000-0003-1109-0876 Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorThomas Choudary Putti MBBS, MD, Thomas Choudary Putti MBBS, MD orcid.org/0000-0001-8791-7257 Department of Pathology, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorPremilla Gopinathan Pillay MBBS, FRCR, Premilla Gopinathan Pillay MBBS, FRCR Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorFelicity Jane Pool MBChB, FRANZCR, Felicity Jane Pool MBChB, FRANZCR orcid.org/0000-0002-3575-8853 Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this authorPooja Jagmohan MBBS, MD, FRCR, Pooja Jagmohan MBBS, MD, FRCR orcid.org/0000-0001-8491-0375 Departments of Diagnostic Imaging, National University Hospital and Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SingaporeSearch for more papers by this author First published: 29 September 2020 https://doi.org/10.1111/tbj.14066Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume26, Issue11November 2020Pages 2260-2262 RelatedInformation

PURPOSE: To determine the accuracy of a handheld ultrasound-guided optoacoustic tomography (US-OT) probe developed for human deep-tissue imaging in ex vivo assessment of tumor margins postlumpectomy. METHODS: A custom-built two-dimensional (2D) US-OT-handheld probe was used to scan 15 lumpectomy breast specimens. Optoacoustic signals acquired at multiple wavelengths between 700 and 1100 nm were reconstructed using model linear algorithm, followed by spectral unmixing for lipid and deoxyhemoglobin (Hb). Distribution maps of lipid and Hb on the anterior, posterior, superior, inferior, medial, and lateral margins of the specimens were inspected for margin involvement, and results were correlated with histopathologic findings. The agreement in tumor margin assessment between US-OT and histopathology was determined using the Bland-Altman plot. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of margin assessment using US-OT were calculated. RESULTS: Ninety margins (6 x 15 specimens) were assessed. The US-OT probe resolved blood vessels and lipid up to a depth of 6 mm. Negative and positive margins were discriminated by marked differences in the distribution patterns of lipid and Hb. US-OT assessments were concordant with histopathologic findings in 87 of 89 margins assessed (one margin was uninterpretable and excluded), with diagnostic accuracy of 97.9% (kappa 0.79). The sensitivity, specificity, PPV, and NPV were 100% (4/4), 97.6% (83/85), 66.7% (4/6), and 100% (83/83), respectively. CONCLUSION: US-OT was capable of providing distribution maps of lipid and Hb in lumpectomy specimens that predicted tumor margins with high sensitivity and specificity, making it a potential tool for intraoperative tumor margin assessment.

The Royal Australian and New Zealand College of Radiologists (RANZCR) Radiology Written Report Guideline was first issued in 2011. A survey-based consultation of clinical radiology members of the college in 2015 found that the vast majority of 235 respondents supported all components of the guideline. Since the original guideline was developed, considerable new research has been published about radiology reporting, particularly regarding structured/template reports. In 2016/17 a RANZCR working group used the consultation results, stakeholder feedback and recent research to develop revised guidelines. This article outlines the consultation survey results and guideline revision process as well as some of the supporting evidence from the literature.

Multispectral optoacoustic tomography (MSOT) is an innovative state-of-the-art imaging modality that has gained popularity for in vivo breast imaging in recent years. Combining high-resolution images with endogenous differentiation of biochemical contents, MSOT could be an accurate ex vivo imaging modality for assessment of tumor margins during breast-conserving surgery to reduce margin positivity rates. Accurate intraoperative assessment of margins could lead to more precise surgery, allowing a smaller volume of breast tissue to be removed without compromising the resection margins. To the authors' knowledge, there has been no ex vivo study conducted to this day that uses MSOT in the assessment of breast tumor margins after lumpectomy. Hence, we would like to present the first case of breast tumor margin assessment using MSOT in a 55-year-old patient who underwent breast-conserving surgery for invasive ductal carcinoma. (C) 2018 The Authors. Published by Elsevier Inc.

Papillary breast lesions encompass a wide spectrum of pathologies ranging from benign lesions, such as solitary intraductal papilloma, to the uncommon papillary carcinoma. These lesions have various clinical presentations and diverse radiological features. Differentiating benign and malignant papillary lesions based on imaging features may often be difficult. Other benign and malignant pathologies can also mimic papillary lesions on imaging, and tissue diagnosis is essential. Imaging plays an important role in lesion identification, assessment of extent, tissue sampling, and follow-up. Surgical excision has been recommended for all papillary lesions due to an increased incidence of high-risk lesions and neoplasia even with percutaneous, biopsy-proven benign papillomas. This review looks at papillary breast lesions from the radiologists' standpoint and discusses the clinical, imaging, and pathological features of these lesions, as well as the role of imaging in their evaluation.

The written radiology report is the dominant method by which radiologists communicate the results of diagnostic and interventional imaging procedures. It has an important impact on decisions about further investigation and management. Its form and content can be influential in reducing harm to patients and mitigating risk for practitioners but varies markedly with little standardisation in practice. Until now, the Royal Australian and New Zealand College of Radiologists has not had a guideline for the written report. International guidelines on this subject are not evidence based and lack description of development methods. The current guideline seeks to improve the quality of the written report by providing evidence-based recommendations for good practice. The following attributes of the report are addressed by recommendations: Content Clinical information available to the radiologist at the time the report was created Technical details of the procedure Examination quality and limitations Findings (both normal and abnormal) Comparison with previous studies Pathophysiological diagnosis Differential diagnoses Clinical correlation and/or answer to the clinical question Recommendations, particularly for further imaging and other investigations Conclusion/opinion/impression Format Length Format Language Confidence and certainty Clarity Readability Accuracy Communication of discrepancies between an original verbal or written report and the final report Proofreading/editing of own and trainee reports.

Purpose: A literature review was carried out, guided by the question, What are the important elements of a high-quality radiology written report?Methods: Two papers known to the authors were used as a basis for 5 PubMed search strategies. Exclusion criteria were applied to retrieved citations. Reference lists of retrieved citations were scanned for additional relevant papers and exclusion criteria applied to these. Web sites of professional radiology organizations were scanned for guidelines relating to the written radiology report. Retrieved guidelines were appraised using the Appraisal of Guidelines for Research & Evaluation instrument. Methodologies of retrieved papers were not suitable for conventional appraisal, and an evidence table was constructed.Results: The search strategy identified 25 published papers and 4 guidelines. Published study methodologies included 1 randomized controlled trial; 1 before-and-after study of interventions; 10 observational studies, audits, or analyses; 12 surveys; and 1 narrative review of the literature.Conclusions: Existing guidelines have a number of weaknesses with regard to scope and purpose, methods of development, stakeholder consultation, and editorial independence and applicability. There is a major gap in published studies relating to testing of interventions to improve report quality using conventional randomized controlled trial methods. Published studies and guidelines generally support report content, including clinical history, examination quality, description of findings, comparison, and diagnosis. Important report attributes include accuracy, clarity, and certainty. There is wide variation in the language used to describe imaging findings and diagnostic certainty. Survey participants strongly preferred reports with structured or itemized formats, but few studies exist regarding the effect of report structure on quality.

Poster: ECR 2016 / C-1204 / High risk lesions of the breast : Review of the current diagnostic and management strategies by: Jagmohan, F. J. Pool, P. G. Pillay, S. Dhanda, S. T. Quek; singapore/SG