BACKGROUND:There has been little examination of rural-urban residency and cancer screening. We investigated rural-urban variations in breast, cervical, and colorectal cancer screening uptake across Scotland. METHODS:Using aggregate data and cross-sectional analyses, we calculated uptake and detection rates (breast and bowel screening). We modelled uptake by urban and rural residency, age group, sex (bowel screening only), year, health board, and deprivation using multivariable logistic regression with appropriate interaction terms. RESULTS:Cervical screening uptake was higher in rural than urban areas (under 50s: 75.9% vs. 69.1%; 50+: 77.5% vs. 75.3%). Mammography uptake was: 77.0% in rural; 71.0% in urban areas. Bowel screening uptake was: 56.6% in urban; 62.5% in rural areas. In multivariable models, two-way interaction effects between residency and deprivation, and between residency and health board, were statistically significant (P < .05). Rural residency did not confer universally higher uptake. Breast and bowel cancer detection were similar in both areas. CONCLUSIONS:The relationship between residence and cancer screening uptake varied across Scotland. Uptake patterns were complex and not consistent across all rural areas, likely reflecting Scotland's topography and characteristics of the screening eligible. Efforts to improve screening uptake would likely benefit from local rather than national public health policies.

Artificial intelligence (AI) tools can improve breast screening performance but different screening sites have varying needs. Here the GEMINI prospective evaluation of 10,889 women, within one UK region, used both live AI integration and simulations to model 17 different ways AI could be used in breast screening. All women received routine care. One AI tool was assessed. When the AI tool recommended recall but routine double reading did not, cases underwent additional human review, detecting 11 additional cancers. The primary AI workflow could improve cancer detection by 10.4% (1 per 1,000), maintain the recall rate (0.8% reduction) and reduce workload by up to 31%. Other workflow variations significantly improved all measured metrics (superiority in cancer detection rate, recall rate, positive predictive value (PPV), sensitivity and specificity) with up to 36% workload savings. Different AI integrations in breast screening could offer various clinical and operational gains, allowing for adaptation to local healthcare needs.

Adipocytes are abundant in the breast tissue microenvironment. In breast cancer, they can change morphologically according to their proximity to tumour cells, with the closest becoming cancer-associated adipocytes (CAAs). It remains unclear whether breast cancer risk factors, including menopausal status, body mass index (BMI), and mammographic density (MD), influence CAAs morphology in breast carcinogenesis. This study aimed to quantify morphological differences in adipocytes across breast cancer pathologies and associated risk factors. Whole slide images of haematoxylin and eosin stained cancer (n = 149) and normal (n = 182) breast tissue samples were analysed. Parameters representative of adipocyte morphology: perimeter, area, concavity, and aspect ratio, were measured using ImageJ. Adipocytes were considered close (≤2 mm) or distant ( > 2 mm) to cancer cells in cancer samples or breast epithelial cells in normal samples. Close adipocytes in cancer samples were designated CAAs. CAAs decreased in size compared to distant adipocytes (p≤0.0001). A similar trend was observed between close and distant adipocytes in normal (p≤0.0001). CAAs size increased post menopause (p ≤ 0.0001). CAAs size positively correlated with BMI (p ≤ 0.0001). In cancer cases, distant adipocyte size increased and concavity decreased with increasing MD (p ≤ 0.01). Smaller CAAs were associated with poorer survival (p≤0.05). Morphological differences were identified in adipocytes dependent on location within the breast, tissue, pathology and risk factors. Understanding what drives these morphological differences could provide mechanistic insight into whether risk factor-induced alterations in adipocytes influence their role in breast carcinogenesis.

Residual microcalcifications on mammograms after neoadjuvant chemotherapy (NACT) pose a challenge in surgical decision-making. This single-centre retrospective review of all patients who had NACT for breast cancer over five years, evaluated the relationship between pathological complete response and residual microcalcifications, controlling for tumour size, nodal stage, grade, and receptor status, as well as the impact of residual microcalcifications on recurrence and survival. There was no significant association between pathological complete response (pCR) and residual microcalcifications (p = 0.763). We computed hazard ratios (HR) for Time to recurrence (TTR) and overall survival (OS) which were both not significant, with HR = 2.599, [0.290, 23.264], p = 0.393 and HR = 1.362 [0.123, 15.062], p = 0.801 respectively. The predictive and prognostic significance of residual microcalcifications remains to be proven. The surgical excision of these lesions should be considered based on individual patient risk.

Background The national breast screening programme in the United Kingdom is under pressure due to workforce shortages and having been paused during the COVID-19 pandemic. Artificial intelligence has the potential to transform how healthcare is delivered by improving care processes and patient outcomes. Research on the clinical and organisational benefits of artificial intelligence is still at an early stage, and numerous concerns have been raised around its implications, including patient safety, acceptance, and accountability for decisions. Reforming the breast screening programme to include artificial intelligence is a complex endeavour because numerous stakeholders influence it. Therefore, a stakeholder analysis was conducted to identify relevant stakeholders, explore their views on the proposed reform (i.e., integrating artificial intelligence algorithms into the Scottish National Breast Screening Service for breast cancer detection) and develop strategies for managing ‘important’ stakeholders. Methods A qualitative study (i.e., focus groups and interviews, March-November 2021) was conducted using the stakeholder analysis guide provided by the World Health Organisation and involving three Scottish health boards: NHS Greater Glasgow & Clyde, NHS Grampian and NHS Lothian. The objectives included: A) Identify possible stakeholders B) Explore stakeholders’ perspectives and describe their characteristics C) Prioritise stakeholders in terms of importance and D) Develop strategies to manage ‘important’ stakeholders. Seven stakeholder characteristics were assessed: their knowledge of the targeted reform, position, interest, alliances, resources, power and leadership. Results Thirty-two participants took part from 14 (out of 17 identified) sub-groups of stakeholders. While they were generally supportive of using artificial intelligence in breast screening programmes, some concerns were raised. Stakeholder knowledge, influence and interests in the reform varied. Key advantages mentioned include service efficiency, quicker results and reduced work pressure. Disadvantages included overdiagnosis or misdiagnosis of cancer, inequalities in detection and the self-learning capacity of the algorithms. Five strategies (with considerations suggested by stakeholders) were developed to maintain and improve the support of ‘important’ stakeholders. Conclusions Health services worldwide face similar challenges of workforce issues to provide patient care. The findings of this study will help others to learn from Scottish experiences and provide guidance to conduct similar studies targeting healthcare reform. Study registration: researchregistry6579, date of registration: 16/02/2021

Objectives: To determine factors influencing reader agreement in breast screening and investigate the relationship between agreement level and patient outcomes. Methods: Reader pair agreement for 83 265 sets of mammograms from the Scottish Breast Screening service (2015-2020) was evaluated using Cohen's kappa statistic. Each mammography examination was read by two readers, per routine screening practice, with the second initially blinded but able to choose to view the first reader's opinion. If the two readers disagreed, a third reader arbitrated. Variation in reader agreement was examined by: whether the reader acted as the first or second reader, reader experience, and recall, cancer detection and arbitration recall rate. Results: Readers' opinions varied by whether they acted as the first or second reader. Furthermore, reader 2 was more likely to agree with reader 1 if reader 1 was more experienced than they were, and less likely to agree if they themselves were more experienced than reader 1 (P<.001). Agreement was not significantly associated with cancer detection rate, overall recall rate or arbitration recall rates (P>.05). Lower agreement between readers led to a higher arbiter workload (P<.001). Conclusions: In mammography screening, the second reader's opinion is influenced by the first reader's opinion, with the degree of influence dependent on the readers' relative experience levels. Advances in knowledge: While less-experienced readers relied on their more experienced reading partner, no adverse impact on service outcomes was observed. Allowing access to the first reader's opinion may benefit newly qualified readers, but reduces independent evaluation, which may lower cancer detection rates.

This article describes an approach to planning and implementing artificial intelligence products in a breast screening service. It highlights the importance of an in-depth understanding of the end-to-end workflow and effective project planning by a multidisciplinary team. It discusses the need for monitoring to ensure that performance is stable and meets expectations, as well as focusing on the potential for inadvertantly generating inequality. New cross-discipline roles and expertise will be needed to enhance service delivery.

Abstract Background This prospective feasibility study explores Field-Cycling Imaging (FCI), a new MRI technology that measures the longitudinal relaxation time across a range of low magnetic field strengths, providing additional information about the molecular properties of tissues. This study aims to assess the performance of FCI and investigate new quantitative biomarkers at low fields within the context of breast cancer. Methods We conducted a study involving 9 people living with breast cancer (10 tumours in total, mean age, 54 ± 10 years). FCI images were obtained at four magnetic field strengths (2.3 mT to 200 mT). FCI images were processed to generate T1 maps and 1/T1 dispersion profiles from regions of tumour, normal adipose tissue, and glandular tissue. The dispersion profiles were subsequently fitted using a power law model. Statistical analysis focused on comparing potential FCI biomarkers using a Mann-Whitney U or Wilcoxon signed rank test. Results We show that low magnetic fields clearly differentiate tumours from adipose and glandular tissues without contrast agents, particularly at 22 mT (1/T1, median [IQR]: 6.8 [3.9–7.8] s−1 vs 9.1 [8.9–10.2] s−1 vs 8.1 [6.2–9.2] s−1, P < 0.01), where the tumour-to-background contrast ratio was highest (62%). Additionally, 1/T1 dispersion indicated a potential to discriminate invasive from non-invasive cancers (median [IQR]: 0.05 [0.03–0.09] vs 0.19 [0.09–0.26], P = 0.038). Conclusions To the best of our knowledge, we described the first application of in vivo FCI in breast cancer, demonstrating relevant biomarkers that could complement diagnosis of current imaging modalities, non-invasively and without contrast agents.

We propose Field-Cycling Imaging (FCI), a new MRI technology accessing a range of low and ultra-low magnetic fields (2mT to 0.2T), to acquire longitudinal relaxation time over 4 orders of magnitude of field strength, and covering the whole body. FCI obtains the Nuclear Magnetic Relaxation Dispersion (NMRD) profiles of tissues, which probes molecular dynamics at micro- to nanometer scales. We present a prospective study including 10 female patients with breast cancers. Low magnetic fields clearly differentiate tumours from adipose and glandular tissues and discriminates true tumour extent beyond that of conventional imaging, matching the true pathological size of the lesion. Using our FCI prototype, T 1 variations at low and ultra-low field discriminate invasive from non-invasive cancers in patients (p < 0.05). To our knowledge, we described the first application of in vivo FCI in breast cancer, demonstrating relevant biomarkers that complement diagnosis of current imaging modalities, non-invasively and without contrast agents.

Field-Cycling imaging (FCI) can image over a range of low magnetic field strengths (0.2 T to 0.2 mT) through rapid switching between magnetic field levels. This allows measuring the field-depended changes of the longitudinal T1 relaxation time, known as T1 dispersion, exploring new approaches for breast tumour contrast. FCI images were acquired from patients with breast tumours to generate multi field-T1 maps and T1 dispersions. The T1 maps exhibited visible contrast of the tumour region. T1 dispersion profiles from tumours differed from those in healthy breast tissues, showing that FCI can detect breast tumours at low field strengths.

Artificial intelligence (AI) tools may assist breast screening mammography programs, but limited evidence supports their generalizability to new settings. This retrospective study used a 3-year dataset (April 1, 2016-March 31, 2019) from a U.K. regional screening program. The performance of a commercially available breast screening AI algorithm was assessed with a prespecified and site-specific decision threshold to evaluate whether its performance was transferable to a new clinical site. The dataset consisted of women (aged approximately 50-70 years) who attended routine screening, excluding self-referrals, those with complex physical requirements, those who had undergone a previous mastectomy, and those who underwent screening that had technical recalls or did not have the four standard image views. In total, 55 916 screening attendees (mean age, 60 years ± 6 [SD]) met the inclusion criteria. The prespecified threshold resulted in high recall rates (48.3%, 21 929 of 45 444), which reduced to 13.0% (5896 of 45 444) following threshold calibration, closer to the observed service level (5.0%, 2774 of 55 916). Recall rates also increased approximately threefold following a software upgrade on the mammography equipment, requiring per-software version thresholds. Using software-specific thresholds, the AI algorithm would have recalled 277 of 303 (91.4%) screen-detected cancers and 47 of 138 (34.1%) interval cancers. AI performance and thresholds should be validated for new clinical settings before deployment, while quality assurance systems should monitor AI performance for consistency. Keywords: Breast, Screening, Mammography, Computer Applications-Detection/Diagnosis, Neoplasms-Primary, Technology Assessment Supplemental material is available for this article. © RSNA, 2023.

Category: AI (artificial intelligence)

Background: Impalpable breast lesions are being diagnosed more often since the introduction of screening programs. Wire Localization has been used for decades to aid the surgeons excisions but more recently new localisation techniques have emerged. Radiofrequency (RF) tag localisation is a new localisation technique used for impalpable breast lesions. We describe our experience at NHS Grampian with the first 100 cases in our unit.

Objectives This study surveyed the views of breast screening readers in the UK on how to incorporate Artificial Intelligence (AI) technology into breast screening mammography. Methods An online questionnaire was circulated to the UK breast screening readers. Questions included their degree of approval of four AI implementation scenarios: AI as triage, AI as a companion reader/reader aid, AI replacing one of the initial two readers, and AI replacing all readers. They were also asked to rank five AI representation options (discrete opinion; mammographic scoring; percentage score with 100% indicating malignancy; region of suspicion; heat map) and indicate which evidence they considered necessary to support the implementation of AI into their practice among six options offered. Results The survey had 87 nationally accredited respondents across the UK; 73 completed the survey in full. Respondents approved of AI replacing one of the initial two human readers and objected to AI replacing all human readers. Participants were divided on AI as triage and AI as a reader companion. A region of suspicion superimposed on the image was the preferred AI representation option. Most screen readers considered national guidelines (77%), studies using a nationally representative dataset (65%) and independent prospective studies (60%) as essential evidence. Participants’ free-text comments highlighted concerns and the need for additional validation. Conclusions Overall, screen readers supported the introduction of AI as a partial replacement of human readers and preferred a graphical indication of the suspected tumour area, with further evidence and national guidelines considered crucial prior to implementation.

Abstract Artificial intelligence (AI) tools may assist breast screening mammography programmes, but evidence gaps remain, including whether AI performance is consistent across sites and over time. This study used a three-year historical dataset (58,209 cases) from a UK regional screening programme with known clinical outcomes. The performance of a commercially available breast screening AI algorithm, used to recall women for further investigation, was evaluated. The AI algorithm was used with a pre-specified and a site-optimised threshold. The pre-specified threshold resulted in high recall rates (47.7%) which reduced to 13.0% following threshold optimisation, closer to the observed service level (5.0%). Stand-alone, the AI algorithm would have recalled 277/303 (91.4%) of cancers detected through the routine screening programme and 14/52 (26.9%) of cancers diagnosed between screening cycles (interval cancers). An approximately three-fold increase in recall rate was observed following a software upgrade on the mammography X-ray units. To ensure safe deployment, AI algorithm performance and decision thresholds should be validated when applied in new clinical settings. Real-time quality assurance systems will need to be in place to monitor AI performance in a clinical setting. Collectively these findings show that the generalisability of a breast screening AI algorithm is not guaranteed.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Innovate UK has funded this research under the UK Research and Innovation Industrial Strategy Challenge Fund. Acknowledgements We are grateful to Friends of Anchor for supplying single-use pens for participants to complete the questionnaires. We would also like to thank the participants and staff at the breast screening unit in NHS Grampian.

Background: Radiofrequency (RF) Tags are new devices used to localize breast lesions for surgery. During the Covid-19 Pandemic these offered the flexibility to be inserted days or weeks before surgery, making the logistics of planning theatres lists much easier especially when most of our breast cancer surgery was moved off site. Materials and Methods: In the 7 weeks following the lockdown in the UK, we reviewed all the planned admissions for breast surgery looking at the types of surgery offered, type of localization used and assessed who wouldn't have had their surgery if RF tags were not available locally. Results: Out of 85 planned admission, 83 had surgery, 11 were for re-excision of margins and 72 for their first breast surgery excision (mastectomy or breast conservation). Out of the 54 that had BCS, 40 needed localization, out of these 27 had RF tags. Looking at theatre order list and the site that surgery was performed, 20 out of the 27, wouldn't have had their surgery if RF tags were not available, that is 50% of patients needing Localization. Conclusion: RF Tags are new devices used for breast lesion localization, like other similar new devices, they offer the flexibility of being inserted days or weeks before surgery making the logistics of theatre planning easier, offering a much-needed flexibility especially during the Covid19 Pandemic. This was approved as an Audit by NHS Grampian Clinical Governance Department

Lower screening uptake could impact cancer survival in rural areas. This systematic review sought studies comparing rural/urban uptake of colorectal, cervical and breast cancer screening in high income countries. Relevant studies (n = 50) were identified systematically by searching Medline, EMBASE and CINAHL. Narrative synthesis found that screening uptake for all three cancers was generally lower in rural areas. In meta-analysis, colorectal cancer screening uptake (OR 0.66, 95 % CI = 0.50-0.87, I2 = 85 %) was significantly lower for rural dwellers than their urban counterparts. The meta-analysis found no relationship between uptake of breast cancer screening and rural versus urban residency (OR 0.93, 95 % CI = 0.80-1.09, I2 = 86 %). However, it is important to note the limitation of the significant statistical heterogeneity found which demonstrates the lack of consistency between the few studies eligible for inclusion in the meta-analyses. Cancer screening uptake is apparently lower for rural dwellers which may contribute to poorer survival. National screening programmes should consider geography in planning.