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Objectives: To assess the predictive value of diffusion weighted imaging (DWI) for survival in women treated for advanced cancer of the cervix with concurrent chemo-radiotherapy.Methods: Twenty women treated for advanced cancer of the cervix were recruited and followed up for a median of 26 (range <1 to 43) months. They each had DWI performed before treatment, 2 weeks after beginning therapy (midtreatment) and at the end of treatment. Apparent diffusion coefficient (ADC) values were calculated from regions of interest (ROI). All participants were reviewed for follow-up data. ADC values were compared with mortality status (Mann-Whitney test). Time to progression and overall survival were assessed (Kaplan-Meier survival graphs).Results: There were 14 survivors. The median midtreatment ADC was statistically significantly higher in those alive compared to the non-survivors, 1.55 and 1.36 (×10(-3)/mm(2)/s), respectively, P = 0.02. The median change in ADC 14 days after treatment commencement was significantly higher in the alive group compared to non-survivors, 0.28 and 0.14 (×10(-3)/mm(2)/s), respectively, P = 0.02. There was no evidence of a difference between survivors and non-survivors for pretreatment baseline or post-therapy ADC values.Conclusion: Functional DWI early in the treatment of advanced cancer of the cervix may provide useful information in predicting survival.Key Points: • Diffusion weighted magnetic resonance imaging (DWI) is increasingly used in cervical cancer. • Functional DWI early in treatment of cervical cancer may help predict survival. • DWI may help clinicians to tailor or individualise treatment appropriately. • This may limit toxicity from ineffective treatment and allow early alternative therapy. [ABSTRACT FROM AUTHOR]

Objectives: To determine the diagnostic accuracy of surveillance mammography for detecting ipsilateral breast tumour recurrence and metachronous contralateral breast cancer in women previously treated for primary breast cancer.Methods: A systematic review of surveillance mammography compared with ultrasound, magnetic resonance imaging (MRI), specialist-led clinical examination or unstructured primary care follow-up, using histopathological assessment for test positives and follow-up for test negatives as the reference standard.Results: Nine studies met our inclusion criteria. Variations in study comparisons precluded meta-analysis. For routine ipsilateral breast tumour detection, surveillance mammography sensitivity ranged from 64-67% and specificity ranged from 85-97%. For MRI, sensitivity ranged from 86-100% and specificity was 93%. For non-routine ipsilateral breast tumour detection, sensitivity and specificity for surveillance mammography ranged from 50-83% and 57-75% and for MRI 93-100% and 88-96%. For routine metachronous contralateral breast cancer detection, one study reported sensitivity of 67% and specificity of 50% for both surveillance mammography and MRI.Conclusion: Although mammography is associated with high sensitivity and specificity, MRI is the most accurate test for detecting ipsilateral breast tumour recurrence and metachronous contralateral breast cancer in women previously treated for primary breast cancer. Results should be interpreted with caution because of the limited evidence base. Key Points • Surveillance mammography is associated with high sensitivity and specificity • Findings suggest that MRI is the most accurate test for detecting further breast cancer • Robust conclusions cannot be made due to the limited evidence base • Further research comparing surveillance mammography and other diagnostic tests is required. [ABSTRACT FROM AUTHOR]

Purpose: To quantify B(1) transmission-field inhomogeneity in breast imaging of normal volunteers at 3T using 3D T(1)-weighted spoiled gradient echo and to assess the resulting errors in enhancement ratio (ER) measured in dynamic contrast-enhanced MRI (DCE-MRI) studies of the breast.Materials and Methods: A total of 25 volunteers underwent breast imaging at 3T and the B(1) transmission-fields were mapped. Gel phantoms that simulate pre- and postcontrast breast tissue T(1) were developed. The effects of B(1)-field inhomogeneity on ER, as measured using a 3D spoiled gradient echo sequence, were investigated by computer simulation and experiments on gel phantoms.Results: It was observed that by using the patient orientation and MR scanner employed in this study, the B(1) transmission-field field is always reduced toward the volunteer's right side. The median B(1)-field in the right breast is reduced around 40% of the expected B(1)-field. For some volunteers the amplitude was reduced by more than 50%. Computer simulation and experiment showed that a reduction in B(1)-field decreases ER. This reduction increases with both B(1)-field error and contrast agent uptake.Conclusion: B(1) transmission-field inhomogeneity is a critical issue in breast imaging at 3T and causes errors in quantifying ER. These errors would be sufficient to reduce the conspicuity of a malignant lesion and could result in reduced sensitivity for cancer detection. [ABSTRACT FROM AUTHOR]

Purpose: To provide an overview of evidence-based medicine (EBM) in relation to radiology and to define a policy for adoption of this principle in the European radiological community.Results: Starting from Sackett's definition of EBM we illustrate the top-down and bottom-up approaches to EBM as well as EBM's limitations. Delayed diffusion and peculiar features of evidence-based radiology (EBR) are defined with emphasis on the need to shift from the demonstration of the increasing ability to see more and better, to the demonstration of a significant change in treatment planning or, at best, of a significant gain in patient outcome. The "as low as reasonably achievable" (ALARA) principle is thought as a dimension of EBR while EBR is proposed as part of the core curriculum of radiology residency. Moreover, we describe the process of health technology assessment in radiology with reference to the six-level scale of hierarchy of studies on diagnostic tests, the main sources of bias in studies on diagnostic performance, and levels of evidence and degrees of recommendations according to the Centre for Evidence-Based Medicine (Oxford, UK) as well as the approach proposed by the GRADE working group. Problems and opportunities offered by evidence-based guidelines in radiology are considered. Finally, we suggest nine points to be actioned by the ESR in order to promote EBR.Conclusion: Radiology will benefit greatly from the improvement in practice that will result from adopting this more rigorous approach to all aspects of our work. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is the most accurate technique for diagnosing and delineating the extent of both invasive and in-situ breast cancer and is increasingly being used as part of the preoperative work-up to assess the local extent of disease. It is proving invaluable in providing information that allows successful single-stage surgery. An inevitable consequence of the high sensitivity of MRI is that it will identify additional lesions that may or may not represent significant extra disease. This may complicate and delay the preoperative process. This paper outlines a strategy for managing MRI-detected lesions to optimize the benefits of breast MRI as a local staging tool while minimizing the false-positive diagnoses. It discusses the importance of good technique to reduce the number of indeterminate lesions. Methods to refine the patient pathway to minimize delays are discussed. The format of MRI reporting is discussed in detail as is the usefulness of discussion of cases at multidisciplinary meetings. Illustrative cases are used to clarify the points made. [ABSTRACT FROM AUTHOR]

Background Deep learning (DL) algorithms have shown promising results in mammographic screening either compared to a single reader or, when deployed in conjunction with a human reader, compared with double reading. Purpose To externally validate the performance of three DL algorithms as mammographic screen readers in an independent UK data set. Materials and Methods Three commercial DL algorithms (DL-1, DL-2, and DL-3) were retrospectively investigated from January 2022 to June 2022 using consecutive full-field digital mammograms collected at two UK sites during 1 year (2017). Normal cases with 3-year follow-up and histopathologically proven cancer cases detected either at screening (that round or next) or within the 3-year interval were included. A preset specificity threshold equivalent to a single reader was applied. Performance was evaluated for stand-alone DL reading compared with single human reading, and for DL reading combined with human reading compared with double reading, using sensitivity and specificity as the primary metrics. P < .025 was considered to indicate statistical significance for noninferiority testing. Results A total of 26 722 cases (median patient age, 59.0 years [IQR, 54.0-63.0 years]) with mammograms acquired using machines from two vendors were included. Cases included 332 screen-detected, 174 interval, and 254 next-round cancers. Two of three stand-alone DL algorithms achieved noninferior sensitivity (DL-1: 64.8%, P < .001; DL-2: 56.7%, P = .03; DL-3: 58.9%, P < .001) compared with the single first reader (62.8%), and specificity was noninferior for DL-1 (92.8%; P < .001) and DL-2 (96.8%; P < .001) and superior for DL-3 (97.9%; P < .001) compared with the single first reader (96.5%). Combining the DL algorithms with human readers achieved noninferior sensitivity (67.0%, 65.6%, and 65.4% for DL-1, DL-2, and DL-3, respectively; P < .001 for all) compared with double reading (67.4%), and superior specificity (97.4%, 97.6%, and 97.6%; P < .001 for all) compared with double reading (97.1%). Conclusion Use of stand-alone DL algorithms in combination with a human reader could maintain screening accuracy while reducing workload. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Background: Integrating artificial intelligence (AI) into mammography screening can support radiologists and improve programme metrics, yet the potential of different strategies for integrating the technology remains understudied. We compared programme-level performance metrics of seven AI integration strategies., Methods: We performed a retrospective comparative evaluation of seven strategies for integrating AI into mammography screening using datasets generated from screening programmes in Germany (n=1 657 068), the UK (n=223 603) and Sweden (n=22 779). The commercially available AI model used was Vara version 2.10, trained from scratch on German data. We simulated the performance of each strategy in terms of cancer detection rate (CDR), recall rate, and workload reduction, and compared the metrics with those of the screening programmes. We also assessed the distribution of the stages and grades of the cancers detected by each strategy and the AI model's ability to correctly localise those cancers., Findings: Compared with the German screening programme (CDR 6·32 per 1000 examinations, recall rate 4·11 per 100 examinations), replacement of both readers (standalone AI strategy) achieved a non-inferior CDR of 6·37 (95% CI 6·10-6·64) at a recall rate of 3·80 (95% CI 3·67-3·93), whereas single reader replacement achieved a CDR of 6·49 (6·31-6·67), a recall rate of 4·01 (3·92-4·10), and a 49% workload reduction. Programme-level decision referral achieved a CDR of 6·85 (6·61-7·11), a recall rate of 3·55 (3·43-3·68), and an 84% workload reduction. Compared with the UK programme CDR of 8·19, the reader-level, programme-level, and deferral to single reader strategies achieved CDRs of 8·24 (7·82-8·71), 8·59 (8·12-9·06), and 8·28 (7·86-8·71), without increasing recall and while reducing workload by 37%, 81%, and 95%, respectively. On the Swedish dataset, programme-level decision referral increased the CDR by 17·7% without increasing recall and while reducing reading workload by 92%., Interpretation: The decision referral strategies offered the largest improvements in cancer detection rates and reduction in recall rates, and all strategies except normal triaging showed potential to improve screening metrics., Funding: Vara., Competing Interests: Declaration of interests ZVF, MB, TM, VŠ, SB, and CL are current or former employees of Vara, with stock options as part of the standard compensation package. FJG, NRP, and SEH have research collaborations with Vara, ScreenPoint, Lunit, Google, Volpara, iCAD, Therapixel, CureMetrix, Sunnybrook Research Institute, and Massachusetts Institute of Technology., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Aims: To establish the safety and feasibility of delivering neoadjuvant radiotherapy and endocrine therapy for oestrogen receptor-positive breast cancers with palpable size 20mm or greater, for which radiotherapy might facilitate more conservative surgery., Materials and Methods: A single-arm feasibility study was conducted. Patients received whole breast radiotherapy with or without radiotherapy to nodal areas. Dose/fractionation was 40Gy in 15 fractions over 3 weeks, with or without either a simultaneous integrated boost to 48Gy or sequential boost to the tumour bed. This was followed by endocrine treatment for 20 weeks, then surgery. The primary endpoint of the study was the proportion of patients successfully completing neoadjuvant radiotherapy and endocrine treatment followed by breast surgery. Response and toxicity endpoints including mastectomy rate, peri/postoperative complications, and pathological response were also evaluated. The primary analysis is descriptive. The study regimen would be considered feasible if more than 70% of patients completed treatment, while it might not be considered feasible if less than 50% did so. With a one-sided 5% significance level and 80% power, a maximum of 43 patients would be required to detect a rate of ≤50% vs ≥70%., Results: 14 patients were recruited out of the planned 43. Due to slow recruitment, particularly during the COVID-19 pandemic, the decision was made to stop the trial in October 2021. One registered patient was found to be ineligible before starting treatment. 13/13 patients (100%, 90% CI: 75.3%, 100%) who received any trial treatment successfully completed all trial treatments. The lower bound of the Clopper-Pearson (exact) 90% confidence interval was 79%, indicating that the primary endpoint would have been met if the planned recruitment had been achieved. 3/13 patients underwent mastectomy. 7/13 had more conservative surgery than had been planned at baseline. 4/13 patients experienced any peri/postoperative complication. The only acute radiotherapy toxicities reported were grade 1/2 dermatitis and grade 1 fatigue. Long-term breast outcomes were clinician assessed as none/mild at all timepoints in 12/13 patients. All tumours showed evidence of some pathological response to treatment, but none had a pathological complete response., Conclusion: This treatment schedule is likely feasible. It is difficult to draw strong conclusions on safety/toxicity given the small numbers, but these seem in keeping with other recent reports of neoadjuvant breast radiotherapy., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Population screening for breast cancer (BC) is currently offered in the UK for women aged 50 to 71 with the aim of reducing mortality. There is additional screening within the national programme for women identified as having a very high risk of BC. There is growing interest in further risk stratification in breast screening, which would require a whole population risk assessment and the subsequent offer of screening tailored to the individual's risk. Some women would be offered more intensive screening than others or no screening. This might provide a better balance of screening benefits and harms for each individual than the current population age-based programme alone. The UK National Screening Committee (UK NSC) is considering using decision-analytic and other models to evaluate different risk stratification screening strategies and identify remaining gaps in evidence. This paper reports the proceedings of a UK NSC workshop where experts in the field discussed both risk prediction models, as well as decision-analytic models providing a benefit-harm analysis/economic evaluation of risk-stratified screening programmes (see Table 1). The aim of the meeting was to present and discuss the current work of experts, including some data which had not been published at the time of the meeting, to inform the UK NSC. The workshop was not intended to present a balanced evaluation of how to deliver screening in future. Areas for further work identified included methods for comparing models to assess accuracy, the optimum risk assessment tools, the digital screening infrastructure, acceptability of stratification, choice of screening test and reducing inequalities. A move to risk stratification of the whole programme would require a careful phased introduction with continuing assessment of real-world evidence during deployment., (© 2024. The Author(s).)

Digital breast tomosynthesis (DBT) with full-field digital mammography (FFDM) exposes women to a higher radiation dose. A synthetic 2D mammogram (S2D) is a two-dimensional image constructed from DBT. We aim to evaluate the S2D performance when used alone or combined with DBT compared to FFDM alone or with DBT. Studies were included if they recruited screening participants and reported on S2D performance. Studies were excluded if they included symptomatic patients, imaging was for diagnostic purposes, or if participants had a breast cancer history. Meta-analyses for cancer detection rates (CDR) and Specificities were conducted where available. Differences in the performance of imaging modalities were calculated within individual studies, and these were pooled by meta-analysis. Out of 3241 records identified, 17 studies were included in the review and 13 in the meta-analysis. The estimated combined difference in CDRs per thousand among individual studies that reported on DBT plus S2D vs. FFDM and those reporting on DBT plus S2D versus DBT plus FFDM was 2.03 (95% CI 0.81-3.25) and - 0.15 (95% CI -1.17 to 0.86), respectively. The estimated difference in percent specificities was 1.13 (95% CI -0.06 to 2.31) in studies comparing DBT plus S2D and FFDM. In studies comparing DBT plus S2D and DBT plus FFDM, the estimated difference in specificities was 1.08 (95% CI 0.59-1.56). DBT plus S2D showed comparable accuracy to FFDM plus DPT and improved cancer detection to FFDM alone. Integrating S2D with DBT in breast cancer screening is safe and preserves performance., (© 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Background Artificial intelligence (AI) systems can be used to identify interval breast cancers, although the localizations are not always accurate. Purpose To evaluate AI localizations of interval cancers (ICs) on screening mammograms by IC category and histopathologic characteristics. Materials and Methods A screening mammography data set (median patient age, 57 years [IQR, 52-64 years]) that had been assessed by two human readers from January 2011 to December 2018 was retrospectively analyzed using a commercial AI system. The AI outputs were lesion locations (heatmaps) and the highest per-lesion risk score (range, 0-100) assigned to each case. AI heatmaps were considered false positive (FP) if they occurred on normal screening mammograms or on IC screening mammograms (ie, in patients subsequently diagnosed with IC) but outside the cancer boundary. A panel of consultant radiology experts classified ICs as normal or benign (true negative [TN]), uncertain (minimal signs of malignancy [MS]), or suspicious (false negative [FN]). Several specificity and sensitivity thresholds were applied. Mann-Whitney U tests, Kruskal-Wallis tests, and χ 2 tests were used to compare groups. Results A total of 2052 screening mammograms (514 ICs and 1548 normal mammograms) were included. The median AI risk score was 50 (IQR, 32-82) for TN ICs, 76 (IQR, 41-90) for ICs with MS, and 89 (IQR, 81-95) for FN ICs ( P = .005). Higher median AI scores were observed for invasive tumors (62 [IQR, 39-88]) than for noninvasive tumors (33 [IQR, 20-55]; P < .01) and for high-grade (grade 2-3) tumors (62 [IQR, 40-87]) than for low-grade (grade 0-1) tumors (45 [IQR, 26-81]; P = .02). At the 96% specificity threshold, the AI algorithm flagged 121 of 514 (23.5%) ICs and correctly localized the IC in 93 of 121 (76.9%) cases, with 48 FP heatmaps on the mammograms for ICs (rate, 0.093 per case) and 74 FP heatmaps on normal mammograms (rate, 0.048 per case). The AI algorithm correctly localized a lower proportion of TN ICs (54 of 427; 12.6%) than ICs with MS (35 of 76; 46%) and FN ICs (four of eight; 50% [95% CI: 13, 88]; P < .001). The AI algorithm localized a higher proportion of node-positive than node-negative cancers ( P = .03). However, no evidence of a difference by cancer type ( P = .09), grade ( P = .27), or hormone receptor status ( P = .12) was found. At 89.8% specificity and 79% sensitivity thresholds, AI detection increased to 181 (35.2%) and 256 (49.8%) of the 514 ICs, respectively, with FP heatmaps on 158 (10.2%) and 307 (19.8%) of the 1548 normal mammograms. Conclusion Use of a standalone AI system improved early cancer detection by correctly identifying some cancers missed by two human readers, with no differences based on histopathologic features except for node-positive cancers. © RSNA, 2024 Supplemental material is available for this article .

Breast complaints are frequent reasons for consultations in primary care or breast clinics. Breast pain, breast lumps, and nipple discharge are the most common complaints. Less common symptoms such as skin changes and axillary abnormalities also require specific diagnostic approaches. Imaging the symptomatic breast should be performed by appropriately trained breast radiologists following the best practice guidelines and quality standards. Full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and breast ultrasound (US) are the main modalities used in this primary setting. The choice depends on the patient's age and symptoms. Women younger than 30-years-old are first imaged by US, whereas women over 40-years-old usually require both FFDM or DBT and US. For women between 30-years-old and 40-years-old, the US is the modality of choice, whereas FFDM or DBT might also be performed if needed. Pregnant or lactating women with palpable lesions or nipple discharge are imaged with US as the first method; FFDM or DBT can also be performed depending on the degree of suspicion as the dose to the fetus is minimal, and shielding may even further reduce the dose. More advanced techniques such as breast magnetic resonance imaging or contrast-enhanced mammography are not indicated in this first diagnostic setting and are reserved for cases of established malignancy (local staging) or rare cases of equivocal findings not otherwise resolved or inflammatory breast cancer. Last, but not least, male breast symptoms should also be addressed with US and/or FFDM. CLINICAL RELEVANCE STATEMENT: It is equally important to correctly diagnose an underlying malignancy and to avoid false positives that would lead to unnecessary biopsies, increased costs, and anxiety for the patient. Proper use of imaging modalities ensures optimal diagnostic approach and minimizes false negatives. KEY POINTS: Ultrasound, full-field digital mammography, or digital breast tomosynthesis are the main imaging modalities in the diagnostic setting, while MRI or contrast-enhanced mammography should be reserved to selected cases. Initial imaging modality includes ultrasound combined with mammography or digital breast tomosynthesis depending on women's age and the presence (or not) of inconclusive findings. A negative imaging evaluation should not deter biopsy when a highly suspicious finding is found on physical examination., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: To assess the performance of breast cancer screening by category of breast density and age in a UK screening cohort., Methods: Raw full-field digital mammography data from a single site in the UK, forming a consecutive 3-year cohort of women aged 50 to 70 years from 2016 to 2018, were obtained retrospectively. Breast density was assessed using Volpara software. Examinations were grouped by density category and age group (50-60 and 61-70 years) to analyse screening performance. Statistical analysis was performed to determine the association between density categories and age groups. Volumetric breast density was assessed as a binary classifier of interval cancers (ICs) to find an optimal density threshold., Results: Forty-nine thousand nine-hundred forty-eight screening examinations (409 screen-detected cancers (SDCs) and 205 ICs) were included in the analysis. Mammographic sensitivity, SDC/(SDC + IC), decreased with increasing breast density from 75.0% for density a (p = 0.839, comparisons made to category b), to 73.5%, 59.8% (p = 0.001), and 51.3% (p < 0.001) in categories b, c, and d, respectively. IC rates were highest in the densest categories with rates of 1.8 (p = 0.039), 3.2, 5.7 (p < 0.001), and 7.9 (p < 0.001) per thousand for categories a, b, c, and d, respectively. The recall rate increased with breast density, leading to more false positive recalls, especially in the younger age group. There was no significant difference between the optimal density threshold found, 6.85, and that Volpara defined as the b/c boundary, 7.5., Conclusions: The performance of screening is significantly reduced with increasing density with IC rates in the densest category four times higher than in women with fatty breasts. False positives are a particular issue for the younger subgroup without prior examinations., Clinical Relevance Statement: In women attending screening there is significant underdiagnosis of breast cancer in those with dense breasts, most marked in the highest density category but still three times higher than in women with fatty breasts in the second highest category., Key Points: Breast density can mask cancers leading to underdiagnosis on mammography. Interval cancer rate increased with breast density categories 'a' to 'd'; 1.8 to 7.9 per thousand. Recall rates increased with increasing breast density, leading to more false positive recalls., (© 2024. The Author(s).)

Background: To study the reproducibility of 23 Na magnetic resonance imaging (MRI) measurements from breast tissue in healthy volunteers., Methods: Using a dual-tuned bilateral 23 Na/ 1 H breast coil at 3-T MRI, high-resolution 23 Na MRI three-dimensional cones sequences were used to quantify total sodium concentration (TSC) and fluid-attenuated sodium concentration (FASC). B 1 -corrected TSC and FASC maps were created. Two readers manually measured mean, minimum and maximum TSC and mean FASC values using two sampling methods: large regions of interest (LROIs) and small regions of interest (SROIs) encompassing fibroglandular tissue (FGT) and the highest signal area at the level of the nipple, respectively. The reproducibility of the measurements and correlations between density, age and FGT apparent diffusion coefficient (ADC) values were evaluatedss., Results: Nine healthy volunteers were included. The inter-reader reproducibility of TSC and FASC using SROIs and LROIs was excellent (intraclass coefficient range 0.945-0.979, p < 0.001), except for the minimum TSC LROI measurements (p = 0.369). The mean/minimum LROI TSC and mean LROI FASC values were lower than the respective SROI values (p < 0.001); the maximum LROI TSC values were higher than the SROI TSC values (p = 0.009). TSC correlated inversely with age but not with FGT ADCs. The mean and maximum FGT TSC and FASC values were higher in dense breasts in comparison to non-dense breasts (p < 0.020)., Conclusions: The chosen sampling method and the selected descriptive value affect the measured TSC and FASC values, although the inter-reader reproducibility of the measurements is in general excellent., Relevance Statement: 23 Na MRI at 3 T allows the quantification of TSC and FASC sodium concentrations. The sodium measurements should be obtained consistently in a uniform manner., Key Points: • 23 Na MRI allows the quantification of total and fluid-attenuated sodium concentrations (TSC/FASC). • Sampling method (large/small region of interest) affects the TSC and FASC values. • Dense breasts have higher TSC and FASC values than non-dense breasts. • The inter-reader reproducibility of TSC and FASC measurements was, in general, excellent. • The results suggest the importance of stratifying the sodium measurements protocol., (© 2024. The Author(s).)

Objectives: To investigate the influence of preoperative breast MRI on mastectomy and reoperation rates in patients with pure ductal carcinoma in situ (DCIS)., Methods: The MIPA observational study database (7245 patients) was searched for patients aged 18-80 years with pure unilateral DCIS diagnosed at core needle or vacuum-assisted biopsy (CNB/VAB) and planned for primary surgery. Patients who underwent preoperative MRI (MRI group) were matched (1:1) to those who did not receive MRI (noMRI group) according to 8 confounding covariates that drive referral to MRI (age; hormonal status; familial risk; posterior-to-nipple diameter; BI-RADS category; lesion diameter; lesion presentation; surgical planning at conventional imaging). Surgical outcomes were compared between the matched groups with nonparametric statistics after calculating odds ratios (ORs)., Results: Of 1005 women with pure unilateral DCIS at CNB/VAB (507 MRI group, 498 noMRI group), 309 remained in each group after matching. First-line mastectomy rate in the MRI group was 20.1% (62/309 patients, OR 2.03) compared to 11.0% in the noMRI group (34/309 patients, p = 0.003). The reoperation rate was 10.0% in the MRI group (31/309, OR for reoperation 0.40) and 22.0% in the noMRI group (68/309, p < 0.001), with a 2.53 OR of avoiding reoperation in the MRI group. The overall mastectomy rate was 23.3% in the MRI group (72/309, OR 1.40) and 17.8% in the noMRI group (55/309, p = 0.111)., Conclusions: Compared to those going directly to surgery, patients with pure DCIS at CNB/VAB who underwent preoperative MRI had a higher OR for first-line mastectomy but a substantially lower OR for reoperation., Clinical Relevance Statement: When confounding factors behind MRI referral are accounted for in the comparison of patients with CNB/VAB-diagnosed pure unilateral DCIS, preoperative MRI yields a reduction of reoperations that is more than twice as high as the increase in overall mastectomies., Key Points: • Confounding factors cause imbalance when investigating the influence of preoperative MRI on surgical outcomes of pure DCIS. • When patient matching is applied to women with pure unilateral DCIS, reoperation rates are significantly reduced in women who underwent preoperative MRI. • The reduction of reoperations brought about by preoperative MRI is more than double the increase in overall mastectomies., (© 2023. The Author(s).)

In the mid-1990s, the identification of BRCA1/2 genes for breast cancer susceptibility led to testing breast MRI accuracy in screening women at increased risk. From 2000 onwards, ten intraindividual comparative studies showed the marked superiority of MRI: the sensitivity ranged 25-58% for mammography, 33-52% for ultrasound, 48-67% for mammography plus ultrasound, and 71-100% for MRI; specificity 93-100%, 91-98%, 89-98%, and 81-98%, respectively. Based on the available evidence, in 2006-2007, the UK National Institute for Clinical Excellence and the American Cancer Society recommended MRI screening of high-risk women, followed by other international guidelines. Despite evidence-based medicine ideally requiring randomised controlled trials (RCTs) for policy changes regarding screening procedures, breast MRI for high-risk screening was adopted in many countries worldwide. In 2019, the results of the "DENSE" RCT were published in favour of breast MRI screening of women with extremely dense breasts compared to mammography alone, showing a reduction of more than 80% of the interval cancer rate in women who attended MRI screening. Even though international recommendations in favour of this practice were issued, substantial obstacles still prevent health systems from adopting breast MRI for screening women with extremely dense breasts. A paradox is evident: we adopted a screening procedure without evidence from RCTs, and now that we have this level-1 evidence for the same procedure, we fail to do so. This critical review tries to explain the differences between the two cases, as examples of the complex pathways of translating radiological research into everyday practice.Critical relevance statement The high-level evidence in favour of breast MRI screening of women with extremely dense breasts is failing to persuade policy makers to translate this into clinical practice.Key points• Breast MRI screening of high-risk women was adopted on basis of the evidence provided by test accuracy comparative studies showing an MRI performance greatly superior to that of mammography.• Breast MRI screening of women with extremely dense breasts has not been adopted although the evidence of a large reduction in interval cancer rate from a RCT.• We illustrate the differences between the two cases, as an example of the complex ways of translation of radiological research in clinical practice according to the EBM theory., (© 2024. The Author(s).)

Objectives: Quality assurance (QA) of image interpretation plays a key role in screening and diagnostic mammography, maintaining minimum standards and supporting continuous improvement in interpreting images. However, the QA structure across Europe shows considerable variation. The European Society of Breast Imaging (EUSOBI) conducted a survey among the members to collect information on radiologists' preferences regarding QA measures in mammography., Materials and Methods: An anonymous online survey consisting of 25 questions was distributed to all EUSOBI members and national breast radiology bodies in Europe. The questions were designed to collect demographic characteristics, information on responders' mammography workload and data about QA measures currently used in their country. Data was analysed using descriptive statistical analysis, the χ 2 test, linear regression, and Durbin-Watson statistic test., Results: In total, 251 breast radiologists from 34 countries completed the survey. Most respondents were providing both screening and symptomatic services (137/251, 54.6%), working in an academic hospital (85/251, 33.9%) and reading 1000-4999 cases per year (109/251, 43.4%). More than half of them (133/251, 53%) had established QA measures in their workplace. Although less than one-third (71/251, 28.3%) had to participate in regular performance testing, the vast majority (190/251, 75.7%) agreed that a mandatory test would be helpful to improve their skills., Conclusion: QA measures were in place for more than half of the respondents working in screening and diagnostic mammography to evaluate their breast imaging performance. Although there were substantial differences between countries, the importance of having QA in the workplace and implemented was widely acknowledged by radiologists., Clinical Relevance Statement: Although several quality assurance (QA) measures of image interpretation are recommended by European bodies or national organisations, the QA in mammography is quite heterogenous between countries and reporting settings, and not always actively implemented across Europe., Key Points: The first survey that presents radiologists' preferences regarding QA measures of image interpretation in mammography. Quality assurance measures in the workplace are better-established for breast screening compared to diagnostic mammography. Radiologists consider that performance tests would help to improve their mammography interpretation skills., (© 2023. The Author(s).)

Background Breast screening enables early detection of cancers; however, most women have normal mammograms, resulting in repetitive and resource-intensive reading tasks. Purpose To investigate if deep learning (DL) algorithms can be used to triage mammograms by identifying normal results to reduce workload or flag cancers that may be overlooked. Materials and Methods In this retrospective study, three commercial DL algorithms were investigated using consecutive mammograms from two UK Breast Screening Program sites from January 2015 to December 2017 and January 2017 to December 2018 on devices from two mammography vendors. Normal mammograms with a 3-year follow-up and histopathologically proven cancer detected at screening, the subsequent round, or in the 3-year interval were included. Two algorithm thresholds were set: in scenario A, 99.0% sensitivity for rule-out triage to a lone reader, and in scenario B, approximately 1.0% additional recall providing a rule-in triage for further assessment. Both thresholds were then applied to the screening workflow in scenario C. The sensitivity and specificity were used to assess the overall predictive performance of each DL algorithm. Results The data set comprised 78 849 patients (median age, 59 years [IQR, 53-63 years]) and 887 screening-detected, 439 interval, and 688 subsequent screening round-detected cancers. In scenario A (rule-out triage), models DL-1, DL-2, and DL-3 triaged 35.0% (27 565 of 78 849), 53.2% (41 937 of 78 849), and 55.6% (43 869 of 78 849) of mammograms, respectively, with 0.0% (0 of 887) to 0.1% (one of 887) of screening-detected cancers undetected. In scenario B, DL algorithms triaged in 4.6% (20 of 439) to 8.2% (36 of 439) of interval and 5.2% (36 of 688) to 6.1% (42 of 688) of subsequent-round cancers when applied after the routine double-reading workflow. Combining both approaches in scenario C resulted in an overall noninferior specificity (difference, -0.9%; P < .001) and superior sensitivity (difference, 2.7%; P < .001) for the adaptive workflow compared with routine double reading for all three algorithms. Conclusion Rule-out and rule-in DL-adapted triage workflows can improve the efficiency and efficacy of mammography breast cancer screening. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Nishikawa and Lu in this issue.

Cross-institution collaborations are constrained by data-sharing challenges. These challenges hamper innovation, particularly in artificial intelligence, where models require diverse data to ensure strong performance. Federated learning (FL) solves data-sharing challenges. In typical collaborations, data is sent to a central repository where models are trained. With FL, models are sent to participating sites, trained locally, and model weights aggregated to create a master model with improved performance. At the 2021 Radiology Society of North America's (RSNA) conference, a panel was conducted titled "Accelerating AI: How Federated Learning Can Protect Privacy, Facilitate Collaboration and Improve Outcomes." Two groups shared insights: researchers from the EXAM study (EMC CXR AI Model) and members of the National Cancer Institute's Early Detection Research Network's (EDRN) pancreatic cancer working group. EXAM brought together 20 institutions to create a model to predict oxygen requirements of patients seen in the emergency department with COVID-19 symptoms. The EDRN collaboration is focused on improving outcomes for pancreatic cancer patients through earlier detection. This paper describes major insights from the panel, including direct quotes. The panelists described the impetus for FL, the long-term potential vision of FL, challenges faced in FL, and the immediate path forward for FL., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Accumulating evidence from retrospective studies demonstrate at least non-inferior performance when using AI algorithms with different strategies versus double-reading in mammography screening. In addition, AI algorithms for mammography screening can reduce work load by moving to single human reading. Prospective trials are essential to avoid unintended adverse consequences before incorporation of AI algorithms into UK's National Health Service (NHS) Breast Screening Programme (BSP). A stakeholders' meeting was organized in Newnham College, Cambridge, UK to undertake a review of the current evidence to enable consensus discussion on next steps required before implementation into a screening programme. It was concluded that a multicentre multivendor testing platform study with opt-out consent is preferred. AI thresholds from different vendors should be determined while maintaining non-inferior screening performance results, particularly ensuring recall rates are not increased. Automatic recall of cases using an agreed high sensitivity AI score versus automatic rule out with a low AI score set at a high sensitivity could be used. A human reader should still be involved in decision making with AI-only recalls requiring human arbitration. Standalone AI algorithms used without prompting maintain unbiased screening reading performance, but reading with prompts should be tested prospectively and ideally provided for arbitration., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Support for this meeting was from Newnham College, University of Cambridge, Cambridge, UK. F.J.G. is supported by a Cancer Research UK early detection programme grant. H.A. is Chief Scientific Officer, Preemptive Medicine and Health, Flagship Pioneering.]., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: To report mastectomy and reoperation rates in women who had breast MRI for screening (S-MRI subgroup) or diagnostic (D-MRI subgroup) purposes, using multivariable analysis for investigating the role of MRI referral/nonreferral and other covariates in driving surgical outcomes., Methods: The MIPA observational study enrolled women aged 18-80 years with newly diagnosed breast cancer destined to have surgery as the primary treatment, in 27 centres worldwide. Mastectomy and reoperation rates were compared using non-parametric tests and multivariable analysis., Results: A total of 5828 patients entered analysis, 2763 (47.4%) did not undergo MRI (noMRI subgroup) and 3065 underwent MRI (52.6%); of the latter, 2441/3065 (79.7%) underwent MRI with preoperative intent (P-MRI subgroup), 510/3065 (16.6%) D-MRI, and 114/3065 S-MRI (3.7%). The reoperation rate was 10.5% for S-MRI, 8.2% for D-MRI, and 8.5% for P-MRI, while it was 11.7% for noMRI (p ≤ 0.023 for comparisons with D-MRI and P-MRI). The overall mastectomy rate (first-line mastectomy plus conversions from conserving surgery to mastectomy) was 39.5% for S-MRI, 36.2% for P-MRI, 24.1% for D-MRI, and 18.0% for noMRI. At multivariable analysis, using noMRI as reference, the odds ratios for overall mastectomy were 2.4 (p < 0.001) for S-MRI, 1.0 (p = 0.957) for D-MRI, and 1.9 (p < 0.001) for P-MRI., Conclusions: Patients from the D-MRI subgroup had the lowest overall mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). This analysis offers an insight into how the initial indication for MRI affects the subsequent surgical treatment of breast cancer., Key Points: • Of 3065 breast MRI examinations, 79.7% were performed with preoperative intent (P-MRI), 16.6% were diagnostic (D-MRI), and 3.7% were screening (S-MRI) examinations. • The D-MRI subgroup had the lowest mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). • The S-MRI subgroup had the highest mastectomy rate (39.5%) which aligns with higher-than-average risk in this subgroup, with a reoperation rate (10.5%) not significantly different to that of all other subgroups., (© 2023. The Author(s).)

Objectives: To explore the relationship between indices of hypoxia and vascular function from 18 F-fluoromisonidazole ([ 18 F]-FMISO)-PET/MRI with immunohistochemical markers of hypoxia and vascularity in oestrogen receptor-positive (ER +) breast cancer., Methods: Women aged  > 18 years with biopsy-confirmed, treatment-naïve primary ER + breast cancer underwent [ 18 F]-FMISO-PET/MRI prior to surgery. Parameters of vascular function were derived from DCE-MRI using the extended Tofts model, whilst hypoxia was assessed using the [ 18 F]-FMISO influx rate constant, K i . Histological tumour sections were stained with CD31, hypoxia-inducible factor (HIF)-1α, and carbonic anhydrase IX (CAIX). The number of tumour microvessels, median vessel diameter, and microvessel density (MVD) were obtained from CD31 immunohistochemistry. HIF-1α and CAIX expression were assessed using histoscores obtained by multiplying the percentage of positive cells stained by the staining intensity. Regression analysis was used to study associations between imaging and immunohistochemistry variables., Results: Of the lesions examined, 14/22 (64%) were ductal cancers, grade 2 or 3 (19/22; 86%), with 17/22 (77%) HER2-negative. [ 18 F]-FMISO K i associated negatively with vessel diameter (p = 0.03), MVD (p = 0.02), and CAIX expression (p = 0.002), whilst no significant relationships were found between DCE-MRI pharmacokinetic parameters and immunohistochemical variables. HIF-1α did not significantly associate with any PET/MR imaging indices., Conclusion: Hypoxia measured by [ 18 F]-FMISO-PET was associated with increased CAIX expression, low MVD, and smaller vessel diameters in ER + breast cancer, further corroborating the link between inadequate vascularity and hypoxia in ER + breast cancer., Key Points: • Hypoxia, measured by [ 18 F]-FMISO-PET, was associated with low microvessel density and small vessel diameters, corroborating the link between inadequate vascularity and hypoxia in ER + breast cancer. • Increased CAIX expression was associated with higher levels of hypoxia measured by [ 18 F]-FMISO-PET. • Morphologic and functional abnormalities of the tumour microvasculature are the major determinants of hypoxia in cancers and support the previously reported perfusion-driven character of hypoxia in breast carcinomas., (© 2023. The Author(s).)