PURPOSE Accurate risk assessment is essential for the success of population screening programs in breast cancer. Models with high sensitivity and specificity would enable programs to target more elaborate screening efforts to high-risk populations, while minimizing overtreatment for the rest. Artificial intelligence (AI)-based risk models have demonstrated a significant advance over risk models used today in clinical practice. However, the responsible deployment of novel AI requires careful validation across diverse populations. To this end, we validate our AI-based model, Mirai, across globally diverse screening populations. METHODS We collected screening mammograms and pathology-confirmed breast cancer outcomes from Massachusetts General Hospital, USA; Novant, USA; Emory, USA; Maccabi-Assuta, Israel; Karolinska, Sweden; Chang Gung Memorial Hospital, Taiwan; and Barretos, Brazil. We evaluated Uno's concordance-index for Mirai in predicting risk of breast cancer at one to five years from the mammogram. RESULTS A total of 128,793 mammograms from 62,185 patients were collected across the seven sites, of which 3,815 were followed by a cancer diagnosis within 5 years. Mirai obtained concordance indices of 0.75 (95% CI, 0.72 to 0.78), 0.75 (95% CI, 0.70 to 0.80), 0.77 (95% CI, 0.75 to 0.79), 0.77 (95% CI, 0.73 to 0.81), 0.81 (95% CI, 0.79 to 0.82), 0.79 (95% CI, 0.76 to 0.83), and 0.84 (95% CI, 0.81 to 0.88) at Massachusetts General Hospital, Novant, Emory, Maccabi-Assuta, Karolinska, Chang Gung Memorial Hospital, and Barretos, respectively. CONCLUSION Mirai, a mammography-based risk model, maintained its accuracy across globally diverse test sets from seven hospitals across five countries. This is the broadest validation to date of an AI-based breast cancer model and suggests that the technology can offer broad and equitable improvements in care.

reast cancer is the second leading cause of cancer-related deaths and the most commonly diagnosed cancer in women across the world (1). Digital mammography (DM) is the primary imaging modality of breast cancer screening in women who are asymptomatic. In a diagnostic workup setting (2), DM has been shown to reduce breast cancer mortality (3). In standard clinical practice, a radiologist reads mammograms and classifies the findings according to the American College of Radiology (4) Breast Imaging Reporting and Data System (BI-RADS) lexicon. An abnormal finding depicted at DM typically requires a diagnostic workup, which may include additional mammographic views or possibly additional imaging modalities. If a lesion is suspicious for cancer, further evaluation with a biopsy is recommended. Analyzing these images is challenging because of the subtle differences between lesions and background fibroglandular tissue, different lesion types, the nonrigid nature of the breast, and the relatively small proportion of cancers in a screening population of women at average risk (2). This leads to substantial intraobserver and interobserver variability (5). The average performance measures for screening mammography by a radiologist was reported by Lehman et al (6) to be 86.9% sensitivity and 88.9% specificity. Breast cancer risk prediction models on the basis of clinical features can help physicians estimate the probability of an individual or population to develop breast cancer within certain time frames. As a result, they are often used to recommend an individual screening plan. In a systematic survey of risk prediction models, Meads et al (7) reported a limited performance when applied to general populations (area under the receiver operating characteristic curve [AUC], 0.67; 95% confidence interval [CI]: 0.65, 0.68), and showed improved results when applied to high-risk populations (AUC, 0.76; 95% CI: 0.70, 0.82).

The aim of this study was to investigate the language development of 20 children who had been exposed to thiamine (vitamin B1) deficiency in infancy due to feeding with soy‐based formula that was accidentally deficient of thiamine. In this case–control study, 20 children (12 males, eight females; mean age 31.8mo [SD 4.1], range 24–39mo) who were fed thiamine‐deficient formula in infancy were compared with 20 children (12 males, eight females; mean age 32.2mo [SD 3.9], range 25–39mo) fed with other milk sources and matched for age, sex, and maternal education. Receptive and expressive language development was assessed with the Preschool Language Scale, 3rd edition. Other assessments included mental development (Bayley Scales of Infant Development, 2nd edition), evaluation for autistic spectrum disorders, and neurological examination. Motor development was compared by age at independent walking. The study and control groups differed significantly in the expressive communication (p<0.001) and auditory comprehension language subscales (p<0.001), the Mental Developmental Index score (p<0.001), and age at independent walking (p=0.001). A significant correlation was found between the receptive language score and age at independent walking, i.e. poorer language associated with later walking (r=–0.601, p=0.005). The conclusion was that thiamine deficiency in infancy could affect language development in childhood.

Background The MyPeBS study is an ongoing randomised controlled trial testing whether a risk-stratified breast cancer screening strategy is non-inferior, or eventually superior, to standard age-based screening at reducing incidence of stage 2 or more cancers. This large European Commission-funded initiative aims to include 85,000 women aged 40 to 70 years, without prior breast cancer and not previously identified at high risk in six countries (Belgium, France, Italy, Israel, Spain, UK). A specific work package within MyPeBS examines psychological, socio-economic and ethical aspects of this new screening strategy. It compares women’s reported data and outcomes in both trial arms on the following issues: general anxiety, cancer-related worry, understanding of breast cancer screening strategy and information-seeking behaviour, socio-demographic and economic characteristics, quality of life, risk perception, intention to change health-related behaviours, satisfaction with the trial. Methods At inclusion, 3-months, 1-year and 4-years, each woman participating in MyPeBS is asked to fill online questionnaires. Descriptive statistics, bivariate analyses, subgroup comparisons and analysis of variations over time will be performed with appropriate tests to assess differences between arms. Multivariate regression models will allow modelling of different patient reported data and outcomes such as comprehension of the information provided, general anxiety or cancer worry, and information seeking behaviour. In addition, a qualitative study (48 semi-structured interviews conducted in France and in the UK with women randomised in the risk-stratified arm), will help further understand participants’ acceptability and comprehension of the trial, and their experience of risk assessment. Discussion Beyond the scientific and medical objectives of this clinical study, it is critical to acknowledge the consequences of such a paradigm shift for women. Indeed, introducing a risk-based screening relying on individual biological differences also implies addressing non-biological differences (e.g. social status or health literacy) from an ethical perspective, to ensure equal access to healthcare. The results of the present study will facilitate making recommendations on implementation at the end of the trial to accompany any potential change in screening strategy. Trial registration Study sponsor: UNICANCER. My personalised breast screening (MyPeBS). Clinicaltrials.gov (2018) available at: https://clinicaltrials.gov/ct2/show/NCT03672331 Contact: Cécile VISSAC SABATIER, PhD, + 33 (0)1 73 79 77 58 ext + 330,142,114,293, contact@mypebs.eu.

We investigated the long-term implications of infantile thiamine (vitamin B1) deficiency on motor function in preschoolers who had been fed during the first 2 years of life with a faulty milk substitute. In this retrospective cohort study, 39 children aged 5-6 years who had been exposed to a thiamine-deficient formula during infancy were compared with 30 age-matched healthy children with unremarkable infant nutritional history. The motor function of the participants was evaluated with The Movement Assessment Battery for Children (M-ABC) and the Zuk Assessment. Both evaluation tools revealed statistically significant differences between the exposed and unexposed groups for gross and fine motor development (p < .001, ball skills p = .01) and grapho-motor development (p = .004). The differences were especially noteworthy on M-ABC testing for balance control functioning (p < .001, OR 5.4; 95% CI 3.4-7.4) and fine motor skills (p < .001, OR 3.2; 95% CI 1.8-4.6). In the exposed group, both assessments concurred on the high rate of children exhibiting motor function difficulties in comparison to unexposed group (M-ABC: 56% vs. 10%, Zuk Assessment: 59% vs. 3%, p < .001). Thiamine deficiency in infancy has long-term implications on gross and fine motor function and balance skills in childhood, thiamine having a crucial role in normal motor development. The study emphasizes the importance of proper infant feeding and regulatory control of breast milk substitutes.

The sestamibi scan (MIBI) and ultrasound (US) are used for preoperative localization of parathyroid adenoma (PTA), with sensitivity as high as 90%. We developed 4-dimensional magnetic resonance imaging (4D MRI) as a novel tool for identifying PTAs. Eleven patients with PTA were enrolled. 4D MRI from the mandible to the aortic arch was used. Optimization of the timing of image acquisition was obtained by changing dynamic and static sequences. PTAs were identified in all except 1 patient. In 9 patients, there was a complete match between the 4D MRI and the US and MIBI, as well as with the operative finding. In 1 patient, the adenoma was correctly localized by 4D MRI, in contrast to the US and MIBI scan. The sensitivity of the 4D MRI was 90% and after optimization, 100%. Specificity was 100%. We concluded that 4D MRI is a reliable technique for identification of PTAs, although more studies are needed.

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Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.THE BIGGER PICTURE During the COVID-19 pandemic, medical imaging (CT, X-ray, ultrasound) has played a key role in addressing the magnified need for speed, low cost, ubiquity, and precision in patient care. The contemporary digitization of medicine and rise of artificial intelligence (AI) induce a quantum leap in medical imaging: AI has proven equipollent to healthcare professionals across a diverse range of tasks, and hopes are high that AI can save time and cost and increase coverage by advancing rapid patient stratification and empowering clinicians. This review bridges medical imaging and AI in the context of COVID-19 and conducts the largest systematic review of the literature in the field. We identify several gaps and evidence significant disparities between clinicians and AI experts and foresee a need for improved, interdisciplinary collaboration to develop robust AI solutions that can be deployed in clinical practice.The key challenges on that roadmap are discussed alongside recommended solutions. ll