Calcium score of the entire thoracic aorta is an independent predictor of all-cause mortality in patients referred to chest computed tomography.

BACKGROUND: Thoracic arterial calcifications (TAC) are not routinely reported or quantified in chest CT scans. We aimed to evaluate the association between TAC of the entire thoracic aorta and all-cause mortality (ACM) in patients referred to standard chest CT. METHODS: A retrospective analysis of consecutive standard chest CT scans (non-gated, non-contrast) for the quantification of TAC, CAC and aortic valve calcification. TAC was divided into 4 sample-derived categories (TAC 1 = 0, TAC 2 = 1-65, TAC 3 = 66-439 and TAC 4 ≥ 440). Data regarding ACM was retrieved from the health care provider database. Multivariate Cox proportional regression models were used to assess associations between the TAC categories and ACM. RESULTS: The study cohort included 415 patients (mean age 67 years, 52% male); 107 ACM events were recorded during a median follow-up of 9 years (inter-quartile range: 7.4-10.4). The rate of ACM was 13%, 25%, 32%, 41% according to TAC category (p < 0.001). The highest TAC category (≥ 440) was a strong and independent predictor of ACM [HR = 1.69 (1.13-2.52; 0.01)] in multivariate analysis. Other independent predictors of ACM included age [HR = 1.07 (1.04-1.10; p < 0.001)], male sex [HR = 2.27 (1.49-3.46; 0.001)] and malignancy [HR = 2.21 (1.49-3.23; < 0.001)]. CONCLUSIONS: Severe TAC (≥ 440) was found to be an independent predictor of ACM. Thus, we suggest that documenting and quantifying TAC should be routinely incorporated into standard chest CT reports.

Accelerating artificial intelligence: How federated learning can protect privacy, facilitate collaboration, and improve outcomes.

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.

Impact of the COVID-19 Pandemic on Clinical Findings in Medical Imaging Exams in a Nationwide Israeli Health Organization: Observational Study.

BACKGROUND: The outbreak of the COVID-19 pandemic had a major effect on the consumption of health care services. Changes in the use of routine diagnostic exams, increased incidences of postacute COVID-19 syndrome (PCS), and other pandemic-related factors may have influenced detected clinical conditions. OBJECTIVE: This study aimed to analyze the impact of COVID-19 on the use of outpatient medical imaging services and clinical findings therein, specifically focusing on the time period after the launch of the Israeli COVID-19 vaccination campaign. In addition, the study tested whether the observed gains in abnormal findings may be linked to PCS or COVID-19 vaccination. METHODS: Our data set included 572,480 ambulatory medical imaging patients in a national health organization from January 1, 2019, to August 31, 2021. We compared different measures of medical imaging utilization and clinical findings therein before and after the surge of the pandemic to identify significant changes. We also inspected the changes in the rate of abnormal findings during the pandemic after adjusting for changes in medical imaging utilization. Finally, for imaging classes that showed increased rates of abnormal findings, we measured the causal associations between SARS-CoV-2 infection, COVID-19-related hospitalization (indicative of COVID-19 complications), and COVID-19 vaccination and future risk for abnormal findings. To adjust for a multitude of confounding factors, we used causal inference methodologies. RESULTS: After the initial drop in the utilization of routine medical imaging due to the first COVID-19 wave, the number of these exams has increased but with lower proportions of older patients, patients with comorbidities, women, and vaccine-hesitant patients. Furthermore, we observed significant gains in the rate of abnormal findings, specifically in musculoskeletal magnetic resonance (MR-MSK) and brain computed tomography (CT-brain) exams. These results also persisted after adjusting for the changes in medical imaging utilization. Demonstrated causal associations included the following: SARS-CoV-2 infection increasing the risk for an abnormal finding in a CT-brain exam (odds ratio [OR] 1.4, 95% CI 1.1-1.7) and COVID-19-related hospitalization increasing the risk for abnormal findings in an MR-MSK exam (OR 3.1, 95% CI 1.9-5.3). CONCLUSIONS: COVID-19 impacted the use of ambulatory imaging exams, with greater avoidance among patients at higher risk for COVID-19 complications: older patients, patients with comorbidities, and nonvaccinated patients. Causal analysis results imply that PCS may have contributed to the observed gains in abnormal findings in MR-MSK and CT-brain exams.

Virtual Biopsy by Using Artificial Intelligence-based Multimodal Modeling of Binational Mammography Data.

Background Computational models based on artificial intelligence (AI) are increasingly used to diagnose malignant breast lesions. However, assessment from radiologic images of the specific pathologic lesion subtypes, as detailed in the results of biopsy procedures, remains a challenge. Purpose To develop an AI-based model to identify breast lesion subtypes with mammograms and linked electronic health records labeled with histopathologic information. Materials and Methods In this retrospective study, 26 569 images were collected in 9234 women who underwent digital mammography to pretrain the algorithms. The training data included individuals who had at least 1 year of clinical and imaging history followed by biopsy-based histopathologic diagnosis from March 2013 to November 2018. A model that combined convolutional neural networks with supervised learning algorithms was independently trained to make breast lesion predictions with data from 2120 women in Israel and 1642 women in the United States. Results were reported using the area under the receiver operating characteristic curve (AUC) with the 95% DeLong approach to estimate CIs. Significance was tested with bootstrapping. Results The Israeli model was validated in 456 women and tested in 441 women (mean age, 51 years ± 11 [SD]). The U.S. model was validated in 350 women and tested in 344 women (mean age, 60 years ± 12). For predicting malignancy in the test sets (consisting of 220 Israeli patient examinations and 126 U.S. patient examinations with ductal carcinoma in situ or invasive cancer), the algorithms obtained an AUC of 0.88 (95% CI: 0.85, 0.91) and 0.80 (95% CI: 0.74, 0.85) for Israeli and U.S. patients, respectively (P = .006). These results may not hold for other cohorts of patients, and generalizability across populations should be further investigated. Conclusion The results offer supporting evidence that artificial intelligence applied to clinical and mammographic images can identify breast lesion subtypes when the data are sufficiently large, which may help assess diagnostic workflow and reduce biopsy sampling errors. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

Study protocol comparing the ethical, psychological and socio-economic impact of personalised breast cancer screening to that of standard screening in the "My Personal Breast Screening" (MyPeBS) randomised clinical trial.

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.

Development of a mobile training app to assist radiographers' diagnostic assessments.

The current study reduced the time lag between performing a diagnostic assessment and identifying a critical finding in CT and MRI exams through improving radiographers' abilities to identify those critical findings. Radiographers' diagnostic assessments in CT and MRI exams were used to develop a mobile training application with the aim to improve radiographers' awareness of critical findings. The current research used data analytics to examine radiographers' interpretation of imaging studies from a privately owned medical group in Israel. During the project, the radiographers' ability to identify critical findings improved. Implementation of the mobile training program yielded positive results where the knowledge gap was reduced and time to identify critical cases was decreased. Specifically, this study showed that radiographers can be trained in ways that enhance their involvement with radiologists to provide high quality services and improve treatment Ultimately, this gives patients higher quality of care and safer treatment.

Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model.

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.

Leveraging Comprehensive Health Records for Breast Cancer Risk Prediction: A Binational Assessment.

Breast cancer (BC) risk models based on electronic health records (EHR) can assist physicians in estimating the probability of an individual with certain risk factors to develop BC in the future. In this retrospective study, we used clinical data combined with machine learning tools to assess the utility of a personalized BC risk model on 13,786 Israeli and 1,695 American women who underwent screening mammography in the years 2012-2018 and 2008-2018, respectively. Clinical features were extracted from EHR, personal questionnaires, and past radiologists' reports. Using a set of 1,547 features, the predictive ability for BC within 12 months was measured in both datasets and in sub-cohorts of interest. Our results highlight the improved performance of our model over previous established BC risk models, their ultimate potential for risk-based screening policies on first time patients and novel clinically relevant risk factors that can compensate for the absence of imaging history information.

Erratum: On the role of artificial intelligence in medical imaging of COVID-19.

[This corrects the article DOI: 10.1016/j.patter.2021.100269.].

On the role of artificial intelligence in medical imaging of COVID-19.

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 Pattern of Use of PET/CT Scans in the Clinical Management of Chronic Lymphocytic Leukemia.

BACKGROUND: The study aimed to evaluate the utilization patterns of positron emission tomography/computed tomography (PET/CT) in chronic lymphocytic leukemia (CLL) patients and to investigate whether the results of these scans influenced treatment decisions. PATIENTS: and Methods: In this observational study, we analyzed patients with CLL or small lymphocytic leukemia (SLL) who underwent at least one PET/CT scan from 2007 to 2018. Patients were divided into two groups: (1) patients who had at least one fluorodeoxyglucose-avid PET/CT scan, and (2) patients who had all negative scans. PET/CT results were retrieved from patients' medical files and were revised by an expert radiologist according to visual score scale, SUVmax/SUVliver mean ratio, and the SUVmax. RESULTS: Of the 524 patients, 160 patients (30.5%) had PET/CT scans, and 120 patients met the inclusion criteria. A total of 219 eligible scans were analyzed; 62 of these scans (28.3%) were reported as positive, and 167 of these scans (76.3%) were performed for staging. There was a significant association between PET/CT results and change of therapy (P < .001); however, 62.9% of the positive PET/CT scans were not followed by a change of treatment. Survival time was not different between the two groups. The SUVmax/SUVliver mean ratio was negatively significantly associated with lymphocytes percent (r = -0.237, P = .042) and positively associated with lactate dehydrogenase levels (r = 0.338, P = .008) among CLL patients. CONCLUSION: Despite the fact that the use of surveillance PET/CT for patients with CLL/SLL is not in the guidelines and that it is not useful for disease management, in practice the test is in frequent use in Israel.

Impact of high-quality ultrasound following community ultrasound on surgical planning and active surveillance in patients with thyroid cancer.

OBJECTIVE: Ultrasound (US) has gained a critical role in thyroid cancer treatment planning, yet it is limited by its user-dependent nature. The aim of this study was to compare the impact of US performed by radiologists specializing in thyroid imaging (hqUS) and US performed by radiographers in the community (cUS) on treatment plans of patients diagnosed with well-differentiated thyroid malignancies. DESIGN: Retrospective single-centre case series with chart review. PATIENTS: Patients diagnosed with thyroid cancer during 2017-2019 that had cUS followed by hqUS pre-operative counselling were included in this retrospective analysis. MEASUREMENTS: The main outcome was management alternations based on one of two sonographic measures: (1) extrathyroid extension (ETE); (2) The presence of central or lateral lymph nodes suspicious for metastases (LNM), which were compared with the final pathology. RESULTS: Among those with non-recurrent tumour (n = 76), ETE was reported 22 times more by hqUS compared with cUS (28.9% vs 1.3%, P < .001). Central and lateral LNM were reported approximately 6.5 and 1.5 times more by hqUS, respectively (25.0% vs 3.9%, P < .001 and 15.8% vs., 9.2%, P = .227, respectively). Overall, hqUS altered the initial treatment plan of 35.5% of patients. In 27.6% of patients, hqUS and its subsequent surgery resulted in a change to the patients' 2015 ATA risk stratification system. In 40% of patients with microcarcinomas, hqUS findings mandated surgery according to findings that were not reported by cUS. False-positive rate was 5.2%. CONCLUSIONS: Community US may under-diagnose important features such as ETE and LNM, leading to potential under-treatment in many patients. High-quality US of the neck should be considered in patients with differentiated thyroid carcinoma before making any treatment decisions.

Personalized Screening for Breast Cancer: Rationale, Present Practices, and Future Directions.

Ever since screening for early breast cancer (BC) diagnosis was shown to decrease mortality from the disease, screening programs have been widely implemented throughout the world. Targeted age groups and schedules vary between countries but the majority use a population-based approach, regardless of personal BC risk. The purpose of this review was to describe current population-based screening practices, point out some of the shortcomings of these practices, describe BC risk factors and risk assessment models, and present ongoing clinical trials of personalized risk-adapted BC screening. Three ongoing, large-scale, randomized controlled clinical trials (WISDOM in the US, MyPEBS in Europe, and TBST in Italy) were identified through a search of the MEDLINE and US National Library of Medicine (ClinicalTrials.gov) databases. In these trials, women either undergo standard or personalized screening. The trials vary in methods of risk stratification and screening modalities, but all aim to examine whether personalized risk-adapted screening can safely replace the current population-based approach and lead to rates of advanced-stage BC at diagnosis comparable with those of current screening regimens. The results of these trials may change current population-based screening practices.

Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization.

"No-shows", defined as missed appointments or late cancellations, is a central problem in healthcare systems. It has appeared to intensify during the COVID-19 pandemic and the nonpharmaceutical interventions, such as closures, taken to slow its spread. No-shows interfere with patients' continuous care, lead to inefficient utilization of medical resources, and increase healthcare costs. We present a comprehensive analysis of no-shows for breast imaging appointments made during 2020 in a large medical network in Israel. We applied advanced machine learning methods to provide insights into novel and known predictors. Additionally, we employed causal inference methodology to infer the effect of closures on no-shows, after accounting for confounding biases, and demonstrate the superiority of adversarial balancing over inverse probability weighting in correcting these biases. Our results imply that a patient's perceived risk of cancer and the COVID-19 time-based factors are major predictors. Further, we reveal that closures impact patients over 60, but not patients undergoing advanced diagnostic examinations.

Whose fault is it? Attribution of causes of patient violence among exposed and unexposed community-based family physicians.

Extensive research has dealt with violence directed at healthcare workers in hospital settings; however, few studies have examined community-based settings. Furthermore, there is also scant literature regarding the perceptions of healthcare providers who were exposed to violence, compared to those who were not. This study aims to narrow these gaps in the literature by examining community-based family physicians' (CBFPs) perceptions in a large national Health Maintenance Organisation (HMO) regarding patient-initiated violence. Using a voluntary online survey, directed at all CBFPs working at the HMO, 412 CBFPs were surveyed on the following issues: exposure to violence initiated by patients or their family members; perceptions of violent occurrences and possible safety measures. The differences between CBFPs who had been exposed to violence and those who had not were compared. The majority of CBFPs reported experiencing verbal attacks (64%), and a small percentage experienced property-related violence (11.7%) or physical violence (3.4%). Comparing CBFPs who were exposed to violence with those who were not, regarding their perceptions of the 'causes of violence', revealed three differentiating factors: 'waiting time', 'failure to meet the patient's expectations' and 'the nature of the physician-patient encounter'. Regarding the desired preventive actions, the four differentiating factors were as follows: 'reduction in the number of patients per physician', 'improved queue management processes', 'longer meetings' and 'violence prevention training'. Conducting separate analyses, according to violence type (verbal abuse, vandalism or physical violence), indicated finer differentiations. In terms of Attribution Theory, one might argue that CBFPs who were directly exposed to patients' aggression attributed internal locus to the attacker, and tended to blame the attacker's personal characteristics and cultural values. Conversely, family CBFPs who were not attacked attributed external locus to situational factors such as waiting time, not receiving service, and the nature of the interaction between the attacker and the CBFP.

Development and efficacy of a computerized decision support system for osteoporosis management in the community.

Computerized alerts for primary care physicians, provided during visits of patients who met treatment guidelines based on their electronic medical records, are an efficient method to raise awareness to many otherwise missed cases, especially after fracture. PURPOSE: Measure the efficacy of an automated real-time alert which was developed to assist osteoporosis management in the community. METHODS: The study population included treatment naïve patients with T-score ≤ - 2.5 or hip or vertebral fracture in a 2 million member Israeli health fund. On each ambulatory visit to a primary care physician or endocrinologist, a pop-up screen reminded the caregiver to consider treatment initiation. A follow-up "smart-set" screen conveniently gathered links to common actions (namely, (a) issue first line therapy prescription, (b) referral to nutritionist consultation, (c) laboratory tests relevant for osteoporosis, and (d) printing an information page for the patient). Time till treatment initiation was compared between the 3 years prior to and following the intervention. RESULTS: Within 2 years since alert activation, a total of n = 21,070 cases were alerted, 52% of which were long standing cases: untreated for over 6 months since the event. During this period, a total of 30% initiated treatment purchases. As compared with the 3 years prior to the intervention, time till treatment initiation decreased following the intervention with HR = 1.05, 1.94, 1.29 (p values = 0.020, < 0.001, 0.005) for T-score, hip, and vertebral cases respectively. Initiation rates within 6 months increased from 52.0 to 59.8%, from 12.3 to 27.7%, and from 17.4 to 27.1% among T-score, hip, and vertebral cases, respectively (p value < 0.001). Male sex, nursing home residence, having diabetes or a cardiovascular disease and age younger than 60 or older than 80 were associated with lower treatment rates. CONCLUSIONS: A computerized decision support system can efficiently raise attention to many otherwise missed high-risk osteoporotic cases, particularly those after fractures.

Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms.

Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of traditional computational models in radiology is a matter of debate. Purpose To evaluate the accuracy and efficiency of a combined machine and deep learning approach for early breast cancer detection applied to a linked set of digital mammography images and electronic health records. Materials and Methods In this retrospective study, 52 936 images were collected in 13 234 women who underwent at least one mammogram between 2013 and 2017, and who had health records for at least 1 year before undergoing mammography. The algorithm was trained on 9611 mammograms and health records of women to make two breast cancer predictions: to predict biopsy malignancy and to differentiate normal from abnormal screening examinations. The study estimated the association of features with outcomes by using t test and Fisher exact test. The model comparisons were performed with a 95% confidence interval (CI) or by using the DeLong test. Results The resulting algorithm was validated in 1055 women and tested in 2548 women (mean age, 55 years ± 10 [standard deviation]). In the test set, the algorithm identified 34 of 71 (48%) false-negative findings on mammograms. For the malignancy prediction objective, the algorithm obtained an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.89, 0.93), with specificity of 77.3% (95% CI: 69.2%, 85.4%) at a sensitivity of 87%. When trained on clinical data alone, the model performed significantly better than the Gail model (AUC, 0.78 vs 0.54, respectively; P < .004). Conclusion The algorithm, which combined machine-learning and deep-learning approaches, can be applied to assess breast cancer at a level comparable to radiologists and has the potential to substantially reduce missed diagnoses of breast cancer. © RSNA, 2019 Online supplemental material is available for this article.

The effect of subclinical infantile thiamine deficiency on motor function in preschool children.

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.

4D MRI for the Localization of Parathyroid Adenoma: A Novel Method in Evolution.

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.