Data System Research Articles

Comparison of golden-angle radial sparse parallel (GRASP) and conventional cartesian sampling in 3D dynamic contrast-enhanced mri for bladder cancer: a preliminary study.

To compare the image quality, inter-reader agreement, and diagnostic capability for muscle-invasive bladder cancer (MIBC) of the reconstructed images in sections orthogonal to the bladder tumor obtained by 3D Dynamic contrast-enhanced (DCE)-MRI using the Golden-angle Radial Sparse Parallel (GRASP) technique with the images directly captured using the Cartesian sampling. This study involved 68 initial cases of bladder cancer examined with DCE-MRI (GRASP: n = 34, Cartesian: n = 34) at 3 Tesla. Four radiologists conducted qualitative evaluations (overall image quality, absence of motion artifact, absence of streak artifact, and tumor conspicuity) using a five-point Likert scale (5 = Excellent/None) and quantitative signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. The areas under the receiver-operating characteristic curves (AUCs) for the Vesical Imaging-Reporting and Data System (VI-RADS) DCE score for MIBC assessment were calculated. Inter-reader agreement was also assessed. GRASP notably enhanced overall image quality (pooled score: GRASP 4 vs. Cartesian 3, P < 0.0001), tumor conspicuity (5 vs. 3, P < 0.05), SNR (Median 38.2 vs. 19.0, P < 0.0001), and CNR (7.9 vs. 6.0, P = 0.005), with fewer motion artifacts (5 vs. 3, P < 0.0001) and minor streak artifacts (5 vs. 5, P > 0.05). Although no significant differences were observed, the GRASP group tended to have higher AUCs for MIBC (pooled AUCs: 0.92 vs. 0.88) and showed a trend toward higher inter-reader agreement (pooled kappa-value: 0.70 vs. 0.63) compared to the Cartesian group. Using the GRASP for 3D DCE-MRI, the reconstructed images in sections orthogonal to the bladder tumor achieved higher image quality and improve the clinical work flow, compared to the images directly captured using the Cartesian. GRASP tended to have higher diagnostic ability for MIBC and showed a trend toward higher inter-reader agreement compared to the Cartesian.

A controlled vocabulary and taxonomy for the submission of quality attributes for therapeutic proteins

Structured product quality data offer tremendous promise to revolutionize the submission of drug applications. However, the quality attributes for biological products do not have a systematic naming taxonomy, and consequently this limit poses a critical challenge in the development of systems for structured regulatory submissions. Here, we describe the creation of a controlled vocabulary with a structured taxonomical naming approach for quality attributes of therapeutic proteins. Additionally, we endeavor to make the case for why such systematic harmonized naming is required to support the successful implementation of structured data systems. We also describe the key principles of our structured naming approach, including a top-down view of the product and protein structure and a distinction between a quality attribute and the test to evaluate the attribute. Finally, we describe how this approach can accommodate emerging product types, advanced manufacturing technologies, and be used across the variety of submission sections in a regulatory dossier that discusses quality attributes.

Diagnostic efficacy of the contrast-enhanced ultrasound thyroid imaging reporting and data system classification for benign and malignant thyroid nodules

Diagnostic efficacy of the contrast-enhanced ultrasound thyroid imaging reporting and data system classification for benign and malignant thyroid nodules

Application of data element mapping and analysis for system definition to enable model‐based systems engineering

AbstractIn the connected age of the model‐based enterprise and model‐based systems engineering (MBSE), new systems engineering tools are needed to move from a functional, document‐centric, hierarchical view of data and information to the individual units of data or data element level view. Data element mapping and analysis (DEMA) is a technology‐agnostic analytical methodology that combines traditional functional analysis techniques, systems engineering elicitation practices, and novel data mapping techniques to provide a holistic view of a system's data and information flows at the individual units of data (data element level). In this research, DEMA was utilized to enable an enhanced system definition for the development of a verification and validation process as applied to a modeling and simulation environment. DEMA uncovered and visually mapped the hidden flow of approximately 1600 data vessel occurrences as inputs or outputs to 79 functional activities in 23 disparate storage locations. The results reveal that DEMA is a practical tool for both improving existing systems and defining new systems. The data element level view captured by DEMA can be used to define the interconnections between the system elements that are input to systems modeling language (SysML) models. Therefore, DEMA is a necessary and novel tool that can be used to enable system digitalization.

Can dynamic contrast-enhanced MR imaging based on radiomics improve the diagnostic efficiency of clinically significant prostate cancer?

Aims/Background Prostate cancer stands out as one of the most prevalent malignant tumours among males. The non-invasive identification of clinically significant prostate cancer via magnetic resonance imaging plays a critical role in circumventing unnecessary biopsies and determining suitable treatment strategies for patients. Our study aimed to evaluate the potential improvement in predictive accuracy for clinically significant prostate cancer by incorporating perfusion data obtained from dynamic contrast-enhanced magnetic resonance imaging acquisition protocols into multiparametric magnetic resonance imaging parameters. Methods Radiomics extracted from perfusion parameters (Ktrans, Kep, Ve) of dynamic contrast-enhanced magnetic resonance imaging were analysed in patients suspected of prostate cancer who underwent 3T multiparametric magnetic resonance imaging between January 2017 and June 2023 in this retrospective study. The pathological findings obtained from biopsy or therapy were categorised into groups based on the Gleason sum score as either clinically significant prostate cancer (Gleason sum score > 7) or non-clinically significant prostate cancer (Gleason sum score ≤ 6). Diagnostic models were constructed using logistic regression analysis, incorporating prostate imaging reporting and data system V2.1 scores and clinical data, with or without radiomics extracted from dynamic contrast-enhanced. The area under curve (AUC) values were compared using the DeLong test. Results Overall, 214 men (clinically significant prostate cancer [n=78] and non-clinically significant prostate cancer [n=136]) were included. The clinical-prostate imaging reporting and data system model demonstrated an AUC of 0.89 (95% confidence interval: 0.84-0.95) in the training cohort and 0.91 (95% confidence interval: 0.84-0.98) in the test cohort. For the clinical-prostate imaging reporting and data system-radscore model, the AUC values were 0.97 (95% confidence interval: 0.95-0.99) for Ktrans, 0.98 (95% confidence interval: 0.96-1.00) for Ve, and 0.96 (95% confidence interval: 0.93-0.98) for Kep in the training cohort, and 0.97 (95% confidence interval: 0.94-1.00) for Ktrans, 0.95 (95% confidence interval: 0.91-1.00) for Ve, and 0.97 (95% confidence interval: 0.94-1.00) for Kep in the test cohort. Radiomics based on perfusion parameters exhibited good diagnostic performance in predicting clinically significant prostate cancer. The clinical-prostate imaging reporting and data system-radscore model demonstrated superior diagnostic capability compared to perfusion-based radiomics or clinical-prostate imaging reporting and data system models alone. Conclusion The application of radiomics, which involves extracting perfusion parameters from dynamic contrast-enhanced imaging, has the potential to enhance diagnostic accuracy for clinically significant prostate cancer.

Enhancing customer experience through AI-driven language processing in service interactions

In today’s competitive service industries, delivering exceptional customer experiences is paramount. This paper explores how AI-driven language processing technologies enhance customer service interactions. Key technologies such as Natural Language Processing (NLP), chatbots, virtual assistants, speech recognition, and machine learning are crucial in transforming customer service. These technologies enable personalized, efficient, and consistent customer interactions, significantly improving overall customer satisfaction. However, implementing these technologies has challenges, including data quality, system integration, privacy concerns, user acceptance, and financial investment. Addressing these challenges is essential for successful AI adoption. Future trends indicate further advancements in AI capabilities, integration with IoT and blockchain technologies, and evolving ethical and regulatory considerations. The long-term impact of AI on service industries promises more efficient operations, cost savings, and superior customer experiences, giving companies a competitive advantage in the market. This paper provides insights into leveraging AI to enhance customer experience, outlining the benefits and challenges while speculating on future directions and implications.

Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study.

To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives. One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions. csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9-77.6/54.5-70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5-84.5/59.4-77.5%) and 56.6/54.2%(95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only. Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

The Legacy Effect of Intensive versus Standard Blood Pressure Control on the Incidence of Dialysis or Kidney Transplantation.

The Systolic Blood Pressure Intervention Trial (SPRINT) showed that intensive lowering of systolic blood pressure (BP) increased the risk of incident chronic kidney disease and episodes of acute kidney injury. Whether intensive treatment changes the risk of kidney failure is unknown. The goal of this study was to estimate the legacy effect of intensive versus standard systolic BP lowering on the longer-term incidence of kidney failure. Secondary analysis of a randomized, open-label clinical trial with observational follow-up. Between 2010 and 2013, patients 50 years and older with hypertension and higher cardiovascular risk excluding those with diabetes mellitus, history of stroke, proteinuria >1g / day or polycystic kidney disease were recruited from 102 clinic sites in the United States and Puerto Rico. Participants were randomized to a systolic BP goal of <120 mm Hg (intensive treatment) or <140 mm Hg (standard treatment group). We linked participants with the US Renal Data System to ascertain kidney failure (initiation of dialysis therapy or transplantation) and the US National Death Index to ascertain all-cause mortality through 2020. Based on analysis of 9279 (99.1%) of 9361 randomized participants, 101 cases of kidney failure occurred over a median follow-up of 8.6 years (interquartile range 8.0 to 9.1 years), with the majority occurring in 74 (73.3%) participants with an eGFR<45 ml/min/1.73 m2 at baseline. Intensive treatment did not significantly increase the risk of kidney failure either overall (cause-specific Hazard Ratio (HR) = 1.20, 95% CI: 0.81 - 1.78) or in the subgroup of participants with baseline eGFR<45 ml/min/1.73 m2 (HR = 1.43, 95% CI: 0.89 - 2.30). Overall, and in patients with eGFR <45 ml/min/1.73 m2, there were higher rates of dialysis or transplantation among SPRINT participants randomized to intensive treatment, but the modest differences observed were not statistically significant.

BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining

Bridges are fundamental facilities in the transportation system, and their operational performance is crucial for economic and social development. Many large bridges are now equipped with structural health monitoring (SHM) systems that collect various types of real-time data. However, our user study found that despite the accumulation of massive amounts of monitoring data, current analysis methods cannot efficiently process large-scale, high-dimensional data. To address this, we have developed BOPVis, a visualization system for bridge monitoring data. BOPVis allows users to intuitively locate sensors and extract corresponding data from a 3D digital model of a bridge. It also provides convenient and flexible interactions for examining trends over time and correlations across hundreds of monitoring channels. A real-world long-span suspension bridge in China is used as a case study to demonstrate the advantages of the BOPVis system for operational performance mining. Through BOPVis, the global temperature deformation behaviors of the bridge are explored and found to align with the physical mechanism documented in the SHM literature. The BOPVis system, with its interactive visualization analysis capabilities, offers a new method for analyzing bridge monitoring data.

Factors associated with pathological up-staging in MRI cT3a prostate cancer - a retrospective study from a high-volume centre.

Multiparametric MRI (mpMRI) parameters of pT3a prostate cancer have not been examined in large cohort studies. Therefore, we aimed to identify factors associated with up-staging of mpMRI cT3a in post-operative histopathological confirmation. Retrospective analysis of a prospectively maintained database of a single UK cancer centre. Only cT3a cases who underwent robotic-assisted radical prostatectomy (RARP) were included (N = 383). MRI and specimen histopathology was reviewed independently by expert uro-radiologists and uro-histopathologists, respectively. Factors included age, BMI, prostate-specific antigen (PSA) level, biopsy international society of urological pathology (ISUP) grade, Prostate Imaging Reporting & Data System (PI-RADS®) score, tumour size, tumour coverage of gland (%), gland weight and surgical margins were analysed as predictors of pT3a prostate cancer. N = 383. Mean age 66 years (58-71), mean BMI 27.1kg/m2 (25.0-30.0). 314 (82.0%) cases down- unchanged or down-staged, and 69 (18.0%) cases upstaged. PSA level (P = 0.002), PI-RADS score (P < 0.001) and ISUP grade (P < 0.001) are positively associated with upstage categories. ISUP grade ≥3 (OR 5.45, CI 1.88, 9.29, P < 0.002), PI-RADS score ≥4 (OR 3.92, CI 1.88-9.29, P < 0.001) and tumour coverage (OR 1.06, CI 1.05-1.08, P < 0.001) significantly positively associated with upstaging disease, with concurrent decreased probability of downstaging (OR 0.55, 0.14, 0.44, respectively, P < 0.05). Tumour coverage was positively correlated with increasing positive surgical margins (P < 0.05). Capsular contact > 15mm was very unlikely to be upstaged (OR 0.36, CI 0.21-0.62, P < 0.001), aligning with published results past the widely accepted significant level for extracapsular disease on MRI. The study has identified PSA level, ISUP, PI-RADS score, tumour volume and percentage coverage are key predictive factors in cT3a upstaging. This study uniquely shows tumour coverage percentage as a predictor of cT3a upstaging on mpMRI. ISUP is the strongest predictor, followed by PI-RADS score and tumour coverage of gland. Multi-institutional studies are needed to confirm our findings.

Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics.

To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. This bicentric retrospective analysis included CEM examinations performed for thework-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). A total of 200 women (median age 56.8years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively. BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Non-genetic factors and breast cancer: an umbrella review of meta-analyses

BackgroundPrevious research has found associations between various non-genetic factors and breast cancer (BrCa) risk. This study summarises and appraises the credibility of the available evidence on the association between non-genetic factors and BrCa risk.MethodsWe conducted an umbrella review of meta-analyses. Medline, Scopus, and the Cochrane databases were systematically searched for meta-analyses examining non-genetic factors and BrCa incidence or mortality. The strength of the evidence was graded in four categories (i.e., weak, suggestive, highly suggestive, convincing).ResultsA total of 781 meta-analyses from 280 publications were evaluated and graded. We included exposures related to anthropometric measurements, biomarkers, breast characteristics and diseases, diet and supplements, environment, exogenous hormones, lifestyle and social factors, medical history, medication, reproductive history, and pregnancy. The largest number of examined associations was found for the category of diet and supplements and for exposures such as aspirin use and active smoking. The statistically significant (P-value < 0.05) meta-analyses were 382 (49%), of which 204 (53.4%) reported factors associated with increased BrCa risk. Most of the statistically significant evidence (n = 224, 58.6%) was graded as weak. Convincing harmful associations with heightened BrCa risk were found for increased body mass index (BMI), BMI and weight gain in postmenopausal women, oral contraceptive use in premenopausal women, increased androstenedione, estradiol, estrone, and testosterone concentrations, high Breast Imaging Reporting and Data System (BIRADS) classification, and increased breast density. Convincing protective factors associated with lower BrCa risk included high fiber intake and high sex hormone binding globulin (SHBG) levels while highly suggestive protective factors included high 25 hydroxy vitamin D [25(OH)D] levels, adherence to healthy lifestyle, and moderate-vigorous physical activity.ConclusionsOur findings suggest some highly modifiable factors that protect from BrCa. Interestingly, while diet was the most studied exposure category, the related associations failed to reach higher levels of evidence, indicating the methodological limitations in the field. To improve the validity of these associations, future research should utilise more robust study designs and better exposure assessment techniques. Overall, our study provides knowledge that supports the development of evidence-based BrCa prevention recommendations and guidance, both at an individual level and for public health initiatives.Trial registrationPROSPERO CRD42022370675.

Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer

BackgroundThe purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC).Methods50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality.ResultsSupervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype.ConclusionThe DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.

What could improve surgical data system at health facilities with high surgical volume in Ethiopia?

BackgroundThe effective management of surgical and anesthesia care relies on quality data and its readily availability for both patient-centered decision-making and facility-level improvement efforts. Recognizing this critical need, the Strengthening Systems for Improved Surgical Outcomes (SSISO) project addressed surgical care data management and information use practices across 23 health facilities from October 2019 to September 2022. This study aimed to evaluate the effectiveness of SSISO interventions in enhancing practices related to surgical data capture, reporting, analysis, and visualization.MethodsThis study employed a mixed method, pre- post intervention evaluation design to assess changes in data management and utilization practices at intervention facilities. The intervention packages included capacity building trainings, monthly mentorship visits facilitated by a hub-and-spoke approach, provision of data capture tools, and reinforcement of performance review teams. Data collection occurred at baseline (February – April 2020) and endline (April – June 2022). The evaluation focused on the availability and appropriate use of data capture tools, as well as changes in performance review practices. Appropriate use of registers was defined as filling all the necessary data onto the registers, and this was verified by completeness of selected key data elements in the registers.ResultsThe proportion of health facilities with Operation Room (OR) scheduling, referral, and surgical site infection registers significantly increased by 34.8%, 56.5% and 87%, respectively, at project endline compared to baseline. Availability of OR and Anesthesia registers remained high throughout the project, at 91.3% and 95.6%, respectively. Furthermore, the appropriate use of these registers improved, with statistically significant increases observed for OR scheduling registers (34.8% increase). Increases were also noted for OR register (9.5% increase) and anesthesia register (4.5% increase), although not statistically significant. Assessing the prior three months reports, the report submissions to the Ministry of Health/Regional Health Bureau (MOH/RHB) rose from 85 to 100%, reflecting complete reporting at endline period. Additionally, the proportion of surgical teams analyzing and displaying data for informed decision-making significantly increased from 30.4% at baseline to 60.8% at endline period.ConclusionThe implemented interventions positively impacted surgical data management and utilization practice at intervention facilities. These positive changes were likely attributable to capacity building trainings and regular mentorship visits via hub-and-spoke approach. Hence, we recommend further investigation into the effectiveness of similar intervention packages in improving surgical data management, data analysis and visualization practices in low- and middle-income country settings.

Behavioral Health Workforce Distribution in Socially Disadvantaged Communities.

This study sought to understand the geographic distribution of three behavioral health clinician (BHC) types in disadvantaged communities in the U.S. across a standardized index of area disadvantage. CMS National Plan and Provider Enumeration System's data were used to identify BHC practice addresses. Addresses were geocoded and mapped to census block groups across Area Disadvantage Index (ADI) scores. Differences in the proportion of BHCs per 100k people in a block group by ADI, clinician type, and rurality were compared. Zero-inflated negative binomial models assessed associations between ADI score with any amount, and expected count, of BHC type in a block group. The sample included 836,780 BHCs (51.5% counselors, 34.5% social workers, 14.0% psychologists). Results indicated there were fewer BHCs in areas of high disadvantage with 351 BHCs in the lowest need versus 267 BHCs in highest need areas, per 100k people. BHCtype was differently associated with the rate of clinicians per 100k by ADI and block groups that were both rural and high ADI had the least BHCs located. Findings suggest the maldistribution of BHCs by ADI underscores how some BHCs may be better positioned to meet the needs of vulnerable communities. Increasing access to behavioral health care requires a workforce equitably positioned in high-need areas. Reforms to payment and practice regulations may support BHCs to deliver services in socially disadvantaged neighborhoods.

Business readiness for dual transformation: an analysis of business capabilities for digital and sustainable transformation

In a dynamic business environment, the convergence of Digital Transformation and Sustainable Transformation has emerged as a comprehensive strategy for companies seeking to thrive in the modern era. For this transformation to successfully develop, the appropriate conditions must be present to carry out a process of digitisation and sustainability. Therefore, this research aimed to identify the readiness level of companies established in the city of Tijuana, Mexico, to adopt a dual transformation process. An analysis of the different digital and sustainable transformation models was carried out, and a self-diagnosis model was designed. Then, a quantitative instrument was applied to a sample of 148 small, medium and large companies in Tijuana between 2022 and mid-2023. The analysis consisted of a descriptive analysis of the data and a correlation analysis. It concludes that the elements that most explain the dual transformation are strategy, culture, organisational and technical capabilities. Likewise, the related factors are definition and communication, alignment, and scope of the objectives within the strategy; change management, knowledge and collaboration in the culture; within the organisation: leadership, processes and organisational structure; and finally in the technical capabilities data management, training and systems integration. The results of this research allowed us to identify and propose strategies to implement the dual transformation and increase the scalability and competitiveness of the adopting companies.

Mortality Surveillance for the COVID-19 Pandemic: Review of the Centers for Disease Control and Prevention's Multiple System Strategy.

Mortality surveillance systems can have limitations, including reporting delays, incomplete reporting, missing data, and insufficient detail on important risk or sociodemographic factors that can impact the accuracy of estimates of current trends, disease severity, and related disparities across subpopulations. The Centers for Disease Control and Prevention used multiple data systems during the COVID-19 emergency response-line-level case‒death surveillance, aggregate death surveillance, and the National Vital Statistics System-to collectively provide more comprehensive and timely information on COVID-19‒associated mortality necessary for informed decisions. This article will review in detail the line-level, aggregate, and National Vital Statistics System surveillance systems and the purpose and use of each. This retrospective review of the hybrid surveillance systems strategy may serve as an example for adaptive informational approaches needed over the course of future public health emergencies. (Am J Public Health. Published online ahead of print July 25, 2024:e1-e10. https://doi.org/10.2105/AJPH.2024.307743).

U.S. State rates of newborns reported to child protection at birth for prenatal substance exposure

BackgroundIn the U.S., the opioid epidemic has revitalized national attention to newborns with prenatal substance exposure (PSE). These newborns and their caregivers have specific health and treatment needs and frequently interact with multiple systems, including child protection systems (CPS). MethodsThis study calculated rates of newborns (less than 15 days old) reported to CPS per 1,000 births due to PSE by state and year using data from the National Child Abuse and Neglect Data System (NCANDS). Given the lack of a clear definition of PSE reports in the data, we calculated rates using three different definitions. To examine the relationship between different state laws regarding the mandated reporting of PSE and PSE reports rates, we used panel data analysis. ResultsRates of newborn reports more than doubled between 2011 and 2019. There was extensive state variability of rates including some states that were consistently more than 100 % greater than and others consistently more than 150 % less than the annual national mean. Reporting rates were not associated with state requirements to report PSE, but were positively associated with rates of diagnosed neonatal abstinence syndrome. ConclusionState-level inconsistencies in identification, reporting, and CPS responses prevent a clear understanding of the scope of the affected population and service needs.

Refining clinically relevant cut-offs of prostate specific antigen density for risk stratification in patients with PI-RADS 3 lesions.

Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions, identified through multiparametric magnetic resonance imaging (mpMRI), present a clinical challenge due to their equivocal nature in predicting clinically significant prostate cancer (csPCa). Aim of the study is to improve risk stratification of patients with PI-RADS 3 lesions and candidates for prostate biopsy. A cohort of 4841 consecutive patients who underwent MRI and subsequent MRI-targeted and systematic biopsies between January 2016 and April 2023 were retrospectively identified from independent prospectively maintained database. Only patients who have PI-RADS 3 lesions were included in the final analysis. A multivariable logistic regression analysis was performed to identify covariables associated with csPCa defined as International Society of Urological Pathology (ISUP) grade group ≥2. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Significant predictors were then selected for further exploration using a Chi-squared Automatic Interaction Detection (CHAID) analysis. Overall, 790 patients had PI-RADS 3 lesions and 151 (19%) had csPCa. Significant associations were observed for age (OR: 1.1 [1.0-1.1]; p = 0.01) and PSA density (OR: 1643 [2717-41,997]; p < 0.01). The CHAID analysis identified PSAd as the sole significant factor influencing the decision tree. Cut-offs for PSAd were 0.13 ng/ml/cc (csPCa detection rate of 1% vs. 18%) for the two-nodes model and 0.09 ng/ml/cc and 0.16 ng/ml/cc for the three-nodes model (csPCa detection rate of 0.5% vs. 2% vs. 17%). For individuals with PI-RADS 3 lesions on prostate mpMRI and a PSAd below 0.13, especially below 0.09, prostate biopsy can be omitted, in order to avoid unnecessary biopsy and overdiagnosis of non-csPCa.

Male breast cancer: Case report and review of literature

Introduction: Male breast cancer (MBC) is an uncommon disease that makes up less than 1% of all breast cancer diagnoses worldwide. While breast carcinomas have some similarities in both sexes, there are significant differences. Case Report: We present a case of breast cancer in a 57-year-old man who had a left breast mass of 3.5 cm classified as breast imaging reporting and data system (BIRADS 4) by the American College of Radiology (ACR). The presence of invasive breast carcinoma of grade II Scarff–Bloom–Richardson (SBR) with a negative molecular phenotype B HER2 was confirmed by a tru-cut biopsy. The axillary cytopuncture did not show any abnormalities. The patient was later treated for a left mastectomy with homolateral axillary dissection. Conclusion: Male breast cancer represents a therapeutic dilemma. A multidisciplinary approach is mandatory.