Data System Research Articles

Effects of Implementation of European Thyroid Imaging Reporting and Data System Risk Stratification in a Thyroid Cancer Program in Western Sweden: A Retrospective Cohort Study

ObjectiveThe European Thyroid Imaging Reporting and Data System (EU-TIRADS) allows for selective fine needle aspiration cytology (FNAC). In 2017, EU-TIRADS was implemented as part of a nationwide standardized care bundle for thyroid cancer in Western Sweden with a population of approximately 1.7 million. The objective of this study was to investigate the clinical value of EU-TIRADS attempting to reduce the number of unnecessary FNACs in referred patients with thyroid nodules. MethodsThe study cohort consisted of all patients referred to Sahlgrenska University Hospital due to a palpable, newly detected or growing thyroid nodules or a positron emission tomography–positive finding for examination with thyroid ultrasound and selective cytology between 2018 and 2022. Medical records on EU-TIRADS classification, corresponding FNAC results, and histopathologic diagnosis were retrospectively collected. Adherence to the EU-TIRADS guidelines, use of selective FNAC, and rate of malignancy in patients who underwent surgery were assessed. ResultsIn total, 1246 thyroid nodules in 990 patients were evaluated. The distributions of EU-TIRADS 2 to 5 (number [percentage]) for all examined nodules were 63 (5%), 462 (37%), 443 (36%), and 278 (22%), respectively. FNAC was omitted in 7% of the investigated patients. FNAC was performed in 124 nodules (10%) despite not fulfilling the EU-TIRADS criteria or absence of positron emission tomography–positive findings. The rate of malignancy was 33% and 1/50 in patients who underwent “unnecessary” FNAC. ConclusionImplementation of EU-TIRADS in routine management of thyroid nodules led to the selective use of FNAC; however, the clinical impact was limited. This study provides real-world data on the value and magnitude of diagnostic improvement by implementing EU-TIRADS in clinical practice.

Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024.

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) standardizes the imaging technique, reporting lexicon, disease categorization, and management for patients with or at risk for hepatocellular carcinoma (HCC). LI-RADS encompasses HCC surveillance with US; HCC diagnosis with CT, MRI, or contrast-enhanced US (CEUS); and treatment response assessment (TRA) with CT or MRI. LI-RADS was recently expanded to include CEUS TRA after nonradiation locoregional therapy or surgical resection. This report provides an overview of LI-RADS CEUS Nonradiation TRA v2024, including a lexicon of imaging findings, techniques, and imaging criteria for posttreatment tumor viability assessment. LI-RADS CEUS Nonradiation TRA v2024 takes into consideration differences in the CEUS appearance of viable tumor and posttreatment changes within and in close proximity to a treated lesion. Due to the high sensitivity of CEUS to vascular flow, posttreatment reactive changes commonly manifest as areas of abnormal perilesional enhancement without washout, especially in the first 3 months after treatment. To improve the accuracy of CEUS for nonradiation TRA, different diagnostic criteria are used to evaluate tumor viability within and outside of the treated lesion margin. Broader criteria for intralesional enhancement increase sensitivity for tumor viability detection. Stricter criteria for perilesional enhancement limit miscategorization of posttreatment reactive changes as viable tumor. Finally, the TRA algorithm reconciles intralesional and perilesional tumor viability assessment and assigns a single LI-RADS treatment response (LR-TR) category: LR-TR nonviable, LR-TR equivocal, or LR-TR viable.

Prediction of Microvascular Invasion and Recurrence After Curative Resection of LI-RADS Category 5 Hepatocellular Carcinoma on Gd-BOPTA Enhanced MRI.

This study aims to investigate the predictive value of Gadobenate dimeglumine (Gd-BOPTA) enhanced MRI features on microvascular invasion (MVI) and recurrence in patients with Liver Imaging Reporting and Data System (LI-RADS) category 5 hepatocellular carcinoma (HCC). A total of 132 patients with LI-RADS category 5 HCC who underwent curative resection and Gd-BOPTA enhanced MRI at our hospital between January 2016 and December 2018 were retrospectively analyzed. Qualitative evaluation based on LI-RADS v2018 imaging features was performed. Logistic regression analyses were conducted to assess the predictive significance of these features for MVI, and the Cox proportional hazards model was used to identify postoperative risk factors of recurrence. The recurrence-free survival (RFS) was analyzed by using the Kaplan-Meier curve and Log rank test. Multivariate logistic regression analysis identified that corona enhancement (odds ratio [OR] = 3.217; p < 0.001), internal arteries (OR = 4.147; p = 0.004), and peritumoral hypointensity on hepatobiliary phase (HBP) (OR = 5.165; p < 0.001) were significantly associated with MVI. Among the 132 patients with LR-5 HCC, 62 patients experienced postoperative recurrence. Multivariate Cox regression analysis showed that mosaic architecture (hazard ratio [HR] = 1.982; p = 0.014), corona enhancement (HR = 1.783; p = 0.039), and peritumoral hypointensity on HBP (HR = 2.130; p = 0.009) were risk factors for poor RFS. MRI features based on Gd-BOPTA can be noninvasively and effectively predict MVI and recurrence of LR-5 HCC patients.

Metabolomics biomarkers of hepatocellular carcinoma in a prospective cohort of patients with cirrhosis

Background & AimsThe effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of the screening modalities. MethodsWe developed a multicenter prospective cohort of cirrhotic patients undergoing surveillance with MRI, and applied global untargeted metabolomics to 612 longitudinal serum samples from 203 patients. Among them, 37 developed HCC during follow-up. ResultsWe identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who didn’t develop HCC (Controls). Tauro-conjugated bile acids and gamma-glutamyl amino acids were increased, while acyl-cholines and deoxycholate derivatives were decreased. Seven amino acids including serine and alanine, had strong associations with HCC risk, while strong protective effects were observed for N-acetylglycine and glycerophosphorylcholine. Machine learning using the 150 metabolites, age, gender, patatin-like phospholipase domain-containing protein 3 (PNPLA3) and transmembrane 6 superfamily member 2 (TMS6SF2) SNPs, identified 15 variables giving optimal performance. Among them, N-acetylglycine had the highest AUC in discriminating Cases and Controls. When restricting Cases to samples collected within 1 year prior to HCC (Cases-12M), additional metabolites including microbiota-derived metabolites were identified. The combination of the top 6 variables identified by machine learning (alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC=0.88 [0.83-0.93]). Finally, 23 metabolites distinguished Cases with Liver Imaging Reporting and Data System (LI-RADS)-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases-LR3. ConclusionsThis study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as main biomarkers of HCC risk, and microbiota-derived metabolites as main contributors to HCC development. Impact and ImplicationsThe effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited. There is an urgent need for improvement in risk stratification and new screening modalities, blood biomarkers in particular. Longitudinal collection of paired blood samples and MRI images from cirrhotic patients is particularly valuable in assessing how early blood and imaging markers become positive during the period when lesions are observed to obtain a diagnosis of HCC. We generated a multicenter prospective cohort of patients with cirrhosis under surveillance with contrast MRI, applied untargeted metabolomics on 612 serum samples from 203 patients and identified metabolites associated with risk of HCC development. Such biomarkers may significantly improve early stage HCC detection for cirrhotic patients undergoing HCC surveillance, a critical step to increasing curative treatment opportunities and reducing mortality.

Breast imaging reporting and data system for sonography: Positive and negative predictive values of sonographic features in Kumasi, Ghana

BackgroundBreast cancer is the most common female cancer globally. The method of choice for screening and diagnosing breast cancer is mammography, which is not widely available in Ghana as compared to ultrasonography. This study aimed to evaluate the sonographic features of solid breast lesions using the new sonographic Breast Imaging- Reporting and Data System (BI-RADS-US) lexicon for malignancy with histopathology as the gold standard. MethodsThis was a prospective quantitative study that sonographically scanned female patients with breast masses and consecutively selected cases recommended for core biopsy from May 2018 to May 2021. Sixty (60) solid breast masses were described using the sonographic BI-RADS lexicon features. Lesion description and biopsy results from histopathology were compared and analyzed using Pearson's Chi-square test. Odds ratios, sensitivity, specificity, and predictive values were also calculated. Statistical significance level was set at p ≤ 0.05. ResultsIrregular shape (p < 0.0001), spiculated mass margins (p < 0.0001), and not parallel mass orientation (p= 0.0007) were more commonly associated with malignant masses. The sensitivity of breast ultrasound for malignancy was 93.9 % and the specificity was 55.6 % with an overall accuracy rate of 76.6 %. The negative predictive value was 88.7 % and the positive predictive value was 72.1 %. Descriptors like irregular shape, non-parallel orientation, angular and spiculated margins, echogenic halo, and markedly hypoechoic internal content, demonstrated higher odds ratios for malignancy. ConclusionsThis study adds valuable insights to the diagnosis of breast cancer using the sonographic BI-RADS lexicon features. The results demonstrate that specific sonographic descriptors can effectively differentiate between benign and malignant breast masses.

Follow-up results of BI-RADS 3 lesions on magnetic resonance imaging: a retrospective study

The categorization of Breast Imaging Reporting and Data System (BI-RADS) 3 lesions is not as clear in magnetic resonance imaging (MRI) as it is in mammography (MG). With the increasing number of MRI scans currently being conducted globally, incidentally detected lesions falling into the probably benign category are frequently being observed. In this study, our aim was to investigate the imaging characteristics and follow-up results of BI-RADS 3 lesions detected by MRI and to determine their malignancy rates. Breast MRI scans performed between January 2010 and January 2020 and classified as BI-RADS 3 lesions were retrospectively analyzed. The study included 216 lesions with known biopsy or surgical excision results or with at least one year of radiological follow-up. We assessed the patients' age, the presence of breast cancer, the follow-up interval, and the imaging findings at the beginning and during the follow-up. Lesions that remained stable, disappeared, or decreased in size and had a benign histopathological diagnosis were classified as benign. Lesions with the histopathological diagnosis of malignancy, identified by either biopsy or surgical excision, were classified as malignant. We determined the malignancy rate based on the histopathology and follow-up results. Considering the follow-up results of all cases, 8% of lesions were excised, 0.5% decreased in size, 1.4% became enlarged, 17.1% disappeared, and 73% remained stable. The malignancy rate was 2.8%. A significant relationship was found between lesion shape and malignancy, as progression to malignancy was more likely in round lesions than in other types. An irregular margin, heterogeneous enhancement, and kinetic curve (type 2) features were significant for lesion upgrade to malignancy. The malignancy rate in BI-RADS 3 lesions detected by MRI is low and falls within the accepted cancer rate for MG and sonography. Changes in size, morphology, and enhancement pattern should be considered in terms of malignancy development during follow-up. The follow-up intervals should be determined on a case-by-case basis.

Evaluating the Efficacy of 6-Month Follow-Up for BI-RADS 3 Lesions Identified by Screening Ultrasound: A Retrospective Study

Background: The Breast Imaging-Reporting and Data System (BI-RADS), developed to standardize mammographic findings, categorizes 'probably benign' lesions as BI-RADS 3, aiming to reduce unnecessary biopsies from false positives, with a cancer likelihood of less than 2%. In some countries, screening programs have been expanded to include ultrasound alongside mammography, particularly in populations with predominantly dense breast tissue, leading to an increased detection of BI-RADS 3 lesions through this additional modality. This study examines the effectiveness of 6-month follow-ups for these ultrasound-identified BI-RADS 3 lesions. The purpose is to evaluate cancer detection rates and diagnostic performance of ultrasound findings leading to decision for biopsy. Materials and Methods: We retrospectively analyzed 7,577 women who underwent mammography and ultrasound screening from January 2016 to December 2021. Of these, 2,907 were classified as BI-RADS 3 based on ultrasound findings. The study focused on 1,163 patients with normal mammography and assessed BI-RADS 3 from ultrasound findings, who completed a 24-month follow-up. Data analysis included assessing demographic data, breast cancer risk, imaging features, and pathological findings. Results: Among the 1,163 patients, the cancer detection of the entire cohort was 0.7%, with no cancers detected during the first six months. The cancer found at 6 months and 12-24 months were 0 and 0.17%, respectively (p&lt;0.001). The median time to be reassesed from BI-RADS 3 to BI-RADS 4 was 18.4 months. Upon the change of BI-RADS, spiculation and angular margins were the most predictive ultrasound features for malignancy (AUROC=0.75 and 0.69, respectively). Lesion size growth alone was found insufficient as a biopsy criterion. A 28% growth cutoff distinguished between benign and malignant lesions better than 20% cutoff (AUROC=0.68 vs 0.64, p=0.038). Conclusion: A 12-month follow-up interval may be more appropriate than the traditional 6-month interval for average-risk patients with BI-RADS 3 lesions detected by screening ultrasound. Combining suspicious imaging features with size increases enhances diagnostic accuracy, proposing a tailored follow-up approach based on individual risk profiles and imaging characteristics.

Evaluation of the ultrasound findings of thyroid gland enlargement in Cape Coast Teaching Hospital, Ghana: A retrospective cross-sectional study.

Goiter is a major source of morbidity in the world, especially in the developing world, where dietary iodine deficiency, a known cause of this condition, is endemic. The diagnosis is mostly by ultrasonography (USG) scan, which can give anatomical, pathological, and functional information for the management of goiter. This study aimed to determine the commonest ultrasound findings of goiter in Ghana. The records of all 213 patients with goiter diagnosed by USG scanover a 5-year period were retrieved. Data collected were sociodemographics, ultrasound features, thyroid nodules diameter, and Thyroid Imaging Reporting and Data System (TI-RADS) scores, which were analyzed using GNU PSPP, version 1.2.0-3.χ 2and two-tailed independent samples t-test were also employed, with p ≤ 0.05. A total of 213 patients with goiter diagnosed by USG scanwere obtained over the study period. The mean age of the participants was 50.01 ± 17.27 years, with an age range of 16-92 years and females constituting the majority (82.16%). The commonest ultrasound features were well-defined solid nodules. The lesion sites for most patients were the whole thyroid (28.17%), both lobes (24.41%), and the right lobe (20.19%). The mean difference in sizes of cysts and solid nodules among genders was 0.26 (CI: -0.14 to 0.67, p = 0.20) and 0.12 (CI: -0.43 to 0.66, p = 0.67), respectively. The TI-RADS score featured TI-RADS 4 (36.62%), TI-RADS 1 (28.17%), TI-RADS 3 (25.82%), TI-RADS 5 (5.16%), and TI-RADS 2 (4.23%). Solid nodules (49.32%, p = 0.001) and cysts (35.71%, p = 0.003) were more common within 41-60 years and less frequent in those <21 years. A p ≤ 0.05 was considered significant in this study. The predominant ultrasound features were well-defined solid nodules, simple cysts, and solid nodules with cystic changes, mostly located in the entire thyroid gland and least located in the isthmus only. Cysts and solid nodules were mostly seen in the 41-60 years age group.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Exploring the possibility of adding DGGS support to the S-100 Universal Hydrographic Data Model

reference systems. A DGGS is a spatial reference system consisting of multiple layers of grids with increasing resolution which partition the globe into polygon shaped cells of equal area. Individual cells can be easily identified and basic Geographic Information System (GIS) operations can be performed on data stored within the cells. Advantages of using DGGS for spatial data include the ability to integrate data layers of different data types in a statistically uniform way, the possibility to avoid distortions caused from projection, and the capability to scale analysis across multiple resolution levels. The benefits of using DGGS for hydrographic data, specifically Electronic Navigational Charts (ENC) are examined. The addition of DGGS to S-100 would provide a uniform gridding system for ENC cells, and would eliminate distortions in the Polar Regions caused when projecting rectangular cells based on latitude and longitude. The Open Geospatial Consortium (OGC) led a pilot project for which one of the participants developed a server that offers S-100 marine protected area data organized according to DGGS. Because of this success and the availability of open-source software for the implementation of DGGS, the BSH (German Federal Maritime and Hydrographic Agency) investigated the feasibility and possible benefits of using Discrete Global Grid Systems for S-100 data. The outcome was successful. However, it is recognized that DGGS as a standard is not yet advanced enough and the available software tools are inadequate for wide implementation. More investigation needs to be done, and additionally the best grid shape, orientation and aperture for a uniform grid for hydrographic data need to be determined. Overall, DGGS should be considered for future versions of S-100, the new universal hydrographic data model, because the benefits for ENCs and hydrographic data are clear and in the future, it is expected that DGGS will revolutionize GIS.

Fibrosis-4 score for screening of metabolic dysfunction-associated steatotic liver disease (MASLD): Data from a population-based sample in Tuscany

Background and aimTo verify the prevalence of positive Fibrosis-4 (FIB-4) score, a screening test for metabolic-associated liver disease, in a large population-based sample in the Tuscany Italian Region, and to identify sub-populations at higher risk which could be targeted by specific screening programs. Methods and resultsPopulation-based survey performed in the Italian region of Tuscany, with Tuscany health informative system's administrative data. We included 594,923 subjects, of which 32% had available data for the FIB-4 calculation. The overall proportion of subjects with an FIB-4 value > 1.3, was 41.6% of those with available exams, and 12,8% of the whole population, whereas 5.4% and 1.7% had FIB-4 >2.67. In those younger than 80 years, FIB >1.3 had a 33.1% and 9.4%. People with diabetes mellitus had higher figures (52.8.% and 28.9% for FIB>1.3). Among subjects aged 70 years or over, 74.9% of those with available data and 38.4% of the general population had a FIB-4>1.3, whereas 32% and 16% had a FIB-4 > 2. ConclusionsThe relevant proportion of FIB-4 positivity in the general population poses a significant burden for further screening with liver elastography. Targeting people with diabetes, excluding people older than 80 years and/or adopting a FIB-4 threshold of 2 in those aged more than 70 years could increase the cost-effectiveness of the screening procedures.

Reinforcement learning based blockchain model for revoking unauthorized access in Virtualized Network Functions‐based Internet of Things Mobile Edge Computing

AbstractVNFs boost data processing efficiency in Mobile Edge Computing (MEC)‐driven Internet of Things (IoT) for healthcare, smart cities, and industrial automation. VNF‐based IoT MEC systems encounter a significant security threat due to unauthorized access, posing risks to data privacy and system integrity. Existing approaches struggle to adapt to dynamic environments and lack tamper‐proof enforcement mechanisms. In this work, we propose a novel system combining Reinforcement Learning (RL) and blockchain technology to revoke unauthorized access in VNF‐based IoT MEC. We introduce the Integrated Action‐selection DRL Algorithm for Unauthorized Access Revocation (IASDRL‐UAR), a novel RL approach that excels in dynamic environments by handling both continuous and discrete actions, enabling real‐time optimization of security risk, execution time, and energy consumption. A behavior control contract (BCC) is proposed and integrated into the RL system, automating behavior checks and enforcement, streamlining security management, and reducing manual intervention. RL feedback plays a pivotal role in steering dynamic security adjustments, gaining valuable perspectives from user behavior via trust scores in the behavior contract. The security features of the proposed method are analyzed. Performance comparisons reveal a substantial improvement, with the proposed system outperforming existing methods by 30% in terms of throughput, 21.7% in system stability, and 26% in access revocation latency. Additionally, the system demonstrates a higher security index, energy efficiency, and scalability.

Integrated pollination traceability system for data sharing and transparency

Integrated pollination traceability system for data sharing and transparency

Transperineal 3-Core Magnetic Resonance Imaging Ultrasound Fusion Targeted Plus Laterally 6-Core Systematic Biopsy in Prostate Cancer Diagnosis

IntroductionIt is important to explore strategies reducing the number of SB cores taken to minimize biopsy-related morbidity and patient's discomfort during biopsy. This study aims to optimize prostate biopsy procedures by reducing the number of systematic biopsy (SB) cores while preserving cancer detection rates in the era of combined biopsy. Patients and MethodsWe prospectively recruited patients with ≥1 magnetic resonance imaging (MRI) lesions and they underwent transperineal combined 12-core SB+3-core targeted prostate biopsy (TB, reference standard). New strategy was defined as a laterally 6-core SB+3-core TB. Patients were served as their own control. Detection rates for overall prostate cancer (PCa) and clinically significant PCa (csPCa) were compared among the standard SB, MRI-TB, 6-core SB +3-core TB, and reference standard. Pathology consistency was assessed using the Kappa test. ResultsA total of 204 men were included, of which 111 (54.41%) and 92 (45.10%) harbored overall PCa and csPCa. Referenced combined biopsy detected significantly 6.86% (P = .0005) or 4.90% (P = .0044) more csPCa than performing only SB or 3-core TB, but was comparable to the new biopsy strategy. (45.10% vs. 43.14%, P = .1336) Similar results persisted when limiting patients in biopsy-naïve men or stratified by Prostate Imaging Reporting and Data System scores, PSAD, and index lesion parameters. Additionally, performing 6-core SB+3-core TB demonstrated high consistency with reference standard in grade group distribution (Kappa coefficient: 0.952 for all, 0.961 for biopsy-naïve men) and achieved superior sensitivity of 95.7% (All: 95% CI: 89.2%-99.8%) and 96.9% (Biopsy-naïve: 95% CI: 91.1%-99.7%), respectively. ConclusionsThe 6-core SB+3-core TB approach maintains expected detection rates while reducing the total core count, offering a promising alternative to the reference standard, which may help to tailor transperineal combined biopsy procedures.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Use of the French National Health Data System (SNDS) in pharmacoepidemiology: A systematic review in its maturation phase

The French National Health Data System (SNDS) comprises healthcare data that cover 99% of the population (over 67 million individuals) in France. The aim of this study was to present an overview of published pharmacoepidemiological studies using the SNDS in its maturation phase. We conducted a systematic literature review of original research articles in the Pubmed and EMBASE databases from January 2012 until August 2018. A total of 316 full-text articles were included, with an annual increase over the study period. Only 16 records were excluded after screening because they did not involve the SNDS but other French healthcare databases. The study design was clearly reported in only 66% of studies of which 57% were retrospective cohorts and 22% cross-sectional studies. The reported study objectives were drug utilization (65%), safety (22%) and effectiveness (9%). Almost all ATC groups were studied but the most frequent ones concerned the nervous system in 149 studies (49%), cardiovascular system drugs in 104 studies (34%) and anti-infectives for systemic use in 50 studies (16%). The SNDS is of growing interest for studies on drug use and safety, which could be conducted more in specific populations, including children, pregnant women and the elderly, as these populations are often not included in clinical trials.

Racial Disparities in Fluid Intake and Plasma Copeptin in Young Adults

INTRO: Underhydration, or habitual low water intake, is associated with increased plasma copeptin, a surrogate marker of arginine vasopressin. Importantly, plasma copeptin is associated with development of cardiovascular disease (CVD) and chronic kidney disease (CKD). Compared with White individuals, Black individuals are more likely to be underhydrated, to develop CVD and end-stage CKD, and exhibit higher plasma copeptin in middle-age and older adulthood. However, there is limited information on potential racial differences in plasma copeptin in young apparently healthy adults, which may have implications for incident CVD and CKD. Therefore, we sought to determine whether young Black adults reported lower fluid intake and exhibited higher plasma copeptin concentration when compared with young White adults. Methods: We analyzed pre-existing data from two registered clinical trials (NCTs 04576338 and 05132556). Participants (n=86, sex; 40F/46M, race; White n=48, Black n=38, age; 21±2 years, BMI; 25±4 kg/m2) completed a three-day food and fluid log and underwent a venous blood draw to measure plasma copeptin concentration (BRAHMS Copeptin proAVP KRYPTOR). Food and fluid records were analyzed using the Nutrition Data System for Research for % fluid from food or water. Separately, records were used to operationalize beverage intake into 8 categories (water, sugar-sweetened beverages (SSB), milk/non-dairy alternatives, 100% juice, non-caloric beverages, plain coffee or tea, milk/sugar added coffee or tea, and alcohol). Statistical analyses included Mann-Whitney U tests for fluid intake data and ANCOVA for plasma copeptin concentration (controlled for site). Plasma copeptin was log transformed. Additionally, we assessed relations between fluid intakes and plasma copeptin concentration using Spearman’s (ρ) correlations. We set α at ≤ 0.05. Results: Compared with White adults, Black adults consumed less total fluid (Black; 1981 ±1035 vs. White; 2780±1242mL), water, alcohol, and coffee/tea (plain and milk/sugar-added), but more juice (all p’s&lt;0.05). There were no differences in % fluid from food or water, milk/non-dairy alternatives, non-caloric beverages, or SSBs. Black individuals exhibited ~22% higher plasma copeptin concentration (Black; 5.5±2.8 vs. White; 4.5±2.8pmol/L, ANCOVA p=0.022). Irrespective of race, copeptin was negatively correlated with water intake (ρ=-0.230, p=0.034). CONCLUSION: Black young adults reported a significantly lower fluid intake and exhibited higher plasma copeptin concentration when compared with White young adults. Future studies should attempt to address racial disparities in hydration practices, which may attenuate racial differences in copeptin and corresponding health disparities. NIH-K01HL147998 (ATR), Auburn University Offce of Vice President for Research Support Program (ATR). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A Model for Predicting Clinically Significant Prostate Cancer Using Prostate MRI and Risk Factors

PurposeThe aim of this study was to develop and validate a predictive model for clinically significant prostate cancer (csPCa) using prostate MRI and patient risk factors. MethodsIn total, 960 men who underwent MRI from 2015 to 2019 and biopsy either 6 months before or 6 months after MRI were identified. Men diagnosed with csPCa were identified, and csPCa risk was modeled using known patient factors (age, race, and prostate-specific antigen [PSA] level) and prostate MRI findings (location, Prostate Imaging Reporting and Data System score, extraprostatic extension, dominant lesion size, and PSA density). csPCa was defined as Gleason score sum ≥ 7. Using a derivation cohort, a multivariable logistic regression model and a point-based scoring system were developed to predict csPCa. Discrimination and calibration were assessed in a separate independent validation cohort. ResultsAmong 960 MRI reports, 552 (57.5%) were from men diagnosed with csPCa. Using the derivation cohort (n = 632), variables that predicted csPCa were Prostate Imaging Reporting and Data System scores of 4 and 5, the presence of extraprostatic extension, and elevated PSA density. Evaluation using the validation cohort (n = 328) resulted in an area under the curve of 0.77, with adequate calibration (Hosmer-Lemeshow P = .58). At a risk threshold of >2 points, the model identified csPCa with sensitivity of 98.4% and negative predictive value of 78.6% but prevented only 4.3% potential biopsies (0-2 points; 14 of 328). At a higher threshold of >5 points, the model identified csPCa with sensitivity of 89.5% and negative predictive value of 70.1% and avoided 20.4% of biopsies (0-5 points; 67 of 328). ConclusionsThe point-based model reported here can potentially identify a vast majority of men at risk for csPCa, while avoiding biopsy in about 1 in 5 men with elevated PSA levels.

Enhancing prostate MRI expertise: educational strategies for radiologists.

The adoption of multiparametric MRI (mpMRI) and the Prostate Imaging Reporting and Data System has significantly changed prostate cancer diagnosis and management. These advancements, alongside novel biomarkers and updated International Society of Uropathology grade groups, have improved cancer detection and prognostication. Despite this progress, varying levels of expertise in mpMRI among radiologists have resulted in inconsistent assessments, potentially leading to unnecessary procedures and diminished confidence in the modality. This review assesses the educational landscape for prostate MRI, highlighting available resources for radiologists at all professional stages. It emphasizes the need for targeted educational strategies to bridge knowledge gaps and improve patient care outcomes in prostate cancer management.

Review Artificial Intelligence Applications in Renewable Energy Systems Integration

The transition to renewable energy (RE) sources is critical for addressing global energy demands and environmental concerns. This review paper focuses on the pivotal role of Machine Learning (ML) and Deep Learning (DL) in optimizing and predicting the performance of RE systems, particularly solar and wind power. We explore various applications of these advanced technologies in forecasting energy demand and consumption, predicting the output power of renewable systems, and optimizing the operation and maintenance of these systems. The paper also delves into the significance of Explainable AI (XAI) in enhancing the transparency and understandability of AI models in energy applications. Our comprehensive analysis reveals that while ML and DL offer transformative potential in the RE sector, challenges such as data complexity, system integration, and model interpretability remain. Concluding, this work aims to provide a foundation for future research and development in this rapidly evolving field, asserting that the continued advancement and integration of AI technologies in RE systems is essential for achieving a sustainable and efficient energy future.