Hemoglobin Threshold for Blood Transfusion in Young Children Hospitalized with Iron Deficiency Anemia.

Current recommendations advise against blood transfusion in hemodynamically stable children with iron deficiency anemia. In an observational study of 125 children aged 6 through 36 months, hospitalized with iron deficiency anemia, we found that hemoglobin level predicted red blood cell transfusion (area under the curve 0.8862). A hemoglobin of 39 g/L had sensitivity 92% and specificity 72% for transfusion.

Predictive Performance of an Updated Polygenic Risk Score for Age-Related Macular Degeneration.

PURPOSE: A recent genome-wide association study of age-related macular degeneration (AMD) identified new AMD-associated risk variants. These variants now can be incorporated into an updated polygenic risk score (PRS). This study aimed to assess the performance of an updated PRS, PRS2023, in an independent cohort of older individuals with retinal imaging data and to compare performance with an older PRS, PRS2016. DESIGN: Cross-sectional study. PARTICIPANTS: A total of 4175 participants of European ancestry, 70 years of age or older, with genotype and retinal imaging data. METHODS: We used logistic regression models and area under the receiver operating characteristic curve (AUC) to assess the performance of PRS2023 compared with PRS2016. AMD status and severity were graded using color fundus photography. MAIN OUTCOME MEASURES: Association of PRS2023 and PRS2016 with AMD risk at baseline. RESULTS: At enrollment among 4175 participants, 2605 participants (62.4%) had no AMD and 853 participants (20.4%), 671 participants (16.1%), and 46 participants (1.1%) had early, intermediate, and late-stage AMD, respectively. More than 27% of the participants with a high PRS2023 (top quartile) had intermediate or late-stage AMD, compared with < 15% for those in the middle 2 quartiles and less than 13% for those in the lowest quartile. Both PRS2023 and PRS2016 were associated significantly with AMD after adjustment for age, sex, smoking status, and lipid levels, with increasing odds ratios (ORs) for worsening AMD grades. PRS2023 outperformed PRS2016 (P = 0.03 for all AMD and P = 0.03 for late AMD, DeLong test comparing AUC). PRS2023 was associated with late-stage AMD with an adjusted OR of 5.05 (95% confidence interval [CI], 3.41-7.47) per standard deviation. The AUC of a model containing conventional or nongenetic risk factors and PRS2023 was 91% (95% CI, 87%-95%) for predicting late-stage AMD, which improved 12% over the model without the PRS (AUC, 79%; P < 0.001 for difference). CONCLUSIONS: A new PRS, PRS2023, for AMD outperforms a previous PRS and predicts increasing risk for late-stage AMD (with stronger association for more severe imaging-confirmed AMD grades). Our findings have clinical implications for the improved prediction and risk stratification of AMD. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Magnetic resonance imaging of anal cancer: tumor characteristics and early prediction of treatment outcome.

PURPOSE: To analyze tumor characteristics derived from pelvic magnetic resonance imaging (MRI) of patients with squamous cell carcinoma of the anus (SCCA) before and during chemoradiotherapy (CRT), and to compare the changes in these characteristics between scans of responders vs. nonresponders to CRT. METHODS: We included 52 patients with a pelvic 3T MRI scan prior to CRT (baseline scan); 39 of these patients received an additional scan during week 2 of CRT (second scan). Volume, diameter, extramural tumor depth (EMTD), and external anal sphincter infiltration (EASI) of the tumor were assessed. Mean, kurtosis, skewness, standard deviation (SD), and entropy values were extracted from apparent diffusion coefficient (ADC) histograms. The main outcome was locoregional treatment failure. Correlations were evaluated with Wilcoxon's signed rank-sum test and Pearson's correlation coefficient, quantile regression, univariate logistic regression, and area under the ROC curve (AUC) analyses. RESULTS: In isolated analyses of the baseline and second MRI scans, none of the characteristics were associated with outcome. Comparison between the scans showed significant changes in several characteristics: volume, diameter, EMTD, and ADC skewness decreased in the second scan, although the mean ADC increased. Small decreases in volume and diameter were associated with treatment failure, and these variables had the highest AUC values (0.73 and 0.76, respectively) among the analyzed characteristics. CONCLUSION: Changes in tumor volume and diameter in an early scan during CRT could represent easily assessable imaging-based biomarkers to eliminate the need for analysis of more complex MRI characteristics.

Predictive factors that influence the clinical efficacy of umbilical cord-derived mesenchymal stromal cells in the treatment of type 2 diabetes mellitus.

BACKGROUND: Our previous single-center, randomized, double-blinded, placebo-controlled phase 2 study evaluated the safety and effectiveness of human umbilical cord mesenchymal stromal cell (UC-MSC) transfusion for treating patients with type 2 diabetes mellitus (T2DM). Indeed, this potential treatment strategy was able to reduce insulin use by half in a considerable number of patients. However, many other patients' responses to UC-MSC transfusion were insignificant. The selection of patients who might benefit from UC-MSC treatment is crucial from a clinical standpoint. METHODS: In this post hoc analysis, 37 patients who received UC-MSC transfusions were divided into two groups based on whether their glycated hemoglobin (hemoglobin A1c, or HbA1c) level was less than 7% after receiving UC-MSC treatment. The baseline differences between the two groups were summarized, and potential factors influencing efficacy of UC-MSCs for T2DM were analyzed by univariate and multivariate logistic regression. The correlations between the relevant hormone levels and the treatment effect were further analyzed. RESULTS: At the 9-week follow-up, 59.5% of patients achieved their targeted HbA1c level. Male patients with lower baseline HbA1c and greater C-peptide area under the curve (AUCC-pep) values responded favorably to UC-MSC transfusion, according to multivariate analysis. The effectiveness of UC-MSCs transfusion was predicted by AUCC-pep (cutoff value: 14.22 ng/h/mL). Further investigation revealed that AUCC-pep was increased in male patients with greater baseline testosterone levels. CONCLUSIONS: Male patients with T2DM with greater AUCC-pep may be more likely to respond clinically to UC-MSC therapy, and further large-scale multi-ethnic clinical studies should be performed to confirm the conclusion.

Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae.

OBJECTIVES: Influenza and Mycoplasma pneumoniae infections often present concurrent and overlapping symptoms in clinical manifestations, making it crucial to accurately differentiate between the two in clinical practice. Therefore, this study aims to explore the potential of using peripheral blood routine parameters to effectively distinguish between influenza and Mycoplasma pneumoniae infections. METHODS: This study selected 209 influenza patients (IV group) and 214 Mycoplasma pneumoniae patients (MP group) from September 2023 to January 2024 at Nansha Division, the First Affiliated Hospital of Sun Yat-sen University. We conducted a routine blood-related index test on all research subjects to develop a diagnostic model. For normally distributed parameters, we used the T-test, and for non-normally distributed parameters, we used the Wilcoxon test. RESULTS: Based on an area under the curve (AUC) threshold of ≥ 0.7, we selected indices such as Lym# (lymphocyte count), Eos# (eosinophil percentage), Mon% (monocyte percentage), PLT (platelet count), HFC# (high fluorescent cell count), and PLR (platelet to lymphocyte ratio) to construct the model. Based on these indicators, we constructed a diagnostic algorithm named IV@MP using the random forest method. CONCLUSIONS: The diagnostic algorithm demonstrated excellent diagnostic performance and was validated in a new population, with an AUC of 0.845. In addition, we developed a web tool to facilitate the diagnosis of influenza and Mycoplasma pneumoniae infections. The results of this study provide an effective tool for clinical practice, enabling physicians to accurately diagnose and differentiate between influenza and Mycoplasma pneumoniae infection, thereby offering patients more precise treatment plans.

MMP12 is a Potential Predictive and Prognostic Biomarker of Various Cancers Including Lung Adenocarcinoma.

OBJECTIVE: This study sought to explore the clinical value of matrix metalloproteinases 12 (MMP12) in multiple cancers, including lung adenocarcinoma (LUAD). METHODS: Using >10,000 samples, this retrospective study demonstrated the first pan-cancer analysis of MMP12. The expression of MMP12 between cancer groups and their control groups was analyzed using Wilcoxon rank-sum tests. The clinical significance of MMP12 expression in multiple cancers was assessed using receiver operating characteristic curves, Kaplan-Meier curves, and univariate Cox analysis. A further LUAD-related analysis based on 4565 multi-center and in-house samples was performed to verify the findings regarding MMP12 in pan-cancer analysis partly. RESULTS: MMP12 mRNA is highly expressed in 13 cancers compared to their controls, and the MMP12 protein level is elevated in some of these cancers (e.g., colon adenocarcinoma) (P < .05). MMP12 expression makes it feasible to distinguish 21 cancer tissues from normal tissues (AUC = 0.86). A high MMP12 expression is a prognosis risk factor in eight cancers, such as adrenocortical carcinoma (hazard ratio >1, P < .05). The elevated MMP12 expression is also a prognosis protective factor in breast-invasive carcinoma and colon adenocarcinoma (hazard ratio <1, P < .05). Some pan-cancer findings regarding MMP12 are verified in LUAD-MMP12 expression is upregulated in LUAD at both the mRNA and protein levels (P < .05), has the potential to distinguish LUAD with considerable accuracy (AUC = .91), and plays a risk prognosis factor for patients with the disease (P < .05). CONCLUSIONS: MMP12 is highly expressed in most cancers and may serve as a novel biomarker for the prediction and prognosis of numerous cancers.

Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

OBJECTIVES: To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients. METHODS: Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated. RESULTS: Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets. CONCLUSION: The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC. CLINICAL RELEVANCE STATEMENT: Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions. KEY POINTS: • The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory. • The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy. • Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy.

Preoperative Modified Frailty Index-11 versus EuroSCORE II in Predicting Postoperative Mortality and Complications in Elderly Patients Who Underwent Elective Open Cardiac Surgery: A Retrospective Cohort Study.

OBJECTIVE: To compare sensitivity, specificity, receiver operating characteristic (ROC), and area under the curve (AUC) values using the modified Frailty Index 11 (mFI-11), EuroSCORE II, and combined mFI-11 and EuroSCORE II to predict in-hospital mortality and composite morbidities. DESIGN: Retrospective cohort study SETTING: Songklanagarind Hospital, a tertiary care center in southern Thailand. PARTICIPANTS: Elderly patients age ≥60 years who underwent elective open-heart surgical procedures on a pump between January 2017 and December 2022 were included. INTERVENTIONS: ROC curves were constructed to evaluate the discriminatory power of EuroSCORE II and mFI-11 for predicting in-hospital mortality and postoperative complications. MEASUREMENTS AND MAIN RESULTS: The actual in-hospital mortality was 2.5% for all patients. The discriminative accuracy of mFI-11, EuroSCORE II, and combined mFI-11 with EuroSCORE II for predicting in-hospital mortality was good, with respective AUC values of 0.733 (95% confidence interval [CI], 0.6157-0.8499), 0.793 (95% CI, 0.6826-0.9026), and 0.78 (95% CI, 0.6686-0.893). The AUC of mFI-11 for predicting postoperative cardiac, respiratory, neurologic, and renal complications was 0.558 (95% CI, 0.5101-0.6063), 0.606 (95% CI, 0.5542-0.6581), 0.543 (95% CI, 0.4533-0.6337), and 0.652 (95% CI, 0.5859-0.7179), respectively, and that of EuroSCORE II was 0.553 (95% CI, 0.5038-0.6013), 0.631 (95% CI, 0.578-0.6836), 0.619 (95% CI, 0.5306-0.7076), and 0.702 (95% CI, 0.6378-0.7657), respectively. CONCLUSIONS: The mFI-11 and EuroSCORE II demonstrated good discrimination in ROC analysis, with EuroSCORE II showing superior predictive accuracy for in-hospital mortality in elderly elective cardiac surgery patients. However, neither score independently predicted mortality in multiple logistic regression, nor did combining them enhance predictive power significantly. Furthermore, both scores were less effective in predicting postoperative complications.

Prediction of neonatal outcomes using gestational age vs ACOG definitions of maternal disease severity in hypertensive disorders of pregnancy.

PURPOSE: Hypertensive disorders of pregnancy cause significant neonatal complications. Disease severity is often used to predict neonatal outcomes, however gestational age (GA) at delivery may be a better predictor. We aimed to assess whether disease severity or GA was more predictive of adverse neonatal outcomes. METHODS: We included 165 participants with confirmed HELLP syndrome or severe preeclampsia (sPE). Two predictive models were constructed to assess the ability of disease severity compared to GA to predict a composite adverse neonatal outcome. The composite outcome included low birth weight, SGA, IUGR, Apgar score, and neonatal death. RESULTS: Using severity as a predictor of binary neonatal outcome had an AUC of 0.73 (0.65-0.81), with a sensitivity (SE) of 70.3% and a specificity (SP) of 64.4%. For GA, we observed an AUC of 0.82 (0.75-0.89), with a SE of 75.7% and a SP of 76.7%. CONCLUSION: For the composite neonatal outcome, GA was a better predictor than ACOG diagnosis (severity). This observation underscores the need for further research to validate these findings in larger cohorts and to determine their applicability to maternal outcomes.

Alzheimer's polygenic risk scores are associated with cognitive phenotypes in Down syndrome.

INTRODUCTION: This study aimed to investigate the influence of the overall Alzheimer's disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) biomarkers. METHODS: AD polygenic risk scores (PRS) were tested for association with DS-related traits. RESULTS: The AD risk PRS was associated with disease status in several cohorts of sporadic late- and early-onset and familial late-onset AD, but not in familial early-onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRSAPOE , p = 2.84 × 10-4 ; PRS excluding APOE, PRSnonAPOE , p = 1.60 × 10-2 ). PRSAPOE exhibited significant associations with Aß42, tTau, pTau, and Aß42/40 ratio in DS. DISCUSSION: These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits. HIGHLIGHTS: Examination of the polygenic risk of AD in DS presented here is the first of its kind. AD PRS influences memory aspects in DS individuals, independently of APOE genotype. These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population. APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults.

Estimating rate of change for nonlinear trajectories in the framework of individual measurement occasions: A new perspective on growth curves.

Researchers are often interested in examining between-individual differences in within-individual processes. If the process under investigation is tracked for a long time, its trajectory may show a certain degree of nonlinearity, so that the rate of change is not constant. A fundamental goal of modeling such nonlinear processes is to estimate model parameters that reflect meaningful aspects of change, including the parameters related to change and other parameters that shed light on substantive hypotheses. However, if the measurement occasion is unstructured, existing models cannot simultaneously estimate these two types of parameters. This article has three goals. First, we view the change over time as the area under the curve (AUC) of the rate of change versus time ( r - t ) graph. Second, using the instantaneous rate of change midway through a time interval to approximate the average rate of change during that interval, we propose a new specification to describe longitudinal processes. In addition to obtaining the individual change-related parameters and other parameters related to specific research questions, the new specification allows for unequally spaced study waves and individual measurement occasions around each wave. Third, we derive the model-based interval-specific change and change from baseline, two common measures to evaluate change over time. We evaluate the proposed specification through a simulation study and a real-world data analysis. We also provide OpenMx and Mplus 8 code for each model with the novel specification.

Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outcomes. This article aims to provide a basic understanding of AI enabled ECG technology. Specific conditions and findings will be discussed, followed by reviewing associated terminology and methodology. In the appendix, definitions of AUC versus accuracy are explained. The application of deep learning models enables detecting diseases from normal electrocardiograms at accuracy not previously achieved by technology or human experts. Results with AI enabled ECG are encouraging as they considerably exceeded current screening models for specific conditions (i.e., atrial fibrillation, left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy). This could potentially lead to a revitalization of the utilization of the ECG in the insurance domain. While we are embracing the findings with this rapidly evolving technology, but cautious optimism is still necessary at this point.

Deep Learning Algorithm Enables Cerebral Venous Thrombosis Detection With Routine Brain Magnetic Resonance Imaging.

BACKGROUND: Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose CVT. This study aimed to develop and evaluate a novel deep learning (DL) algorithm for detecting CVT using routine brain magnetic resonance imaging. METHODS: Routine brain magnetic resonance imaging, including T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images of patients suspected of CVT from April 2014 through December 2019 who were enrolled from a CVT registry, were collected. The images were divided into 2 data sets: a development set and a test set. Different DL algorithms were constructed in the development set using 5-fold cross-validation. Four radiologists with various levels of expertise independently read the images and performed diagnosis within the test set. The diagnostic performance on per-patient and per-segment diagnosis levels of the DL algorithms and radiologist's assessment were evaluated and compared. RESULTS: A total of 392 patients, including 294 patients with CVT (37±14 years, 151 women) and 98 patients without CVT (42±15 years, 65 women), were enrolled. Of these, 100 patients (50 CVT and 50 non-CVT) were randomly assigned to the test set, and the other 292 patients comprised the development set. In the test set, the optimal DL algorithm (multisequence multitask deep learning algorithm) achieved an area under the curve of 0.96, with a sensitivity of 96% (48/50) and a specificity of 88% (44/50) on per-patient diagnosis level, as well as a sensitivity of 88% (129/146) and a specificity of 80% (521/654) on per-segment diagnosis level. Compared with 4 radiologists, multisequence multitask deep learning algorithm showed higher sensitivity both on per-patient (all P<0.05) and per-segment diagnosis levels (all P<0.001). CONCLUSIONS: The CVT-detected DL algorithm herein improved diagnostic performance of routine brain magnetic resonance imaging, with high sensitivity and specificity, which provides a promising approach for detecting CVT.

Simulation Tests of Methods in Evolution, Ecology, and Systematics: Pitfalls, Progress, and Principles.

Complex statistical methods are continuously developed across the fields of ecology, evolution, and systematics (EES). These fields, however, lack standardized principles for evaluating methods, which has led to high variability in the rigor with which methods are tested, a lack of clarity regarding their limitations, and the potential for misapplication. In this review, we illustrate the common pitfalls of method evaluations in EES, the advantages of testing methods with simulated data, and best practices for method evaluations. We highlight the difference between method evaluation and validation and review how simulations, when appropriately designed, can refine the domain in which a method can be reliably applied. We also discuss the strengths and limitations of different evaluation metrics. The potential for misapplication of methods would be greatly reduced if funding agencies, reviewers, and journals required principled method evaluation.

Area under the curve: Comparing the value of factor VIII replacement therapies in haemophilia A.

INTRODUCTION: In factor VIII (FVIII) prophylaxis for haemophilia A, cost comparisons have used price per international unit (IU) based on the once reasonable assumption of equivalent outcome per IU. Now, with several extended half-life (EHL) products available, new outcome-oriented ways to compare products are needed. Area under the curve (AUC) quantifies FVIII levels over time after infusion providing comparable data. AIM: To develop a decision analytical model for making indirect comparisons of FVIII replacement products based on AUC. METHODS: A literature search identified 11 crossover studies with relevant pharmacokinetic data. A common comparator FVIII level curve was calculated using pooled data from selected studies. Absolute curves for other products were estimated based on relative differences to the common comparator (% difference vs the anchor). Three scenarios were investigated: (1) Kogenate® versus Kovaltry® and Jivi® ; (2) Advate® versus Elocta® , NovoEight® , Kovaltry, Adynovate® , Afstyla® , and ReFacto® ; and (3) Jivi versus Elocta, Adynovate, and Kogenate. Sensitivity analyses investigated effects of assay type and dose. RESULTS: In scenario 1, Jivi (+50%) and Kovaltry (+14%) showed larger AUCs versus Kogenate. In scenario 2, EHL products, Elocta and Adynovate, had the largest AUC (+64% and +58%, respectively) versus Advate. Compared with all other products in scenario 3, Jivi had the largest AUC by +13%-28%. CONCLUSION: This analysis concludes that EHL products differ in relative AUC, have a larger AUC compared with standard half-life, and thus, different FVIII levels over time after infusion. This model may aid decision makers in the absence of head-to-head data.

Comparison of the blink reflex in classical and idiopathic trigeminal neuralgia.

BACKGROUND: Previous findings indicate that the blink reflex is useful to distinguish between primary (classical/idiopathic) and secondary trigeminal neuralgia. No prior studies have investigated whether the blink reflex could identify differences in electrophysiological responses between classical and idiopathic trigeminal neuralgia. With this in mind, we investigated the blink reflex in a cohort of classical and idiopathic trigeminal neuralgia patients. METHODS: Participants were consecutively enrolled in the study. According to magnetic resonance imaging findings, the patients were subgrouped into either classical or idiopathic trigeminal neuralgia. Assessors were blinded to the subgroup and pain side, and the blink reflex was examined to assess R1 and R2 latencies, as well as the area under the curve. RESULTS: The study group constituted of 55 patients with primary trigeminal neuralgia: 25 patients with classical trigeminal neuralgia and 30 patients with idiopathic trigeminal neuralgia. None of the blink reflex latencies (R1 and R2) or the area under the curve significantly differed between the two subgroups when adjusted for age and sex (p > 0.05). CONCLUSIONS: Our findings suggest that the blink reflex cannot be used to differentiate classical and idiopathic trigeminal neuralgia patients, and that both subgroups may share common pathophysiological mechanisms.Trial Registration: ClinicalTrials.gov Identifier: NCT05328661.

Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation.

OBJECTIVE: This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients. METHODS: A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively. RESULTS: A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732-0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823-0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613-0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728-0.881) in the testing set. CONCLUSION: This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization. CLINICAL RELEVANCE STATEMENT: This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment. KEY POINTS: • Combination model that included rad-score and clinical features had a great predicting performance. • Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models.

Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening.

OBJECTIVES: To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density. METHODS: Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D). RESULTS: Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively). CONCLUSIONS: Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts. CLINICAL RELEVANCE STATEMENT: Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts. KEY POINTS: • Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.

Mycophenolic acid therapeutic drug monitoring using area under the curve in pediatric heart transplant recipients.

INTRODUCTION: Pharmacokinetics of mycophenolic acid (MPA) display substantial interpatient variability, with up to 10-fold difference of exposure in individual patients under a fixed-dose regimen. MPA trough level (C0) monitoring is common in clinical practice but has not proven sufficiently informative in predicting MPA exposure or patient outcomes, especially in children. No limited sampling strategies (LSSs) have been generated from pediatric heart transplant (HTx) recipients to estimate MPA AUC. METHODS: Single-center, observational analysis of 135 de novo pediatric HTx recipients ≤21 years old who underwent MPA AUC between 2011 and 2021. RESULTS: Median age was 4 years (IQR .6-12.1). Median time from transplant to MPA AUC sampling was 15 days (IQR 11-19). MMF doses (mg or mg/day) had low, negative Pearson correlation coefficients (r) while doses adjusted for weight or body surface area had low correlation with Trapezoidal MPA AUC0-24 h (r = .3 and .383, respectively). MPA C0 had weak association (r = .451) with Trapezoidal MPA AUC0-24 h . LSS with two pharmacokinetic sampling time points at 90 (C3 ) and 360 (C5 ) min after MMF administration (estimated AUC0-24 h  = 32.82 + 4.12 × C3  + 11.53 × C5 ) showed strong correlation with Trapezoidal MPA AUC0-24 h (r = .87). CONCLUSION: MMF at fixed or weight-adjusted doses, as well as MPA trough levels, correlate poorly with MPA AUC0-24 h . We developed novel LSSs to estimate Trapezoidal MPA AUC from a large cohort of pediatric HTx recipients. Validation of our LSSs should be completed in a separate cohort of pediatric HTx recipients.

Biomarkers for distinguishing tuberculous pleural effusion from non-tuberculosis effusion: a retrospective study.

BACKGROUND: Pleural effusion (PE) is a common clinical feature that presents a diagnostic challenge for clinicians. In this retrospective study, we aimed to assess the biomarkers, ratios, and multiple indicators in serum and Pleural effusion for the differential diagnosis of tuberculous pleural effusion (TPE) from non-tuberculosis effusion (non-TPE). METHODS: The participants, who were divided into two groups: TPE and non-TPE (MPE and PPE), from Ningbo First Hospital, were incorporated in this study. The clinical and laboratory features were collected and analyzed using logistic regression analysis. Twelve biomarkers and their ratios in serum and PE were investigated for TPE versus non-TPE. Additionally, the value of multiple indicators for joint diagnosis was estimated. RESULTS: Biomarkers and ratios showed good diagnostic performance. The five variables including Serum ADA, IGRA, Effusion ADA, Effusion ADA/Serum ADA and Effusion LDH/Effusion ADA were identified as valuable parameters for differential diagnosis of TPE from non-TPE. The combined diagnosis of the five indexes yielded the highest diagnostic accuracy for TPE with an AUC (0.919), sensitivity (90.30%), and specificity (94.50%). CONCLUSIONS: The biomarkers and ratios demonstrated strong diagnostic performance, and the utilization of multiple indicators for joint diagnosis can improve the diagnostic efficacy of tuberculous pleurisy.