The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection.

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

The diagnostic value of sST2 for myocardial fibrosis in idiopathic inflammatory myopathies in subclinical stage of cardiac involvement.

OBJECTIVE: Myocardial fibrosis occurs in the early subclinical stage of cardiac involvement in idiopathic inflammatory myopathies (IIMs). Soluble suppression of tumorigenicity 2 (sST2) is known to have an immunomodulatory impact during autoimmune disease development. The current study investigated the diagnostic value of sST2 for myocardial fibrosis during early stage of cardiac involvement in IIM. METHODS: A total of 44 IIM patients with normal heart function and 32 age- and gender-matched healthy controls (HCs) were enrolled. Serum sST2 levels were measured by ELISA and cardiac magnetic resonance (CMR) parameters for myocardial fibrosis [native T1, extracellular volume (ECV), late-gadolinium enhancement (LGE)] and oedema (T2 values) were analysed. RESULTS: IIM patients had significantly higher sST2 levels than HCs [67.5 ng/ml (s.d. 30.4)] vs 14.4 (5.5), P < 0.001] and levels correlated positively with diffuse myocardial fibrosis parameters, native T1 (r = 0.531, P = 0.000), ECV (r = 0.371, P = 0.013) and focal myocardial fibrosis index and LGE (r = 0.339, P = 0.024) by Spearman's correlation analysis. sST2 was an independent predictive factor for diffuse and focal myocardial fibrosis after adjustment for age, gender, BMI and ESR. Risk increased ≈15.4% for diffuse [odds ratio (OR) 1.154 (95% CI 1.021, 1.305), P = 0.022] and 3.8% for focal [OR 1.038 (95% CI 1.006, 1.072), P = 0.020] myocardial fibrosis per unit increase of sST2. Cut-off values for diagnosing diffuse and focal myocardial fibrosis were sST2 ≥51.3 ng/ml [area under the curve (AUC) = 0.942, sensitivity = 85.7%, specificity = 98.9%, P < 0.001] and 53.3 ng/ml (AUC = 0.753, sensitivity = 87.5%, specificity = 58.3%, P < 0.01), respectively. CONCLUSION: sST2 showed a marked elevation during the subclinical stage of cardiac involvement in IIM and has potential as a biomarker for predicting diffuse and focal myocardial fibrosis in IIM.

The impact of type 2 diabetes mellitus on the clinical profile, myocardial fibrosis, and prognosis in non-ischemic dilated cardiomyopathy: a prospective cohort study.

BACKGROUND: The impact of the coexistence of type 2 diabetes mellitus (T2DM) in patients with non-ischemic dilated cardiomyopathy (DCM) on clinical profiles, myocardial fibrosis, and outcomes remain incompletely understood. METHOD: A total of 1152 patients diagnosed with non-ischemic DCM were prospectively enrolled from June 2012 to October 2021 and categorized into T2DM and non-T2DM groups. Clinical characteristics, cardiac function, and myocardial fibrosis evaluated by CMR were compared between the two groups. The primary endpoint included both all-cause mortality and heart transplantation. Cause of mortality was classified into heart failure death, sudden cardiac death, and non-cardiac death. Cox regression analysis and Kaplan-Meier analysis were performed to identify the association between T2DM and clinical outcomes. Propensity score matching (PSM) cohort including 438 patients was analyzed to reduce the bias from confounding covariates. RESULTS: Among the 1152 included DCM patients, 155 (13%) patients had T2DM. Patients with T2DM were older (55 ± 12 vs. 47 ± 14 years, P < 0.001), had higher New York Heart Association (NYHA) functional class (P = 0.003), higher prevalence of hypertension (37% vs. 21%, P < 0.001), atrial fibrillation (31% vs. 16%, P < 0.001), lower left ventricular (LV) ejection fraction (EF) (23 ± 9% vs. 27 ± 12%, P < 0.001), higher late gadolinium enhancement (LGE) presence (55% vs. 45%, P = 0.02), and significantly elevated native T1 (1323 ± 81ms vs. 1305 ± 73ms, P = 0.01) and extracellular volume fraction (ECV) (32.7 ± 6.3% vs. 31.3 ± 5.9%, P = 0.01) values. After a median follow-up of 38 months (interquartile range: 20-57 months), 239 patients reached primary endpoint. Kaplan-Meier analysis showed that patients with T2DM had worse clinical outcomes compared with those without T2DM in the overall cohort (annual events rate: 10.2% vs. 5.7%, P < 0.001). T2DM was independently associated with an increased risk of primary endpoint in the overall (Hazard ratio [HR]: 1.61, 95% CI: 1.13-2.33, P = 0.01) and PSM (HR: 1.54, 95% CI: 1.05-2.24, P = 0.02) cohorts. Furthermore, T2DM was associated with a higher risk of heart failure death (P = 0.006) and non-cardiac death (P = 0.02), but not sudden cardiac death (P = 0.16). CONCLUSIONS: Patients with T2DM represented a more severe clinical profile and experienced more adverse outcomes compared to those without T2DM in a large DCM cohort. TRIAL REGISTRATION: Trial registration number: ChiCTR1800017058; URL: https://www. CLINICALTRIALS: gov .

Liver T1 Mapping Derived From Cardiac Magnetic Resonance Imaging: A Potential Prognostic Marker in Idiopathic Dilated Cardiomyopathy.

BACKGROUND: Hepatic alterations are common aftereffects of heart failure (HF) and ventricular dysfunction. The prognostic value of liver injury markers derived from cardiac MRI studies in nonischemic dilated cardiomyopathy (DCM) patients is unclear. PURPOSE: Evaluate the prognostic performance of liver injury markers derived from cardiac MRI studies in DCM patients. STUDY TYPE: Prospective. POPULATION: Three hundred fifty-six consecutive DCM patients diagnosed according to ESC guidelines (age 48.7 ± 14.2 years, males 72.6%). FIELD STRENGTH/SEQUENCE: Steady-state free precession, modified Look-Locker inversion recovery T1 mapping and phase sensitive inversion recovery late gadolinium enhancement (LGE) sequences at 3 T. ASSESSMENT: Clinical characteristics, conventional MRI parameters (ventricular volumes, function, mass), native myocardial and liver T1, liver extracellular volume (ECV), and myocardial LGE presence were assessed. Patients were followed up for a median duration of 48.3 months (interquartile range 42.0-69.9 months). Primary endpoints included HF death, sudden cardiac death, heart transplantation, and HF readmission; secondary endpoints included HF death, sudden cardiac death, and heart transplantation. Models were developed to predict endpoints and the incremental value of including liver parameters assessed. STATISTICAL TESTS: Optimal cut-off value was determined using receiver operating characteristic curve and Youden method. Survival analysis was performed using Kaplan-Meier and Cox proportional hazard. Discriminative power of models was compared using net reclassification improvement and integrated discriminatory index. P value <0.05 was considered statistically significant. RESULTS: 47.2% patients reached primary endpoints; 25.8% patients reached secondary endpoints. Patients with elevated liver ECV (cut-off 34.4%) had significantly higher risk reaching primary and secondary endpoints. Cox regression showed liver ECV was an independent prognostic predictor, and showed independent prognostic value for primary endpoints and long-term HF readmission compared to conventional clinical and cardiac MRI parameters. DATA CONCLUSIONS: Liver ECV is an independent prognostic predictor and may serve as an innovative approach for risk stratification for DCM. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Prognostic value of mid-term cardiovascular magnetic resonance follow-up in patients with non-ischemic dilated cardiomyopathy: a prospective cohort study.

BACKGROUND: The prognostic value of follow-up cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) patients is unclear. We aimed to investigate the prognostic value of cardiac function, structure, and tissue characteristics at mid-term CMR follow-up. METHODS: The study population was a prospectively enrolled cohort of DCM patients who underwent guideline-directed medical therapy with baseline and follow-up CMR, which included measurement of biventricular volume and ejection fraction, late gadolinium enhancement, native T1, native T2, and extracellular volume. During follow-up, major adverse cardiac events (MACE) were defined as a composite endpoint of cardiovascular death, heart transplantation, and heart-failure readmission. RESULTS: Among 235 DCM patients (median CMR interval: 15.3 months; interquartile range: 12.5-19.2 months), 54 (23.0%) experienced MACE during follow-up (median: 31.2 months; interquartile range: 20.8-50.0 months). In multivariable Cox regression, follow-up CMR models showed significantly superior predictive value than baseline CMR models. Stepwise multivariate Cox regression showed that follow-up left ventricular ejection fraction (LVEF; hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.91-0.96; p < 0.001) and native T1 (HR, 1.01; 95% CI, 1.00-1.01; p = 0.030) were independent predictors of MACE. Follow-up LVEF ≥ 40% or stable LVEF < 40% with T1 ≤ 1273 ms indicated low risk (annual event rate < 4%), while stable LVEF < 40% and T1 > 1273 ms or LVEF < 40% with deterioration indicated high risk (annual event rate > 15%). CONCLUSIONS: Follow-up CMR provided better risk stratification than baseline CMR. Improvements in the LVEF and T1 mapping are associated with a lower risk of MACE.

Triage in major incidents: development and external validation of novel machine learning-derived primary and secondary triage tools.

BACKGROUND: Major incidents (MIs) are an important cause of death and disability. Triage tools are crucial to identifying priority 1 (P1) patients-those needing time-critical, life-saving interventions. Existing expert opinion-derived tools have limited evidence supporting their use. This study employs machine learning (ML) to develop and validate models for novel primary and secondary triage tools. METHODS: Adults (16+ years) from the UK Trauma Audit and Research Network (TARN) registry (January 2008-December 2017) served as surrogates for MI victims, with P1 patients identified using predefined criteria. The TARN database was split chronologically into model training and testing (70:30) datasets. Input variables included physiological parameters, age, mechanism and anatomical location of injury. Random forest, extreme gradient boosted tree, logistic regression and decision tree models were trained to predict P1 status, and compared with existing tools (Battlefield Casualty Drills (BCD) Triage Sieve, CareFlight, Modified Physiological Triage Tool, MPTT-24, MSTART, National Ambulance Resilience Unit Triage Sieve and RAMP). Primary and secondary candidate models were selected; the latter was externally validated on patients from the UK military's Joint Theatre Trauma Registry (JTTR). RESULTS: Models were internally tested in 57 979 TARN patients. The best existing tool was the BCD Triage Sieve (sensitivity 68.2%, area under the receiver operating curve (AUC) 0.688). Inability to breathe spontaneously, presence of chest injury and mental status were most predictive of P1 status. A decision tree model including these three variables exhibited the best test characteristics (sensitivity 73.0%, AUC 0.782), forming the candidate primary tool. The proposed secondary tool (sensitivity 77.9%, AUC 0.817), applicable via a portable device, includes a fourth variable (injury mechanism). This performed favourably on external validation (sensitivity of 97.6%, AUC 0.778) in 5956 JTTR patients. CONCLUSION: Novel triage tools developed using ML outperform existing tools in a nationally representative trauma population. The proposed primary tool requires external validation prior to consideration for practical use. The secondary tool demonstrates good external validity and may be used to support decision-making by healthcare workers responding to MIs.

Cardiac MRI to Predict Sudden Cardiac Death Risk in Dilated Cardiomyopathy.

Background Sudden cardiac death (SCD) is one of the leading causes of death in individuals with nonischemic dilated cardiomyopathy (DCM). However, the risk stratification of SCD events remains challenging in clinical practice. Purpose To determine whether myocardial tissue characterization with cardiac MRI could be used to predict SCD events and to explore a SCD stratification algorithm in nonischemic DCM. Materials and Methods In this prospective single-center study, adults with nonischemic DCM who underwent cardiac MRI between June 2012 and August 2020 were enrolled. SCD-related events included SCD, appropriate implantable cardioverter-defibrillator shock, and resuscitation after cardiac arrest. Competing risk regression analysis and Kaplan-Meier analysis were performed to identify the association of myocardial tissue characterization with outcomes. Results Among the 858 participants (mean age, 48 years; age range, 18-83 years; 603 men), 70 (8%) participants experienced SCD-related events during a median follow-up of 33.0 months. In multivariable competing risk analysis, late gadolinium enhancement (LGE) (hazard ratio [HR], 1.87; 95% CI: 1.07, 3.27; P = .03), native T1 (per 10-msec increase: HR, 1.07; 95% CI: 1.04, 1.11; P < .001), and extracellular volume fraction (per 3% increase: HR, 1.26; 95% CI: 1.11, 1.44; P < .001) were independent predictors of SCD-related events after adjustment of systolic blood pressure, atrial fibrillation, and left ventricular ejection fraction. An SCD risk stratification category was developed with a combination of native T1 and LGE. Participants with a native T1 value 4 or more SDs above the mean (1382 msec) had the highest annual SCD-related events rate of 9.3%, and participants with a native T1 value 2 SDs below the mean (1292 msec) and negative LGE had the lowest rate of 0.6%. This category showed good prediction ability (C statistic = 0.74) and could be used to discriminate SCD risk and competing heart failure risk. Conclusion Myocardial tissue characteristics derived from cardiac MRI were independent predictors of sudden cardiac death (SCD)-related events in individuals with nonischemic dilated cardiomyopathy and could be used to stratify participants according to different SCD risk categories. Clinical trial registration no. ChiCTR1800017058 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Sakuma in this issue.

CACONET: a novel classification framework for microbial correlation networks.

MOTIVATION: Existing microbiome-based disease prediction relies on the ability of machine learning methods to differentiate disease from healthy subjects based on the observed taxa abundance across samples. Despite numerous microbes have been implicated as potential biomarkers, challenges remain due to not only the statistical nature of microbiome data but also the lack of understanding of microbial interactions which can be indicative of the disease. RESULTS: We propose CACONET (classification of Compositional-Aware COrrelation NETworks), a computational framework that learns to classify microbial correlation networks and extracts potential signature interactions, taking as input taxa relative abundance across samples and their health status. By using Bayesian compositional-aware correlation inference, a collection of posterior correlation networks can be drawn and used for graph-level classification, thus incorporating uncertainty in the estimates. CACONET then employs a deep learning approach for graph classification, achieving excellent performance metrics by exploiting the correlation structure. We test the framework on both simulated data and a large real-world dataset pertaining to microbiome samples of colorectal cancer (CRC) and healthy subjects, and identify potential network substructure characteristic of CRC microbiota. CACONET is customizable and can be adapted to further improve its utility. AVAILABILITY AND IMPLEMENTATION: CACONET is available at https://github.com/yuanwxu/corr-net-classify. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Predictive Value of Myocardial Native T1 Mapping Radiomics in Dilated Cardiomyopathy: A Study in a Chinese Population.

BACKGROUND: Investigation of the factors influencing dilated cardiomyopathy (DCM) prognosis is important as it could facilitate risk stratification and guide clinical decision-making. PURPOSE: To assess the prognostic value of magnetic resonance imaging (MRI) radiomics analysis of native T1 mapping in DCM. STUDY TYPE: Prospective. SUBJECTS: Three hundred and thirty consecutive patients with non-ischemic DCM (mean age 48.42 ± 14.20 years, 247 males). FIELD STRENGTH/SEQUENCE: Balanced steady-state free precession and modified Look-Locker inversion recovery T1 mapping sequences at 3 T. ASSESSMENT: Clinical characteristics, conventional MRI parameters (ventricular volumes, function, and mass), native myocardial T1, and radiomics features extracted from native T1 mapping were obtained. The study endpoint was defined as all-cause mortality or heart transplantation. Models were developed based on 1) clinical data; 2) radiomics data based on T1 mapping; 3) clinical and conventional MRI data; 4) clinical, conventional MRI, and native T1 data; and 5) clinical, conventional MRI, and radiomics T1 mapping data. Each prediction model was trained according to follow-up results with AdaBoost, random forest, and logistic regression classifiers. STATISTICAL TESTS: The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score by 5-fold cross-validation. RESULTS: During a median follow-up of 53.5 months (interquartile range, 41.6-69.5 months), 77 patients with DCM experienced all-cause mortality or heart transplantation. The random forest model based on radiomics combined with clinical and conventional MRI parameters achieved the best performance, with AUC and F1 score of 0.95 and 0.89, respectively. DATA CONCLUSION: A machine-learning framework based on radiomics analysis of T1 mapping prognosis prediction in DCM. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Pulmonary Transit Time Derived from First-Pass Perfusion Cardiac MR Imaging: A Potential New Marker for Cardiac Involvement and Prognosis in Light-Chain Amyloidosis.

BACKGROUND: First-pass perfusion cardiac MR imaging could reflect pulmonary hemodynamics. However, the clinical value of pulmonary transit time (PTT) derived from first-pass perfusion MRI in light-chain (AL) amyloidosis requires further evaluation. PURPOSE: To assess the clinical and prognostic value of PTT in patients with AL amyloidosis. STUDY TYPE: Prospective observational study. POPULATION: 226 biopsy-proven systemic AL amyloidosis patients (age 58.62 ± 10.10 years, 135 males) and 43 healthy controls (age 42 ± 16.2 years, 20 males). FIELD STRENGTH/SEQUENCE: SSFP cine and phase sensitive inversion recovery late gadolinium enhancement (LGE) sequences, and multislice first-pass myocardial perfusion imaging with a saturation recovery turbo fast low-angle shot (SR-TurboFLASH) pulse sequence at 3.0T. ASSESSMENT: PTT was measured as the time interval between the peaks of right and left ventricular cavity arterial input function curves on first-pass perfusion MR images. STATISTICAL TESTS: Independent-sample t test, Mann-Whitney U test, Chi-square test, Fisher's exact test, analysis of variance, or Kruskal-Wallis test, as appropriate; univariable and multivariable Cox proportional hazards models and Kaplan-Meier curves, area under receiver operating characteristic curve were used to determine statistical significance. RESULTS: PTT could differentiate AL amyloidosis patients with (N = 188) and without (N = 38) cardiac involvement (area under the curve [AUC] = 0.839). During a median follow-up of 35 months, 160 patients (70.8%) demonstrated all-cause mortality. After adjustments for clinical (Hazard ratio [HR] 1.061, confidence interval [CI]: 1.021-1.102), biochemical (HR 1.055, CI: 1.014-1.097), cardiac MRI-derived (HR 1.077, CI: 1.034-1.123), and therapeutic (HR 1.063, CI: 1.024-1.103) factors, PTT predicted mortality independently in patients with AL amyloidosis. Finally, PTT could identify worse outcomes in patients demonstrating New York Heart Association class III, Mayo 2004 stage III, and transmural LGE pattern. DATA CONCLUSION: PTT may serve as a new imaging predictor of cardiac involvement and prognosis in AL amyloidosis. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.