This was a narrative review of select studies published through September of 2024. We review the shift toward multi-dimensional scores such as HCC early detection screening (HES), GALAD, ASAP, and mt-HBT represents a significant advancement in biomarker research for hepatocellular carcinoma (HCC) detection. Unlike single biomarker approaches, these scores integrate various clinical and biochemical factors to enhance predictive accuracy by reflecting different complementary aspects of disease progression and HCC oncogenesis. Proper testing and validation of biomarker scores in phase 3 biomarker studies is essential before wide use can be recommended. We also review the comparative performance of biomarker scores in phase 3 studies. The new version of HES (HES V2.0) which includes AFP, AFP L3, DCP, and changes in their levels the past one year, if available, in addition to age, platelets, albumin, ALT and underlying liver disease etiology outperforms GALAD in detecting early-stage HCC with overall 6.7% higher sensitivity, and ASAP with 13.4%-18.0% higher sensitivity, both at fixed 90% specificity. HES V2.0 is a leading candidate biomarker score for prospective testing in clinical studies of early HCC detection.

To compare the diagnostic value of transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI) in cesarean scar pregnancy (CSP) by a method of meta-analysis.

Studies on TVS and MRI for CSP were collected from PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang data, and Chinese Scientific Journal Database (VIP database) until April 1, 2024. Stata 15.0 software was used for data analysis. Mann-Whitney U-test was applied to compare the diagnostic efficiency of the TVS and MRI groups.

Nine articles with 713 subjects were involved in this review. The pooled sensitivity (0.96, 95%CI: 0.94-0.97), specificity (0.90, 95%CI: 0.84-0.94), and DOR (197.28, 95%CI: 99.71-390.31) in the MRI group were higher than those (Sensitivity = 0.83, 95%CI: 0.77-0.87; Specificity= 0.74, 95%CI: 0.63-0.83; DOR = 13.66, 95%CI: 7.84-23.79) in the TVS group. The positive likelihood ratio and negative likelihood ratio of the MRI group were 9.56 (95%CI: 8.82-15.72) and 0.05 (95%CI: 0.03-0.07), while those of the TVS group were 3.21 (95%CI:2.18-4.74) and 0.24 (95%CI: 0.18-0.31), respectively. In the MRI and TVS groups, the area under the curve (AUC) of the summary receiver operating characteristic was 0.9497 and 0.86, respectively. The results of Mann-Whitney U-tests of the two groups showed significant differences in the pooled sensitivity (Z= -3.311, p < 0.001), specificity (Z= -2.123, p = 0.034), and DOR (Z= -3.272, p = 0.001).

Both MRI and TVS can effectively diagnose CSP. However, compared with TVS, MRI has better diagnostic accuracy for CSP, with higher sensitivity and specificity. Considering the good diagnostic accuracy of ultrasound, patients with ultrasound suspicion of CSP should be sent to a reference center where MRI can express its full diagnostic potential regarding depth, topography of invasion and myometral residue, which is useful for subsequent management.

Current hepatocellular carcinoma (HCC) surveillance strategy has its limitations, consequently delaying early detection. The GALAD model has been validated in retrospective studies, with two published cut-off values yielding different sensitivities for HCCs of different etiologies. We evaluated the performance of GALAD model in HCC surveillance and determined the ideal cut-off value for our cohort.

Patients undergoing 6-monthly HCC surveillance in Singapore General Hospital were recruited between December 2017-October 2018. Study serum specimens were prospectively collected and retrospectively tested using the μTASWako alpha-fetoprotein (AFP), AFP-L3, and protein induced by vitamin K antagonism-II (PIVKA-II) kits. GALAD score was calculated and compared with individual biomarkers using area under the curve (AUC) analysis. Published GALAD cut-offs of -0.63 and -1.95 were compared for their performance in HCC detection.

There were 207 patients (median age 59 years, 55.1% males). Hepatitis B was the commonest etiology (72.9%). By February 2023, with a median follow-up of 48.9 months, 20 patients had developed HCC. Eight patients developed HCC within 1 year from specimen collection. For HCC developing within 1 year, GALAD model detected HCC with an AUC of 0.84, greater than AFP (AUC 0.77), AFP-L3 (AUC 0.60), and PIVKA-II (AUC 0.67). GALAD at cut-off -1.95 achieved sensitivity and specificity of 75% and 92.5% for HCCs detected within 1 year, superior to cut-off -0.63 (sensitivity 12.5%, specificity 100%).

In this prospective study of HCC surveillance, the GALAD model performed better than individual biomarkers. The cut-off of -1.95 was more useful in our predominantly chronic hepatitis B cohort.

The heterogeneous biology of cancer subtypes, especially in lung cancer, poses significant challenges for biomarker development. Standard model building techniques often fall short in accurately incorporating various histologic subtypes because of their diverse biological characteristics. This study explores a nested biomarker model to address this issue, aiming to improve lung cancer early detection.

The study included 337 patients from two clinical sites. Blood biomarkers were analyzed and various statistical methods employed to develop a nested model. This model was designed to account for the biological heterogeneity across histologic subtypes, compared against traditional logistic regression models.

The patient cohort included a range of malignant and benign nodules and included different cancer subtypes reflecting lung cancer heterogeneity. The nested model had comparable performance overall with the Mayo Clinic model and a standard logistic regression model with an AUC of 77.6 (95% confidence interval, 72.2-83.0) in training and 77.3 (95% confidence interval, 65.8-88.9) in testing. The nested subtype versus benign model had the best performance in the training set overall and had a particular advantage for small cell subtype prediction.

This study highlights the challenges cancer heterogeneity present for biomarker development and the potential for nested biomarker models to improve early cancer detection. Validation of this approach in larger cohorts is essential to prove its predictive benefit in biologically diverse cancers.

This work addresses the challenge of biological heterogeneity in biomarker development. A nested modeling approach may assist in developing more effective multicancer early detection strategies.

There is accumulating evidence linking the microbiome and cardiovascular diseases. Nevertheless, no existing studies have been conducted on atrial fibrillation (AF) and the oral microbiome.

We collected and sequenced 245 AF tongue-coating samples and 26 AF samples after catheter ablation from Zhengzhou and Guangshan, China. We characterized tongue coating microbiome, constructed microbial classifiers in the discovery cohort, and verified their diagnostic potential in a cross-regional cohort.

Tongue coating microbial richness and diversity were significantly increased in the AF group compared to the control group, indicating increased bacterial colonization. The classifiers based on four optimal tongue coating microbial markers achieved good diagnostic efficiency in AF cohorts, with area under the curve (AUC) of 99.10 and 98.62% in the discovery and validation cohorts, respectively, and 97.97% in the cross-regional cohort. Paroxysmal AF and persistent AF shared similar taxonomic features, but some specific differential bacteria acted in the AF progression. Moreover, the outcomes revealed that catheter ablation contributed to rehabilitating oral bacterial disorders.

This was the first cross-sectional and longitudinal research of oral microbiome in AF patients and the alternations after catheter ablation, which offers promising new perspectives for AF clinical diagnosis and management.

Over the past 30 years, academic and industrial research investigators have developed molecular reporters to visualize cell death in complex biological systems. In parallel, clinical researchers, chemists, biochemists, and molecular biologists have endeavored to translate these molecular tools into clinical imaging agents. Despite these efforts, there are no clinically approved imaging methodologies with which to image cell death consistently and quantitatively. One reason may reside in the intrinsic mismatch between the sampling frequency of translational molecular imaging and the biochemical kinetics that define cell death. Beyond cell death imaging, many active research programs are now attempting to create translational diagnostic pharmaceuticals to image immunological, fibrotic, amyloidotic, and metabolic pathways. Each of these pathways is defined by a unique set of biochemical rate constants, some of which are associated with key predictive pathways. Exhaustively sampling all permutations of pathways and kinetic constants would seem to be an intractable strategy for target identification and validation. Sampling theory, if applied to these pathways, could accelerate the translation of high-impact diagnostics through prioritization of pathways for either AI enhanced diagnostic imaging or AI-enhanced wearable devices. In this perspective, we identify the Nyquist sampling rate as a key criterion for evaluating the optimal application for novel diagnostics. Sampling theory states that to fully characterize a band-limited, stationary, temporal data set, the signal must be sampled at more than twice the rate of the fastest frequency in the signal or, for diagnostics, the discriminatory signal. Through the study of the medical imaging process chain, Nyquist sampling rates of 0.25 day-1 and, more likely, slower than 0.02 day-1 were determined to provide high quality information. By prioritizing low-frequency predictive processes, or "state changes,", imaging researchers may improve the "hit rate" of research programs by appropriately matching the rate of change in diagnostic and predictive information with the limiting sampling rate of medical imaging. Critically, however, high-frequency diagnostic information (and therefore high-frequency biological processes) need not be ignored; these processes are simply better interrogated through continuous monitoring, e.g., by wearable devices combined with machine learning or artificial intelligence.

The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that addresses the low signal-to-noise ratio of blood biomarkers by using cells as biosensors. The assay exposes small volumes of patient serum to standardized cells in culture and classifies disease by machine learning analysis of the gene expression readout from the cells. We developed the lung cancer iCAP (LC-iCAP) as a rule-out test for nodule management in computed tomography (CT)-based lung-cancer screening. We performed analytical optimization, rigorous reproducibility testing, and assessed performance in a study with prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) design. LC-iCAP achieved an AUC of 0.64 (95% CI, 0.51-0.76) on the ROC curve in validation. Post-validation integration of the assay readout with CT-based features showed improved clinical utility compared to the Mayo Clinic model, with 90% sensitivity, 64% specificity, and 95% negative predictive value at 25% prevalence. The lung-cancer specific readout was enriched for hypoxia-responsive genes and was reproducible across different indicator cell lineages. This is the first validation study of an iCAP and the first application for early cancer detection. The LC-iCAP uses immortalized cells, is scalable and cost-effective and has a multivariate readout. This study supports its potential as a next-generation multivalent platform for precision medicine applications in multi-cancer screening and drug development.

We developed the LC-iCAP, novel approach for liquid biopsies that uses cultured cells as biosensors. The cells detect cancer signals in serum and transduce them into standardized gene expression profiles, which are analyzed by machine learning for disease classification. The assay is inexpensive and scalable and has a multivariate readout with potential utility for precision medicine and multi-cancer early detection.A LC-iCAP-based lung cancer risk classifier demonstrated improved specificity compared to existing tests, suggesting meaningful clinical utility for managing indeterminate pulmonary nodules.We identified a lung-cancer specific transcriptional response to hypoxia in the assay readout, implicating HIF1A and HIF2A activity in the response consistent with known lung cancer biology and highlighting the platform's mechanistic relevance.Standardized controls and validation studies demonstrated assay reproducibility, lineage stability, and detection of technical errors-supporting the platform's readiness for clinical deployment.

Activation of type M2 macrophages has been implicated in the pathogenesis of chronic pancreatitis (CP). In a clinical pilot study, we investigated blood-based markers of macrophage activation at different stages of CP.

We performed a cross-sectional analysis of prospectively collected plasma samples from healthy controls and patients with suspected or definitive CP according to the M-ANNHEIM criteria. Plasma concentrations of soluble CD163 (sCD163), soluble CD206 (sCD206), and monocyte chemoattractant protein-1 (MCP-1) were analyzed using enzyme-linked immunosorbent assays. Group and pairwise comparisons of analytes were performed using regression models and area under the receiver operating curves (AUC-ROC).

In total, 73 subjects with CP (28 suspected CP and 45 definitive CP) and 40 controls were included. Compared to controls, the median plasma concentrations of sCD163 ( P  = 0.019) and sCD206 ( P  = 0.033) were elevated in patients with definitive CP. sCD206 was also elevated in patients with definitive CP ( P  = 0.042) compared to suspected CP. ROC analysis revealed the optimal sCD163 cutpoint to distinguish definitive CP from controls was 1.84 mg/mL (AUC-ROC 0.65; 95% confidence interval [CI], 0.54-0.77). The optimal sCD206 cutpoint to distinguish definitive CP from controls was 0.24 mg/mL (AUC-ROC 0.66; 95% CI, 0.54-0.78). MCP-1 concentrations showed no differences across subgroups.

Our study demonstrates that subjects with definitive CP, sampled during a clinically quiescent phase, exhibited increased levels of sCD163 and sCD206. This indicates the presence of activated M2 macrophages in patients with CP at advanced, but not early, clinical stages.

Hepatocellular carcinoma (HCC) risk stratification is an urgent unmet need for cost-effective HCC screening and early detection in patients with cirrhosis to improve poor HCC prognosis.

Molecular (prognostic liver secretome signature with α-fetoprotein) and clinical (aMAP [age, male sex, albumin-bilirubin, and platelets] score) variable-based scores were integrated into PAaM (prognostic liver secretome signature with α-fetoprotein plus age, male sex, albumin-bilirubin, and platelets), which was subsequently validated in 2 phase 3 biomarker validation studies: the statewide Texas HCC Consortium and nationwide HCC Early Detection Strategy prospective cohorts, following the prospective specimen collection, retrospective blinded evaluation design. The associations between baseline PAaM and incident HCC were assessed using Fine-Gray regression, with overall death and liver transplantation as competing events.

Of 2156 patients with cirrhosis in the Texas HCC Consortium, PAaM identified 404 (19%) high-risk, 903 (42%) intermediate-risk, and 849 (39%) low-risk patients with annual HCC incidence rates of 5.3%, 2.7%, and 0.6%, respectively. Compared with low-risk patients, high- and intermediate-risk groups had sub-distribution hazard ratios for incident HCC of 7.51 (95% CI, 4.42-12.8) and 4.20 (95% CI, 2.52-7.01), respectively. Of 1328 patients with cirrhosis in the HCC early detection strategy, PAaM identified 201 high-risk (15%), 540 intermediate-risk (41%), and 587 low-risk (44%) patients, with annual HCC incidence rates of 6.2%, 1.8%, and 0.8%, respectively. High- and intermediate-risk groups were associated with sub-distribution hazard ratios for incident HCC of 6.54 (95% CI, 3.85-11.1) and 1.77 (95% CI, 1.02-3.08), respectively. Subgroup analysis showed robust risk stratification across HCC etiologies, including metabolic dysfunction-associated steatotic liver disease and cured hepatitis C infection.

PAaM enables accurate HCC risk stratification in patients with cirrhosis from contemporary etiologies.

Gut dysbiosis is thought to be involved in the pathogenesis and progression of chronic kidney disease and end-stage kidney disease (ESKD). However, differences in the composition and function of gut microbiota in hemodialysis patients are not consistently concluded.

A total of 20 patients receiving maintenance hemodialysis (MHD) treatment at the Blood Purification Center of Bethune International Peace Hospital from March 2021 to December 2022 were included based on the inclusion criteria. Additionally, 20 healthy volunteers matched for age, gender, and body mass index were recruited from the Health Examination Center as the healthy control (HC) group. The structure of the gut microbiota community in the study subjects was analyzed using second-generation high-throughput sequencing technology based on 16S rRNA and amplicon sequence variants (ASV) analysis.

There were significant differences in gut microbial communities between the two groups. At the genus level, significant differences were found in 19 genera. Among them, Escherichia-Shigella, Lachnospira, Parasutterella, [Ruminococcus]-torques-group, Butyricicoccus, and Streptococcus were significantly decreased, while Phascolarctobacterium, Ruminococcaceae-UBA1819, Erysipelotrichaceae-UCG-003, Flavonifractor, and Erysipelatoclostridium were significantly increased in MHD patients. In particular, the abnormal decrease in the abundance of p-Proteobacteria.c-Gammaproteobacteria.o-Enterobacterales.f-Enterobacteriaceae.g-Escherichia-Shigella might be a significant characteristic of gut microbiota in MHD patients.

The decreased abundance of Escherichia-Shigella is a signature gut microbiota alteration in patients with ESKD undergoing MHD, and Escherichia-Shigella may represent a key bacterial group warranting exploration in the field of hemodialysis. The dysbiosis of gut microbiota holds promise as a therapeutic target and biomarker for the diagnosis and treatment of MHD.

In this systematic review we have sought to summarise the current knowledge concerning biomarkers that can distinguish between steroid-resistant nephrotic syndrome and steroid-sensitive nephrotic syndrome. Additionally, we aim to select biomarkers that have the best evidence-base and should be prioritised for further research. Pub med and web of science databases were searched using "steroid resistant nephrotic syndrome AND biomarker". Papers published between 01/01/2012 and 10/05/2022 were included. Papers that did not compare steroid resistant and steroid sensitive nephrotic syndrome, did not report sensitivity/specificity or area under curve and reviews/letters were excluded. The selected papers were then assessed for bias using the QUADAS-2 tool. The source of the biomarker, cut off, sensitivity/specificity, area under curve and sample size were all extracted. Quality assessment was performed using the BIOCROSS tool. 17 studies were included, comprising 15 case-control studies and 2 cross-sectional studies. Given the rarity of nephrotic syndrome and difficulty in recruiting large cohorts, case-control studies were accepted despite their limitations. We present a range of candidate biomarkers along with scores relating to the quality of the original publications and the risk of bias to inform future investigations. None of the selected papers stated whether the authors were blinded to the patient's disease when assessing the index test in the cohort. Highlighting a key problem in the field that needs to be addressed. These candidate biomarkers must now be tested with much larger sample sizes. Using new biobanks such as the one built by the NURTuRE-INS team will be very helpful in this regard.

Preeclampsia (PE) is a serious pregnancy complication that contributes to maternal and perinatal morbidity and mortality. Understanding its pathogenesis and revealing predictive biomarkers are essential for guiding treatment decisions. In order to explore the global changes of serum metabolites in PE patients and identify potential predictive biomarkers for suspected PE patients (pregnant women who had already shown PE-related symptoms in the middle to late stages of pregnancy, but were not yet confirmatively diagnosed as PE.), a large-scale serum metabolomic analysis was conducted in this study with a prospective cohort of 328 suspected PE patients in the middle or late pregnancy stages, as well as a retrospective cohort of 30 healthy pregnant women and 30 PE patients. Using liquid chromatography mass spectrometry (LC - MS), serum metabolomic profiling revealed that the development of PE was closely associated with disturbed amino acid metabolism. Moreover, a panel of seven predictive biomarkers including 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, gamma-glutamyl-leucine, 2-hydroxyvaleric acid, LysoPC(16:1(9Z)/0:0), PC(DiMe(13,5)/MonoMe(13,5)), ADP-D-glycero-beta-D-manno-heptose and phenylalanyl-tryptophan were identified for PE development by performing multiple statistical analysis and LASSO regression analysis. The combination of these biomarkers showed promise in the prediction of PE development for suspected PE patients, with an AUC of 0.753 and 0.885 for the discovery and validation cohorts, respectively. These findings highlight the potential of large-scale prospective metabolomic studies combined with machine learning algorithms in identifying key biomarkers for predicting PE development, while retrospective metabolomics studies provide insights into the pathogenesis of PE.

The original hepatocellular carcinoma early detection screening (HES) score, which combines alpha-fetoprotein (AFP) with age, alanine aminotransferase, and platelets, has better performance than AFP alone for early HCC detection. We have developed HES V2.0 by adding AFP-L3 and des-gamma-carboxy prothrombin to the score and compared its performance to GALAD and ASAP scores among patients with cirrhosis.

We conducted a prospective-specimen collection, retrospective-blinded-evaluation phase 3 biomarker cohort study in patients with cirrhosis enrolled in imaging and AFP surveillance. True-positive rate (TPR)/sensitivity and false-positive rate for any or early HCC were calculated for GALAD, ASAP, and HES V2.0 scores within 6, 12, and 24 months of HCC diagnosis. We calculated the AUROC curve and estimated TPR based on an optimal threshold at a fixed false-positive rate of 10%. We analyzed 2331 patients, of whom 125 developed HCC (71% in the early stages). For any HCC, HES V2.0 had higher TPR than GALAD overall (+7.2%), at 6 months (+3.6%), at 12 months (+7.2%), and 24 months (+13.0%) before HCC diagnosis. HES V2.0 had higher TPR than ASAP for all time points (+5.9% to +12.0%). For early HCC, HES V2.0 had higher sensitivity/TPR than GALAD overall (+6.7%), at 12 months (+6.3%), and 24 months (+14.6%) but not at 6 months (+0.0%) and higher than ASAP for all time points (+13.4% to +18.0%).

In a prospective cohort study, HES V2.0 had a significantly higher performance for identifying new HCC, including early stage, than GALAD or ASAP.

Background and Aims: This investigation examined the association of pancreatitis and pancreatitis-related pain with serum levels of two endocannabinoid molecules such as anandamide (AEA) and 2-arachidonoylglycerol (2-AG) and two paracannabinoid molecules such as oleoylethanolamide (OEA) and palmitoylethanolamide (PEA). Methods: A case-control study was conducted within the Prospective Evaluation of Chronic Pancreatitis for Epidemiological and Translational Studies, including participants with no pancreas disease (N = 56), chronic abdominal pain of suspected pancreatic origin or indeterminate chronic pancreatitis (CP) (N = 22), acute pancreatitis (N = 33), recurrent acute pancreatitis (N = 57), and definite CP (N = 63). Results: Circulating AEA concentrations were higher in women than in men (p = 0.0499), and PEA concentrations were higher in obese participants than those who were underweight/normal or overweight (p = 0.003). Asymptomatic controls with no pancreatic disease had significantly (p = 0.03) lower concentrations of AEA compared with all disease groups combined. The highest concentrations of AEA were observed in participants with acute pancreatitis, followed by those with recurrent acute pancreatitis, chronic abdominal pain/indeterminant CP, and definite CP. Participants with pancreatitis reporting abdominal pain in the past year had significantly (p = 0.04) higher concentrations of AEA compared with asymptomatic controls. Levels of 2-AG were significantly lower (p = 0.02) among participants reporting abdominal pain in the past week, and pain intensity was inversely associated with concentrations of 2-AG and OEA. Conclusions: Endocannabinoid levels may be associated with stage of pancreatitis, perhaps through activation of the CB1 receptor. Validation of our findings would support the investigation of novel therapeutics, including cannabinoid receptor-1 antagonists, in this patient population.

Determining the benign or malignant status of indeterminate pulmonary nodules (IPN) with intermediate malignancy risk is a significant clinical challenge. Oral microbiota-lung cancer (LC) interactions have qualified oral microbiota as a promising non-invasive predictive biomarker in IPN.

Prospectively collected saliva, throat swabs, and tongue coating samples from 1040 IPN patients and 70 healthy controls across three hospitals. Following up, the IPNs were diagnosed as benign (BPN) or malignant pulmonary nodules (MPN). Through 16S rRNA sequencing, bioinformatics analysis, fluorescence in situ hybridization (FISH), and seven machine learning algorithms (support vector machine, logistic regression, naïve Bayes, multi-layer perceptron, random forest, gradient-boosting decision tree, and LightGBM), we revealed the oral microbiota characteristics at different stages of HC-BPN-MPN, identified the sample types with the highest predictive potential, constructed and evaluated the optimal MPN prediction model for predictive efficacy, and determined microbial biomarkers. Additionally, based on the SHAP algorithm interpretation of the ML model's output, we have developed a visualized IPN risk prediction system on the web.

Saliva, tongue coating, and throat swab microbiotas exhibit site-specific characteristics, with saliva microbiota being the optimal sample type for disease prediction. The saliva-LightGBM model demonstrated the best predictive performance (AUC = 0.887, 95%CI: 0.865-0.918), and identified Actinomyces, Rothia, Streptococcus, Prevotella, Porphyromonas , and Veillonella as biomarkers for predicting MPN. FISH was used to confirm the presence of a microbiota within tumors, and external data from a LC cohort, along with three non-IPN disease cohorts, were employed to validate the specificity of the microbial biomarkers. Notably, coabundance analysis of the ecological network revealed that microbial biomarkers exhibit richer interspecies connections within the MPN, which may contribute to the pathogenesis of MPN.

This study presents a new predictive strategy for the clinic to determine MPNs from BPNs, which aids in the surgical decision-making for IPN.

Better surveillance tests for hepatocellular carcinoma (HCC) are needed. The GALAD score (gender, age, α-fetoprotein [AFP] L3, AFP, and des-γ carboxyprothrombin) has been shown to have excellent sensitivity and specificity for HCC in phase 2 studies. We performed a phase 3 biomarker validation study to compare GALAD with AFP in detecting HCC.

This is a prospective study of patients with cirrhosis enrolled at 7 centers. Surveillance for HCC was performed every 6 months at each site, and HCC diagnosis was confirmed per American Association for the Study of Liver Diseases guidelines. Blood for biomarker research was obtained at each follow-up visit and stored in a biorepository. Measurements of AFP, AFP-L3, and des-γ carboxyprothrombin) were performed in a FujiFilm laboratory by staff blinded to clinical data. The performance of GALAD in detecting HCC was retrospectively evaluated within 12 months before the clinical diagnosis. All analyses were conducted by an unblinded statistician in the Early Detection Research Network data management and coordinating center.

A total of 1,558 patients with cirrhosis were enrolled and followed for a median of 2.2 years. A total of 109 patients developed HCC (76 very early or early stage), with an annual incident rate of 2.4%. The areas under the curve for AFP and GALAD within 12 months before HCC were 0.66 and 0.78 (P < .001), respectively. Using a cutoff for GALAD of -1.36, the specificity was 82%, and the sensitivity at 12 months before HCC diagnosis was 62%. For comparison, performance of AFP at 82% specificity showed 41% sensitivity at 12 months before HCC diagnosis (P = .001).

GALAD score, compared to AFP, improves the detection of HCC within 12 months before the actual diagnosis.

Breast cancer (BC) is one of the most significant neoplasms globally due to its high incidence and mortality, particularly among females. As a highly heterogeneous pathology, biomarkers are essential for characterizing specific tumors. Currently, several biological processes are well-described in the context of this neoplasm, such as alterations in BRCA1/2, HER, and pathways involving estrogen and progesterone hormone receptors. These studies have enabled the use of these findings as more precise methods for diagnosis, prognosis, and treatment. However, beyond patients who do not exhibit these classic markers, some individuals within the same risk group respond differently to treatment. Therefore, the search for biological markers that can improve diagnosis, aid in stratification, or serve as therapeutic targets is continuous and urgent. Genetic signatures have led to molecular tests currently used in clinical practice, though certain limitations persist. Understanding genetic and epigenetic mechanisms facilitates the identification of potential biomarkers. Biomarker targets must undergo experimental and clinical trials on samples of significant size before reaching clinical utility. In this review, we compile the classical markers and describe the potential use of other markers associated with the biological processes of this neoplasm.

Golgi protein 73 (GP73) is implicated in key pathogenic processes, particularly those related to inflammation and fibrogenesis. In the last years, its measurement has emerged as a promising biomarker for detection of liver fibrosis (LF), a common consequence of chronic liver disease that can progress to cirrhosis and eventually hepatocellular carcinoma. GP73 concentrations in blood appear significantly increased in LF patients, correlating with disease severity, making this biomarker a possible non-invasive alternative for detecting and monitoring this condition regardless of etiology. Understanding the molecular mechanisms involving GP73 expression could also lead to new therapeutic strategies aimed at modulating its synthesis or function to prevent or reverse LF. Despite its clinical potential, GP73 as a LF biomarker faces several challenges. The lack of demonstrated comparability among different assays as well as the lack of knowledge of individual variability can make difficult the result interpretation. Further research is therefore needed focusing on robust clinical validation of GP73 as a LF biomarker. Addressing analytical, biological, and clinical limitations will be critical to exploiting its potential for improving detection and monitoring of advanced LF.

The standard treatment for acute graft-vs-host disease (GVHD), a common complication following allogeneic hematopoietic cell transplant, remains prolonged courses of high dose corticosteroids. Previous attempts to decrease corticosteroid exposure during GVHD therapy failed because physicians lack the tools necessary to safely reduce and shorten therapy and fear loss of GVHD control in responding patients. Prior studies have shown that a serum biomarker risk score, the MAGIC algorithm probability (MAP), provided prognostic value within groups with similar clinical severity and that patients with GVHD that is Minnesota standard risk by clinical symptoms and who have a low MAP at the start of corticosteroid treatment represent a low risk group with good outcomes.

This study tested the hypothesis that serial monitoring of GVHD symptoms and the MAP score in patients with low risk GVHD could provide further risk stratification, and would identify a subset with exceptionally low rates of failure with standard treatment, which we term ultra-low risk (ULR) GVHD who might benefit from reduced corticosteroid treatment.

Weekly monitoring of clinical symptoms and MAPs from initiation to day 14 of treatment was used to further divide 450 patients with low risk GVHD into groups with different outcomes, such as overall response rates at day 28 and non-relapse mortality at six-months.

310/450 low risk patients (69%) who achieved clinical response by day 14 and had low MAPs at days 7 and 14 constituted an ultra-low risk (ULR) group. that experienced a significantly higher overall response rate at day 28 (93% vs 50%, p<0.001) that was sustained to day 56 (84% vs 45%, p<0.001) and significantly lower six-month NRM (4% vs 13%, p<0.001) compared to the non-ULR patients. Patients who achieved clinical response by day 14 but who developed a high MAP during monitoring (n=20) experienced six-fold higher six-month NRM than the ULR group (25% vs 4%, p<0.001). Among 120 patients who did not achieve a clinical response by day 14, the overwhelming majority (n=112) who maintained low MAPs at both days 7 and 14 of treatment experienced six-fold lower NRM at six months compared to patients with a high MAP at either time point (8% vs 50%, p<0.001). The majority of deaths within the ULR group were due to infections in patients with complete and sustained control of GVHD symptoms while the majority of deaths in the non-ULR group were due to poorly controlled GVHD.

Serial monitoring during treatment can identify a large subset of patients by day 14 who achieve excellent GVHD control but remain at risk for treatment complications with standard treatment and who might be suitable candidates for testing abbreviated corticosteroid courses.

The objective of this study was to evaluate the use of telomere length measurements as diagnostic biomarkers during early screening for lung cancer in high-risk patients.

This was a prospective study of patients undergoing lung cancer diagnosis at two Spanish hospitals between April 2017 and January 2020. Telomeres from peripheral blood lymphocytes were analysed by Telomere Analysis Technology, which is based in high-throughput quantitative fluorescent in situ hybridization. Analytical predictive models were developed using Random Forest from the dataset of telomere-associated variables (TAV). Receiver Operating Characteristic curves were used to characterize model performance.

From 233 patients undergoing lung cancer diagnosis, 106 patients aged 55-75 with lung cancer or lung cancer and COPD were selected. A control group (N = 453) included individuals of similar age with COPD or healthy. Telomere analysis showed that patients in the cancer cohort had a higher proportion of short telomeres compared to the control cohort. A TAV-based predictive model assuming a prevalence of 5 % of lung cancer among screened subjects showed an AUC of 0.98 %, a positive predictive value of 0.60 (95 % CI, 0.49-0.70) and a negative predictive value of 0.99 (95 % CI, 0.98-0.99) for prediction of lung cancer.

The results of this study suggest that TAV analysis in peripheral lymphocytes can be considered a useful diagnostic tool during screening for lung cancer in high-risk patients. TAV-based models could improve the predictive power of current initial diagnostic pathways, but further work is needed to integrate them into routine clinical evaluation.

Life Length SL.

Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that is challenging to detect at an early stage. Biomarkers are needed that can detect PDAC early in the course of disease when interventions lead to the best outcomes. We highlight study design and statistical considerations that inform pancreatic cancer early detection biomarker evaluation.

We describe experimental design strategies in this setting useful for streamlining biomarker evaluation at each Early Detection Research Network (EDRN) phase of biomarker development. We break the early EDRN phases into sub-phases, proposing objectives, study design strategies, and biomarker performance benchmarks.

The goal of early detection in populations at high-risk of PDAC is described. Evaluating biomarker behavior in patients under surveillance without disease can winnow candidate biomarkers. Potential resources for biomarker validation studies are described.

Multisite and multidisciplinary collaboration can facilitate study design strategies in this lethal but low incidence disease and streamline the path from biomarker discovery to clinical use. Improvements in analytical and experimental design methods could help accelerate biomarker evaluation through the phases of biomarker development.

Acute graft-versus-host disease (GVHD) is a significant complication following hematopoietic stem cell transplantation (HCT). Although recent advancements in GVHD prophylaxis have resulted in successful HCT across HLA barriers and expanded access to HCT for racial minorities, less is known about how race affects the severity and outcomes of acute GVHD. This study examines differences in the clinical course of acute GVHD and the prognostic value of GVHD biomarkers for Black and White recipients. We conducted a retrospective analysis of patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) database who underwent HCT between 2014 and 2021 to describe the difference in clinical course of acute GVHD and significance of GVHD biomarkers between Black and White recipients. We used propensity score matching to generate a 1:3 matched cohort of 234 Black patients and 702 White patients with similar baseline characteristics. In the first year after HCT Black patients experienced a higher cumulative incidence of grade III-IV acute GVHD (17% versus 12%, P = 0.050), higher nonrelapse mortality (NRM; 18% versus 12%, P = .009), and lower overall survival that trended toward statistical significance (73% versus 79%, P = .071) compared to White patients. The difference in NRM in the first year was even greater among Black patients who developed GVHD than White patients (24% versus 14%, P = .041). The distribution of low, intermediate, and high MAGIC biomarker scores at the time of treatment was similar across racial groups (P = .847), however, Black patients with high biomarker scores experienced significantly worse NRM than White patients (71% versus 32%, P = .010). Our data indicate that Black patients are at a higher risk of NRM following HCT, primarily from a higher incidence of severe GVHD. Serum biomarkers at treatment initiation can stratify patients for risk of NRM across races, however Black patients with high biomarker scores had a significantly greater NRM risk. These results suggest a need for strategies that mitigate the higher risk for poor GVHD outcomes among Black patients.

Previous studies have reported higher circulating bile acid levels in patients with HCC compared to healthy controls. However, the association between prediagnostic bile acid levels and HCC risk among patients with cirrhosis is unclear.

We measured total BA (TBA) concentration in serum samples collected from a prospective cohort of patients with cirrhosis who were followed until the development of HCC, death, or last study date. Competing risk proportional hazard-adjusted models were used to estimate the association between tertiles of serum TBA levels and the risk of developing HCC. We quantified the incremental predictive value of serum bile acid when added to a previously validated clinical model.

We analyzed data from 940 patients with cirrhosis, of whom 68 patients progressed to HCC during 3406 person-years of follow-up. Higher baseline serum TBA level was significantly associated with an increased risk of developing HCC with an adjusted HR of 3.69 (95% CI = 1.85-7.37) for the highest versus lowest tertile. TBA levels significantly increased predictive ability for progression to HCC at 2 years of follow-up; the c statistic increased from 0.74 to 0.80 (p < 0.001). There was evidence for a significant interaction between TBA level and hepatitis C (p = 0.04).

In a large prospective cohort study, the prediagnostic serum level of TBAs was associated with a significant increase in the risk of developing HCC among patients with multi-etiology cirrhosis. The TBA-associated risk was additive to that of established demographic and clinical predictors.

Localized prostate tumors show significant spatial heterogeneity, with regions of high-grade disease adjacent to lower grade disease. Consequently, prostate cancer biopsies are prone to sampling bias, potentially leading to underestimation of tumor grade. To study the clinical, epidemiologic, and molecular hallmarks of this phenomenon, we conducted a prospective study of grade upgrading: differences in detected prostate cancer grade between biopsy and surgery.

We established a prospective, multi-institutional cohort of men with grade group 1 (GG1) prostate cancer on biopsy who underwent radical prostatectomy. Upgrading was defined as detection of GG2+ in the resected tumor. Germline DNA from 192 subjects was subjected to whole-genome sequencing to quantify ancestry, pathogenic variants in DNA damage response genes, and polygenic risk.

Of 285 men, 67% upgraded at surgery. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores were significantly associated with upgrading. No assessed genetic risk factor was predictive of upgrading, including polygenic risk scores for prostate cancer diagnosis.

In a cohort of patients with low-grade prostate cancer, a majority upgraded at radical prostatectomy. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores portended the presence of higher-grade disease, while germline genetics was not informative in this setting. Patients with low-risk prostate cancer, but elevated PSA density or percent cancer in positive biopsy cores, may benefit from repeat biopsy, additional imaging or other approaches to complement active surveillance.

Further risk stratification of patients with low-risk prostate cancer may provide useful context for active surveillance decision-making.

To evaluate diagnostic tests for low prevalence conditions, classification accuracy metrics such as sensitivity, specificity, and positive likelihood ratio (PLR) and negative likelihood ratio (NLR) are advantageous because they are prevalence-independent and thus estimable in studies enriched for the condition. However, classification accuracy goals are often chosen without a clear understanding of whether they are clinically meaningful. Pennello (2021) proposed a risk stratification framework for determining classification accuracy goals. A software application is needed to determine the goals and provide data analysis.

We introduce DxGoals, a freely available, R-Shiny software application for determining, visualizing, and analyzing classification accuracy goals for diagnostic tests. Given prevalence p for the target condition and specification that a test's positive and negative predictive values PPVand NPV=1-cNPV should satisfy PPV>PPV* and cNPV

RESULTS

We illustrate DxGoals on tests for penicillin allergy, ovarian cancer, and cervical cancer. The inputs cNPV*,p, and PPV* were informed by clinical management guidelines.

CONCLUSIONS

DxGoals facilitates determination, visualization, and analysis of clinically meaningful standalone and comparative classification accuracy goals. It is a potentially useful tool for diagnostic test evaluation.

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Before implementing a biomarker in routine clinical care, it must demonstrate clinical utility by leading to clinical actions that positively affect patient-relevant outcomes. Randomly controlled early detection utility trials, especially those targeting mortality endpoint, are challenging due to their high costs and prolonged duration. Special design considerations are required to determine the clinical utility of early detection assays. This commentary reports on discussions among the National Cancer Institute's Early Detection Research Network investigators, outlining the recommended process for carrying out single-organ biomarker-driven clinical utility studies. We present the early detection utility studies in the context of phased biomarker development. We describe aspects of the studies related to the features of biomarker tests, the clinical context of endpoints, the performance criteria for later phase evaluation, and study size. We discuss novel adaptive design approaches for improving the efficiency and practicality of clinical utility trials. We recommend using multiple strategies, including adopting real-world evidence, emulated trials, and mathematical modeling to circumvent the challenges in conducting early detection utility trials.

To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based methodologies to assess if multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies.

A nested case-control design evaluated the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location.

Three university hospitals.

A total of 218 individuals with pain and/or bleeding in early pregnancy: 75 had an ongoing intrauterine gestation; 68 had ectopic pregnancies (EPs); and 75 had miscarriages.

Serum levels of 24 biomarkers were assessed in the same patients. Multiple machine learning-based methodologies to evaluate combinations of these top candidates to develop a multiplexed prediction model for the identification of a nonviable pregnancy (ongoing intrauterine pregnancy vs. miscarriage or EP) and an EP (EP vs. ongoing intrauterine pregnancy or miscarriage).

The predicted classification using each model was compared with the actual diagnosis, and sensitivity, specificity, positive predictive value, negative predictive value, conclusive classification, and accuracy were calculated.

Models using classification regression tree analysis using 3 (pregnancy-specific beta-1-glycoprotein 3 [PSG3], chorionic gonadotropin-alpha subunit, and pregnancy-associated plasma protein-A) biomarkers were able to predict a maximum sensitivity of 93.3% and a maximum specificity of 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of 3 (soluble fms-like tyrosine kinase-1, PSG3, and tissue factor pathway inhibitor 2) biomarkers achieved a maximum sensitivity of 98.5% and a maximum specificity of 95.3%. The model with the highest accuracy was 94.4% (with 65.6% receiving classification). When the models were used simultaneously, the conclusive classification increased to 72.7% with an accuracy of 95.9%. The predictive ability of the biomarkers in the random forest produced similar test characteristics when using 11 predictive biomarkers.

We have demonstrated a pool of biomarkers from divergent biological pathways that can be used to classify individuals with potential early pregnancy loss. The biomarkers choriogonadotropin alpha, pregnancy-associated plasma protein-A, and PSG3 can be used to predict viability, and soluble fms-like tyrosine kinase-1, tissue factor pathway inhibitor 2, and PSG3 can be used to predict pregnancy location.