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.

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.

Ductal features alone may not offer high diagnostic sensitivity or most accurate disease severity of chronic pancreatitis (CP).

Diagnose CP based on multiparametric MRI and MRCP features.

Prospective.

Between February 2019 and May 2021, 46 control (23 males, 49.3 ± 14.1 years), 45 suspected (20 males, 48.7 ± 12.5 years), and 46 definite (20 males, 53.7 ± 14.6 years) CP patients were enrolled at seven hospitals enrolled in the MINIMAP study. CP classification was based on imaging findings and clinical presentation.

1.5 T. T1-weighted (T1W) spoiled gradient echo, T1 map with variable flip angle, dual-echo Dixon, secretin-enhanced MRCP before and after secretin infusion.

Dual-echo fat fraction (FF), T1 relaxation time, extracellular volume (ECV), T1 signal intensity ratio of the pancreas to the spleen (T1 score), arterial-to-venous enhancement ratio (AVR), pancreatic tail diameter (PTD), pancreas volume, late gadolinium enhancement, pancreatic ductal elasticity (PDE), and duodenal filling grade of secretin-enhanced MRCP were measured.

Logistic regression analysis generated CP-MRI and secretin-enhanced CP-SMRI scores. Receiver operating characteristics analysis was used to differentiate definite CP from control. Interobserver agreement was assessed using Lin's concordance correlation coefficient.

Compared to control, definite CP cohort showed significantly higher dual-echo FF (7% vs. 11%), lower AVR (1.35 vs. 0.85), smaller PTD (2.5 cm vs. 1.95 cm), higher ECV (28% vs. 38%), and higher incidence of PDE loss (6.5% vs. 50%). With the cut-off of >2.5 CP-MRI score (dual-echo FF, AVR, and PTD) and CP-SMRI score (dual-echo FF, AVR, PTD, and PDE) had cross-validated area under the curves of 0.84 (sensitivity 87%, specificity 68%) and 0.86 (sensitivity 89%, specificity 67%), respectively. Interobserver agreement for both CP-MRI and CP-SMRI scores was 0.74.

The CP-MRI and CP-SMRI scores yielded acceptable performance and interobserver agreement for the diagnosis of CP.

1 TECHNICAL EFFICACY: Stage 2.

Abbreviated MRI (AMRI) has been proposed as an alternative to ultrasound for hepatocellular carcinoma (HCC) surveillance; however, comparative data for AMRI and ultrasound are needed. Thus, we evaluated the sensitivity and specificity of dynamic contrast-enhanced (DCE)-AMRI and ultrasound for early-stage HCC detection in patients with cirrhosis.

We conducted a multicenter retrospective case-control study among patients with cirrhosis (cases with early-stage HCC as per Milan Criteria; controls without HCC) who underwent an ultrasound and a DCE-MRI within a 6-month period between 2012 and 2019. HCC diagnosis was confirmed by imaging alone in 85% and by histopathology in 15% of patients. Dynamic AMRI examinations were simulated from the full MRI by selecting relevant sequences. Independent, blinded interpretations of ultrasounds and AMRI results were performed using Liver Imaging Reporting and Data System algorithms. Ultrasounds were considered positive if US-3 observations were detected. AMRI was considered positive if LR-4, LR-5, or LR-M were detected. Per-patient sensitivity and specificity for early-stage HCC detection were estimated, and cross-modality differences were tested.

We included 216 cases and 432 controls. Patient-level sensitivity and specificity of AMRI were significantly higher compared with ultrasound: 80.1% (95% CI 76.1-83.6) vs. 71.1% (95% CI 66.6-75.2), p <0.001, and 91.9% (95% CI 89.9-93.5) vs. 72.3% (95% CI 69.3-75.2), p <0.001, respectively. AMRI sensitivity was significantly higher compared with ultrasound among patients with Child-Pugh B cirrhosis (80.8% vs. 57.4%, p <0.001) but not among those with Child-Pugh A (84.7% vs. 78.6%, p = 0.07) or Child-Pugh C cirrhosis (52.6% vs. 68.4%, p = 0.18).

Dynamic AMRI may be more sensitive and specific for early-stage HCC detection in patients with cirrhosis compared with ultrasound, although its relative benefit might be smaller in patients with Child-Pugh A cirrhosis. Larger direct comparative data sets are needed, particularly among patients with Child-Pugh C cirrhosis who may benefit from alternative surveillance strategies.

Abbreviated MRI (AMRI) is increasingly recognized as an alternative to ultrasound for hepatocellular carcinoma (HCC) surveillance. However, existing data are limited by single-center samples, spectrum bias, and lack of comparative data for AMRI vs. ultrasound. We found that AMRI had significantly higher per-patient sensitivity and specificity compared with ultrasound for the detection of early-stage HCC, although its relative benefit might be smaller in patients with Child-Pugh A cirrhosis, and both modalities underperformed in patients with Child-Pugh C cirrhosis. If sufficiently validated, AMRI could be adopted into practice guidelines for HCC surveillance and serve as a preferred alternative in select subgroups of patients.

Hematological malignancies often lack defined risk factors and present with non-specific symptoms, underscoring the urgent need for simple and reliable detection methods. To address this challenge, Hirotsu et al. innovated N-NOSE, a novel, non-invasive cancer screening test that utilizes the chemotaxis response of the nematode Caenorhabditis elegans to detect tumor-related odors in urine. In this clinical study, we assessed the performance of N-NOSE in patients with various hematological malignancies at diagnosis and during treatment. Urine samples were collected from 30 healthy individuals and 89 patients, including those with leukemia (n = 13), malignant lymphoma (n = 53), multiple myeloma (n = 15), primary AL amyloidosis (n = 3), Waldenström's macroglobulinemia (n = 2), myelodysplastic syndrome (n = 2), and blastic plasmacytoid dendritic cell neoplasm (n = 1). Based on the optimal cut-off values in detecting hematological malignancies, N-NOSE demonstrated high positivity rates in treatment-naïve patients: leukemia and multiple myeloma were very high (over 90%), whereas malignant lymphoma was slightly lower than 80%. In the small subset of malignant lymphoma patients who tested N-NOSE-negative, confounding factors included steroid administration and hemodialysis. Importantly, no significant correlation emerged between N-NOSE index values and baseline characteristics or comorbidities other than the presence of cancer. Moreover, in all 32 patients who achieved clinical response following chemotherapy, the N-NOSE index declined, reflecting disease status. These findings highlight N-NOSE's strong potential as a sensitive, non-invasive screening tool for hematological malignancies-particularly multiple myeloma-and support its use in initial detection and monitoring of therapeutic response.

Actionable candidates of hepatocarcinogenesis remain elusive, and tools for early detection are suboptimal. Our aim was to demonstrate that serum metabolome profiles reflect the initiation of hepatocellular carcinoma (HCC) and enable the identification of biomarkers for early HCC detection and actionable candidates for chemoprevention.

This global cohort study included 654 patients and 801 biospecimens. Following serum metabolome profiling across the spectrum of hepatocarcinogenesis, we conducted a phase II biomarker case-control study for early HCC detection. Findings were independently validated through in silico analysis, mRNA sequencing, and proteome profiling of primary HCC and non-tumoral tissue, and in vitro experiments.

Aspartic acid, glutamic acid, taurine, and hypoxanthine were differentially abundant in the serum across chronic liver disease, cirrhosis, initial HCC, and progressed HCC, independent of sex, age, and etiology. In a phase II biomarker case-control study, a blood-based metabolite signature yielded an AUC of 94% to discriminate between patients with early-stage HCC and controls with cirrhosis, including independent validation. Unsupervised biclustering (MoSBi), lipid network analysis (LINEX2), and pathway enrichment analysis confirmed alterations in amino acid-, lipid-, and nucleotide-related pathways. In tumor tissue, these pathways were significantly deregulated regarding gene and protein expression in two independent datasets, including actionable targets RRM2, GMPS, BCAT1, PYCR2, and NEU1. In vitro knockdown confirmed a functional role in proliferation and migration, as exemplified for PYCR2.

These findings demonstrate that serum metabolome profiling indicates deregulated metabolites and pathways during hepatocarcinogenesis. Our liquid biopsy approach accurately detects early-stage HCC outperforming currently recommended surveillance tools and facilitates identification of actionable candidates for chemoprevention.

Deregulated cellular metabolism is a hallmark of cancer. In smaller studies, circulating metabolite profiles have been associated with HCC, although mainly in the context of fatty liver disease. Translation strategies for primary prevention or early detection are lacking. In this global study, we present an unsupervised landscape of the altered serum metabolome profile during hepatocarcinogenesis, independent of age, sex, and etiology. We provide a blood-based metabolite signature that accurately identifies early-stage HCC in a phase II biomarker study including independent validation. Further RRM2, GMPS, BCAT1, PYCR2, and NEU1 are identified in tumor tissue as actionable candidates for prevention. Our data provide the rationale for clinical trials testing liquid biopsy metabolome-based signatures for early HCC detection and the development of chemoprevention strategies.

MRI is a potential alternative to ultrasound for hepatocellular carcinoma (HCC) detection. We evaluated the impact of ultrasound and dynamic abbreviated MRI (AMRI) exam quality on early-stage HCC detection.

We conducted a multicenter case-control study among patients with cirrhosis (cases with early-stage HCC per Milan Criteria; controls without HCC) who underwent both a liver ultrasound and dynamic contrast-enhanced (DCE) AMRI within 6 months in 2012-2019. Two radiologists performed independent, blinded interpretations of both exams for HCC detection and scored exam quality as no/mild, moderate, or severe limitations. Associations between exam quality, patient characteristics, and HCC detection were assessed by odds ratios (OR).

Of 216 cases and 432 controls, severe limitations were reported in 7% and 8% of ultrasounds and DCE-AMRIs, respectively. Severe limitations at ultrasound were associated with obesity (OR 2.08, 95%CI [1.32-3.32]) and metabolic dysfunction-associated steatotic liver disease (MASLD) (OR 1.98 [1.12-3.44]) but not for DCE-AMRI. Decompensated cirrhosis (Child-Pugh C) was associated with severe limitations for both ultrasound (OR 2.54 [1.37-4.58]) and DCE-AMRI (OR 3.96 [2.36-6.58]). Compared to exams with no/mild limitations, exams with severe limitations had lower sensitivity for ultrasound (79.6% vs. 21.7%, P < 0.001) and AMRI (86.1% vs. 50.0%, P = 0.001). In patients in whom ultrasound was severely limited, DCE-AMRI had significantly higher odds of early-stage HCC detection than ultrasound (OR 8.23 [1.25-54.02]).

HCC detection by DCE-AMRI may be preferred in patients with severe limitations at ultrasound due to obesity and MASLD. Both modalities remain limited for patients with decompensated cirrhosis, for whom alternative strategies may be needed.

This study explores new screening strategies to enhance liver cancer screening effectiveness. In a prospective study, 2605 participants underwent baseline, 6-months self-reported, and 1-year follow-up screenings using abdominal ultrasonography, AFP, AFP-L3%, and DCP. The results demonstrated the GALADUS protocol exhibited superior performance with higher AUC (0.935 vs. 0.836; DeLong P < 0.001), sensitivity (91.0% vs. 70.8%; P < 0.001), detection (3.1% vs. 2.4%; P < 0.001), and early diagnosis rates (64.2% vs. 58.7%) compared to the AFP/US protocol. Notably, among individuals with an aMAP score ≥ 60, GALADUS had significantly outperformed AFP/US in AUC (0.923 vs. 0.826; DeLong P < 0.001), sensitivity (94.2% vs. 69.6%; P < 0.001), detection (9.7% vs. 7.2%; P < 0.001), and early diagnosis rates (63.1% vs. 54.2%). However, for those with an aMAP score < 60, GALADUS offered no significant advantages. Introducing the "aMAP triage" protocol, combining GALADUS for aMAP ≥ 60 and AFP/US for aMAP < 60, further enhanced AUC to 0.925 (DeLong P < 0.001), improved sensitivity by 19.1% (89.9% vs. 70.8%; P < 0.001), and increased detection (3.1% vs. 2.4%; P < 0.001) and early diagnosis rates (65.0% vs. 58.7%), being cost-effective compared to GALADUS. In conclusion, this study highlights the potential of a stratified precision screening strategy in identifying high-risk individuals, applying tailored early detection protocols to improve liver cancer screening efficacy.

For many cancers, biomarkers have served as an important tool across the cancer care continuum from risk stratification and early detection to diagnosis and treatment. Alpha-fetoprotein (AFP) remains one of the few validated biomarkers for patients with HCC. Although AFP has shown potential for each of these steps, its performance, when used alone, has often been suboptimal. There continue to be discordant recommendations about AFP's value when combined with ultrasound for surveillance, as well as its role in diagnostic algorithms. Conversely, high AFP levels are associated with aggressive tumor biology and survival, so it remains a key factor for the selection of candidates for liver transplant. There have been immense efforts to identify and validate additional biomarkers for each of these steps in the HCC care continuum. Indeed, biomarker panels have shown promising data for HCC risk stratification and surveillance among patients with cirrhosis, as well as prognostication and detection of minimal residual disease in patients undergoing HCC treatment. Several large prospective studies are currently ongoing to evaluate the role of these emerging biomarkers in clinical practice.

Hepatocellular carcinoma (HCC) is one of the five most common cancers and the second leading cause of cancer-related death worldwide. In this study, three monoclonal antibodies were developed for the early detection of HCC. The enzyme-linked immunosorbent assay (ELISA) method is used to detect antigens causing HCC. The final working dilutions of the coated antigen, monoclonal antibody, and enzyme-labeled secondary antibody were determined to be 1:5, 1:100, and 1:15,000, respectively. The optimal dilution of blocking buffer was 1.5% BSA phosphate buffer. The cutoff values were determined to be 0.1989, 0.2539, and 0.3059 for the Des-gamma carboxyprothrombin (DCP), Alpha-fetoprotein (AFP) and Thioredoxin (TXN) antigens, respectively. There is no cross-reaction between antigens and antibodies of different types. The coincidence rates between the indirect ELISA and commercial kits for detecting DCP, AFP, and TXN antigens were 95.24%, 95.24%, and 96.83%, respectively. In addition, a procedure to detect genes encoding TXN, DCP, and AFP via PCR has been developed. The results of the indirect ELISA and PCR methods are similar. In summary, we successfully constructed an indirect ELISA method to detect HCC-causing antigens via three monoclonal antibodies and designed primers to amplify HCC-causing gene fragments, which can be used for diagnosis and screening in clinical medicine.

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.

Early identification of malignant biliary strictures (MBSs) is challenging, with up to 20% classified as indeterminants after preliminary testing and tissue sampling with endoscopic retrograde cholangiopancreatography. We aimed to evaluate the use of methylated DNA markers (MDMs) from biliary brushings to enhance MBS detection in a prospective cohort.

Candidate MDMs were evaluated for their utility in MBS diagnosis through a series of discovery and validation phases. DNA was extracted from biliary brushing samples, quantified, bisulfite-converted, and then subjected to methylation-specific droplet digital polymerase chain reaction.  Patients were considered to have no malignancy if the sampling was negative and there was no evidence of malignancy after 1 year or definitive negative surgical histopathology.

Fourteen candidate MDMs were evaluated in the discovery phase, with top-performing and new markers evaluated in the technical validation phase. The top 4 MDMs were TWIST1, HOXA1, VSTM2B, and CLEC11A, which individually achieved AUC values of 0.82, 0.81, 0.83, and 0.78, respectively, with sensitivities of 59.4%, 53.1%, 62.5%, and 50.0%, respectively, at high specificities for malignancy of 95.2%-95.3% for the final biologic validation phase. When combined as a panel, the AUC was 0.86, achieving 73.4% sensitivity and 92.9% specificity, which outperformed cytology and fluorescence in situ hybridization (FISH).

The selected MDMs demonstrated improved performance characteristics for the detection of MBS compared to cytology and FISH. Therefore, MDMs should be considered viable candidates for inclusion in diagnostic testing algorithms.

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.

Lymphoma is one of the leading causes of cancer and cancer deaths and yet has not been amenable to population screening. The role of methylated DNA markers (MDMs) in the detection of lymphoma has not been extensively studied. We aimed to discover, validate, and test tissue-derived MDMs of lymphoma in archival plasma specimens. Reduced representation bisulfite sequencing (RRBS) was performed on a discovery set of frozen tissues. MDMs identified were converted to methylation-specific PCR assays and validated on independent formalin-fixed, paraffin-embedded (FFPE) tissues. Target enrichment long-probe quantitative-amplified signal (TELQAS) assays were developed and assayed in plasma-extracted, bisulfite-converted DNA from independent treatment-naïve lymphoma patients and healthy controls. Prediction of cancer status was modeled using random forest model with in silico cross-validation. After discovery and validation in tissue, 16 TELQAS assays (ZNF503, VWA5B1, HOXA9, GABRG3, ITGA5, MAX.chr17.7190, BNC1, CDK20, MAX.chr4.4069, TPBG, DNAH14, SYT6, CACNG8, FAM110B, ADRA1D, and NRN1) were selected for testing in plasma. These detected 78% (95% CI, 74%-82%) of lymphoma cases at 90% specificity. Excluding marginal zone and T-cell lymphomas, sensitivity increased to 84% (80%-88%). MDMs in plasma show promise to detect lymphoma and are candidates for inclusion in multi-cancer detection studies.

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.

The global incidence of bladder cancer is more than half a million diagnoses each year. Bladder cancer can be categorized into non-muscle-invasive bladder cancer (NMIBC), which accounts for ~75% of diagnoses, and muscle-invasive bladder cancer (MIBC). Up to 45% of patients with NMIBC develop disease progression to MIBC, which is associated with a poor outcome, highlighting a clinical need to identify these patients. Current risk stratification has a prognostic value, but relies solely on clinicopathological parameters that might not fully capture the complexity of disease progression. Molecular research has led to identification of multiple crucial players involved in NMIBC progression. Identified biomarkers of progression are related to cell cycle, MAPK pathways, apoptosis, tumour microenvironment, chromatin stability and DNA-damage response. However, none of these biomarkers has been prospectively validated. Reported gene signatures of progression do not improve NMIBC risk stratification. Molecular subtypes of NMIBC have improved our understanding of NMIBC progression, but these subtypes are currently unsuitable for clinical implementation owing to a lack of prospective validation, limited predictive value as a result of intratumour subtype heterogeneity, technical challenges, costs and turnaround time. Future steps include the development of consensus molecular NMIBC subtypes that might improve conventional clinicopathological risk stratification. Prospective implementation studies of biomarkers and the design of biomarker-guided clinical trials are required for the integration of molecular biomarkers into clinical practice.

Herein, we report a label-free cancer biosensor designed for carcinoembryonic antigen (CEA) detection using a nanohybrid comprising CeO2 nanoparticles, carbon nanoparticles (CNPs), and chitosan (Ch). CeO2 nanoparticles were prepared using a simple green synthesis process. A thin film of the CeO2-CNPs-Ch nanohybrid was formed on indium tin oxide (ITO)-coated glass plates that endowed a high surface area, excellent stability, and good adsorption for the efficient loading of CEA antibodies. Quantitative and selective determination of CEA antigen was achieved by immobilizing monoclonal CEA antibodies (anti-CEA) on the CeO2-CNPs-Ch/ITO platform. The electrochemical response of the anti-CEA/CeO2-CNPs-Ch/ITO immunoelectrode was evaluated in a label-free immunoassay format using differential pulse voltammetry (DPV). The response studies of immunoelectrodes indicated wider linearity with respect to the CEA concentration in the range of 0.05-100 ng mL-1. The electrochemical cancer biosensor exhibited a higher sensitivity of 22.40 μA cm-2 per decade change in concentration along with storage stability for up to 35 days. The limit of detection (LOD) was 0.037 ng mL-1. Furthermore, this cancer biosensor exhibited good specificity and reproducibility. Thus, the proposed CeO2-CNPs-Ch nanocomposite-based platform provides an efficient method for the analysis of other antigen-antibody interactions and biomolecule detection. The efficacy of the anti-CEA/CeO2-CNPs-Ch/ITO immunoelectrode was further examined by measuring CEA levels in human serum.

Multi-cancer early detection (MCED) tests are already being marketed as noninvasive, convenient opportunities to test for multiple cancer types with a single blood sample. The technology varies-involving detection of circulating tumor DNA, fragments of DNA, RNA, or proteins unique to each targeted cancer. The priorities and tradeoffs of reaching diagnostic resolution in the setting of possible false positives and negatives remain under active study. Given the well-established role of imaging in lesion detection and characterization for most cancers, radiologists have an essential role to play in selecting diagnostic pathways, determining the validity of test results, resolving false-positive MCED test results, and evaluating tradeoffs for clinical policy. Appropriate access to and use of imaging tests will also factor into clinical guidelines. Thus, all clinicians potentially involved with MCED tests for cancer screening will need to weigh the benefits and harms of MCED testing, including consideration of how the tests will be used alongside or in place of other screening options, how diagnostic confirmation tests should be selected, and what the implications are for policy and reimbursement decisions. Further, patients will need regular support to make informed decisions about screening using MCED tests in the context of their personal cancer risks, health-related values, and access to care.

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.

The aim of this study was to explore the microbial variations and biomarkers in the oral environment of patients with persistent pulmonary nodules (pPNs) and to reveal the potential biological functions of the salivary microbiota in pPNs.

This study included a total of 483 participants (141 healthy controls and 342 patients with pPNs) from June 2022 and January 2024. Saliva samples were subjected to sequencing of the V3-V4 region of the 16S rRNA gene to assess microbial diversity and differential abundance. Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. The Shapley Additive exPlanations (SHAP) algorithm was employed to explain the contribution of these core microbiotas to the predictive model. Additionally, the PICRUSt2 algorithm was used to predict the microbial functions.

The salivary microbial composition in pPNs group showed significantly lower α- and β-diversity compared to healthy controls. A high-accuracy LightGBM model was developed, identifying six core genera-Fusobacterium, Solobacterium, Actinomyces, Porphyromonas, Atopobium, and Peptostreptococcus-as pPNs biomarkers. Additionally, a visualization pPNs risk prediction system was developed. The immune responses and metabolic activities differences in salivary microbiota between the patients with pPNs and healthy controls were revealed.

This study highlights the potential clinical applications of the salivary microbiota for enable earlier detection and targeted interventions, offering significant promise for advancing clinical management and improving patient outcomes in pPNs.

Alzheimer's disease (AD) affects a significant portion of the aging population, presenting a serious challenge due to the limited availability of effective therapies during its progression. The disease advances rapidly, underscoring the need for early diagnosis and the application of preventative measures. Current diagnostic methods for AD are often expensive and invasive, restricting access for the general public. One potential solution is the use of biomarkers, which can facilitate early detection and treatment through objective, non-invasive, and cost-effective evaluations of AD. This review critically investigates the function and role of biofluid biomarkers in detecting AD, with a specific focus on cerebrospinal fluid (CSF), blood-based, and saliva biomarkers.

CSF biomarkers have demonstrated potential for accurate diagnosis and valuable prognostic insights, while blood biomarkers offer a minimally invasive and cost-effective approach for diagnosing cognitive issues. However, while current biomarkers for AD show significant potential, none have yet achieved the precision needed to replace expensive PET scans and CSF assays. The lack of a single accurate biomarker underscores the need for further research to identify novel or combined biomarkers to enhance the clinical efficacy of existing diagnostic tests. In this context, artificial intelligence (AI) and deep-learning (DL) tools present promising avenues for improving biomarker analysis and interpretation, enabling more precise and timely diagnoses.

Further research is essential to confirm the utility of all AD biomarkers in clinical settings. Combining biomarker data with AI tools offers a promising path toward revolutionizing the personalized characterization and early diagnosis of AD symptoms.

Artificial intelligence (AI) has numerous applications in radiology. Clinical research studies to evaluate the AI models are also diverse. Consequently, diverse outcome metrics and measures are employed in the clinical evaluation of AI, presenting a challenge for clinical radiologists. This review aims to provide conceptually intuitive explanations of the outcome metrics and measures that are most frequently used in clinical research, specifically tailored for clinicians. While we briefly discuss performance metrics for AI models in binary classification, detection, or segmentation tasks, our primary focus is on less frequently addressed topics in published literature. These include metrics and measures for evaluating multiclass classification; those for evaluating generative AI models, such as models used in image generation or modification and large language models; and outcome measures beyond performance metrics, including patient-centered outcome measures. Our explanations aim to guide clinicians in the appropriate use of these metrics and measures.

Professional guidelines recommend HCC screening in at-risk patients using semi-annual ultrasound with or without alpha-fetoprotein (AFP); however, this strategy has limited effectiveness due to low adherence and sensitivity. Increasing data support the potential role of blood-based biomarker panels, which could improve both aspects. The biomarker panel GALAD, comprised of sex, age, and 3 blood biomarkers (AFP, AFP-L3, and des-carboxy prothrombin des-carboxy prothrombin), has shown high sensitivity and specificity in biomarker phase II (case-control) and phase III (retrospective cohort) validation studies. However, prospective validation in a large phase IV biomarker clinical utility trial is necessary before its adoption in practice.

The National Liver Cancer Screening Trial is an adaptive pragmatic randomized phase IV trial, which began enrollment in January 2024, comparing ultrasound-based versus biomarker-based screening in 5500 patients with chronic hepatitis B infection or cirrhosis from any etiology. Eligible patients are randomly assigned in a 1:1 ratio to semi-annual screening with ultrasound ± alpha-fetoprotein (arm A) or semi-annual screening with GALAD (arm B). Randomization is stratified by enrollment site, liver disease severity (per Child-Pugh class), liver disease etiology (viral, nonviral, and noncirrhotic HBV), and sex. Patients are being recruited from 15 sites (a mix of tertiary care academic referral centers, safety-net health systems, and large community health systems) over a 3-year period, and the primary endpoint, reduction in late-stage HCC, will be assessed at the end of year 5.5.

The results of this trial will inform the best strategy for HCC screening and early-stage detection in patients with chronic liver diseases. If GALAD shows superiority, HCC screening would primarily shift from an ultrasound-based strategy to the adoption of the biomarker panel.

NCT06084234.

The TRACER Study is actively enrolling.

Cancer remains a leading cause of morbidity and mortality worldwide, necessitating the ongoing investigation of molecular targets for improved diagnosis, prognosis, and therapy. Among these targets, RNA modifications, particularly N5-methylcytosine (m5C) in RNA, have emerged as critical regulators of gene expression and cellular functions. NOP2/Sun RNA methyltransferase family member 2 (NSUN2) is a key enzyme in m5C modification, significantly influencing various biological processes and tumorigenesis. NSUN2 methylates multiple RNA species, including transfer RNAs (tRNAs), messenger RNAs (mRNAs), and non-coding RNAs, impacting RNA stability, translation efficiency, and cellular stress responses. These modifications, in turn, affect cell proliferation, differentiation, and survival. In cancer, NSUN2 is frequently upregulated, associated with aggressive tumor phenotypes, poor prognosis, and therapy resistance. Its role in oncogenic signaling pathways further underscores its importance in cancer biology. This review offers a comprehensive overview of NSUN2's role in cancer, focusing on its involvement in RNA methylation and its implications for tumor initiation and progression. Additionally, we explore the potential of NSUN2 as a biomarker for cancer diagnosis and prognosis, and its promise as a therapeutic target.

Hepatocellular carcinoma (HCC) is one of the few cancers with a 5-year survival that has remained below 20%; however, prognosis differs by tumor stage at diagnosis. Curative treatment options among patients with early-stage HCC afford a median survival of 5-10 years. Accordingly, international society guidelines recommend semi-annual HCC surveillance in at-risk patients, including those with cirrhosis or high-risk chronic hepatitis B infection. Surveillance is associated with increased early-stage HCC detection and curative treatments, leading to reduced HCC-related mortality. Abdominal ultrasound has been the cornerstone for HCC surveillance for the past two decades, but recent data have highlighted its suboptimal sensitivity for early-stage HCC detection, particularly in patients with obesity and those with non-viral etiologies of liver disease. The combination of ultrasound plus alpha fetoprotein (AFP) has higher sensitivity for early-stage HCC detection than ultrasound alone, although the combination still misses over one-third of HCC at an early stage. Emerging imaging and blood-based biomarker strategies have promising data in biomarker phase 2 (case-control) and phase 3 (cohort) studies. Beyond ultrasound, Magnetic resonance imaging (MRI) is the best-studied imaging strategy, with superior sensitivity and specificity compared to ultrasound in a cohort study. Abbreviated MRI protocols have been proposed to address concerns about MRI radiological capacity, costs, and patient acceptance. Of biomarker strategies, GALAD (a panel including gender, age, AFP, AFP-L3, and DCP) is the best validated, with promising sensitivity for early-stage HCC detection in a national multi-center cohort study. Liquid biopsy biomarkers, including methylated DNA markers, have also shown promising accuracy in case-control studies. Abbreviated MRI and GALAD are now entering prospective trials that examine clinical outcomes such as early-stage HCC detection and screening-related harms, which are essential data to understand for adoption in clinical practice. As additional surveillance strategies become available, it will allow an era of precision surveillance in which optimal surveillance modalities are tailored to individual patient risk and expected test performance.

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.

Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.

MicroRNAs are involved in breast cancer development and progression, holding potential as biomarkers and therapeutic targets or tools. The roles of miR-20a-5p, a member of the oncogenic miR-17-92 cluster, remain poorly understood in the context of breast cancer. In this study, we elucidate the role of miR-20a-5p in breast cancer by examining its associations with breast cancer risk factors and clinicopathological features, and its functional roles in vitro. Tissue microarrays from 313 CAMO cohort breast cancer surgical specimens were constructed, in situ hybridization was performed and miR-20a-5p expression was semiquantitatively scored in tumor stromal fibroblasts, and in the cytoplasm and nuclei of cancer cells. In vitro analysis of the effect of miR-20a-5p transfection on proliferation, migration and invasion was performed in three breast cancer cell lines. High stromal miR-20a-5p was associated with higher Ki67 expression, and higher odds of relapse, compared to low expression. Compared to postmenopausal women, women who were premenopausal at diagnosis had higher odds of high stromal and cytoplasmic miR-20a-5p expression. Cytoplasmic miR-20a-5p was significantly associated with tumor grade. In tumors with high cytoplasmic miR-20a-5p expression compared to low expression, there was a tendency towards having a basal-like subtype and high Ki67. In contrast, high nuclear miR-20a-5p in cancer cells was associated with smaller tumor size and lower odds of lymph node metastasis, compared to low nuclear expression. Transfection with miR-20a-5p in breast cancer cell lines led to increased migration and invasion in vitro. While the majority of our results point towards an oncogenic role, some of our findings indicate that the associations of miR-20a-5p with breast cancer related risk factors and outcomes may vary based on tissue- and subcellular location. Larger studies are needed to validate our findings and further investigate the clinical utility of miR-20a-5p.

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality globally. Most patients present with late diagnosis, leading to poor prognosis. This narrative review explores novel biomarkers for early HCC detection. We conducted a comprehensive literature review analyzing protein, circulating nucleic acid, metabolite, and quantitative proteomics-based biomarkers, evaluating the advantages and limitations of each approach. While established markers like alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, and AFP-L3 remain relevant, promising candidates include circulating tumor DNA, microRNAs, long noncoding RNAs, extracellular vesicle, and metabolomic biomarkers. Multi-biomarker panels like the GALAD score, Oncoguard, and Helio liver test show promise for improved diagnostic accuracy. Non-invasive approaches like urine and gut microbiome analysis are also emerging possibilities. Integrating these novel biomarkers with current screening protocols holds significant potential for earlier HCC detection and improved patient outcomes. Future research should explore multi-biomarker panels, omics technologies, and artificial intelligence to further enhance early HCC diagnosis and management.

Breast cancer remains one of the leading causes of death among women worldwide, and recent research highlights its growing connection to alterations in the microbiota. This review delves into the intricate relationship between microbiotas and breast cancer, exploring its presence in healthy breast tissue, its changes during cancer progression, and its considerable impact on both the tumor microenvironment (TME) and the tumor immune microenvironment (TIME). We extensively analyze how the microbiota influences cancer growth, invasion, metastasis, resistance to drugs, and the evasion of the immune system, with a special focus on its effects on the TIME. Furthermore, we investigate distinct microbial profiles associated with the four primary molecular subtypes of breast cancer, examining how the microbiota in tumor tissues compares with that in adjacent normal tissues. Emerging studies suggest that microbiotas could serve as valuable diagnostic and prognostic biomarkers, as well as targets for therapy. This review emphasizes the urgent need for further research to improve strategies for breast cancer prevention, diagnosis, and treatment. By offering a detailed examination of the microbiota's critical role in breast cancer, this review aims to foster the development of novel microbiota-based approaches for managing the disease.