Chanavee Toh

Background and Aims: Hepatocellular carcinoma (HCC) is frequently diagnosed at an advanced stage, and serum alpha-fetoprotein (AFP), the most widely used biomarker for HCC surveillance, exhibits substantial interpatient variability. However, longitudinal AFP patterns preceding HCC diagnosis and their clinical correlates remain incompletely characterized. We aimed to characterize pre-diagnostic AFP trajectories and identify clinical features associated with rising versus non-rising AFP patterns. Methods: We analyzed 529 patients with newly diagnosed HCC at Songklanagarind Hospital, each with at least three serum AFP measurements obtained within five years before diagnosis. Log-transformed AFP values were modeled using latent class mixed models with natural cubic splines and linear fixed and random effects. The optimal model was selected based on Bayesian Information Criterion (BIC), entropy, and clinical interpretability. Multinomial logistic regression and Cox proportional hazards models were used to evaluate associations between AFP trajectory class, baseline characteristics, and overall survival. Results: A two-class natural spline model (df = 3) provided the optimal fit (BIC = 9003). The non-rising trajectory (n = 440; 83.2%) showed stable AFP levels throughout follow-up, whereas the rising trajectory (n = 89; 16.8%) demonstrated exponential increase beginning 18–24 months before diagnosis. Compared to the non-rising group, patients in the rising group were significantly more likely to present with tumors >2 cm (78% vs. 62%; p = 0.009). The rising trajectory was associated with shorter overall survival in univariable analysis (hazard ratio [HR] 1.33, 95% confidence interval [CI] 1.02–1.73; p = 0.033), but not after adjustment for clinical and tumor characteristics, including Barcelona Clinic Liver Cancer stage (adjusted HR 1.01, 95% CI 0.77–1.33; p = 0.920). Conclusions: Two distinct AFP trajectories precede HCC diagnosis. Most patients did not demonstrate a substantial rise in AFP, underscoring the need for complementary surveillance biomarkers. Prospective validation in independent cohorts is required before AFP trajectory-based approaches can be incorporated into HCC surveillance practice. These findings are exploratory and hypothesis-generating rather than a validated clinical prediction tool.

Objectives: To assess inter-rater agreement and diagnostic performance of six United States National Institute for Occupational Safety and Health-certified B readers (physicians certified in standardized chest X-ray (CXR) classification for pneumoconiosis) in diagnosing tuberculosis (TB) on CXR, with the goal of developing a reliable dataset to support external validation of artificial intelligence (AI) models for TB detection. Materials and methods: 1097 CXRs from five institutions were analyzed by six B readers. Patients aged ≥ 15 years were included, excluding those with HIV or opportunistic infections. CXRs were classified as unremarkable or abnormal. Abnormalities were categorized using a modified International Labor Organization classification and classified as consistent or inconsistent with TB. Microbiological references included sputum smears, cultures, or molecular tests. Descriptive statistics summarized the CXR and microbiological findings. Inter-rater agreement was assessed with Fleiss’ kappa. Diagnostic performance was evaluated by comparing CXR findings to microbiological references. Results: Of the 3117 readings, 69% of CXRs were abnormal, and 31% were unremarkable. Microbiological results confirmed that 87% of abnormal CXRs were TB cases, while 83% of unremarkable CXRs were non-TB. Inter-rater agreements were κ = 0.83 for all findings, κ = 0.67 for findings consistent with TB, and κ = 0.76 for active TB findings. For findings consistent with TB, the sensitivity, specificity, and accuracy ranged from 77.2% to 91.1%, 87.4% to 98.6%, and 84.1% to 90.1%, respectively. Conclusion: B readers exhibited strong agreement and high accuracy in diagnosing TB on CXR, providing a robust dataset that could be used for external validation of AI models in TB diagnosis. Critical relevance statement: With substantial inter-rater agreement and strong diagnostic performance, B readers’ interpretations of CXRs provide a reliable dataset to support external validation of AI models for TB diagnosis. Key Points: B readers can provide a reliable reference for validating AI models in TB diagnosis. Less variability and high accuracy were observed among B readers in CXR-based TB diagnosis.

Hepatocellular carcinoma (HCC) remains a major cause of morbidity and mortality among patients with chronic hepatitis B (CHB). Existing HCC risk scores are largely based on baseline variables and do not account for longitudinal changes over time. We aimed to develop and validate a longitudinal predictive model for HCC using time-dependent Cox regression. We retrospectively analyzed 4,431 patients with CHB followed between 2009 and 2022. After excluding patients with viral co-infections, early HCC, or incomplete data, 2,245 patients were included. The cohort was randomly divided into a training set (n = 1,685) and a test set (n = 562). Time-updated demographic, clinical, and laboratory variables were incorporated into the model. During follow-up, 121 patients developed HCC. Independent predictors of HCC included age, sex, platelet count, cirrhosis, serum albumin, and α-fetoprotein (AFP). These variables were incorporated into the final model (PSU HCC score): (0.023 × age) − (0.627 × female sex) − (0.847 × albumin) − (0.005 × platelet count [× 10³]) + (1.052 × log₁₀AFP) + (1.817 × cirrhosis). In the test cohort, the PSU HCC score demonstrated excellent discrimination (C-index 0.909; 95% CI, 0.870–0.947) and good calibration, outperforming ALBI, aMAP, REACH-B, CU-HCC, PAGE-B, and mPAGE-B scores. Risk stratification revealed significantly higher cumulative HCC incidence among patients in the high-risk group. Sensitivity analyses evaluating longer-term prediction at 3 and 5 years confirmed stable and robust performance. The PSU HCC score is a longitudinal prediction model that integrates dynamic clinical and laboratory parameters and provides superior HCC risk prediction in patients with CHB. This approach may support individualized, risk-based HCC surveillance strategies.