Song Z, et al. Korean J Radiol. 2020 Jul;21:e160. https://doi.org/10.3348/kjr.2020.0254

Background: Assessing liver function is crucial for managing chronic liver diseases. This study aimed to evaluate the efficacy of hepatocyte uptake and the longitudinal relaxation time (T1) mapping indices from gadoxetic acid-enhanced magnetic resonance imaging (MRI) for evaluating liver function and its correlation with the albumin-bilirubin (ALBI) grading system. Methods: We retrospectively studied 183 patients who were grouped based on ALBI score: normal liver function (NLF), ALBI 1, ALBI 2, and ALBI 3. We calculated T1 indices and analyzed their correlation with ALBI grade, and differences among ALBI groups were evaluated. Receiver operating characteristic curves were used to assess the discriminative power of hepatocyte uptake and T1 indices for liver function groups, with significance set at P<0.05. Results: Significant differences were observed in hepatocyte uptake and T1 indices across the NLF and ALBI groups (P<0.001). T1 value before enhancement (T1pre), T1 value after enhancement (T1post) prolonged, and rate of decrease in the T1 relaxation time (ΔT1), hepatocyte uptake rate (K_hep) decreased with the advancement of liver function impairment, except for T1pre shortened in ALBI 3 grade. T1post (rho =0.762, P<0.001), K_hep (rho =−0.759, P<0.001) and ΔT1 (rho =−0.673, P<0.01) showed strong correlations with ALBI grades. T1post and K_hep were superior to T1pre and ΔT1 across all liver function groups not only in pairwise comparison but also in stratified analysis. Conclusions: K_hep and T1post provide good diagnostic performance in distinguishing ALBI groups. T1post exhibits the highest area under the curve (AUC) when predicting lower liver function groups, whereas K_hep excels in predicting high-grade liver function. Gadoxetic acid-enhanced MRI with T1 mapping shows potential as a tool for assessing liver function.

Background: Limited evidence exists on the diagnostic performance of Artificial Intelligence (AI)‐assisted Simplified Prostate Imaging Reporting and Data System version 2.1 (S‐PI‐RADS v2.1) combined with quantitative MRI parameters for detecting clinically significant prostate cancer (csPCa). Purpose: To develop and validate a nomogram incorporating AI‐assisted S‐PI‐RADS v2.1 (based on biparametric MRI [bpMRI]) and R2* mapping for csPCa prediction. Methods: This prospective study enrolled 345 patients grouped by pathology: non‐csPCa with benign prostatic hyperplasia ( n = 230) and csPCa ( n = 115). Clinical (age, body mass index [BMI], prostate‐specific antigen [PSA], free PSA) and imaging parameters (prostate volume [PV], S‐PI‐RADS score, R2*) were analyzed. Independent predictors were identified via logistic regression. A nomogram was developed using R software with the DynNom package (Version 2.0) and validated (1000 bootstrap iterations), with performance assessed by area under the curve (AUC), calibration, decision curve analysis (DCA), and DeLong test ( p < 0.05 significant). Results: Independent csPCa predictors included BMI, PSA ≥ 10 ng/mL, PV, S‐PI‐RADS scores 4–5, and R2* (all p < 0.05). The full model (BMI + PSA + PV + S‐PI‐RADS + R2*) showed superior discrimination (AUC = 0.915) versus the baseline model (AUC = 0.891, p = 0.008), with 85.2% sensitivity and 80.9% specificity. Internal validation was robust (C‐index = 0.884). DCA confirmed clinical utility. An interactive nomogram was deployed ( https://aiguangyong2025.shinyapps.io/dynnomapp/ ). Conclusion: The AI‐enhanced nomogram integrating clinical and multiparametric MRI data accurately predicts csPCa noninvasively, with R2* significantly improving performance. This tool facilitates personalized clinical decision‐making.