| Characteristic | F N = 151 |
M N = 151 |
Overall N = 301 |
|---|---|---|---|
| Age (years) | 46.0 (37.0, 61.0) | 49.0 (41.0, 61.0) | 47.5 (40.0, 61.0) |
| BMI (kg/m²) | 29.9 (26.8, 35.8) | 30.5 (27.4, 34.1) | 30.2 (27.4, 34.1) |
| Etiology of CLD | |||
| Autoimmune, viral | 2 (13%) | 2 (13%) | 4 (13%) |
| MASLD/MASH/EtOH | 11 (73%) | 9 (60%) | 20 (67%) |
| Other (Cardiac Hepatopathy) | 0 (0%) | 1 (6.7%) | 1 (3.3%) |
| Other (Granulomatous Hepatitis) | 1 (6.7%) | 0 (0%) | 1 (3.3%) |
| Other (Normal Liver) | 1 (6.7%) | 0 (0%) | 1 (3.3%) |
| Other (Unknown) | 0 (0%) | 1 (6.7%) | 1 (3.3%) |
| PBC, PSC | 0 (0%) | 2 (13%) | 2 (6.7%) |
| Fibrosis Stage | |||
| 0 | 2 (13%) | 1 (6.7%) | 3 (10%) |
| 1 | 3 (20%) | 3 (20%) | 6 (20%) |
| 2 | 6 (40%) | 7 (47%) | 13 (43%) |
| 3 | 3 (20%) | 2 (13%) | 5 (17%) |
| 4 | 1 (6.7%) | 2 (13%) | 3 (10%) |
| NAS Score | |||
| 0 | 4 (27%) | 5 (33%) | 9 (30%) |
| 1 | 1 (6.7%) | 2 (13%) | 3 (10%) |
| 3 | 4 (27%) | 5 (33%) | 9 (30%) |
| 4 | 4 (27%) | 1 (6.7%) | 5 (17%) |
| 5 | 1 (6.7%) | 1 (6.7%) | 2 (6.7%) |
| 6 | 1 (6.7%) | 1 (6.7%) | 2 (6.7%) |
| Pathology Steatosis Grade (%) | 20.0 (0.0, 35.0) | 15.0 (0.0, 25.0) | 17.5 (0.0, 30.0) |
| ALT (U/L) | 70.0 (41.0, 254.0) | 102.0 (49.0, 199.0) | 82.5 (48.0, 199.0) |
| AST (U/L) | 75.0 (38.0, 195.0) | 67.0 (45.0, 149.0) | 71.0 (45.0, 149.0) |
| ALP (U/L) | 95.0 (69.0, 122.0) | 74.0 (60.0, 143.0) | 83.5 (60.0, 127.0) |
| Total Bilirubin (mg/dL) | 0.5 (0.4, 0.8) | 0.7 (0.6, 0.9) | 0.6 (0.4, 0.8) |
| INR | |||
| 0.84 | 0 (0%) | 1 (6.7%) | 1 (3.3%) |
| 0.9 | 1 (6.7%) | 2 (13%) | 3 (10%) |
| 1 | 8 (53%) | 8 (53%) | 16 (53%) |
| 1.1 | 5 (33%) | 3 (20%) | 8 (27%) |
| 1.2 | 0 (0%) | 1 (6.7%) | 1 (3.3%) |
| 1.3 | 1 (6.7%) | 0 (0%) | 1 (3.3%) |
| 2D MRE Liver Stiffness (kPa) | 3.0 (2.5, 3.7) | 3.2 (2.9, 4.0) | 3.1 (2.5, 3.7) |
| Unknown | 0 | 1 | 1 |
| PDFF (%) | 9.3 (2.3, 11.7) | 7.4 (2.7, 11.0) | 8.4 (2.5, 11.5) |
| Unknown | 0 | 2 | 2 |
| MONO ADC (×10⁻⁶ mm²/s) | 971.7 (941.8, 1,011.9) | 1,100.1 (1,060.5, 1,234.9) | 1,036.2 (950.7, 1,123.1) |
| MODI ADC (×10⁻⁶ mm²/s) | 1,134.1 (1,046.3, 1,265.1) | 1,135.5 (1,090.8, 1,253.3) | 1,134.8 (1,072.7, 1,255.0) |
| MODI LRES ADC (×10⁻⁶ mm²/s) | 1,196.1 (1,111.0, 1,306.0) | 1,191.1 (1,119.4, 1,265.5) | 1,191.8 (1,119.4, 1,265.5) |
| 1 Median (Q1, Q3); n (%) | |||
Motion-optimized diffusion MRI for assessment of liver fibrosis
Statistical analysis
Categorical variables were summarized as counts and percentages; continuous variables are reported as median (interquartile range). Associations between each ADC sequence (MONO, MODI, MODI LRES) and fibrosis stage were assessed using Spearman rank correlation, with differences across fibrosis stages evaluated by the Kruskal-Wallis test followed by pairwise Mann-Whitney U tests with Bonferroni correction. Pearson correlation was used to quantify the relationships between ADC values and 2D MRE liver stiffness and between ADC values and collagen proportionate area (CPA).
Diagnostic performance of each ADC sequence and 2D MRE stiffness for three binary fibrosis thresholds — significant fibrosis (F≥2 vs F0–1), advanced fibrosis (F≥3 vs F0–2), and cirrhosis (F4 vs F0–3) — was evaluated by receiver operating characteristic (ROC) analysis; area under the ROC curve (AUC) with 95% DeLong confidence intervals was computed for each predictor. Three clinically meaningful operating points were identified per curve: a Youden-based cutoff maximizing the sum of sensitivity and specificity, a rule-in cutoff targeting ≥90% specificity, and a rule-out cutoff targeting ≥90% sensitivity.
To assess the incremental diagnostic value of ADC for significant fibrosis, BIC-based stepwise logistic regression (forward and backward selection, penalty k = log n) was applied separately for each ADC sequence using candidate pools with and without ADC alongside 2D MRE stiffness, PDFF, age, sex, BMI, ALT, and AST; AUCs derived from model-fitted probabilities were compared pairwise using DeLong’s test for dependent ROC curves. All analyses were performed in R (version 4.5.3).
Results
Patient characteristics
Table 1 summarizes the demographic and clinical characteristics of the 30 patients, stratified by sex.
ADC vs fibrosis stage
Correlation of ADC (MONO, MODI, and MODI LRES) with Fibrosis Stage (Spearman correlation): Boxplot of ADC values by fibrosis stage with p-values from trend test.
ADC vs 2D MRE stiffness
ADC vs CPA
ADC and MRE stiffness by fibrosis groups
| Significant fibrosis | |||
|---|---|---|---|
| Measure | F0–1 (n=9) | F2–4 (n=21) | p |
| MONO ADC | 1065 (947–1123) | 1012 (972–1102) | p=0.928 |
| MODI ADC | 1231 (1143–1273) | 1110 (1073–1243) | p=0.189 |
| MODI LRES ADC | 1306 (1196–1355) | 1178 (1111–1218) | p=0.027 |
| 2D MRE (kPa) | 3 (2–3) | 3 (3–5) | p=0.048 |
| Advanced fibrosis | |||
|---|---|---|---|
| Measure | F0–2 (n=22) | F3–4 (n=8) | p |
| MONO ADC | 1036 (943–1118) | 1036 (998–1101) | p=0.590 |
| MODI ADC | 1220 (1077–1271) | 1099 (1076–1116) | p=0.079 |
| MODI LRES ADC | 1214 (1186–1319) | 1114 (1098–1138) | p=0.001 |
| 2D MRE (kPa) | 3 (2–3) | 5 (4–6) | p<0.001 |
| Cirrhosis | |||
|---|---|---|---|
| Measure | F0–3 (n=27) | F4 (n=3) | p |
| MONO ADC | 1012 (949–1101) | 1206 (1104–1235) | p=0.147 |
| MODI ADC | 1143 (1075–1260) | 1091 (1062–1099) | p=0.167 |
| MODI LRES ADC | 1196 (1163–1286) | 1074 (1055–1102) | p=0.032 |
| 2D MRE (kPa) | 3 (2–4) | 5 (4–6) | p=0.058 |
ROC analysis - univariate
Univariate ROC analysis of ADC (MONO, MODI, MODI LRES) and 2D MRE as predictors of fibrosis stage. Multiple ROCs (F0–1 vs F2–4, F0–2 vs F3–4, F0–3 vs F4). With
- Youden-based cutoff that maximizes the sum of sensitivity and specificity;
- a rule-in cutoff (a cutoff providing 90% or highest possible specificity);
- a rule-out cutoff (a cutoff providing 90% or highest possible sensitivity).
F0-1 vs F2-4 (significant fibrosis)
| Predictor | AUC (95% CI) |
Youden
|
Rule-in
|
Rule-out
|
||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Threshold | Sn (%) | Sp (%) | Threshold | Sn (%) | Sp (%) | Threshold | Sn (%) | Sp (%) | ||
| MONO ADC | 0.51 (0.27–0.76) | 964.6 | 76 | 44 | 1244.6 | 10 | 100 | 913.5 | 90 | 22 |
| MODI ADC | 0.66 (0.42–0.89) | 1139.0 | 67 | 78 | 964.8 | 10 | 100 | 1324.8 | 95 | 22 |
| MODI LRES | 0.76 (0.54–0.98) | 1285.8 | 90 | 56 | 1044.8 | 10 | 100 | 1285.8 | 90 | 56 |
| 2D MRE | 0.74 (0.56–0.93) | 3.5 | 48 | 100 | 3.5 | 48 | 100 | 2.2 | 100 | 25 |
F0-2 vs F3-4 (advanced fibrosis)
| Predictor | AUC (95% CI) |
Youden
|
Rule-in
|
Rule-out
|
||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Threshold | Sn (%) | Sp (%) | Threshold | Sn (%) | Sp (%) | Threshold | Sn (%) | Sp (%) | ||
| MONO ADC | 0.57 (0.35–0.79) | 948.6 | 100 | 32 | 1236.5 | 12 | 91 | 948.6 | 100 | 32 |
| MODI ADC | 0.72 (0.53–0.90) | 1139.0 | 100 | 64 | 964.8 | 0 | 91 | 1139.0 | 100 | 64 |
| MODI LRES | 0.89 (0.77–1.00) | 1169.6 | 100 | 86 | 1109.4 | 50 | 91 | 1169.6 | 100 | 86 |
| 2D MRE | 0.91 (0.77–1.00) | 4.2 | 75 | 100 | 3.7 | 75 | 90 | 2.8 | 100 | 43 |
F0-3 vs F4 (cirrhosis)
| Predictor | AUC (95% CI) |
Youden
|
Rule-in
|
Rule-out
|
||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Threshold | Sn (%) | Sp (%) | Threshold | Sn (%) | Sp (%) | Threshold | Sn (%) | Sp (%) | ||
| MONO ADC | 0.77 (0.43–1.00) | 1166.1 | 67 | 85 | 1236.5 | 33 | 93 | 1002.0 | 100 | 44 |
| MODI ADC | 0.75 (0.56–0.95) | 1108.5 | 100 | 67 | 964.8 | 0 | 93 | 1108.5 | 100 | 67 |
| MODI LRES | 0.89 (0.74–1.00) | 1145.6 | 100 | 78 | 1090.1 | 67 | 93 | 1145.6 | 100 | 78 |
| 2D MRE | 0.85 (0.68–1.00) | 3.5 | 100 | 73 | 5.5 | 33 | 92 | 3.5 | 100 | 73 |
Multivariate ROC analysis
To assess the incremental diagnostic value of ADC beyond established imaging and clinical biomarkers for significant fibrosis (F≥2), BIC-based stepwise logistic regression was applied to three candidate pools — one per ADC sequence — each including 2D MRE stiffness, PDFF, age, sex, BMI, ALT, and AST (n = 27 with complete data).
In all cases, BIC selection retained 2D MRE stiffness as the sole predictor, with neither ADC nor PDFF entering any model; the BIC-selected models with and without ADC were therefore identical. Among single-predictor models, MODI LRES achieved the highest AUC (0.73), comparable to 2D MRE alone (0.72), while no ADC sequence differed significantly from MRE by DeLong’s test. These results suggest that ADC does not provide incremental value over 2D MRE for predicting significant fibrosis in this cohort, though the small sample size limits definitive conclusions.
| Model | Selected predictors | AUC (95% CI) |
|---|---|---|
| MODI LRES | ||
| MODI LRES alone | modi_lres | 0.73 (0.49–0.97) |
| BIC model with ADC | mre_2d | 0.72 (0.52–0.93) |
| BIC model without ADC | mre_2d | 0.72 (0.52–0.93) |
| MODI ADC | ||
| MODI ADC alone | modi_adc | 0.64 (0.39–0.90) |
| BIC model with ADC | mre_2d | 0.72 (0.52–0.93) |
| BIC model without ADC | mre_2d | 0.72 (0.52–0.93) |
| MONO ADC | ||
| MONO ADC alone | mono_adc | 0.55 (0.29–0.80) |
| BIC model with ADC | mre_2d | 0.72 (0.52–0.93) |
| BIC model without ADC | mre_2d | 0.72 (0.52–0.93) |