Objectives: To develop and compare noninvasive models for differentiating between combined hepatocellular-cholangiocarcinoma (cHCC-CCA) and HCC based on serum tumor markers, contrast-enhanced ultrasound (CEUS), and computed tomography (CECT). Methods: From January 2010 to December 2021, patients with pathologically confirmed cHCC-CCA or HCC who underwent both preoperative CEUS and CECT were retrospectively enrolled. Propensity scores were calculated to match cHCC-CCA and HCC patients with a near-neighbor ratio of 1:2. Two predicted models, a CEUS-predominant (CEUS features plus tumor markers) and a CECT-predominant model (CECT features plus tumor markers), were constructed using logistic regression analyses. Model performance was evaluated by the area under the curve (AUC), sensitivity, specificity, and accuracy. Results: A total of 135 patients (mean age, 51.3 years ± 10.9; 122 men) with 135 tumors (45 cHCC-CCA and 90 HCC) were included. By logistic regression analysis, unclear boundary in the intratumoral nonenhanced area, partial washout on CEUS, CA 19-9 > 100 U/mL, lack of cirrhosis, incomplete tumor capsule, and nonrim arterial phase hyperenhancement (APHE) volume < 50% on CECT were independent factors for a diagnosis of cHCC-CCA. The CECT-predominant model showed almost perfect sensitivity for cHCC-CCA, unlike the CEUS-predominant model (93.3% vs. 55.6%, p < 0.001). The CEUS-predominant model showed higher diagnostic specificity than the CECT-predominant model (80.0% vs. 63.3%; p = 0.020), especially in the ≤ 5 cm subgroup (92.0% vs. 70.0%; p = 0.013). Conclusions: The CECT-predominant model provides higher diagnostic sensitivity than the CEUS-predominant model for CHCC-CCA. Combining CECT features with serum CA 19-9 > 100 U/mL shows excellent sensitivity. Critical relevance statement: Combining lack of cirrhosis, incomplete tumor capsule, and nonrim arterial phase hyperenhancement (APHE) volume < 50% on CECT with serum CA 19-9 > 100 U/mL shows excellent sensitivity in differentiating cHCC-CCA from HCC. Key points: 1. Accurate differentiation between cHCC-CCA and HCC is essential for treatment decisions. 2. The CECT-predominant model provides higher accuracy than the CEUS-predominant model for CHCC-CCA. 3. Combining CECT features and CA 19-9 levels shows a sensitivity of 93.3% in diagnosing cHCC-CCA.
Keywords: Liver neoplasms; Ultrasonography; Tomography (X-ray Computed); Diagnosis (Differential)
Jie Yang and Yun Zhang contributed equally to this work.
Graph
Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) accounts for 0.4–4.2% of primary liver cancer cases and demonstrates hepatocytic and biliary differentiation in the same tumor [[
However, the treatment strategies for cHCC-CCA and HCC differ. For example, liver transplantation has been accepted as an effective curative-intent treatment option for HCC, but it is not recommended for cHCC-CCA because of frequent recurrence (54% at 5 years) and suboptimal long-term survival (41% at 5 years) [[
Contrast-enhanced ultrasound (CEUS) and contrast-enhanced computed tomography (CECT) are two of the main imaging modalities for diagnosing liver tumors [[
Here, we aimed to develop diagnostic models integrating clinical and readily accessible CEUS and CECT features to differentiate between cHCC-CCA and HCC in a propensity score-matched study and to compare the two models.
From January 2010 to December 2021, patients who underwent curative-intent liver resection for surgically proven HCC or cHCC-CCA were consecutively enrolled. The inclusion criteria were as follows: (a) pathologically proven HCC or cHCC-CCA, (b) both CECT and CEUS examinations within 1 month before surgery, and (c) chronic hepatitis B/C virus infection or cirrhosis. Patients were excluded if (a) they had received any prior antitumoral treatment, (b) key laboratory data were not available, or (c) CECT and/or CEUS images were degraded or missing. The inclusion and exclusion flowchart is shown in Fig. 1.
Graph: Fig. 1Flowchart of included patients. cHCC-CCA, combined hepatocellular-cholangiocarcinoma; HCC, hepatocellular carcinoma; CEUS, contrast-enhanced ultrasound; CECT, contrast-enhanced computed tomography
The imaging acquisition recommendation and the detailed parameters are presented in eMethods 1 in Supplement 1.
All image analyses were conducted on a per-lesion basis by two ultrasonographers (K-Y.Z. and J-Y.H., with 8 and 10 years of experience in CEUS images, respectively) and two radiologists (Y.Z. and Y-D.C., with 8 and 11 years of experience in liver imaging in CECT images, respectively). All disagreements between the reviewers regarding the imaging features were resolved by consensus. For patients with multiple lesions, the largest targeted lesion was selected for feature-related analyses.
The CEUS imaging features and LI-RADS categories according to ACR CEUS LI-RADS version 2017 [[
Graph: Fig. 2The definition of the partial imaging features of the lesions on CEUS (a) and CECT (b)
The CECT imaging features and LI-RADS categories according to ACR CECT LI-RADS version 2018 [[
The pathological characteristics of the lesions were retrospectively recorded according to the pathological reporting system in our hospital. These included the maximum size of the main lesion (the largest lesion in the case of multiple lesions), the Edmondson-Steiner grade of the HCC, and the HCC/intrahepatic cholangiocarcinoma (ICC)-predominant components of cHCC-CCA [[
Propensity score-matching was performed to minimize the effect of potential selection bias and confounding factors between patients with HCC and cHCC-CCA.
The predictive models, based separately on tumor markers and CEUS and CECT features, were constructed using logistic regression analyses, and their performance was compared with that of pathology. The variables with p < 0.05 by the χ
Model discrimination was assessed by computing the area under the receiver operating characteristic curve (AUC) value and compared using the DeLong test. Model calibration was evaluated by the Hosmer–Lemeshow (H-L) test and calibration curves. The McNemar test was used to compare pairwise sensitivities, specificities, and accuracies of the two diagnostic models. The subgroup comparison of the diagnostic efficacy between CEUS and CECT was also evaluated for smaller lesions, with a diameter of 5 cm.
All statistical analyses were performed with the R software (R Foundation for Statistical Computing, version 3.2.5,
A total of 971 patients were initially identified. After propensity score matching, 135 patients (mean age, 51.3 ± 10.9 years, 122 males [90.4%]) with 135 nodules (45 cHCC-CCAs and 90 HCCs) were included for further analysis.
Serum CA 19-9 > 100 U/mL was more frequently found in patients with cHCC-CCA than in those with HCC (11.1% vs. 2.2%, p = 0.029), while serum AFP > 400 μg/L was more frequently observed in patients with HCC than in those with cHCC-CCA (36.7% vs. 20.0%, p = 0.050). The key clinical features of the patients are summarized in Table 1.
Table 1 Basic clinical and pathological characteristics of patients with cHCC-CCA and HCC
Characteristics cHCC-CCA ( HCC ( Age (years) 52 ± 9.2 51 ± 11.7 0.630 Sex 0.681 Male 40 (88.9) 82 (91.1) Female 5 (11.1) 8 (8.9) Hepatitis status 1.000 HBV (+) 44 (97.8) 87 (96.7) HCV (+) 1 (2.2) 1 (1.1) Others 0 (0) 2 (2.2) AFP level (μg/L) 0.050 0–400 36 (80.0) 57 (63.3) > 400 9 (20.0) 33 (36.7) CA 19-9 level (U/mL) 0.029 0–100 40 (88.9) 88 (97.8) > 100 5 (11.1) 2 (2.2) Size (cm) 5.6 ± 3.8 5.5 ± 3.5 0.851 Size (cm) 0.807 ≤ 5 26 (57.8) 50 (55.6) > 5 19 (42.2) 40 (44.6) Edmondson-Steiner grade < 0.001 1 1 (2.2) 2 (2.2) 2 3 (6.7) 50 (55.6) 3 11 (24.4) 14 (15.6) Both 2–3c 7 (15.6) 23 (25.6) Not available 23 (51.1) 1 (1.1)
cHCC-CCA combined hepatocellular-cholangiocarcinoma, HCC hepatocellular carcinoma, HBV hepatitis B virus, HCV hepatitis C virus, AFP alpha-fetoprotein, CA 19-9 carbohydrate antigen 19-9
Based on the CEUS LI-RADS classification, 46.6% and 37.8% of cHCC-CCA patients were classified as LR-4/5 and LR-M, respectively; for HCC, 65.6% and 20% were classified as LR-4/5 and LR-M, respectively. Based on the CECT LI-RADS classification, 44.5% and 51.1% of cHCC-CCA patients were classified as LR-4/5 and LR-M, respectively, compared with 76.7% for LR-4/5 and 12.2% for LR-M among the HCC patients.
On CEUS, the following features were more frequent in patients with cHCC-CCA than in those with HCC: hypoenhancement in the PVP images (88.9% vs. 64.4%), unclear boundary in the intratumoral nonenhanced area (71.1% vs. 37.8%), and partial washout (71.1% vs. 40.0%). The baseline CEUS imaging features of all lesions are presented in Table 2.
Table 2 The CEUS features of included lesions
Imaging features cHCC-CCAa ( HCC ( Size (cm) 5.6 ± 3.5 5.8 ± 3.8 0.799 Number of tumors (single) 18 (40.0) 26 (28.9) 0.243 Cirrhosis 23 (51.1) 43 (47.8) Nodule echo (hypo-) 40 (88.9) 68 (75.6) 0.109 Boundary (well) 13 (28.9) 35 (38.9) 0.170 Shape (regular) 16 (35.6) 45 (50.0) 0.143 Enhancement level in the AP 1.000 Hyperenhancement 44 (97.8) 89 (98.9) Isoenhancement 1 (2.2) 1 (1.1) Hypoenhancement 0 (0.0) 0 (0.0) Enhancement level in the PVP 0.002 Hyperenhancement 0 (0.0) 0 (0.0) Isoenhancement 5 (11.1) 32 (35.6) Hypoenhancement 40 (88.9) 58 (64.4) Enhancement level in the LP 0.424 Hyperenhancement 0 (0.0) 0 (0.0) Isoenhancement 1 (2.2) 6 (6.7) Hypoenhancement 44 (97.8) 84 (93.3) Rim APHE 1 (2.3) 1 (1.1) 1.000 Early washout 19 (42.4) 23 (25.6) 0.075 Marked washout within two minutes 0 (0.0) 1 (1.1) 1.000 Mild and late washout 44 (97.8) 84 (93.3) 1.000 Tumor in vein 7 (15.6) 13 (14.4) 1.000 LI-RADS category 0.113 LR-4 1 (2.2) 6 (6.7) LR-5 20 (44.4) 53 (58.9) LR-M 17 (37.8) 18 (20) LR-TIV 7 (15.6) 13 (14.4) Nodule-in-nodule architecture 0 (0.0) 0 (0.0) NA Mosaic architecture 9 (20.0) 14 (15.6) 0.628 Tumor supply artery 24 (53.3) 44 (48.9) 0.716 Circumscribed enhancement (well) 20 (44.4) 45 (50.0) 0.587 Unclear boundary in the intratumoral nonenhanced area 32 (71.1) 34 (37.8) < 0.001 Intratumoral vein in LP 12 (26.7) 17 (18.9) 0.206 The proportion of washout (partial) 32 (71.1) 36 (40.0) < 0.001 Necrosis or severe ischemia 4 (8.9) 17 (18.9) 0.207
cHCC-CCA combined hepatocellular-cholangiocarcinoma, HCC hepatocellular carcinoma, CEUS contrast-enhanced ultrasound, AP arterial phase, PVP portal venous phase, LP late phase, NA not available, APHE arterial phase hyperenhancement, LI-RADS liver imaging reporting and data system, LR liver imaging reporting and data system category
On CECT, the following features were more commonly observed in cHCC-CCA: nonrim APHE volume < 50% (57.8% vs. 12.2%), rim APHE (37.8% vs. 5.6%), nonperipheral washout volume < 50% (48.9% vs. 20.0%), peripheral washout (48.9% vs. 8.9%), LR-M category (51.1% vs. 12.2%), and incomplete tumor capsule (60.0% vs. 40.0%). The following features were more frequently detected for HCC: cirrhosis (68.9% vs. 44.4%) and single nodular type (tumor growth subtype 1) (68.9% vs. 51.1%). The baseline CECT imaging features of all lesions are presented in Table 3.
Table 3 The CECT features of included lesions
Imaging features cHCC-CCAa ( HCC ( Size (cm) 5.5 ± 3.6 5.3 ± 3.4 0.719 Number of tumors (single) 33 (73.3) 82 (80.0) 0.511 Cirrhosis 20 (44.4) 62 (68.9) 0.009 Nonrim APHE volume (< 50%) 26 (57.8) 11 (12.2) < 0.001 Rim APHE 17 (37.8) 5 (5.6) < 0.001 Nonperipheral washout volume (< 50%) 22 (48.9) 18 (20.0) < 0.001 Peripheral washout 22 (48.9) 8 (8.9) < 0.001 Enhancing capsule 28 (62.2) 52 (57.8) 0.621 Tumor in vein 1 (2.2) 8 (9.0) 0.169 LI-RADS category < 0.001 LR-3 1 (2.2) 2 (2.2) LR-4 8 (17.8) 8 (8.9) LR-5 12 (26.7) 61 (67.8) LR-M 23 (51.1) 11(12.2) LR-TIV 1 (2.2) 8 (8.9) Corona enhancement 11 (24.4) 21 (23.3) 0.889 Nonenhancing capsule 4 (8.9) 17 (18.9) 0.132 Nodule-in-nodule architecture 9 (20.0) 23 (25.6) 0.526 Mosaic architecture 11 (24.4) 30 (33.3) 0.326 Blood products in mass 2 (4.4) 5 (5.6) 1.000 Fat in mass, more than adjacent liver 0 (0.0) 1 (1.1) 1.000 Delayed central enhancement 2 (4.4) 7 (7.8) 0.464 Internal artery 14 (31.1) 35 (38.9) 0.449 Necrosis or severe ischemia 18 (40.0) 48 (53.3) 0.201 Infiltrative appearance 20 (44.4) 38 (42.2) 0.855 Tumor capsule integrity (incomplete) 27 (60.0) 36 (40.0) 0.044 Tumor margin (smooth) 0.855 Tumor growth subtype < 0.001 Type 1: single nodular type 23 (51.1) 62 (68.9) Type 2: single nodule with extranodular growth 12 (26.7) 26 (28.9) Type 3: multiple confluent nodules 10 (22.2) 2 (2.2) Lesion with LR-M featuresb 24 (53.3) 11 (12.2) < 0.001
cHCC-CCA combined hepatocellular-cholangiocarcinoma, HCC hepatocellular carcinoma, CECT contrast-enhanced computed tomography, APHE arterial phase hyperenhancement, LI-RADS liver imaging reporting and data system, LR liver imaging reporting and data system category
Cohen's kappa values ranged from 0.312 to 0.765 for CEUS and from 0.380 to 0.717 for CECT. The interrater agreement of imaging features on CEUS and CECT are summarized in eTable 3 in Supplement 1.
The CEUS-predominant model was developed by combining CEUS features and tumor markers (AFP > 400 μg/L and CA 19-9 > 100 U/mL). The univariate variable selection is presented in eMethods 3 in Supplement 1. By multivariate regression analysis, unclear boundary in the intratumoral nonenhanced area (OR = 2.765; 95% confidence interval [CI]: 1.209, 6.541; p = 0.018) and partial washout (OR = 2.607; 95% CI: 1.152, 6.079; p = 0.023) were independent factors for a diagnosis of cHCC-CCA (shown in Table 4). The AUC value of the prediction model was 0.720 (95% CI: 0.632, 0.808). The sensitivity, specificity, and accuracy were 55.6%, 80.0%, and 71.9%, respectively. Regression coefficient-based nomograms were constructed based on the CEUS-predominant model (Fig. 3a). The calibration curve of the nomogram for the probability of cHCC-CCA demonstrated good agreement between prediction and observation (eFigure 1a). The H-L test yielded a nonsignificant statistic (p = 1.000).
Table 4 Univariate and multivariate logistic regression analyses for diagnosing cHCC-CCA with the CEUS-predominant and CECT-predominant models
Variables Univariable analysis Multivariable analysis OR (95% CI) OR (95% CI) CA19-9 level > 100 (U/mL) 1.705 5.500 (1.023, 29.567) 0.047 .. .. .. Unclear boundary in the intratumoral nonenhanced area 1.4 4.054 (1.872, 8.780) < 0.001 1.017 2.765 (1.209, 6.541) 0.018 Partial washout 1.306 3.692 (1.709, 7.977) 0.001 0.958 2.607 (1.152, 6.079) 0.023 Hypoenhancement in the PVP − 1.485 0.227 (0.081, 0.631) 0.045 .. .. .. CA19-9 level > 100 (U/mL) 1.705 5.500 (1.023, 29.567) 0.047 2.149 8.573 (1.217, 82.845) 0.038 Cirrhosis − 1.018 0.361 (0.173, 0.756) 0.007 − 1.179 0.308 (0.113, 0.795) 0.017 Rim APHEa 2.334 10.324 (3.489, 30.538) < 0.001 .. .. .. Peripheral washout 2.104 8.200 (3.222, 20.871) < 0.001 .. .. .. Nonrim APHE volume < 50% 2.285 9.828 (4.139, 23.335) < 0.001 2.414 11.180 (3.475, 41.419) < 0.001 Nonperipheral washout volume < 50% 1.342 3.826 (1.754, 8.347) 0.001 .. .. .. Incomplete tumor capsule 1.325 3.763 (1.771, 7.997) 0.001 1.944 7.348 (2.394, 25.929) < 0.001 Tumor growth subtype 2 or 3b − 0.750 0.472 (0.225, 0.984) 0.045 .. .. .. Lesion with LR-M featuresc 2.105 8.208 (3.471, 19.410) < 0.001 .. .. ..
cHCC-CCA combined hepatocellular-cholangiocarcinoma, CEUS contrast-enhanced ultrasound, CECT contrast-enhanced computed tomography, CA 19-9 carbohydrate antigen 19-9, OR odds ratio, PVP portal venous phase, APHE arterial phase hyperenhancement, CI confidence interval, LR liver imaging reporting and data system category
Graph: Fig. 3Nomograms of the CEUS-predominant (a) and CECT-predominant models (b)
The CECT-predominant model was developed by combining CECT features and tumor markers. The univariate variable selection is presented in eMethods 3 in Supplement 1. On multivariate regression analysis, CA 19-9 > 100 U/mL (OR = 8.573; 95% CI: 1.217, 82.845; p = 0.038), cirrhosis (OR = 0.308; 95% CI: 0.113, 0.795; p = 0.017), incomplete tumor capsule (OR = 7.348; 95% CI: 2.394, 25.929; p < 0.001), and nonrim APHE volume < 50% (OR = 11.180; 95% CI, 3.475, 41.419; p < 0.001) were found to be independent factors for diagnosing cHCC-CCA (shown in Table 4). The AUC value of the prediction model was 0.874 (95% CI: 0.816, 0.931), with a sensitivity, specificity, and accuracy of 93.3%, 63.3%, and 73.3%, respectively. A regression coefficient-based nomogram was constructed based on the CECT-predominant model (Fig. 3b). The calibration curve of the nomogram for the probability of cHCC-CCA demonstrated good agreement between prediction and observation (eFigure 1b). The H-L test yielded a nonsignificant statistic (p > 0.05).
The diagnostic performance was compared between the CEUS-predominant model and the CECT-predominant model (shown in Table 5 and Fig. 4). The CECT-predominant model had a higher diagnostic sensitivity (93.3%) than the CEUS-predominant model (55.6%; p < 0.001) but a lower diagnostic specificity (CECT vs. CEUS: 63.3% vs. 80.0%; p = 0.020). The two models had comparable diagnostic accuracy (CECT vs. CEUS: 73.3% vs. 71.9%; p = 0.583). In addition, we compared the AUC values between the models and found that the AUC value of the CECT-predominant model (AUC
Table 5 Comparison of the diagnostic performance between the CEUS-predominant and CECT-predominant models
The CEUS-predominant model The CECT-predominant model Sensitivity (%) 55.6 93.3 < 0.001 Specificity (%) 80.0 63.3 0.020 Accuracy (%) 71.9 73.3 0.583 AUC (95% CI)a 0.720 (0.632, 0.808) 0.874 (0.816, 0.931) 0.001 ≤ Sensitivity (%) 50.0 88.5 0.006 Specificity (%) 92.0 70.0 0.013 Accuracy (%) 77.6 76.3 1.000 AUC (95% CI) 0.710 (0.595, 0.808) 0.792 (0.684, 0.877) 0.226 Sensitivity (%) 63.2 100 0.016 Specificity (%) 65.0 55.0 0.503 Accuracy (%) 64.4 69.5 0.557 AUC (95% CI) 0.641 (0.505, 0.762) 0.775 (0.648, 0.873) 0.093
CEUS contrast-enhanced ultrasound, CECT contrast-enhanced computed tomography, AUC area under the curve, CI confidence interval
Graph: Fig. 4CEUS and CECT images of a 56-year-old man with chronic hepatitis B and CA 19-9 < 100 U/mL. A 7.3-cm mass was detected in segment IV of the liver (A). A hypoechoic mass with poor boundary on conventional ultrasound (A, a); on CEUS, the mass showed hyperenhancement, a nonsmooth tumor margin (stars), and tumor supply artery (arrowhead) at 18 s (A, b); in the late phase (179 s), the hyperenhanced area in the arterial phase of mass exhibited partial washout with partial isoenhancement (stars) and partial hypoenhancement area (arrowhead, A, c). Based on these features, the likelihood of this mass being diagnosed as cHCC-CCA was smaller than 30% according to the CEUS-predominant model (B). There was no obvious cirrhotic liver background, and the mass showed low density on abdominal CT image (A, d), rim enhancement and < 50% nonrim enhancement (mainly the right posterior part of the lesion, arrow) in the arterial phase (A, e), "washout" absence, nonsmooth tumor margin, and a thin incomplete enhanced capsule (arrow) seen in the portal venous phase (A, f). Based on these features, the likelihood of this mass being diagnosed as cHCC-CCA was higher than 90.0% according to the CECT-predominant model (C). The mass was pathologically proven to be combined hepatocellular-cholangiocarcinoma
Graph: Fig. 5The diagnostic performance of the CEUS-predominant and CECT-predominant models was assessed through ROC curve and AUC analyses
For the smaller nodules (≤ 5 cm, based on the pathology results) group, the CECT-predominant model had higher diagnostic sensitivity for cHCC-CCA than the CEUS-predominant model (88.5% vs. 50.0%; p = 0.006), while the CEUS-predominant model presented better diagnostic specificity than the CECT-predominant model (92.0% vs. 70.0%; p = 0.013). The two models showed comparable diagnostic performance in differentiating cHCC-CCA from HCC (AUC
Combining tumor biomarkers and imaging features is critical in diagnosing cHCC-CCA due to its overlapping features with HCC. This propensity score-matched study found that approximately 44.4% of cHCC-CCAs on CEUS and 26.7% of cHCC-CCAs on CECT were evaluated as LR-5, which can easily mimic HCC. Therefore, we constructed and compared two imaging-predominant diagnostic models based on clinical data and nodule features on CEUS and CECT imaging to identify cHCC-CCA. The results indicated that the CECT-predominant model exhibited nearly perfect diagnostic sensitivity (93.3%), which was significantly higher than that of the CEUS-predominant model (55.6%; p < 0.001). On the other hand, the CEUS-predominant model demonstrated commendable diagnostic specificity, particularly for lesions smaller than 5 cm (92.0% vs. 70.0%; p = 0.013).
Cirrhosis detected by CECT is highly suggestive of HCC. In this study, we found that few at-risk patients with cHCC-CCA had a cirrhotic liver background due to the different origins of HCC and cHCC-CCA, which is similar to the findings of the latest studies [[
On CEUS images, the presence of unclear boundaries in the intratumoral nonenhanced areas was an independent risk factor for cHCC-CCA. This might be elucidated by the fibrotic pathological findings (relying on the ICC component), similar to previous findings [[
Several studies have evaluated the performance of imaging characteristics in differentiating cHCC-CCA and HCC in recent years [[
Some limitations of this study should be mentioned. First, there was an unavoidable selection bias due to the single-center retrospective nature of the study, although we used PSM to lessen this bias. Second, no validation data were available to test and refine our models due to the limited size of the cHCC-CCA population. Third, we did not include ICC patients in this differential diagnostic study due to the limited number of ICC patients with HCC risk factors. Finally, the results of this study were based on a case-control design rather than a cohort design, which might not reflect real-world clinical epidemiological conditions. Therefore, large-scale multicenter studies are warranted to validate our findings.
The CECT-predominant model provides higher diagnostic sensitivity compared to the CEUS-predominant model for cHCC-CCA. Combining the CECT features with serum CA 19-9 > 100 U/mL showed excellent diagnostic sensitivity in differentiating cHCC-CCA from HCC, while the CEUS features could enhance diagnostic specificity, especially in the ≤ 5 cm subgroup.
We thank American Journal Experts (https://china.aje.com/) for the professional writing service.
JY: conceptualization; data curation; formal analysis; writing original draft. YZ: conceptualization; investigation; methodology; writing original draft. WygB: data curation; formal analysis; investigation; methodology; resources. YdC: investigation; writing, review and editing. JyH: data curation. KyZ: data curation. HJ: writing, review and editing; formal analysis. BS: formal analysis. ZxH: conceptualization; writing, review and editing. QL: conceptualization; funding acquisition; writing, review and editing. All authors had access to the study data and reviewed and approved the final manuscript for publication.
This work was supported by the National Natural Scientific Foundation of China [grant number 82171952].
The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request.
This single-center retrospective study was approved by the Institutional Review Board of West China Hospital, Sichuan University, and the requirement for informed patient consent was waived.
Not applicable.
The authors declare that they have no competing interests. Bin Song is a member of the Insights into Imaging Editorial Board; he has not taken part in the review or selection process of this article.
Graph: Additional file 1: Supplement 1: eMethods 1. Imaging acquisition protocols eMethods 2. Variable definition eMethods 3. The univariable selection of the CEUS-predominant and the CECT-predominant model eTable 1. Vascular phases of the liver lesions on CEUS eTable 2. Multi-phase contrast-enhanced CT scan eTable 3. The kappa analysis of imaging features assessment on CEUS and CECT between reviewers eTable 4. The multicollinearity analysis between variables of the CEUS-predominant model eTable 5. The multicollinearity analysis between variables of the CECT-predominant model eFigure 1. The calibration analysis of the CEUS-predominant model and the CECT-predominant models eFigure 2. The ROC curve of the two models in the subgroup analysis.
• AFP
- Alpha-fetoprotein
• APHE
- Arterial phase hyperenhancement
• CA 19-9
- Carbohydrate antigen 19-9
• CECT
- Contrast-enhanced computed tomography
• CEUS
- Contrast-enhanced ultrasound
- cHCC-CCA
- Combined hepatocellular-cholangiocarcinoma
• CI
- Confidence interval
• HBV
- Hepatitis B virus
• HCC
- Hepatocellular carcinoma
• HCV
- Hepatitis C virus
• LI-RADS
- Liver Imaging Reporting and Data System
• LR
- Liver Imaging Reporting and Data System category
• OR
- Odds ratio
• PSM
- Propensity score matching
• PVP
- Portal venous phase
• TIV
- Tumor in vein
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
By Jie Yang; Yun Zhang; Wu-yong-ga Bao; Yi-di Chen; Hanyu Jiang; Jia-yan Huang; Ke-yu Zeng; Bin Song; Zi-xing Huang and Qiang Lu
Reported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author