Amide proton transfer-weighted imaging in predicting aggressiveness of hepatocellular carcinoma: comparison with diffusion-weighted imaging

Introduction

Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Surgical interventions, including partial hepatectomy or liver transplantation, remain the primary management approach and best option for cure (1). A recent study, however, showed that about 70% of HCC patients relapsed within 5 years after surgical resection and 35% relapsed within 5 years after liver transplantation, with a median survival of less than 2 years following recurrence (2-4). Several histopathologic factors have been identified as independent risk factors for recurrence and decreased survival in patients with HCC after hepatectomy, including microvascular invasion (MVI), Ki-67 labeling index (LI), and histologic grading (5-9), but these require biopsy, an invasive procedure with risks of bleeding, sample errors and tumor cell seeding (10,11). Therefore, a noninvasive imaging technique for the preoperative evaluation of MVI, Ki-67 LI, and histologic grade of HCC holds great potential in enhancing patient prognosis (12).

Diffusion-weighted imaging (DWI) plays a valuable role in the diagnosis and characterization of focal liver lesions (13-16). The DWI technique operationalizes the apparent diffusion coefficient (ADC) to quantify water diffusion. This ADC value is instrumental in probing key tissue biological features, most notably cellularity and water content (17). Previous studies have demonstrated the potential of ADC in predicting early recurrence of HCC following curative resection, as well as noninvasively estimating MVI, Ki-67 LI, and histologic grade in HCC before resection (18). Nevertheless, the predictive performance of ADC requires further enhancement and rigorous evaluation.

Amide proton transfer-weighted (APTw) imaging is defined as a chemical exchange saturation transfer (CEST) technique that probes the exchange of amide protons (at 3.5 ppm) in mobile cellular proteins and peptides with bulk water (19,20). Emerging evidence supports APTw magnetic resonance imaging (MRI) as a valuable diagnostic tool in oncology, demonstrating utility for noninvasive assessment of tumor histologic grade and prediction of proliferative activity (via Ki-67 LI) across diverse malignancies. Validated applications include glioma World Health Organization (WHO) grading, Ki-67 correlation in rectal adenocarcinoma, and molecular subtyping in breast cancer—highlighting its broad potential for characterizing tumor aggressiveness (21-23). While APTw has been explored for HCC grading (24-26), its capacity to concurrently evaluate key HCC aggressiveness markers—MVI and Ki-67 index—remains inadequately investigated.

Therefore, this study aimed to investigate the potential of amide proton transfer (APT) imaging in simultaneously predicting three key markers of HCC aggressiveness—MVI, Ki-67 LI, and histologic grade—and compare with DWI. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1105/rc).

Methods

Patients

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Medical Ethics Committee of the Northern Jiangsu People’s Hospital (No. 2023JS051) and informed consent was taken from all the patients. Between June 2021 and September 2022, consecutive patients with suspected primary liver lesions were enrolled from Northern Jiangsu People’s Hospital and underwent abdominal MRI with APTw and DWI sequences. The inclusion criteria were: (I) patients with suspected HCC based on prior computed tomography (CT) or ultrasound; (II) no history of any prior treatment (e.g., chemotherapy or surgery) before the MRI examination; (III) complete records of postoperative pathological and immunohistochemical examination of MVI, Ki-67 LI, tumor grade; (IV) the interval between abdominal MRI examination and histologic acquisition was not more than two weeks. The exclusion criteria were: (I) maximum diameter of the liver lesion less than 20 mm (susceptible to slice misregistration during respiratory-triggered acquisition, causing artifacts); (II) preoperative imaging evidence of portal vein tumor thrombus (indicating macrovascular invasion); (III) incomplete clinical and imaging data; (IV) poor image quality that precluded quantitative analysis. In patients with multiple lesions, only the largest (dominant) lesion was selected for analysis.

MRI acquisition

All MRI examinations were performed on a 3-T scanner (GE Discovery MR750W; Waukesha, WI, USA) using a 32-channel phased-array torso coil. To prevent potential interference with the APT signal, patients were instructed to fast for 4–6 hours and to refrain from any contrast-enhanced imaging studies for 24 hours prior to the MRI examination. The liver tumor imaging protocol included breath-hold, transverse T1-weighted sequences liver acquisition with volume acceleration-flex (LAVA-Flex) with repetition time (TR) =4.9 ms, echo time (TE) =1.15/2.3 ms, matrix =296×192, field of view (FOV) =420 mm × 336 mm, slice thickness =2.5 mm, no slice gap, a respiratory triggered fast-spin echo based T2-weighted Propeller imaging at coronal and transverse views (effective TR =3,529 ms, TE =90 ms, matrix =320×320, FOV =380 mm × 360 mm, slice thickness =5 mm, slice interval =1 mm); and a respiratory triggered DWI (effective TR =3,529 ms, TE =66.2 ms, matrix =128×128, FOV =380 mm × 304 mm, slice thickness =5 mm, slice gap =1 mm, two b-values of 0 and 1,000 s/mm² with separate 2 and 4 number of excitations applied).

Before contrast injection, APTw imaging was performed with a two-dimensional (2D) respiratory triggered single shot fast spin-echo echo-planar-imaging based imaging sequence. Based on non-contrast T1-weighted and T2-weighted images, a single slice encompassing the largest cross-sectional area of the lesion was selected for APTw imaging. For APTw imaging, a total of 29 saturation frequencies were applied, ranging from −896 to +896 Hz in 64 Hz increments. A separate S₀ image without saturation was also acquired for signal normalization. The saturation power (B₁) was set to 2 µT and the saturation duration time to 2,000 ms. The remaining imaging parameters were as follows: effective TR =3,529 ms, TE=27.4 ms; FOV, 350 mm × 245 mm; matrix size 128×128; and slice thickness =8 mm with slice interval of 2 mm. An extra 11 saturated images at frequencies ranging from −240 to 240 Hz with 48 Hz increment were acquired with identical scan parameters but lower saturation power of 0.5 µT for B₀ inhomogeneity correction with Water Saturation Shift Referencing (WASSR) algorithm (27). The scan time was about 3 minutes with respiratory rate of 12 per minute.

Imaging measurement and analysis

The image review was performed independently by two abdominal radiologists (with 5 and 13 years of experience) using a dedicated post-processing workstation (Advantage Windows Workstation, GE Healthcare, Waukesha, WI, USA). All disagreements were resolved afterward by consensus reached after discussion. Both radiologists knew that the lesion was HCC, but they did not know other clinical, laboratory or histological information. Two radiologists independently evaluated the following image features of HCC: (I) maximum tumor diameter: defined as the maximum diameter measured on the axial portal phase image; (II) tumor margin: the interface between tumor and normal liver parenchyma on MRI images, the tumor margin was categorized as smooth or non-smooth on portal or delayed phase images. “Smooth” corresponded to a nodular mass with smooth outline. “Non-smooth” corresponded to irregular edge or tumor edge budding into the surrounding liver parenchyma; (III) presence of a capsule: a smooth, uniform and well-defined enhancement area around the tumor edge on the portal venous phase or delayed phase; (IV) peritumoral enhancement: defined as peritumor enhancement in the late arterial phase or early portal venous phase; (V) enhancement pattern: classified as typical dynamic enhancement, with arterial hypervascularity, and portal washout, as well as atypical dynamic enhancement.

APTw images, reflecting the magnetization transfer ratio asymmetry (MTR_asym) at 3.5 ppm, were reconstructed on the scanner console. The APT effect was quantified using the following MTR_asym calculation at 3.5 ppm downfield from the water signal:

MTRasym(3.5ppm)=[Ssat(−3.5ppm)−Ssat(+3.5ppm)]/S0[1]

where S_sat and S₀ represent the signal intensity with and without the saturation pulse, respectively. ADC maps were automatically calculated on the GE Advantage Workstation using the conventional mono-exponential model. The average values of APTw and ADC from three regions of interest (ROIs) were obtained by each radiologist. With reference to axial T2-weighted and portal venous phase images, three circular ROIs (approximately 30–40 mm²) were manually delineated within the solid tumor component on the APTw images and ADC maps for each patient (22). All ROIs were carefully placed to avoid cystic, necrotic, calcified, or hemorrhagic areas, as well as major blood vessels.

Histologic data acquisition

The pathological specimens were read by two pathologists with 11 and 9 years’ experience in histologic diagnosis with a final result reached by discussion and consensus.

All histologic specimens were routinely fixed with 10% formaldehyde solution and embedded in paraffin, and the serial sections of 5 µm thickness were examined by immunohistochemical streptavidin-peroxidase method. According to the results of immunohistochemical staining about the proportion of positive cells, Ki-67 ≤10% was defined as low expression, and >10% was defined as high expression (28).

Based on the final pathological reports, all tumors were graded from 1 to 4 according to the Edmondson-Steiner classification. For analytical purposes, we defined grades I and II as low-grade and grades III and IV as high-grade HCCs. Furthermore, MVI positivity was defined as the presence of tumor cells within endothelial-lined vessels (portal vein, hepatic vein, or large capsular vessels), identifiable only upon microscopic examination (29).

Statistical assessment

Intra-group correlation coefficient (ICC) was used to evaluate the inter-observer consistency of APTw and ADC measured by two radiologists. ICC >0.75 means good reproducibility. Normality was assessed by the Shapiro-Wilk test. Normally distributed data were reported as mean and standard deviation (SD) and the independent sample t-test was used for the comparison. Non-normal distribution data were reported as median and interquartile range (IQR) and Mann-Whitney U test was employed for the comparison. The qualitative parameters were expressed by the number of cases, and the differences between the two were assessed using the chi-square test or Fisher test to assess for significance. Receiver operating characteristic (ROC) curves were constructed to evaluate the predictive performance of the imaging parameters. The area under the ROC curve (AUC) and its 95% confidence interval (CI) were calculated. The optimal cutoff value, along with its corresponding sensitivity and specificity, was determined. All statistical analyses were performed using SPSS (version 23.0) and MedCalc (version 15.8). Two-sided P values of <0.05 were considered statistically significant.

Results

Participants characteristics

Patients (n=160) with at least one liver lesion undergoing conventional MRI were enrolled and had the additional APTw imaging sequence covering their lesions. According to the inclusion and exclusion criteria, 104 patients were excluded, including 38 without histologic results, 23 cholangiocarcinoma, 5 metastasis, 3 hepatic cavernous hemangioma, 2 hepatic angiomyolipoma, 1 hepatic epithelioid hemangioendothelioma and 8 with tumor diameter less than 2 cm. There were 9 cases with history of HCC treatment, 5 cases of poor APTw image quality and 10 cases of incomplete clinical data. Finally, 56 patients were included in this study (Figure 1) including 50 males and 6 females. The mean age of the participants was 58 years with a SD of 9, ranging from 27 to 76 years old. There were 27 MVI⁺ patients and 29 MVI⁻ patients. A significant difference was observed between the two groups in terms of tumor maximum diameter (MVI⁺, 6.7±3.3 vs. MVI⁻, 4.2±2.2 cm, P=0.024). The overall average value of Ki-67 was 24%±18%, with a range of 1–90%. According to the Ki-67 index threshold of 10%, the patients were divided into high Ki-67 LI (n=39) and low Ki-67 LI (n=17).

Figure 1 Participant inclusion flowchart. APTw, amide proton transfer-weighted; DWI, diffusion-weighted imaging; HCC, hepatocellular carcinoma; MRI, magnetic resonance imaging.

There were 33 cases determined to be high grade (16 cases of Edmondson-Steiner grades III and 17 cases of IV) and 23 cases were low grade (8 cases of Edmondson-Steiner grades I and 15 cases of II). AFP and background liver cirrhosis were significantly different between the two groups as shown in Table 1.

MR imaging measurements

The intraclass correlation coefficients of the APTw and ADC values measured by the two radiologists were 0.89 (95% CI: 0.81–0.93) and 0.91 (95% CI: 0.85–0.94), respectively, indicating good agreement.

Among all 56 patients with HCC, the average APTw value was 0.91%±1.37%, with range of (−3.20%, 4.77%). There was a moderate positive correlation between APTw value and Ki-67 LI (ρ=0.49, P<0.001). The average ADC value was (0.88±0.16)×10⁻³ mm²/sec, ranging from 0.63×10⁻³ to 1.26×10⁻³ mm²/sec. The tumor ADC showed a slight negative correlation with Ki-67 LI (ρ=−0.27, P=0.04). The average tumor maximum diameter was 56±31 mm, and there was no significant correlation between the tumor maximum diameter and Ki-67 LI (ρ=0.18, P=0.19).

Comparison of APTw and ADC values in different aggressive groups

MVI⁺ HCC exhibited higher APTw values compared to MVI⁻ HCC (1.68%±1.02% vs. 0.21%±1.29%; P<0.001). ADC values were significantly lower in the MVI⁺ group than in the MVI⁻ group (0.84±0.13 vs. 0.92±0.17 ×10⁻³ mm²/sec; P=0.04) (Table 2, Figure 2).

Figure 2 Box plots comparing the distributions of (A) APTw and (B) ADC values across different pathologic groups: MVI⁺vs. MVI⁻, high vs. low Ki-67 labeling index, and high-grade vs. low-grade HCC. *, P<0.05; **, P<0.01; ***, P<0.001. ADC, apparent diffusion coefficient; APTw, amide proton transfer-weighted; H-G, high grade; H-Ki-67, high Ki-67 labeling index; L-G, low grade; L-Ki-67, low Ki-67 labeling index; MVI, microvascular invasion.

HCC tumors with high Ki-67 LI had higher APTw values compared to HCC tumors with low Ki-67 LI [1.57% (IQR, 1.06% to 1.92%) vs. 0.16% (IQR, −0.67% to 0.72%); P<0.001]. ADC values were significantly lower in HCCs with high Ki-67 LI compared to those with low Ki-67 LI [0.82 (IQR, 0.75–0.90) vs. 1.01 (IQR, 0.87–1.10) ×10⁻³ mm²/sec; P=0.003] (Table 2, Figure 2).

High-grade HCCs demonstrated significantly higher APTw values than low-grade HCCs (1.54%±1.10% vs. 0.02%±1.25%; P<0.001). However, there was no significant difference in ADC between high-grade and low-grade HCC (0.86±0.16 vs. 0.91±0.16 ×10⁻³ mm²/sec; P=0.26) (Table 2, Figure 2).

Typical examples of highly aggressive and less aggressive HCC were shown in Figure 3 and Figure 4, respectively.

Figure 3 A 50-year-old man with high-grade, MVI-positive HCC. MR images include (A) T2-weighted, (B) diffusion-weighted (b=1,000 sec/mm²), (C) ADC, (D) S₀, and (E) APTw map overlaid on S₀. (F) Ki-67 immunohistochemistry (×100) shows a high proliferative index of 40%. The mean ADC and APTw values were 0.67×10⁻³ mm²/sec and 1.82%, respectively. ADC, apparent diffusion coefficient; APTw, amide proton transfer-weighted; HCC, hepatocellular carcinoma; MR, magnetic resonance; MVI, microvascular invasion.

Figure 4 A 68-year-old man with low-grade, MVI-negative HCC. MR images include (A) T2-weighted, (B) diffusion-weighted (b=1,000 sec/mm²), (C) ADC, (D) S₀, and (E) APTw map overlaid on S₀. (F) Ki-67 immunohistochemistry (×100) shows a low proliferative index of 3%. The consensus mean ADC and APTw values were 0.81×10⁻³ mm²/sec and 0.21%, respectively. ADC, apparent diffusion coefficient; APTw, amide proton transfer-weighted; HCC, hepatocellular carcinoma; MR, magnetic resonance; MVI, microvascular invasion.

Histopathologic tumor marker prediction efficacy

The AUC value of APTw for differentiating HCC with MVI⁺ was 0.82 (95% CI: 0.69–0.91). The optimal cut-off value for APTw was determined to be 0.69%, with a sensitivity of 89% and specificity of 62%. The AUC value of ADC for distinguishing MVI⁺ was 0.65 (95% CI: 0.51–0.77), with the cut-off value of 0.86×10⁻³ mm²/sec, sensitivity of 74%, and specificity of 62%. There was a significant difference in the discrimination efficiency between APTw and ADC values (P=0.03). The results were shown in Figure 5A and Table 3.

Figure 5 ROC curves demonstrating the performance of APTw and ADC values in stratifying hepatocellular carcinoma by (A) MVI status, (B) Ki-67 labeling index, and (C) histologic grade. ADC, apparent diffusion coefficient; APTw, amide proton transfer-weighted; AUC, area under the curve; MVI, microvascular invasion; ROC, receiver operating characteristic.

The AUC value of APTw to distinguish the high and low expression state of Ki-67 was 0.85 (95% CI: 0.72–0.93; cut-off value =1.24%; sensitivity =67%; specificity =100%). The AUC value of ADC to distinguish the high and low expression of Ki-67 was 0.76 (95% CI: 0.62–0.86; cut-off value =0.90×10⁻³ mm²/sec; sensitivity =80%; specificity =71%). There was no significant difference in the discrimination efficiency between APTw and ADC (P=0.28). The results were shown in Figure 5B and Table 3.

The AUC value of APTw value for the diagnosis of high-grade HCC was 0.82 (95% CI: 0.71–0.92), with the cut-off value of 0.64%, sensitivity of 88%, and specificity of 70%. The results were shown in Figure 5C and Table 3.

Discussion

In this study, we systematically evaluated APTw imaging and compared it with DWI for predicted the presence of histopathologic markers of HCC tumor aggressiveness. High-grade HCC with MVI⁺ and high Ki-67 LI exhibited significantly higher APT values. Additionally, HCC with MVI⁺ and high Ki-67 LI demonstrated lower ADC signal intensity. Consistent with this trend, higher-grade HCC also showed lower ADC values compared to lower-grade HCC. Compared to DWI, APTw imaging demonstrated a more robust performance in assessing HCC, especially in the assessment of MVI and tumor grading. This indicated that APTw imaging hold promise in providing valuable insights for the assessment and characterization of aggressive HCC.

The APTw signal intensity is primarily driven by the abundance of labile amide protons found in mobile cellular proteins (19,30). Moreover, angiogenesis, micro-necrosis, and nuclear atypia mucin can also affect APT signal intensity (31,32). Our study highlighted the potential of APTw imaging in predicting the aggressiveness of HCC prior to surgical intervention. We found that high-grade HCC with high Ki-67 LI exhibited higher APTw values. Ki-67 LI and the APTw values of HCC had moderately positive correlation (ρ=0.49, P<0.001), which was consistent with previous studies on HCC and other tumors (22,26,33). This finding suggests that the elevated APTw signal in high-grade and high Ki-67 index HCC can be attributed to increased levels of mobile proteins and peptides, which collectively contribute to accelerated cell proliferation, heightened protein synthesis, and elevated cellular density. Furthermore, hepatic cellular nuclear atypia, which induces the interaction of macromolecules with hydrophobic cell membranes and the release of proteins and peptides, may also contribute to the higher APTw values (34,35). In addition to these factors, our study demonstrated that MVI⁺ exhibited higher APTw values. It is suggested that this association could be attributed to increased expression of vascular endothelial growth factor A (VEGF-A) in HCC with MVI⁺ (33). VEGF-A can promote the enhancement of vascular permeability and angiogenesis, enrich the blood supply, and lead to the increase of tumor blood protein and polypeptide content. The ADC value, reflecting the diffusion of water molecules in tissues, is a sensitive indicator of cellular changes. Our findings revealed that HCC with MVI⁺ and high Ki-67 LI had lower ADC values. Ki-67 LI and the ADC values of tumors have moderately positive correlation (ρ=−0.27, P=0.04), consistent with previous studies (36,37). In MVI⁺ tumors with high Ki-67 LI, the increased tissue cell density significantly limits water molecule diffusion, leading to lower ADC values. Interestingly, our study revealed no significant difference in ADC values between high-grade and low-grade HCC, consistent with prior research (37,38). Previous studies have indicated that high-grade HCC tends to exhibit decreased ADC values. In general, the histopathologic grade of a malignant neoplasm is determined by both cellular atypia and structural atypia. The cellularity detected by DWI primarily reflects structural atypia. Therefore, our study’s results can be considered appropriate. Alternatively, the observed results might also be influenced by our relatively small sample size.

In our comparative study, ROC curve analysis revealed that APTw MRI outperformed DWI in predicting MVI in HCC. Although there was no significant difference in predicting Ki-67 expression, APTw MRI demonstrated higher specificity. One potential explanation for the superior performance of APTw MRI compared to DWI relates to inherent limitations of the DWI model. First, ADC is not a pure reflection of water diffusion, as it is significantly influenced by tissue T2 relaxation time (39). Second, ADC represents a composite parameter that captures both true molecular diffusion and perfusion-related contributions. Additionally, it may be affected by factors such as intravoxel T2 heterogeneity, necrosis, cystic degeneration, or abundant vascularity (40). On the other hand, APTw imaging, which detects endogenous proteins and peptides, is not affected by this factor and may provide a more accurate representation of tissue characteristics.

Despite the promising diagnostic performance, it is important to recognize that the APTw signal derived from MTRasym at 3.5 ppm is not entirely specific to amide protons. The measured contrast may include overlapping contributions from exchange-relayed nuclear Overhauser effect (rNOE) at –3.5 ppm, conventional magnetization transfer (MT), and direct water saturation (DS) effects, all of which may vary with tumor composition and be influenced by B₀ and B₁ field inhomogeneities (30,41). Additionally, the APTw signal may also be affected by the longitudinal relaxation of water (T1w), which is often prolonged in tumor tissue. Although MTRasym does not explicitly correct for T1w, previous studies have shown that under non-steady-state acquisition conditions, this effect is limited and chemical exchange remains the dominant contrast mechanism (42). Moreover, APTw imaging performed at moderate saturation power (e.g., 2 µT) has been reported to yield stable values even in the presence of altered tissue relaxation properties (43,44). Taken together, these factors suggest that the APTw signal represents a composite CEST effect and should be interpreted as a surrogate marker that integrates both protein content and the tumor microenvironment, rather than a direct readout of protein concentration alone (45).

There are several limitations in our study. First, the sample size was small. Second, the manually selected ROIs did not cover entire tumor volume, potentially underrepresenting tumor heterogeneity in HCC. Furthermore, an automated, standardized ROI placement method could improve reproducibility in future studies. Third, our respiratory-triggered APT sequence only acquired a single 2D image and thus could not assess the intratumoral signal heterogeneity of the whole tumor. Recent studies have shown that free-breathing 3D CEST MRI has been used in healthy volunteers (46), offering the opportunity to expand this work to a 3D assessment of the entire tumor. Moreover, combining readily available clinical features with advanced MRI parameters can improve diagnostic accuracy.

Conclusions

APTw can predict more aggressive HCC, specifically high-grade tumors with microvascular invasion and a high Ki-67 labeling index. When compared to DWI, APTw demonstrated superior ability in predicting MVI⁺ and tumor grading. Therefore, APTw imaging could potentially provide additional value in assessing HCC aggressiveness, complementing the information obtained from DWI. This may have significant implications for treatment planning and prognosis, ultimately benefiting the patient.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1105/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1105/dss

Funding: This work was supported by Jiangsu Province “333” Project from Jiangsu Province Human Resources and Social Security Department (No. 2022-3-6-139) and the Eighth Batch of Hospital-Level Support Technology Projectof North Jiangsu People’s Hospital (No. FCJS202521); Yangzhou City Basic Research Program (Joint Special Project): Health Projects (No. 2024-4-03).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1105/coif). D.S. reports that this work was supported by Yangzhou City Basic Research Program (Joint Special Project): Health Projects (No. 2024-4-03). J.S. and W.D. are employees of GE Healthcare, MR Research China, Beijing, China, who support APT sequence but had no control over any data or information submitted for publication nor any control over any parts of data or information included in this study. X.L. reports that this work was supported by Jiangsu Province “333” Project from Jiangsu Province Human Resources and Social Security Department (No. 2022-3-6-139) and the Eighth Batch of Hospital-Level Support Technology Project of North Jiangsu People’s Hospital (No. FCJS202521). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Medical Ethics Committee of the Northern Jiangsu People’s Hospital (No. 2023JS051) and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS. Hepatocellular carcinoma. Nat Rev Dis Primers 2021;7:6. [Crossref] [PubMed]
2. Rajendran L, Ivanics T, Claasen MP, Muaddi H, Sapisochin G. The management of post-transplantation recurrence of hepatocellular carcinoma. Clin Mol Hepatol 2022;28:1-16. [Crossref] [PubMed]
3. Yao LQ, Chen ZL, Feng ZH, Diao YK, Li C, Sun HY, Zhong JH, Chen TH, Gu WM, Zhou YH, Zhang WG, Wang H, Zeng YY, Wu H, Wang MD, Xu XF, Pawlik TM, Lau WY, Shen F, Yang T. Clinical Features of Recurrence After Hepatic Resection for Early-Stage Hepatocellular Carcinoma and Long-Term Survival Outcomes of Patients with Recurrence: A Multi-institutional Analysis. Ann Surg Oncol 2022; Epub ahead of print. [Crossref]
4. Lv K, Cao X, Du P, Fu JY, Geng DY, Zhang J. Radiomics for the detection of microvascular invasion in hepatocellular carcinoma. World J Gastroenterol 2022;28:2176-83. [Crossref] [PubMed]
5. Nitta H, Allard MA, Sebagh M, Ciacio O, Pittau G, Vibert E, Sa Cunha A, Cherqui D, Castaing D, Bismuth H, Guettier C, Lewin M, Samuel D, Baba H, Adam R. Prognostic Value and Prediction of Extratumoral Microvascular Invasion for Hepatocellular Carcinoma. Ann Surg Oncol 2019;26:2568-76. [Crossref] [PubMed]
6. Hong YM, Cho M, Yoon KT, Chu CW, Yang KH, Park YM, Rhu JH. Risk factors of early recurrence after curative hepatectomy in hepatocellular carcinoma. Tumour Biol 2017;39:1010428317720863. [Crossref] [PubMed]
7. Yang C, Su H, Liao X, Han C, Yu T, Zhu G, Wang X, Winkler CA, O'Brien SJ, Peng T. Marker of proliferation Ki-67 expression is associated with transforming growth factor beta 1 and can predict the prognosis of patients with hepatic B virus-related hepatocellular carcinoma. Cancer Manag Res 2018;10:679-96. [Crossref] [PubMed]
8. Lu M, Qu Q, Xu L, Zhang J, Liu M, Jiang J, Shen W, Zhang T, Zhang X. Prediction for Aggressiveness and Postoperative Recurrence of Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced Magnetic Resonance Imaging. Acad Radiol 2023;30:841-52. [Crossref] [PubMed]
9. Zhang L, Chen J, Jiang H, Rong D, Guo N, Yang H, Zhu J, Hu B, He B, Yin M, Venkatesh SK, Ehman RL, Wang J. MR elastography as a biomarker for prediction of early and late recurrence in HBV-related hepatocellular carcinoma patients before hepatectomy. Eur J Radiol 2022;152:110340. [Crossref] [PubMed]
10. Court CM, Harlander-Locke MP, Markovic D, French SW, Naini BV, Lu DS, Raman SS, Kaldas FM, Zarrinpar A, Farmer DG, Finn RS, Sadeghi S, Tomlinson JS, Busuttil RW, Agopian VG. Determination of hepatocellular carcinoma grade by needle biopsy is unreliable for liver transplant candidate selection. Liver Transpl 2017;23:1123-32. [Crossref] [PubMed]
11. Russo FP, Imondi A, Lynch EN, Farinati F. When and how should we perform a biopsy for HCC in patients with liver cirrhosis in 2018? A review. Dig Liver Dis 2018;50:640-6. [Crossref] [PubMed]
12. Yao J, Li K, Yang H, Lu S, Ding H, Luo Y, et al. Analysis of Sonazoid contrast-enhanced ultrasound for predicting the risk of microvascular invasion in hepatocellular carcinoma: a prospective multicenter study. Eur Radiol 2023;33:7066-76. [Crossref] [PubMed]
13. Galea N, Cantisani V, Taouli B. Liver lesion detection and characterization: role of diffusion-weighted imaging. J Magn Reson Imaging 2013;37:1260-76. [Crossref] [PubMed]
14. Alwalid O, Wang Y, Fan W, Han P. Value of gadoxetic acid-enhanced MR imaging and DWI in classification, characterization and confidence in diagnosis of solid focal liver lesions. Scand J Gastroenterol 2021;56:72-80. [Crossref] [PubMed]
15. Lewis S, Peti S, Hectors SJ, King M, Rosen A, Kamath A, Putra J, Thung S, Taouli B. Volumetric quantitative histogram analysis using diffusion-weighted magnetic resonance imaging to differentiate HCC from other primary liver cancers. Abdom Radiol (NY) 2019;44:912-22. [Crossref] [PubMed]
16. Wang G, Jian W, Cen X, Zhang L, Guo H, Liu Z, Liang C, Zhou W. Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Preoperative Diffusion-Weighted MR Using Deep Learning. Acad Radiol 2021;28:S118-27. [Crossref] [PubMed]
17. Taouli B, Koh DM. Diffusion-weighted MR imaging of the liver. Radiology 2010;254:47-66. [Crossref] [PubMed]
18. Jing M, Cao Y, Zhang P, Zhang B, Lin X, Deng L, Han T, Zhou J. The Benefit of Apparent Diffusion Coefficient in Evaluating the Invasiveness of Hepatocellular Carcinoma. Front Oncol 2021;11:719480. [Crossref] [PubMed]
19. Zhou J, Payen JF, Wilson DA, Traystman RJ, van Zijl PC. Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI. Nat Med 2003;9:1085-90. [Crossref] [PubMed]
20. Zhou J, Lal B, Wilson DA, Laterra J, van Zijl PC. Amide proton transfer (APT) contrast for imaging of brain tumors. Magn Reson Med 2003;50:1120-6. [Crossref] [PubMed]
21. Nishie A, Takayama Y, Asayama Y, Ishigami K, Ushijima Y, Okamoto D, Fujita N, Tsurumaru D, Togao O, Manabe T, Oki E, Kubo Y, Hida T, Hirahashi-Fujiwara M, Keupp J, Honda H. Amide proton transfer imaging can predict tumor grade in rectal cancer. Magn Reson Imaging 2018;51:96-103. [Crossref] [PubMed]
22. Yu H, Wen X, Wu P, Chen Y, Zou T, Wang X, Jiang S, Zhou J, Wen Z. Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma? Eur Radiol 2019;29:5298-306. [Crossref] [PubMed]
23. Meng N, Wang X, Sun J, Han D, Bai Y, Wei W, Wang Z, Jia F, Wang K, Wang M. A comparative study of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in the diagnosis and evaluation of breast cancer. Eur Radiol 2021;31:1707-17. [Crossref] [PubMed]
24. Wu B, Jia F, Li X, Zhang M, Han D, Jia Z. Amide Proton Transfer Imaging vs Diffusion Kurtosis Imaging for Predicting Histological Grade of Hepatocellular Carcinoma. J Hepatocell Carcinoma 2020;7:159-68. [Crossref] [PubMed]
25. Wu B, Jia F, Li X, Li L, Wang K, Han D. Comparative Study of Amide Proton Transfer Imaging and Intravoxel Incoherent Motion Imaging for Predicting Histologic Grade of Hepatocellular Carcinoma. Front Oncol 2020;10:562049. [Crossref] [PubMed]
26. Lin Y, Luo X, Yu L, Zhang Y, Zhou J, Jiang Y, Zhang C, Zhang J, Li C, Chen M. Amide proton transfer-weighted MRI for predicting histological grade of hepatocellular carcinoma: comparison with diffusion-weighted imaging. Quant Imaging Med Surg 2019;9:1641-51. [Crossref] [PubMed]
27. Kim M, Gillen J, Landman BA, Zhou J, van Zijl PC. Water saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments. Magn Reson Med 2009;61:1441-50. [Crossref] [PubMed]
28. Li Y, Chen J, Weng S, Sun H, Yan C, Xu X, Ye R, Hong J. Small hepatocellular carcinoma: using MRI to predict histological grade and Ki-67 expression. Clin Radiol 2019;74:653.e1-9. [Crossref] [PubMed]
29. Cong WM, Bu H, Chen J, Dong H, Zhu YY, Feng LH, Chen J. Guideline Committee. Practice guidelines for the pathological diagnosis of primary liver cancer: 2015 update. World J Gastroenterol 2016;22:9279-87. [Crossref] [PubMed]
30. Zhou J, Heo HY, Knutsson L, van Zijl PCM, Jiang S. APT-weighted MRI: Techniques, current neuro applications, and challenging issues. J Magn Reson Imaging 2019;50:347-64. [Crossref] [PubMed]
31. Meng N, Wang X, Sun J, Han D, Ma X, Wang K, Wang M. Application of the amide proton transfer-weighted imaging and diffusion kurtosis imaging in the study of cervical cancer. Eur Radiol 2020;30:5758-67. [Crossref] [PubMed]
32. Zheng S, van der Bom IM, Zu Z, Lin G, Zhao Y, Gounis MJ. Chemical exchange saturation transfer effect in blood. Magn Reson Med 2014;71:1082-92. [Crossref] [PubMed]
33. Fu F, Meng N, Huang Z, Sun J, Wang X, Shang J, Fang T, Feng P, Wang K, Han D, Wang M. Identification of histological features of endometrioid adenocarcinoma based on amide proton transfer-weighted imaging and multimodel diffusion-weighted imaging. Quant Imaging Med Surg 2022;12:1311-23. [Crossref] [PubMed]
34. Whittaker CS, Coady A, Culver L, Rustin G, Padwick M, Padhani AR. Diffusion-weighted MR imaging of female pelvic tumors: a pictorial review. Radiographics 2009;29:759-74; discussion 774-8. [Crossref] [PubMed]
35. Tang Y, Dundamadappa SK, Thangasamy S, Flood T, Moser R, Smith T, Cauley K, Takhtani D. Correlation of apparent diffusion coefficient with Ki-67 proliferation index in grading meningioma. AJR Am J Roentgenol 2014;202:1303-8. [Crossref] [PubMed]
36. Huang Z, Xu X, Meng X, Hou Z, Liu F, Hua Q, Liu Q, Xiu J. Correlations between ADC values and molecular markers of Ki-67 and HIF-1α in hepatocellular carcinoma. Eur J Radiol 2015;84:2464-9. [Crossref] [PubMed]
37. Xu P, Zeng M, Liu K, Shan Y, Xu C, Lin J. Microvascular invasion in small hepatocellular carcinoma: is it predictable with preoperative diffusion-weighted imaging? J Gastroenterol Hepatol 2014;29:330-6. [Crossref] [PubMed]
38. Nasu K, Kuroki Y, Tsukamoto T, Nakajima H, Mori K, Minami M. Diffusion-weighted imaging of surgically resected hepatocellular carcinoma: imaging characteristics and relationship among signal intensity, apparent diffusion coefficient, and histopathologic grade. AJR Am J Roentgenol 2009;193:438-44. [Crossref] [PubMed]
39. Wáng YXJ, Ma FZ. A tri-phasic relationship between T2 relaxation time and magnetic resonance imaging (MRI)-derived apparent diffusion coefficient (ADC). Quant Imaging Med Surg 2023;13:8873-80. [Crossref] [PubMed]
40. Wáng YXJ. An explanation for the triphasic dependency of apparent diffusion coefficient (ADC) on T2 relaxation time: the multiple T2 compartments model. Quant Imaging Med Surg 2025;15:3779-91. [Crossref] [PubMed]
41. Zhou J, Hong X, Zhao X, Gao JH, Yuan J. APT-weighted and NOE-weighted image contrasts in glioma with different RF saturation powers based on magnetization transfer ratio asymmetry analyses. Magn Reson Med 2013;70:320-7. [Crossref] [PubMed]
42. Zu Z. Towards the complex dependence of MTR(asym) on T(1w) in amide proton transfer (APT) imaging. NMR Biomed 2018;31:e3934. [Crossref] [PubMed]
43. Heo HY, Lee DH, Zhang Y, Zhao X, Jiang S, Chen M, Zhou J. Insight into the quantitative metrics of chemical exchange saturation transfer (CEST) imaging. Magn Reson Med 2017;77:1853-65. [Crossref] [PubMed]
44. Cui J, Zhao Y, Sun C, Xu J, Zu Z. Evaluation of contributors to amide proton transfer-weighted imaging and nuclear Overhauser enhancement-weighted imaging contrast in tumors at a high magnetic field. Magn Reson Med 2023;90:596-614. [Crossref] [PubMed]
45. Wáng YXJ, Dou W, Shen Z, Zhang Y. An update on liver chemical exchange saturation transfer imaging with a focus on clinical translation. Quant Imaging Med Surg 2023;13:4057-76. [Crossref] [PubMed]
46. Han P, Cheema K, Cao T, Lee HL, Han F, Wang N, Han H, Xie Y, Christodoulou AG, Li D. Free-breathing 3D CEST MRI of human liver at 3.0 T. Magn Reson Med 2023;89:738-45. [Crossref] [PubMed]