Keywords: Liver, Data Analysis

Motivation: Hepatocellular carcinoma (HCC) can be categorized into proliferative and non-proliferative classes, with proliferative HCC exhibiting aggressive characteristics and a poor prognosis.

Goal(s): To develop a predictive model for proliferative HCC using Liver Imaging Reporting and Data System (LI-RADS) and to investigate its prognostic value for HCC.

Approach: A logistic regression nomogram was constructed based on LI-RADS features to identify proliferative HCC. The implication of model-predicted proliferative HCC for different therapeutic outcomes in HCC was investigated.

Results: The predictive model for proliferative HCC performed well and is a risk factor for postoperative recurrence in HCC, associated with favorable outcomes in systemic therapy.

Impact: The MR-based model, utilizing LI-RADS v2018, could predict proliferative HCC before treatment. Patients with model-predicted proliferative HCC had more post-hepatectomy recurrences but better responses to systemic therapy, which may facilitate clinical decision-making for more precise and rational therapeutic strategies.

Keywords: Liver, fMRI, Hepatocellular Carcinoma； Immunotherapy； Nanocarrier; IVIM-MRI; Tumor Microenvironment

Motivation: To enhance the efficacy of hepatocellular carcinoma immunotherapy using a nanocarrier and to explore IVIM-MRI for monitoring the tumor immune microenvironment.

Goal(s): To synthesize iRGD-targeted liposomes to enhance the treatment efficacy of hepatocellular carcinoma and to develop effective biomarkers for the tumor microenvironment.

Approach: We synthesized iRGD-modified liposomal co-encapsulating Lenvatinib and BMS-202. IVIM-MRI was performed before and at 6 and 12 days after treatments, followed by pathological examination after the final scan.

Results:  iRGD-lip@Len/BMS-202 promotes tumor vascular normalization and effectively activates an anti-tumor immune response. Importantly, the derived parameters D* and f are significantly correlated with tumor vascular normalization and immune activation.

Impact: The iRGD-targeted dual-drug liposomal nanoparticles exhibited potent synergistic anti-tumor effects. Additionally, IVIM-MRI facilitated the monitoring of changes in the tumor microenvironment, with the D* and f parameters serving as valuable indicators for evaluating tumor vascular network and immune microenvironment modulation.

Keywords: Liver, Tumor, Hepatocellular carcinoma

Motivation: Our report targets precise timing in hepatocellular carcinoma (HCC) diagnosis via MRI. Timely recognition of the late hepatic arterial phase is crucial to avoid unnecessary invasive biopsies and minimize patient risks.

Goal(s): Our report evaluates the TWIST technique's effectiveness in HCC diagnosis, aiming to reduce the need for percutaneous biopsies.

Approach: We integrated TWIST into our liver MRI protocols, capturing images at preset intervals, enhancing temporal resolution for HCC diagnosis.

Results: Our report successfully employs the TWIST technique to diagnose HCC by capturing arterial hyperenhancement. This innovation improves diagnostic accuracy and diminishes the necessity for invasive procedures, benefiting patients.

Impact: The implementation of the TWIST technique promises safer and more accurate hepatocellular carcinoma (HCC) diagnosis. This technique minimizes invasive biopsies, reshaping clinical practice and offering a new perspective on non-invasive diagnostic techniques.

Keywords: Liver, Radiomics, Magnetic resonance imaging, IVIM, Hepatocellular carcinoma, tertiary lymphoid structures

Motivation: Intra-tumoral tertiary lymphoid structures (TLSs) are associated with a favorable prognosis for patients with hepatocellular carcinoma (HCC). Intravoxel incoherent motion (IVIM) sequences describe heterogeneity of tumor components.

Goal(s): We aimed to explore the value of intravoxel incoherent motion (IVIM) sequences in predicting TLSs.

Approach:  IVIM quantitative parameters and radiomics features were obtained. A fusion model based on the above and clinical characteristics was constructed. Receiver operating characteristic  curve analysis was performed to assess the diagnostic performance of different models for TLSs prediction.

Results: Radiomics features based on IVIM-DWI can be better conducive to preoperative prediction of TLSs in patients than IVIM parameter maps.

Impact: TLSs exhibit considerable promise in prognostic prediction and the identification of appropriate candidates for immunotherapy. The fusion model based on the IVIM-DWI showed great performance in predicting TLSs, assisting the selection of clinical immunotherapy patients.

Keywords: Liver, Liver, hepatocellular carcinoma

Motivation: Hepatocellular carcinoma (HCC) exhibits significant intertumoral heterogeneity, which contributes significantly to treatment resistance and failure. Noninvasive imaging and radiomics for preoperative decoding of the subtypes and prognosis may be valuable in clinical management.

Goal(s): To preoperatively develop and validate clustering analysis of HCC based on MRI radiomics features for identifying subtypes with discrete prognosis.

Approach: We performed clustering analysis of HCC based on MRI radiomics features to detect distinct subtypes, and subsequently clinicopathological parameters and prognosis were compared and evaluated between different subtypes.

Results: Based on the radiomics features of MRI, clustering analysis identified two distinct subtypes with discrete prognosis in HCC patients.

Impact: Clustering analysis based on the radiomics features of multiparametric MRI is a potential noninvasive decision-making method for the management of patients with HCC in clinical practice.

Keywords: Liver, Liver

Motivation: Prognostic value of microvascular invasion (MVI) in combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) was verified, and an effective prediction model is warranted to facilitate risk stratification and individual management.

Goal(s): To establish an MRI-based radiomics model for predicting MVI status of cHCC-CCA, and to investigate biological processes underlying the radiomics model.

Approach: Clinical data, conventional MR features, MR-based radiomics features and RNA sequencing data were collected and analyzed.

Results: A robust MRI-based radiomics model was established for predicting MVI status in cHCC-CCA, in which potential prognostic value and underlying biological processes that regulate immune response were demonstrated.

Impact: MVI is a significant manifestation of tumor invasiveness,and the MR-based radiomics model established in our study will facilitate risk stratification. Furthermore, underlying biological processes demonstrated in radiomics model will offer valuable insights for guiding immunotherapy strategies.

Keywords: Liver, Liver

Motivation: Noninvasive assessment of high-risk histopathology (microvascular invasion or Edmondson-Steiner G3/4) for early HCC is critical but challenging.

Goal(s): To develop an MRI-based diagnostic model for high-risk histopathology.

Approach: This dual-center retrospective study included consecutive patients who underwent contrast-enhanced MRI and subsequent curative resection or RFA for solitary BCLC 0/A HCC≤5 cm. A diagnostic model was developed against pathology based on resection-treated patients. 

Results: 554 patients were included. Serum α-fetoprotein, non-simple nodular growth subtype, and the VICT2 trait constituted the model (testing center AUC, 0.828). Adjuvant therapies were associated with improved RFS (resection, P=.009; RFA, P=.009) for the model-positive patients.

Impact: This dual-center study developed and externally validated a diagnostic model which could effectively predict high-risk histopathology and adjuvant therapy benefit for patients receiving curative resection or radiofrequency ablation for solitary BCLC 0 or A HCCs ≤5 cm.

Keywords: Liver, Quantitative Imaging, Hepatocellular carcinoma (HCC); apparent diffusion coefficient (ADC); diffusion kurtosis imaging (DKI); Intravoxel incoherent motion diffusion-weighted imaging (IVIM); histologic grade; microvascular invasion (MVI)

Motivation: IVIM and DKI, effective in HCC malignancy prediction, face limitations due to lengthy scan times. The Simultaneous Multi-Slice (SMS) technology has successfully reduced scan times for ADC studies, but its application in IVIM and DKI remains underexplored.

Goal(s): To evaluate SMS-accelerated IVIM and DKI's efficacy in predicting HCC microvascular invasion (MVI) and tumor grading compared to conventional methods.

Approach: The study enrolled 42 HCC patients, conducting MRI with both conventional and SMS-accelerated DWI, DKI, and IVIM.

Results: SMS significantly reduced MRI scan times while maintaining reliable diffusion metrics, proving more effective than ADC in predicting MVI and tumor grades in HCC.

Impact: Integrating SMS into IVIM and DKI protocols can notably shorten scan times while preserving diagnostic accuracy in MVI and tumor grading, potentially improving clinical efficiency and patient management.

Keywords: Liver, Cancer

Motivation: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related death.

Goal(s): This study investigated the value of gadobenate dimeglumine-enhanced quantitative parameters for predicting the proliferative subtype of HCC and patients’ prognosis.

Approach: All HCC lesions were resected and pathologically confirmed. The lesion-to-liver contrast enhancement ratio (LLCER) was measured in the hepatobiliary phase.

Results: LLCER was identified as an independent predictor of proliferative HCC. Patients with LLCER < -4.59% had a significantly higher incidence of proliferative HCC. In addition, patients with LLCER < -4.61% showed poorer overall survival than those with LLCER ≥ -4.61%.

Impact: Quantitative information from gadobenate dimeglumine–enhanced MRI can provide crucial information on hepatocellular carcinoma subtypes. It might be valuable to design novel therapeutic strategies, such as targeted therapies or immunotherapy.