Early Recurrence Of Hepatocellular Carcinoma Research Articles

Serum Galectin-9 mirrors immune-evasive microenvironment and predicts early recurrence in hepatocellular carcinoma

Serum Galectin-9 mirrors immune-evasive microenvironment and predicts early recurrence in hepatocellular carcinoma

Nomogram for predicting early recurrence of hepatocellular carcinoma with narrow resection margin

PurposeNarrow resection margin hepatocellular carcinoma (NRM-HCC) has a high incidence of early recurrence. Our study was designed to identify prognostic factors in patients with NRM-HCC, establish and validate a nomogram model to predict early recurrence of NRM-HCC patients.MethodsWe retrospectively analyzed data from 2957 NRM-HCC patients who underwent radical hepatectomy at three medical centers between December 2009 and January 2015. Patients were randomly assigned to a training cohort (n = 2069) and a validation cohort (n = 888). Using univariate and multivariate COX regression to determine early relapse factors in NRM-HCC patients, and used these factors to construct a nomogram. The accuracy of the prediction was evaluated using the C-index, receiver operating characteristic (ROC) and calibration curve. Decision curve analysis (DCA) assessed the predictive value of the models. Finally, the recurrence-free survival of different risks was analyzed using Kaplan-Meier (K-M) method.ResultsThe nomogram of NRM model contains alpha-fetoprotein (AFP), alkaline phosphatase (ALP), tumor size, tumor number, microvascular invasion (MVI), tumor capsular, and satellite nodules. The model shows good discrimination with C-indexes of 0.71 (95% CI: 0.69–0.72) and 0.72 (95% CI: 0.70–0.75) in the train cohort and test cohort respectively. Decision curve analysis demonstrated that the model is clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different early recurrence. The web-based tools are convenient for clinical practice.ConclusionsNRM model demonstrated favorable performance in predicting early recurrence in NRM-HCC patients. This novel model will be helpful to guide postoperative follow-up and adjuvant therapy.

Self-supervised multi-modal feature fusion for predicting early recurrence of hepatocellular carcinoma

Surgical resection stands as the primary treatment option for early-stage hepatocellular carcinoma (HCC) patients. Postoperative early recurrence (ER) is a significant factor contributing to the mortality of HCC patients. Therefore, accurately predicting the risk of ER after curative resection is crucial for clinical decision-making and improving patient prognosis. This study leverages a self-supervised multi-modal feature fusion approach, combining multi-phase MRI and clinical features, to predict ER of HCC. Specifically, we utilized attention mechanisms to suppress redundant features, enabling efficient extraction and fusion of multi-phase features. Through self-supervised learning (SSL), we pretrained an encoder on our dataset to extract more generalizable feature representations. Finally, we achieved effective multi-modal information fusion via attention modules. To enhance explainability, we employed Score-CAM to visualize the key regions influencing the model’s predictions. We evaluated the effectiveness of the proposed method on our dataset and found that predictions based on multi-phase feature fusion outperformed those based on single-phase features. Additionally, predictions based on multi-modal feature fusion were superior to those based on single-modal features.

ViT-based quantification of intratumoral heterogeneity for predicting the early recurrence in HCC following multiple ablation.

This study aimed to develop a quantitative intratumoral heterogeneity (ITH) model for assessing the risk of early recurrence (ER) in pre-treatment multimodal imaging for hepatocellular carcinoma (HCC) patients undergoing ablation treatments. This multi-centre study enrolled 633 HCC patients who underwent ultrasound-guided local ablation between January 2015 and September 2022. Among them, 422, 85, 57 and 69 patients underwent radiofrequency ablation (RFA), microwave ablation (MWA), laser ablation (LA) and irreversible electroporation (IRE) ablation, respectively. Vision-Transformer-based quantitative ITH (ViT-Q-ITH) features were extracted from the US and MRI sequences. Multivariable logistic regression analysis was used to identify variables associated with ER. A combined model integrated clinic-radiologic and ViT-Q-ITH scores. The prediction performance was evaluated concerning calibration, clinical usefulness and discrimination. The final training cohort and internal validation cohort included 318 patients and 83 patients, respectively, who underwent RFA and MWA. The three external testing cohorts comprised of 106 patients treated with RFA, 57 patients treated with LA and 69 patients who underwent IRE ablation. The combined model showed excellent predictive performance for ER in the training (AUC: .99, 95% CI: .99-1.00), internal validation (AUC: .86, 95% CI: .78-.94), external testing (AUC: .83, 95% CI: .73-.92), LA (AUC: .84, 95% CI: .73-.95) and IRE (AUC: .82, 95% CI: .72-.93) cohorts, respectively. Decision curve analysis further affirmed the clinical utility of the combined model. The multimodal-based model, incorporating clinic-radiologic factors and ITH features, demonstrated superior performance in predicting ER among early-stage HCC patients undergoing different ablation modalities.

Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection.

To explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection. Total 165 HCC patients (ER, n = 96 vs. non-early recurrence (NER), n = 69) were retrospectively collected and divided into a training cohort (n = 132) and a validation cohort (n = 33). From pretreatment CEMR images, a total of 3111 radiomics features were extracted, and radiomics models were constructed using five machine learning classifiers (logistic regression, support vector machine, k-nearest neighbor, extreme gradient Boosting, and multilayer perceptron). DL models were established via three variations of ResNet architecture. The clinical-radiological (CR), radiomics combined with clinical-radiological (RCR), and deep learning combined with RCR (DLRCR) models were constructed. Model discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, anddecision curve analysis, respectively. The best-performing model wascompared with the widely used staging systems and preoperative prognostic indexes. The RCR model (area under the curve (AUC): 0.841 and 0.811) and the optimal radiomics model (AUC: 0.839 and 0.804) achieved better performance than the CR model (AUC: 0.662 and 0.752) in the training and validation cohorts, respectively. The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. The DL, radiomics, and CR predictors (aspartate aminotransferase (AST) and tumor diameter) were combined to construct the DLRCR model. The DLRCR model presented the best performance over any model, yielding an AUC, an accuracy, a sensitivity, a specificity of 0.917, 0.886, 0.889, and 0.882 in the training cohort and of 0.844, 0.818, 0.800, and 0.846 in the validation cohort, respectively. The DLRCR model achieved better clinical utility compared to the clinical staging systems and prognostic indexes. Both radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection.

Combined Bone Mineral Density (BMD) and Monocyte-to-Lymphocyte Ratio (MLR) Predicts Recurrence and Prognosis in Hepatocellular Carcinoma Patients Following Liver Resection.

Bone mineral density (BMD) and monocyte-to-lymphocyte ratio (MLR) were recently identified as novel risk factors for patients with several malignancies. The objective of this study was to validate the role of preoperative BMD/MLR as a potential prognostic biomarker in patients with hepatocellular carcinoma (HCC) undergoing liver resection. This investigation enrolled 442 adult patients diagnosed with HCC who underwent liver resection. The patients were classified into high- and low-BMD/MLR groups based on the median, and forward stepwise logistic regression was employed to identify independent predictors for early HCC recurrence. To mitigate the impact of confounding factors, a propensity score matching (PSM) analysis was conducted between patients in the high- and low-BMD/MLR groups. The Kaplan-Meier method was employed to assess and compare the disease-free survival (DFS) and overall survival (OS) between the two cohorts. The study categorized patients into high-BMD/MLR and low-BMD/MLR groups. Forward stepwise logistic regression analysis revealed that low BMD/MLR (P < 0.001), tumor size > 50 mm (P < 0.001), and AFP > 200 ug/L (P = 0.001) were significantly associated with the early recurrence of HCC. Moreover, the results suggested that DFS and OS were significantly shorter in the low-BMD/MLR group compared to the high-BMD/MLR group, both before and after PSM (P < 0.05). Preoperative BMD/MLR held promise as a prognostic biomarker for early recurrence and prognosis in patients with HCC who underwent liver resection. Furthermore, the integration of tumor size, AFP level, and BMD/MLR demonstrated a robust predictive capacity for early recurrence within this patient population.

Construction of a 2.5D Deep Learning Model for Predicting Early Postoperative Recurrence of Hepatocellular Carcinoma Using Multi-View and Multi-Phase CT Images.

To construct a 2.5-dimensional (2.5D) CT radiomics-based deep learning (DL) model to predict early postoperative recurrence of hepatocellular carcinoma (HCC). We retrospectively analyzed the data of patients who underwent HCC resection at 2 centers. The 232 patients from center 1 were randomly divided into the training (162 patients) and internal validation cohorts (70 patients); 91 patients from center 2 formed the external validation cohort. We developed a 2.5D DL model based on a central 2D image with the maximum tumor cross-section and adjacent slices. Multiple views (transverse, sagittal, and coronal) and phases (arterial, plain, and portal) were incorporated. Multi-instance learning techniques were applied to the extracted data; the resulting comprehensive feature set was modeled using Logistic Regression, RandomForest, ExtraTrees, XGBoost, and LightGBM, with 5-fold cross validation and hyperparameter optimization with Grid-search. Receiver operating characteristic curves, calibration curves, DeLong test, and decision curve analysis were used to evaluate model performance. The 2.5D DL model performed well in the training (AUC: 0.920), internal validation (AUC: 0.825), and external validation cohorts (AUC: 0.795). The 3D DL model performed well in the training cohort and poorly in the internal and external validation cohorts (AUCs: 0.751, 0.666, and 0.567, respectively), indicating overfitting. The combined model (2.5D DL+clinical) performed well in all cohorts (AUCs: 0.921, 0.835, 0.804). The Hosmer-Lemeshow test, DeLong test, and decision curve analysis confirmed the superiority of the combined model over the other signatures. The combined model integrating 2.5D DL and clinical features accurately predicts early postoperative HCC recurrence.

Preoperative serum glutathione reductase activity and alpha-fetoprotein level are associated with early postoperative recurrence of hepatocellular carcinoma

Abstract Objectives The aim of this study is to investigate the correlation between preoperative serum glutathione reductase (GR) activity, alpha-fetoprotein (AFP) level, and early postoperative recurrence in patients diagnosed with hepatocellular carcinoma (HCC). Methods The data of 91 patients with HCC who underwent hepatectomy at Jinhua Hospital from January 2020 to December 2021 were retrospectively analyzed. A comparison of clinical characteristics between Non-Recurrent group and Recurrence group was conducted, and the association between GR activity, AFP levels, and early postoperative recurrence in HCC was investigated. Results Recurrence group (n=50) had a significantly higher AFP levels (median: 226.7 vs. 99.7 μg/L, p&lt;0.001) and significantly lower GR activity (median: 55.0 vs. 68.0 U/L, p&lt;0.0001) compared with Non-Recurrent group (n=41). The GR activity was negatively correlated with the AFP level (r=−0.4275, p&lt;0.01). Low GR activity (OR=0.948; 95 % CI: 0.910–0.988; p=0.011) and high AFP levels (OR=1.003; 95 % CI: 1.000–1.006; p=0.036) independently contribute to an increased risk of early postoperative recurrence in HCC patients. The area under receiver operating characteristic curve of GR activity and AFP level for predicting early postoperative recurrence of HCC was 0.790 and 0.708, respectively. Patients with GR &gt;60U/L had a higher early postoperative non-recurrence rate than patients with GR ≤60U/L (71.4 % [30/42] vs. 22.4 % [11/49]; HR=4.026; 95 % CI: 2.254–7.188; p&lt;0.01); Patients with AFP ≤100 μg/L had a higher early postoperative non-recurrence rate than patients with AFP &gt;100 μg/L (65.6 % [21/32] vs. 33.9 % [20/59]; HR=2.490; 95 % CI: 1.397–4.438; p&lt;0.01). Conclusions The preoperative serum GR activity and AFP level hold significant predictive value for early postoperative recurrence in HCC patients.

Definition of early recurrence of hepatocellular carcinoma based on the concordance index and optimal treatment strategy

BackgroundHepatocellular carcinoma (HCC) has a high rate of recurrence and requires multiple treatment options. Early recurrence has been associated with poor prognosis; however, the timing of early recurrence varies in the literature. Therefore, in this study, we aimed to determine the timing of early recurrence with the best prognostic value and clarify the appropriate treatment strategy. MethodsThis retrospective study conducted to determine prognosis and treatments included 274 patients with HCC who underwent liver resection at the Saitama Cancer Center. The prognostic value of early recurrence at each surveillance period was calculated using Harrell's concordance index (C-index). ResultsThe C-index at 9 months postoperatively was 0.656, which was the highest cut-off value for early recurrence. Notably, 122 patients had no recurrence, 58 had early recurrence (<9 months), and 94 had late recurrence (≥9 months). The 5-year overall survival (OS) of patients with early recurrence was worse (16.5 %, p<0.001), whereas that of those with late and no recurrences were similar (79.2 % vs. 70.9 %, p=0.86). The 5-year OS rate of patients with late intrahepatic recurrence in the surgery group was 100 %. ConclusionThe optimal cutoff value for the timing of early recurrence was 9 months postoperatively. The early recurrence group had a high rate of extrahepatic recurrence and poor OS, whereas late recurrences were mostly intrahepatic, with no difference in prognosis between the late and no recurrence groups. Patients with intrahepatic recurrence after >9 months are considered for rehepatectomy because of its good outcomes. SynopsisThe C-index-based optimal cutoff for the timing of early HCC recurrence was 9 months postoperatively. The early recurrence group had a high rate of extrahepatic recurrence and poor OS, whereas late recurrences were mostly intrahepatic with a good prognosis.

Multiparametric MRI based deep learning model for prediction of early recurrence of hepatocellular carcinoma after SR following TACE

BackgroundSurgical resection (SR) following transarterial chemoembolization (TACE) is a promising treatment for unresectable hepatocellular carcinoma (uHCC). However, biomarkers for the prediction of postoperative recurrence are needed.PurposeTo develop and validate a model combining deep learning (DL) and clinical data for early recurrence (ER) in uHCC patients after TACE.MethodsA total of 511 patients who received SR following TACE were assigned to derivation (n = 413) and validation (n = 98) cohorts. Deep learning features were taken from the largest tumor area in liver MRI. A nomogram using DL signatures and clinical data was made to forecast early recurrence risk in uHCC patients. Model performance was evaluated using area under the curve (AUC).ResultsA total of 2278 subsequences and 31,346 slices multiparametric MRI including contrast-enhanced T1WI, T2WI and DWI were input in the DL model simultaneously. Multivariable analysis identified three independent predictors for the development of the nomogram: tumor number (hazard ratio [HR]:3.42, 95% confidence interval [CI]: 2.75–4.31, P = 0.003), microvascular invasion (HR: 9.21, 6.24–32.14; P < 0.001), and DL scores (HR: 17.46, 95% CI: 12.94–23.57, P < 0.001). The AUC of the nomogram was 0.872 and 0.862 in two cohorts, significantly outperforming single-subsequence-based DL mode and clinical model (all, P < 0.001). The nomogram provided two risk strata for cumulative overall survival in two cohorts, showing significant statistical results (P < 0.001).ConclusionsThe DL-based nomogram is essential to identify patients with uHCC suitable for treatment with SR following TACE and may potentially benefit personalized decision-making.

Prognostic implications of CK19 positivity in patients with early recurrent hepatocellular carcinoma after hepatic resection undergoing transarterial chemoembolization

BackgroundThis study aimed to compare the survival outcomes of transarterial chemoembolization (TACE) between patients with early recurrent hepatocellular carcinoma (rHCC) after hepatic resection, stratified by cytokeratin (CK) 19 expression.MethodsA retrospective analysis was conducted on 63 patients with early rHCC after hepatic resection who underwent TACE between January 2017 and December 2021. Patients were divided into two groups based on CK19 expression: CK19-negative (n=31) and CK19-positive (n=32). Overall survival (OS) and progression-free survival (PFS) were compared between the two groups using the Kaplan-Meier method and log-rank test. Cox regression analysis was performed to identify independent risk factors for OS and PFS.ResultsThe CK19-negative group demonstrated a significantly longer median OS compared to the CK19-positive group (635 days vs. 432 days, p=0.013). Similarly, the CK19-negative group had a longer median PFS than the CK19-positive group (291 days vs. 117 days, p=0.014). Multivariate Cox analysis identified Child-Pugh A grade, CK19-negative expression, and increased TACE sessions as protective factors for OS. No severe TACE-related adverse events were observed.ConclusionIn patients with early rHCC after hepatic resection, those with CK19-positive expression had poorer survival outcomes following TACE compared to CK19-negative patients. These findings suggest the need for additional therapies to improve survival in CK19-positive individuals.

Predictors of early recurrence of hepatocellular carcinoma after hepatic resection in a single Egyptian institute: A retrospective study.

Predictors of early recurrence of hepatocellular carcinoma after hepatic resection in a single Egyptian institute: A retrospective study.

Assessing microvascular invasion in HBV-related hepatocellular carcinoma: an online interactive nomogram integrating inflammatory markers, radiomics, and convolutional neural networks.

The early recurrence of hepatocellular carcinoma (HCC) correlates with decreased overall survival. Microvascular invasion (MVI) stands out as a prominent hazard influencing post-resection survival status and metastasis in patients with HBV-related HCC. The study focused on developing a web-based nomogram for preoperative prediction of MVI in HBV-HCC. 173 HBV-HCC patients from 2017 to 2022 with complete preoperative clinical data and Gadopentetate dimeglumine-enhanced magnetic resonance images were randomly divided into two groups for the purpose of model training and validation, using a ratio of 7:3. MRI signatures were extracted by pyradiomics and the deep neural network, 3D ResNet. Clinical factors, blood-cell-inflammation markers, and MRI signatures selected by LASSO were incorporated into the predictive nomogram. The evaluation of the predictive accuracy involved assessing the area under the receiver operating characteristic (ROC) curve (AUC), the concordance index (C-index), along with analyses of calibration and decision curves. Inflammation marker, neutrophil-to-lymphocyte ratio (NLR), was positively correlated with independent MRI radiomics risk factors for MVI. The performance of prediction model combined serum AFP, AST, NLR, 15 radiomics features and 7 deep features was better than clinical and radiomics models. The combined model achieved C-index values of 0.926 and 0.917, with AUCs of 0.911 and 0.907, respectively. NLR showed a positive correlation with MRI radiomics and deep learning features. The nomogram, incorporating NLR and MRI features, accurately predicted individualized MVI risk preoperatively.

Single-cell RNA sequencing reveals intratumoral heterogeneity and multicellular community in primary hepatocellular carcinoma underlying microvascular invasion

BackgroundMicrovascular invasion (MVI) is associated with an unfavorable prognosis and early recurrence of hepatocellular carcinoma (HCC), which is the crucial pathological hallmark of immunotherapy. While microvascular invasion (MVI) in hepatocellular carcinoma (HCC) currently lacks a detailed single-cell analysis of the tumor microenvironment (TME), it holds significant promise for immunotherapy using immune checkpoint inhibitors (ICI). MethodsWe performed single-cell RNA sequencing (scRNA-seq) on 3 MVI positive (MVIP) and 14 MVI-negative (MVIN) tumor tissues, as well as their paired adjacent non-tumoral tissues. ResultsWe identified SPP1+ macrophages and CD4+ proliferative T cells as intertumoral populations critical for the formation of cold tumors and immunosuppressive environments in MVI-positive patients and verified their prognostic value in correlation with MVIP HCC patients. Additionally, we identified SPP1+ dominated interactions between SPP1+ macrophages and the immunosuppressive T population as contributors to MVI destruction and tumorigenesis. ConclusionsWe provide a comprehensive single-cell atlas of HCC patients with MVI, shedding light on the immunosuppressive ecosystem and upregulated signaling associated with MVI. These findings demonstrate that intercellular mechanisms drive MVI and provide a potential immunotherapeutic target for HCC patients with HCC and underlying MVI.

A simple model to predict early recurrence of hepatocellular carcinoma after liver resection.

Multiple studies have reported models for predicting early recurrence of hepatocellular carcinoma (HCC) after liver resection (LR). However, these models are too complex to use in daily practice. We aimed to develop a simple model. We enrolled 1133 patients with newly diagnosed HCC undergoing LR. The Kaplan - Meier method and log-rank test were used for survival analysis and Cox proportional hazards analysis to identify prognostic factors associated with early recurrence (i.e., recurrence within two years after LR). Early recurrence was identified in 403 (35.1%) patients. In multivariate analysis, alpha-fetoprotein (AFP) 20-399 vs. < 20 ng/ml (HR = 1.282 [95% confidence interval = 1.002-1.639]; p = 0.048); AFP ≥ 400 vs. < 20 ng/ml (HR = 1.755 [1.382-2.229]; p < 0.001); 7th edition American Joint Committee on Cancer (AJCC) stage 2 vs. 1 (HR = 1.958 [1.505-2.547]; p < 0.001); AJCC stage 3 vs. 1 (HR = 4.099 [3.043-5.520]; p < 0.001); and pathology-defined cirrhosis (HR = 1.46 [1.200-1.775]; p < 0.001) were associated with early recurrence. We constructed a predictive model with these variables, which provided three risk strata for recurrence-free survival (RFS): low risk, intermediate risk, and high risk, with two-year RFS of 79%, 57%, and 35%, respectively (p < 0.001). We developed a simple model to predict early recurrence risk for patients undergoing LR for HCC.

Risk of early hepatocellular carcinoma recurrence following liver resection: arbitrary specification or possible target to improve outcome?

Risk of early hepatocellular carcinoma recurrence following liver resection: arbitrary specification or possible target to improve outcome?

Prediction power of radiomics in early recurrence of hepatocellular carcinoma: A systematic review and meta-analysis.

Raiomics is an emerging auxiliary diagnostic tool, but there are still differences in whether it can be applied to predict early recurrence of hepatocellular carcinoma (HCC). The purpose of this meta-analysis was to systematically evaluate the predictive power of radiomics in the early recurrence (ER) of HCC. Comprehensive studies on the application of radiomics to predict ER in HCC patients after hepatectomy or curative ablation were systematically screened in Embase, PubMed, and Web of Science. Ten studies which is involving a total of 1929 patients were reviewed. The overall estimates of radiomic models for sensitivity and specificity in predicting the ER of HCC were 0.79 (95% confidence interval [CI]: 0.68-0.87) and 0.83 (95% CI: 0.73-0.90), respectively. The area under the summary receiver operating characteristic curve (SROC) was 0.88 (95% CI: 0.85-0.91). The imaging method is a reliable method for diagnosing HCC. Radiomics, which is based on medical imaging, has excellent power in predicting the ER of HCC. With the help of radiomics, we can predict the recurrence of HCC after surgery more effectively and provide a useful reference for clinical practice.

Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Ultrasound-guided Microwave Ablation

Ultrasound-guided microwave ablation (MWA) is recommended as a first-line treatment for early liver cancer due to its minimally invasive, efficient, and cost-effective nature. It utilizes microwave radiation to heat and destroy tumor cells as a local thermal therapy and offers the benefits of being minimally invasive, repeatable, and applicable to tumors of various sizes and locations. However, despite the efficacy of MWA, early recurrence after treatment remains a challenge, particularly when it occurs within a year and has a significant impact on the prognosis of the patient. This study aimed to identify the risk factors for early recurrence after MWA in patients with hepatocellular carcinoma (HCC) and establish a predictive model. A total of 119 patients with hepatocellular carcinoma (HCC) treated in the Department of Ultrasound at the First Affiliated Hospital of the Air Force Medical University from January, 2020 to April, 2022 were included in this study. Patients were categorized into the early recurrence group and the non-early recurrence group based on whether recurrence occurred within 1 year. We conducted univariate analysis on 29 variables. A predictive model was developed using multiple-factor logistic regression analysis, and a risk column graph was created. A total of 28 patients were included in the early recurrence group, with an early recurrence rate of 23%. Tumor size ≥ 3cm, multiple tumors, AST > 35 U/L, low pathological differentiation, CD34 positive, Ki67 level, quantitative parameters mean transit time (mTT), and rise time (RT) were confirmed as risk factors affecting early recurrence after ablation (P < 0.05). Furthermore, the model constructed based on these 5 predictive factors, including tumor size, tumor number, pathological differentiation, CD34, and quantitative analysis parameter mTT, demonstrated good predictive ability, with an AUC of 0.93 in the training set and 0.86 in the validation set. Our research indicates that the risk column graph can be utilized to predict the risk of early postoperative recurrence in patients after MWA. This contributes to guiding personalized clinical treatment decisions and provides important references for improving the prognosis of patients.

Decision-Tree Models Indicative of Microvascular Invasion on MRI Predict Survival in Patients with Hepatocellular Carcinoma Following Tumor Ablation.

Histological microvascular invasion (MVI) is a risk factor for poor survival and early recurrence in hepatocellular carcinoma (HCC) after surgery. Its prognostic value in the setting of locoregional therapies (LRT), where no tissue samples are obtained, remains unknown. This study aims to establish CT-derived indices indicative of MVI on liver MRI with superior soft tissue contrast and evaluate their association with patient survival after ablation via interstitial brachytherapy (iBT) versus iBT combined with prior conventional transarterial chemoembolization (cTACE). Ninety-five consecutive patients, who underwent ablation via iBT alone (n = 47) or combined with cTACE (n = 48), were retrospectively included between 01/2016 and 12/2017. All patients received contrast-enhanced MRI prior to LRT. Overall (OS), progression-free survival (PFS), and time-to-progression (TTP) were assessed. Decision-tree models to determine Radiogenomic Venous Invasion (RVI) and Two-Trait Predictor of Venous Invasion (TTPVI) on baseline MRI were established, validated on an external test set (TCGA-LIHC), and applied in the study cohorts to investigate their prognostic value for patient survival. Statistics included Fisher's exact and t-test, Kaplan-Meier and cox-regression analysis, area under the receiver operating characteristic curve (AUC-ROC) and Pearson's correlation. OS, PFS, and TTP were similar in both treatment groups. In the external dataset, RVI showed low sensitivity but relatively high specificity (AUC-ROC = 0.53), and TTPVI high sensitivity but only low specificity (AUC-ROC = 0.61) for histological MVI. In patients following iBT alone, positive RVI and TTPVI traits were associated with poorer OS (RVI: p < 0.01; TTPVI: p = 0.08), PFS (p = 0.04; p = 0.04), and TTP (p = 0.14; p = 0.03), respectively. However, when patients with combined cTACE and iBT were stratified by RVI or TTPVI, no differences in OS (p = 0.75; p = 0.55), PFS (p = 0.70; p = 0.43), or TTP (p = 0.33; p = 0.27) were observed. The study underscores the role of non-invasive imaging biomarkers indicative of MVI to identify patients, who would potentially benefit from embolotherapy via cTACE prior to ablation rather than ablation alone.

Preoperative and postoperative MRI-based models versus clinical staging systems for predicting early recurrence in hepatocellular carcinoma

BackgroundTo predict the early recurrence of HCC patients who received radical resection using preoperative variables based on Gd-EOB-DTPA enhanced MRI, followed by the comparison with the postoperative model and clinical staging systems. MethodsOne hundred and twenty-nine HCC patients who received radical resection were categorized into the early recurrence group (n = 48) and the early recurrence-free group (n = 81). Through COX regression analysis, statistically significant variables of laboratory, pathologic, and Gd-EOB-DTPA enhanced MRI results were identified. The preoperative and postoperative models were established to predict early recurrence, and the prognostic performances and differences were compared between the two models and clinical staging systems. ResultsSix variables were incorporated into the preoperative model, including alpha-fetoprotein (AFP) level, aspartate aminotransferase/platelet ratio index (APRI), rim arterial phase hyperenhancement (rim APHE), peritumoral hypointensity on hepatobiliary phase (HBP), CERHBP (tumor-to-liver SI ratio on hepatobiliary phase imaging), and ADC value. Moreover, the postoperative model was developed by adding microvascular invasion (MVI) and histological grade. The C-index of the preoperative model and postoperative model were 0.889 and 0.901 (p = 0.211) respectively. Using receiver operating characteristic curve analysis (ROC) and decision curve analysis (DCA), it was determined that the innovative models we developed had superior predictive capabilities for early recurrence in comparison to current clinical staging systems. HCC patients who received radical resection were stratified into low-, medium-, and high-risk groups on the basis of the preoperative and postoperative models. ConclusionThe preoperative and postoperative MRI-based models built in this study were more competent compared with clinical staging systems to predict the early recurrence in hepatocellular carcinoma.