Imaging Predictors of Survival in Patients with Single Small Hepatocellular Carcinoma Treated with Transarterial Chemoembolization

Clinical outcomes of transarterial chemoembolization in Child-Turcotte Pugh class A patients with a single small (≤3 cm) hepatocellular carcinoma

BACKGROUND AND AIM

Transarterial chemoembolization (TACE) is one of the standard modalities used to treat unresectable hepatocellular carcinoma (HCC), but the effectiveness of TACE for treating patients with a solitary small (≤3 cm) HCC and well-preserved liver function has not been definitively established. This study aimed to determine the therapeutic impact of TACE in patients with these characteristics.

METHODS

This multicenter (four university hospitals) retrospective cohort study analyzed the medical records of 250 patients with a solitary small (≤3 cm) HCC and Child-Turcotte-Pugh (CTP) class A liver function diagnosed over 10 years. Posttreatment outcomes, including overall survival (OS), recurrence-free survival (RFS), and adverse events, were assessed following TACE therapy.

RESULTS

One hundred and thirty-eight of the 250 patients (55.2%) treated with TACE achieved complete remission (CR). Overall median OS was 77.7 months, and median OS was significantly longer in the CR group than in the non-CR group (89.1 vs. 58.8 months, P = 0.001). Median RFS was 19.1 months in the CR group. Subgroup analysis identified hypertension, an elevated serum albumin level, and achieving CR as significant positive predictors of OS, whereas diabetes, hepatitis c virus infection, and tumor size (>2 cm) were poor prognostic factors of OS.

CONCLUSIONS

The study demonstrates the effectiveness of TACE as a viable alternative for treating solitary small (≤3 cm) HCC in CTP class A patients.

A Deep Learning Model Combining Multimodal Factors to Predict the Overall Survival of Transarterial Chemoembolization

Background

To develop and validate an overall survival (OS) prediction model for transarterial chemoembolization (TACE).

Methods

In this retrospective study, 301 patients with hepatocellular carcinoma (HCC) who received TACE from 2012 to 2015 were collected. The residual network was used to extract prognostic information from CT images, which was then combined with the clinical factors adjusted by COX regression to predict survival using a modified deep learning model (DLOPCombin). The DLOPCombin model was compared with the residual network model (DLOPCTR), multiple COX regression model (DLOPCox), Radiomic model (Radiomic), and clinical model.

Results

In the validation cohort, DLOPCombin shows the highest TD AUC of all cohorts, which compared with Radiomic (TD AUC: 0.96vs 0.63) and clinical model (TD AUC: 0.96 vs 0.62) model. DLOPCombin showed significant difference in C index compared with DLOPCTR and DLOPCox models (P < 0.05). Moreover, the DLOPCombin showed good calibration and overall net benefit. Patients with DLOPCombin model score ≤ 0.902 had better OS (33 months vs 15.5 months, P < 0.0001).

Conclusion

The deep learning model can effectively predict the patients' overall survival of TACE.

Chemoembolization versus radiofrequency ablation for single small (≤ 3 cm) hepatocellular carcinoma: a propensity score matching analysis

OBJECTIVES

To compare the efficacy of transarterial chemoembolization (TACE) and radiofrequency ablation (RFA) for patients with single small (≤ 3 cm) hepatocellular carcinoma (HCC) and preserved liver function (Child-Pugh class A).

MATERIALS AND METHODS

The clinical features of treatment-naïve patients who underwent TACE and RFA as first-line treatment were balanced through propensity score matching (PSM). The primary endpoint was overall survival (OS), and the secondary endpoints were local tumor recurrence (LTR) and recurrence-free survival (RFS).

RESULTS

The analysis included 440 patients who received TACE, and 430 patients who received RFA. After PSM adjustment (323 pairs), the 5- and 10-year OS rates were 81% and 61%, respectively, in patients who underwent RFA, and 77% and 51%, respectively, for patients who underwent TACE (p = 0.021). Subgroup analyses showed that OS, LTR, and RFS were homogeneously better in the RFA group.

CONCLUSION

RFA was associated with better survival outcomes than TACE in patients with single small HCC and preserved liver function.

CLINICAL RELEVANCE STATEMENT

This large-scale comparative study provides evidence that radiofrequency ablation has a better overall survival rate than chemoembolization for small (≤ 3 cm) hepatocellular carcinomas.

KEY POINTS

• The relative effectiveness of transarterial chemoembolization (TACE) and radiofrequency ablation (RFA) for early HCC is unclear. • Overall survival rate was significantly higher in the RFA group. • The effects of RFA on overall survival, local tumor recurrence, and recurrence-free survival were homogeneously better in all subgroups.

Complications Related to Transarterial Treatment of Hepatocellular Carcinoma: A Comprehensive Review

Currently, various types of transarterial treatments are performed for hepatocellular carcinoma from the early to advanced stages. Its indications and efficacy have been widely investigated. However, procedure-related complications have not been updated in the literature, although new types of transarterial treatments, such as drug-eluting bead transarterial chemoembolization and transarterial radioembolization, are common in daily practice. Herein, a comprehensive literature review was carried out, and complications were organized according to the organs affected and treatment modalities.

Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm acceptable AI performance through adequate external testing and confirm the benefits of AI-assisted care compared with conventional care through appropriately designed and conducted studies, for which prospective studies are desirable. This article explains some of the fundamental methodological points that should be considered when designing and appraising the clinical evaluation of AI algorithms for medical diagnosis. The specific topics addressed include the following: (a) the importance of external testing of AI algorithms and strategies for conducting the external testing effectively, (b) the various metrics and graphical methods for evaluating the AI performance as well as essential methodological points to note in using and interpreting them, (c) paired study designs primarily for comparative performance evaluation of conventional and AI-assisted diagnoses, (d) parallel study designs primarily for evaluating the effect of AI intervention with an emphasis on randomized clinical trials, and (e) up-to-date guidelines for reporting clinical studies on AI, with an emphasis on guidelines registered in the EQUATOR Network library. Sound methodological knowledge of these topics will aid the design, execution, reporting, and appraisal of clinical evaluation of AI.

Metformin administration is associated with enhanced response to transarterial chemoembolization for hepatocellular carcinoma in type 2 diabetes patients

Transarterial chemoembolization (TACE) is often used as a locoregional therapy for early hepatocellular carcinoma (HCC) when local ablation or resection are not feasible, but incomplete response and recurrence are commonly observed. In this study, we sought to determine the association between metformin administration and TACE outcomes for single nodular HCC in patients with type 2 diabetes mellitus (T2DM). The retrospective cohort analysis included 164 T2DM patients with single nodular HCC who underwent TACE as an initial treatment, and 91 were exposed to metformin before and after TACE. Propensity score (PS) matching was used to balance covariates. Logistic regression analysis was used to determine the predictors of tumor response after TACE, and Cox regression analysis assessed independent predictors of local tumor recurrence (LTR) in patients with complete response after TACE. Metformin use was associated with significantly higher objective response rate (ORR) in the overall and PS-matched cohort (79.1% vs. 60.3 and 78.7% vs. 57.5%; p = 0.008 and p = 0.029, respectively). Logistic regression analysis showed that metformin use was an independent predictor of ORR in all and PS-matched patients (odds ratio = 2.65 and 3.06; p = 0.016 and 0.034, respectively). Cox regression analysis showed metformin administration was an independent predictor for lower LTR in all and PS-matched patients (hazard ratio = 0.28 and 0.27; p = 0.001 and 0.007, respectively). Metformin administration is associated with better initial response and lower local recurrence after TACE for single nodular HCC in T2DM.

Radiomics Analysis on Gadoxetate Disodium-Enhanced MRI Predicts Response to Transarterial Embolization in Patients with HCC

Objectives: To explore the potential of radiomics on gadoxetate disodium-enhanced MRI for predicting hepatocellular carcinoma (HCC) response after transarterial embolization (TAE). Methods: This retrospective study included cirrhotic patients treated with TAE for unifocal HCC naïve to treatments. Each patient underwent gadoxetate disodium-enhanced MRI. Radiomics analysis was performed by segmenting the lesions on portal venous (PVP), 3-min transitional, and 20-min hepatobiliary (HBP) phases. Clinical data, laboratory variables, and qualitative features based on LI-RADSv2018 were assessed. Reference standard was based on mRECIST response criteria. Two different radiomics models were constructed, a statistical model based on logistic regression with elastic net penalty (model 1) and a computational model based on a hybrid descriptive-inferential feature extraction method (model 2). Areas under the ROC curves (AUC) were calculated. Results: The final population included 51 patients with HCC (median size 20 mm). Complete and objective responses were obtained in 14 (27.4%) and 29 (56.9%) patients, respectively. Model 1 showed the highest performance on PVP for predicting objective response with an AUC of 0.733, sensitivity of 100%, and specificity of 40.0% in the test set. Model 2 demonstrated similar performances on PVP and HBP for predicting objective response, with an AUC of 0.791, sensitivity of 71.3%, specificity of 61.7% on PVP, and AUC of 0.790, sensitivity of 58.8%, and specificity of 90.1% on HBP. Conclusions: Radiomics models based on gadoxetate disodium-enhanced MRI can achieve good performance for predicting response of HCCs treated with TAE.

Gadoxetic Acid-Enhanced MRI Features for Predicting Treatment Outcomes of Early Hepatocellular Carcinoma (< 3 cm) After Transarterial Chemoembolization

RATIONALE AND OBJECTIVES

Magnetic resonance imaging (MRI) is the most useful imaging tool for small hepatocellular carcinoma (HCC) evaluation. Patients undergoing transarterial chemoembolization (TACE) might have predictive imaging prognostic factors. This study aimed to find predictive gadoxetic acid (GA)-enhanced MRI features that affect tumor response and outcomes in patients with early HCC who underwent conventional TACE.

MATERIALS AND METHODS

Among patients who underwent conventional TACE as a first-line treatment for Barcelona clinic liver cancer stage 0 or A (<3 cm), 135 patients who underwent GA-enhanced MRI before treatment were included in this retrospective study. The patients' pretreatment clinical characteristics and MRI features were evaluated. Post-treatment tumor response, progression-free survival (PFS), and overall survival (OS) were also investigated.

RESULTS

The median follow-up period was 47 (range: 7-133) months, with 90 (67%) patients showing complete remission (CR) at the 1-month follow-up after TACE. Tumor number (odds ratio [OR] 0.602, 95% confidence interval [CI]: 0.375-0.967), central location (OR: 0.349, 95% CI: 0.145-0.837) were inversely associated with CR achievement. Median PFS and OS time were 22 (range: 1-133) and 67 (range: 7-133) months, respectively. The MRI features affecting poor survival outcomes were tumor number (PFS: hazard ratio [HR]=1.444, 95% CI=1.124-1.854; OS: HR=1.459, 95% CI=1.018-2.090), central location (PFS: HR=1.664, 95% CI=1.038-2.667; OS: HR=1.890, 95% CI=1.021-3.497), and marginal irregularity (PFS: HR=3.099, 95% CI=1.953-4.979; OS: HR=1.985, 95% CI=1.084-3.634).

CONCLUSION

Multiplicity, central location, and marginal irregularity of HCC on GA-enhanced MRI were significant factors associated with poor prognosis of patients with early HCC after conventional TACE.

Liver MRI and clinical findings to predict response after drug eluting bead transarterial chemoembolization in hepatocellular carcinoma

To identify the gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) and laboratory findings that enable prediction of treatment response and disease-free survival (DFS) after the first session of drug eluting bead transarterial chemoembolization (DEB-TACE) in patients with hepatocellular carcinoma (HCC). A total of 55 patients who underwent GA-enhanced MRI and DEB-TACE from January 2014 to December 2018 were included. All MRI features were reviewed by two radiologists. Treatment response was evaluated according to the modified Response Evaluation Criteria in Solid Tumors. Univariate and multivariate logistic regression analyses were used to determine predictive factors of treatment response and DFS, respectively. A total of 27 patients (49.1%) achieved complete response (CR) after one session of treatment. There were no significant differences between the two groups in terms of clinical and laboratory characteristics. Heterogeneous signal intensity in the hepatobiliary phase (HBP) was the only independent predictor of non-CR (odds ratio, 4.807; p = 0.048). Recurrent HCC was detected in 19 patients (70.4%) after CR. In the multivariate analysis, elevated serum alpha-fetoprotein (AFP) level (≥ 30 ng/mL) was the only significant parameter associated with DFS (hazard ratio, 2.916; p = 0.040). This preliminary study demonstrated that heterogeneous signal intensity in the HBP and high serum AFP were useful predictive factors for poor treatment response and short DFS after DEB-TACE, respectively.

Chemoembolization for Single Large Hepatocellular Carcinoma with Preserved Liver Function: Analysis of Factors Predicting Clinical Outcomes in a 302 Patient Cohort

The purpose of this study was to define the role of transcatheter arterial chemoembolization (TACE) in patients with a single large hepatocellular carcinoma (HCC) and define the patient groups benefiting from TACE. Treatment-naïve patients with preserved liver function who received TACE as the first-line treatment for single large (>5 cm) HCC without macrovascular invasion and extrahepatic metastasis between 2007 and 2019 were retrospectively analyzed. Overall survival, progression-free survival, radiologic tumor response, complications, and predictors of survival were analyzed using multivariate analysis, and then a pretreatment risk-prediction model was created using the four predictive factors of tumor size, tumor type, ALBI grade, and ECOG performance status. Patients with scores of 0 (n = 54), 1-2 (n = 170), and 3-6 (n = 78) according to the model were classified as low-, intermediate-, and high-risk, respectively. The corresponding median OS values were 141, 55, and 28 months, respectively. The percentage of major complications increased as tumor size increased (4-21%). Asymptomatic, nodular HCC patients with a tumor size of 5-7 cm and ALBI grade 1 benefited the most from TACE. By contrast, the value of TACE in the treatment of single huge HCC (>10 cm) with high complication rates remains unclear.

Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches)

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

Hepatobiliary phase of gadoxetic acid-enhanced MRI in patients with HCC: prognostic features before resection, ablation, or TACE

OBJECTIVES

Patients with hepatocellular carcinoma (HCC) receiving different treatments might have specific prognostic factors that can be captured in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced magnetic resonance imaging (GA-MRI). We aimed to identify the clinical findings and HBP features with prognostic value in patients with HCC.

METHODS

In this retrospective, single-institution study, we included patients with Barcelona Clinic Liver Cancer very early/early stage HCC who underwent GA-MRI before treatment. After performing propensity score matching, 183 patients received the following treatments: resection, radiofrequency ablation (RFA), and transarterial chemoembolization (TACE) (n = 61 for each). Cox regression models were used to identify clinical factors and HBP features associated with disease-free survival (DFS) and overall survival (OS).

RESULTS

In the resection group, large tumor size was associated with poor DFS (hazard ratio [HR] 4.159 per centimeter; 95% confidence interval [CI], 1.669-10.365) and poor OS (HR 8.498 per centimeter; 95% CI, 1.072-67.338). In the RFA group, satellite nodules on HBP images were associated with poor DFS (HR 5.037; 95% CI, 1.061-23.903) and poor OS (HR 9.398; 95% CI, 1.480-59.668). Peritumoral hypointensity on HBP images was also associated with poor OS (HR 13.062; 95% CI, 1.627-104.840). In addition, serum albumin levels and the prothrombin time-international normalized ratio were associated with DFS and/or OS. Finally, in the TACE group, no variables were associated with DFS/OS.

CONCLUSIONS

Different HBP features and clinical factors were associated with DFS/OS among patients with HCC receiving different treatments.

KEY POINTS

• In patients who underwent resection for HCC, a large tumor size on HBP images was associated with poor disease-free survival and overall survival. • In the RFA group, satellite nodules and peritumoral hypointensity on HBP images, along with decreased serum albumin levels and PT-INR, were associated with poor disease-free survival and/or overall survival. • In the TACE group, no clinical or HBP imaging features were associated with disease-free survival or overall survival.

Tumor infiltrating immune cells (TIICs) as a biomarker for prognosis benefits in patients with osteosarcoma

BACKGROUND

Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma.

METHODS

CIBERSORTX algorithm was used to calculate the proportion of 22 TIIC types in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIIC types. Forward stepwise approach was used to screen a minimal set of immune cell types. Multivariate Cox PHR analysis was performed to construct an immune risk score model.

RESULTS

Osteosarcoma samples with CIBERSORTX output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p = 0.047) and Monocytes (p = 0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cell types, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC = 0.724). Besides, a nomogram model base on eight immune cell types was constructed to predict the survival rate of patients with osteosarcoma.

CONCLUSIONS

TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.