Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study

PD-1/PD-L1 blockade therapy in hepatocellular carcinoma: Current status and potential biomarkers

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related death and the sixth most prevalent cancer worldwide. However, most patients with HCC are at an advanced stage at the time of clinical diagnosis, making surgery impossible. In the past, targeted therapeutic drugs such as sorafenib and lenvatinib were the main treatments. With recent breakthroughs in medicine, immunotherapy, particularly immune checkpoint inhibitors (ICIs), has garnered interest and has been extensively studied for clinical treatment. In addition to single-agent therapies, combination regimens involving ICIs have also been developed. Despite this progress, not all patients with HCC benefit from immunotherapy. Therefore, to improve the treatment response rates, it is crucial to identify patients with HCC who are suitable for immunotherapy. The exploration and validation of markers to predict the outcomes of immunotherapeutic treatments in patients with HCC are of clinical importance. In this article, we provide a comprehensive review of research progress in immunotherapy, particularly ICIs and combination therapies, for HCC. Furthermore, we summarize the clinical indicators and tumor markers discovered in recent years to forecast immunotherapy outcomes in patients with HCC. We also outline predictive markers for the occurrence of immune-related adverse events in patients with HCC receiving immunotherapy and discuss future research directions in the immunotherapeutic treatment landscape.

Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization

BACKGROUND

This study was undertaken to develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) for predicting recurrence in patients with hepatocellular carcinoma (HCC) following postoperative adjuvant transarterial chemoembolization (PA-TACE).

METHODS

In this retrospective study, 149 HCC patients (81 for training, 36 for internal validation, 32 for external validation) treated with PA-TACE were included in two medical centers. Multiparametric radiomics features were extracted from three MRI sequences. Least absolute shrinkage and selection operator (LASSO)-COX regression was utilized to select radiomics features. Optimal clinical characteristics selected by multivariate Cox analysis were integrated with Rad-score to develop a recurrence-free survival (RFS) prediction model. The model performance was evaluated by time-dependent receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), and calibration curve.

RESULTS

Fifteen optimal radiomic features were selected and the median Rad-score value was 0.434. Multivariate Cox analysis indicated that neutrophil-to-lymphocyte ratio (NLR) (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.1-2.1, P = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1-1.5, P = 0.001) were the independent predictors of RFS after PA-TACE. A combined model was established by integrating Rad-score, NLR, and tumor size in the training cohort (C-index 0.822; 95% CI 0.805-0.861), internal validation cohort (0.823; 95% CI 0.771-0.876) and external validation cohort (0.846; 95% CI 0.768-0.924). The calibration curve exhibited a satisfactory correspondence.

CONCLUSION

A multiparametric MRI-based radiomics model can predict RFS of HCC patients receiving PA-TACE and a nomogram can be served as an individualized tool for prognosis.

The evolving landscape of biomarkers for systemic therapy in advanced hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains one of the most prevalent and deadliest cancers. With the approval of multiple first- and second-line agents, especially the combination therapies based on immune checkpoint inhibitor (ICI) regimens, the landscape of systemic therapy for advanced HCC (aHCC) is more diverse than ever before. The efficacy of current systemic therapies shows great heterogeneity in patients with aHCC, thereby identifying biomarkers for response prediction and patient stratification has become an urgent need. The main biomarkers for systemic therapy in hepatocellular carcinoma are derived from peripheral blood, tissues, and imaging. Currently, the understanding of the clinical response to systemic therapy indicates unequivocally that a single biomarker cannot be used to identify patients who are likely to benefit from these treatments. In this review, we provide an integrated landscape of the recent development in molecular targeted therapies and ICIs-based therapies, especially focusing on the role of clinically applicable predictive biomarkers. Additionally, we further highlight the latest advancements in biomarker-driven therapies, including targeted treatments, adoptive cell therapies, and bispecific antibodies.

Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma

The tumor microenvironment (TME) of hepatocellular carcinoma (HCC) has garnered significant attention, especially with the rise of immunotherapy as a treatment strategy. Radiomics, an innovative technique, offers valuable insights into the intricate structure of the TME. This review highlights recent advancements in radiomics for analyzing the HCC TME, identifies key areas that warrant further research, and explores comprehensive multi-omics approaches that extend the potential of radiomics to new frontiers.

CCL26 as a prognostic biomarker in hepatocellular carcinoma: integrating bioinformatics analysis, clinical validation, and radiomics score

BACKGROUND

CCL26 has been identified as a potential prognostic biomarker in hepatocellular carcinoma (HCC). This study aimed to assess the prognostic significance of CCL26 and develop a radiomics score (Rad-score) for predicting outcomes in HCC patients.

METHODS

Data from 316 HCC patients, including genomic information, computed tomography (CT) images, and clinicopathological data, were analyzed. The prognostic value of CCL26 was evaluated in 295 TCGA patients using Kaplan-Meier and Cox regression analyses, and validated in 21 patients from Jiujiang No. 1 People's Hospital. Gene set variation and immune cell infiltration analyses were conducted to elucidate the biological functions of CCL26. Radiomic models for predicting CCL26 expression were constructed using CT images and genomic data from 34 TCGA patients. Radiomic features were extracted from tumor regions and screened using maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE). Two Rad-scores were generated via logistic regression and validated using internal fivefold cross-validation. A prognostic nomogram incorporating the optimal Rad-score, gender, and hepatic inflammation was developed using Cox proportional hazards regression.

RESULTS

Elevated CCL26 levels correlated with poor prognosis, as confirmed by immunohistochemistry. The optimal Rad-score, combined with gender and hepatic inflammation, accurately predicted overall survival (OS), with areas under the receiver operating characteristic curve (AUCs) of 0.819, 0.902, and 0.982 for 24-, 36-, and 48 month survival, respectively. Calibration curves and decision curve analysis (DCA) demonstrated the accuracy and clinical utility of the model.

CONCLUSIONS

CCL26 serves as a significant prognostic biomarker in HCC. The developed Rad-score provides an effective, non-invasive tool for predicting patient outcomes and enhancing clinical decision-making. This study not only highlights the prognostic role of CCL26 but also offers a novel approach for evaluating HCC patient prognosis through radiomics.

Systemic chemotherapy improves outcome of hepatocellular carcinoma patients treated with transarterial chemoembolization

BACKGROUND

Transarterial chemoembolization (TACE) based neoadjuvant therapy was proven effective in hepatocellular carcinoma (HCC). Recently, tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) also showed promise in HCC treatment. However, the prognostic benefits associated with these treatments remain uncertain. This study aimed to explore the relationship between pathologic response and prognostic features in HCC patients who received neoadjuvant therapy.

METHODS

HCC patients who received TACE either with or without TKIs/ICIs as neoadjuvant therapy before liver resection were retrospectively collected from the First Affiliated Hospital, Zhejiang University School of Medicine in China. Pathologic response was determined by calculating the proportion of non-viable area within the tumor. Major pathologic response (MPR) was defined as the presence of non-viable tumor cells reaching a minimum of 90%. Complete pathologic response (CPR) was characterized by the absence of viable cells observed in the tumor.

RESULTS

A total of 481 patients meeting the inclusion criteria were enrolled, with 76 patients (15.8%) achieving CPR and 179 (37.2%) reaching MPR. The median recurrence-free survival (mRFS) in the CPR + MPR group was significantly higher than the non-MPR group (31.3 vs. 25.1 months). The difference in 3-year overall survival (OS) rate was not significant. Multivariate Cox regression analysis identified failure to achieve MPR (hazard ratio = 1.548, 95% confidence interval: 1.122-2.134; P = 0.008), HBsAg positivity (HR = 1.818, 95% CI: 1.062-3.115, P = 0.030), multiple lesions (HR = 2.278, 95% CI: 1.621-3.195, P < 0.001), and baseline tumor size > 5 cm (HR = 1.712, 95% CI: 1.031-2.849, P = 0.038) were independent risk factors for RFS. Subgroup analysis showed that 67 of 93 (72.0%) patients who received the combination of TACE, TKIs, and ICIs achieved MPR + CPR.

CONCLUSIONS

In individuals who received TACE-based neoadjuvant therapy for HCC, failure to achieve MPR emerges as an independent risk factor for RFS. Notably, the combination of TACE, TKIs, and ICIs demonstrated the highest rate of MPR.

Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study

RATIONALE AND OBJECTIVES

To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multiparametric magnetic resonance imaging (MRI) radiomic features for predicting treatment response to transarterial chemoembolization combined with molecular targeted therapy plus immunotherapy in unresectable hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

This retrospective study involved 276 patients with unresectable HCC who received combination therapy from 4 medical centers. Patients were divided into one training cohort and two independent external validation cohorts. 16 radiomic features from six multiparametric MRI sequences and 2 clinical features were used to build six machine learning models. The models were evaluated using the area under the curve (AUC), decision curve analysis, and incremental predictive value.

RESULTS

Alpha-fetoprotein and neutrophil-to-lymphocyte ratio are clinical independent predictors of treatment response. In the training cohort and two external validation cohorts, the AUCs and 95% confidence intervals for predicting treatment response were respectively 0.782 (0.698-0.857) 0.695 (0.566-0.823), and 0.679 (0.542-0.810) for the clinical model; 0.942 (0.903-0.974), 0.869 (0.761-0.949), and 0.868 (0.769-0.942) for the radiomics model; and 0.956 (0.920-0.984), 0.895 (0.810-0.967), and 0.892 (0.804-0.957) for the combined clinical-radiomics model. In the three cohorts, the incremental predictive value of the radiomics model over the clinical model was 49.2% (P < 0.001), 28.8% (P < 0.001), and 31.5% (P < 0.001).

CONCLUSION

The combined clinical-radiomics model may provide a reliable and non-invasive tool to predict individual treatment responses and guide and improve clinical decision-making in combination therapy of HCC patients.

Alpha-fetoprotein combined with initial tumor shape irregularity in predicting the survival of patients with advanced hepatocellular carcinoma treated with immune-checkpoint inhibitors: a retrospective multi-center cohort study

BACKGROUND

Immune checkpoint inhibitors (ICIs) are playing a significant role in the treatment of hepatocellular carcinoma (HCC). This study aims to explore the prognostic value of alpha-fetoprotein (AFP) and initial tumor shape irregularity in patients treated with ICIs.

METHODS

In this retrospective, multi-center study, 296 HCC patients were randomly divided into the training set and the validation set in a 3:2 ratio. The training set was used to evaluate prognostic factors and to develop an easily applicable ATSI (AFP and Tumor Shape Irregularity) score, which was verified in the validation set.

RESULTS

The ATSI score was developed from two independent prognostic risk factors: baseline AFP ≥ 400 ng/ml (HR 1.73, 95% CI 1.01-2.96, P = 0.046) and initial tumor shape irregularity (HR 1.94, 95% CI 1.03-3.65, P = 0.041). The median overall survival (OS) was not reached (95% CI 28.20-NA) in patients who met no criteria (0 points), 25.8 months (95% CI 14.17-NA) in patients who met one criterion (1 point), and 17.03 months (95% CI 11.73-23.83) in patients who met two criteria (2 points) (P = 0.001). The median progression-free survival (PFS) was 10.83 months (95% CI 9.27-14.33) for 0 points, 8.03 months (95% CI 6.77-10.57) for 1 point, and 5.03 months (95% CI 3.83-9.67) for 2 points (P < 0.001). The validation set effectively verified these results (median OS, 37.43/24.27/14.03 months for 0/1/2 points, P = 0.028; median PFS, 13.93/8.30/4.90 months for 0/1/2 points, P < 0.001).

CONCLUSIONS

The ATSI score can effectively predict prognosis in HCC patients receiving ICIs.

Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study

BACKGROUND

Intratumoral tertiary lymphoid structures (iTLS) in hepatocellular carcinoma (HCC) are associated with improved survival and may influence treatment decisions. However, their non-invasive detection remains challenging in HCC. We aim to develop a non-invasive model using baseline contrast-enhanced MRI to predict the iTLS status.

METHODS

A total of 660 patients with HCC who underwent surgery were retrospectively recruited from four centers between October 2015 and January 2023 and divided into training, internal test, and external validation sets. After features dimensionality and selection, corresponding features were used to construct transfer learning radiomic (TLR) models for diagnosing iTLS, and model interpretability was explored with pathway analysis in The Cancer Genome Atlas-Liver HCC. The performances of models were assessed using the area under the receiver operating characteristic curve (AUC). The log-rank test was used to evaluate the prognostic value of the TLR model. The combination therapy set of 101 patients with advanced HCC treated with first-line anti-programmed death 1 or ligand 1 plus antiangiogenic treatment between January 2021 and January 2024 was used to investigate the value of the TLR model for evaluating the treatment response.

RESULTS

The presence of iTLS was identified in 46.0% (n=308) patients. The TLR model demonstrated excellent performance in predicting the presence of iTLS in training (AUC=0.91, 95% CI: 0.87, 0.94), internal test (AUC=0.85, 95% CI: 0.77, 0.93) and external validation set (AUC=0.85, 95% CI: 0.81, 0.90). The TLR model-predicted iTLS group has favorable overall survival (HR=0.66; 95% CI: 0.48, 0.90; p=0.007) and relapse-free survival (HR=0.64; 95% CI: 0.48, 0.85; p=0.001) in the external validation set. The model-predicted iTLS status was associated with inflammatory response and specific tumor-associated signaling activation (all p<0.001). The proportion of treatment responders was significantly higher in the model-predicted group with iTLS than in the group without iTLS (36% vs 13.73%, p=0.009).

CONCLUSION

The TLR model has indicated accurate prediction of iTLS status, which may assist in the risk stratification for patients with HCC in clinical practice.

The prognostic value of systemic immune-inflammation index in patients with unresectable hepatocellular carcinoma treated with immune-based therapy

BACKGROUND

Predicting the efficacy of immune-based therapy in patients with unresectable hepatocellular carcinoma (HCC) remains a clinical challenge. This study aims to evaluate the prognostic value of the systemic immune-inflammation index (SII) in forecasting treatment response and survival outcomes for HCC patients undergoing immune-based therapy.

METHODS

We analyzed a cohort of 268 HCC patients treated with immune-based therapy from January 2019 to March 2023. A training cohort of 93 patients received atezolizumab plus bevacizumab (T + A), while a validation cohort of 175 patients underwent treatment with tyrosine kinase inhibitors (TKIs) combined with anti-PD-(L)1 therapy. The SII cutoff value, determined using X-tile analysis based on overall survival (OS) in the training cohort, divided patients into high (> 752*109) and low (≤ 752*109) SII groups. Prognostic factors were identified through univariate and multivariate logistic and Cox regression analyses, and survival outcomes were assessed using Kaplan-Meier methods. The predictive accuracy of SII was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

An optimal SII cutoff of 752*109 stratified patients into high and low SII groups. Univariate and multivariate logistic regression indicated that SII was a significant predictor of the objective response rate (ORR), which was markedly different between the low and high SII subgroups (34.72% vs. 9.52%, P = 0.019). This finding was consistent in the validation cohort (34.09% vs. 16.28%, P = 0.026). SII also demonstrated prognostic value in Cox regression and Kaplan-Meier analyses. ROC curves confirmed that SII had superior predictive accuracy compared to common clinical indicators, with predictive relevance even in AFP-negative patients. Furthermore, a lower SII was associated with a higher T cell ratio and an increased number of CD8+ T cells and Granzyme B+ CD8+ T cells in peripheral blood.

CONCLUSION

SII is a promising predictor of both therapeutic efficacy and prognosis in HCC patients undergoing immune-based treatments. Its application may enhance clinical decision-making, thereby improving patient outcomes from immune-based therapy.

Intertumoral Heterogeneity Based on MRI Radiomic Features Estimates Recurrence in Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) heterogeneity impacts prognosis, and imaging is a potential indicator.

PURPOSE

To characterize HCC image subtypes in MRI and correlate subtypes with recurrence.

STUDY TYPE

Retrospective.

POPULATION

A total of 440 patients (training cohort = 213, internal test cohort = 140, external test cohort = 87) from three centers.

FIELD STRENGTH/SEQUENCE

1.5-T/3.0-T, fast/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, contrast-enhanced three-dimensional gradient-recalled-echo T1-weighted with extracellular agents (Gd-DTPA, Gd-DTPA-BMA, and Gd-BOPTA).

ASSESSMENT

Three-dimensional volume-of-interest of HCC was contoured on portal venous phase, then coregistered with precontrast and late arterial phases. Subtypes were identified using non-negative matrix factorization by analyzing radiomics features from volume-of-interests, and correlated with recurrence. Clinical (demographic and laboratory data), pathological, and radiologic features were compared across subtypes. Among clinical, radiologic features and subtypes, variables with variance inflation factor above 10 were excluded. Variables (P < 0.10) in univariate Cox regression were included in stepwise multivariate analysis. Three recurrence estimation models were built: clinical-radiologic model, subtype model, hybrid model integrating clinical-radiologic characteristics, and subtypes.

STATISTICAL TESTS

Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, Fisher's exact test, Kaplan-Meier curves, log-rank test, concordance index (C-index). Significance level: P < 0.05.

RESULTS

Two subtypes were identified across three cohorts (subtype 1:subtype 2 of 86:127, 60:80, and 36:51, respectively). Subtype 1 showed higher microvascular invasion (MVI)-positive rates (53%-57% vs. 26%-31%), and worse recurrence-free survival. Hazard ratio (HR) for the subtype is 6.10 in subtype model. Clinical-radiologic model included alpha-fetoprotein (HR: 3.01), macrovascular invasion (HR: 2.32), nonsmooth tumor margin (HR: 1.81), rim enhancement (HR: 3.13), and intratumoral artery (HR: 2.21). Hybrid model included alpha-fetoprotein (HR: 2.70), nonsmooth tumor margin (HR: 1.51), rim enhancement (HR: 3.25), and subtypes (HR: 5.34). Subtype model was comparable to clinical-radiologic model (C-index: 0.71-0.73 vs. 0.71-0.73), but hybrid model outperformed both (C-index: 0.77-0.79).

CONCLUSION

MRI radiomics-based clustering identified two HCC subtypes with distinct MVI status and recurrence-free survival. Hybrid model showed superior capability to estimate recurrence.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study

BACKGROUND

Tertiary lymphoid structures (TLS) within the tumor microenvironment have been associated with cancer prognosis and therapeutic response. However, the immunological pattern of a high peritumoral TLS (pTLS) density and its clinical potential in hepatocellular carcinoma (HCC) remain poor. This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.

METHODS

Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. Baseline MRI images from 660 patients with HCC who had undergone surgery treatment between October 2015 and January 2023 were retrospectively recruited for model development and validation. This included training (n=307) and temporal validation (n=76) cohorts from Xiangya Hospital, and external validation cohorts from three independent hospitals (n=277). Radiomic features were extracted from intratumoral and peritumoral regions of interest and analyzed using machine learning algorithms to develop a predictive classifier. The classifier's performance was evaluated using the area under the curve (AUC), with prognostic and predictive value assessed across four independent cohorts and in a dual-center outcome cohort of 41 patients who received immunotherapy.

RESULTS

Patients with HCC and a high pTLS density experienced prolonged median overall survival (p<0.05) and favorable immunotherapy response (p=0.03). Moreover, immune infiltration by mature B cells was observed in the high pTLS density region. Spatial pseudotime analysis and immunohistochemistry staining revealed that expansion of pTLS in HCC was associated with elevated CXCL9 and CXCL10 co-expression. We developed an optimal radiomic-based classifier with excellent discrimination for predicting pTLS density, achieving an AUC of 0.91 (95% CI 0.87, 0.94) in the external validation cohort. This classifier also exhibited promising stratification ability in terms of overall survival (p<0.01), relapse-free survival (p<0.05), and immunotherapy response (p<0.05).

CONCLUSION

We identified key regulators of pTLS density in patients with HCC and proposed a non-invasive radiomic classifier capable of assisting in stratification for prognosis and treatment.

Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinoma: a two-center study

Objective

The objective of this study is to build and verify the performance of machine learning-based ultrasomics in predicting the objective response to combination therapy involving a tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody for individuals with unresectable hepatocellular carcinoma (HCC). Radiomic features can reflect the internal heterogeneity of the tumor and changes in its microenvironment. These features are closely related to pathological changes observed in histology, such as cellular necrosis and fibrosis, providing crucial non-invasive biomarkers to predict patient treatment response and prognosis.

Methods

Clinical, pathological, and pre-treatment ultrasound image data of 134 patients with recurrent unresectable or advanced HCC who treated with a combination of TKI and anti-PD-1 antibody therapy at Henan Provincial People's Hospital and the First Affiliated Hospital of Zhengzhou University between December 2019 and November 2023 were collected and retrospectively analyzed. Using stratified random sampling, patients from the two hospitals were assigned to training cohort (n = 93) and validation cohort (n = 41) at a 7:3 ratio. After preprocessing the ultrasound images, regions of interest (ROIs) were delineated. Ultrasomic features were extracted from the images for dimensionality reduction and feature selection. By utilizing the extreme gradient boosting (XGBoost) algorithm, three models were developed: a clinical model, an ultrasomic model, and a combined model. By analyzing the area under the receiver operating characteristic (ROC) curve (AUC), specificity, sensitivity, and accuracy, the predicted performance of the models was evaluated. In addition, we identified the optimal cutoff for the radiomic score using the Youden index and applied it to stratify patients. The Kaplan-Meier (KM) survival curves were used to examine differences in progression-free survival (PFS) between the two groups.

Results

Twenty ultrasomic features were selected for the construction of the ultrasomic model. The AUC of the ultrasomic model for the training cohort and validation cohort were 0.999 (95%CI: 0.997-1.000) and 0.828 (95%CI: 0.690-0.966), which compared significant favorably to those of the clinical model [AUC = 0.876 (95%CI: 0.815-0.936) for the training cohort, 0.766 (95%CI: 0.597-0.935) for the validation cohort]. Compared to the ultrasomic model, the combined model demonstrated comparable performance within the training cohort (AUC = 0.977, 95%CI: 0.957-0.998) but higher performance in the validation cohort (AUC = 0.881, 95%CI: 0.758-1.000). However, there was no statistically significant difference (p > 0.05). Furthermore, ultrasomic features were associated with PFS, which was significantly different between patients with radiomic scores (Rad-score) greater than 0.057 and those with Rad-score less than 0.057 in both the training (HR = 0.488, 95% CI: 0.299-0.796, p = 0.003) and validation cohorts (HR = 0.451, 95% CI: 0.229-0.887, p = 0.02).

Conclusion

The ultrasomic features demonstrates excellent performance in accurately predicting the objective response to TKI in combination with anti-PD-1 antibody immunotherapy among patients with unresectable or advanced HCC.

Unveiling Tim-3 immune checkpoint expression in hepatocellular carcinoma through abdominal contrast-enhanced CT habitat radiomics

Introduction

Immune checkpoint inhibitors (ICIs) are important systemic therapeutic agents for hepatocellular carcinoma (HCC), among which T-cell immunoglobulin and mucin-domain containing protein 3 (Tim-3) is considered an emerging target for ICI therapy. This study aims to evaluate the prognostic value of Tim-3 expression and develop a predictive model for Tim-3 infiltration in HCC.

Methods

We collected data from 424 HCC patients in The Cancer Genome Atlas (TCGA) and data from 102 pathologically confirmed HCC patients from our center for prognostic analysis. Multivariate Cox regression analyses were performed on both datasets to determine the prognostic significance of Tim-3 expression. In radiomics analysis, we used the K-means algorithm to cluster regions of interest in arterial phase enhancement and venous phase enhancement images from patients at our center. Radiomic features were extracted from three subregions as well as the entire tumor using pyradiomics. Five machine learning methods were employed to construct Habitat models based on habitat features and Rad models based on traditional radiomic features. The predictive performance of the models was compared using ROC curves, DCA curves, and calibration curves.

Results

Multivariate Cox analyses from both our center and the TCGA database indicated that high Tim-3 expression is an independent risk factor for poor prognosis in HCC patients. Higher levels of Tim-3 expression were significantly associated with worse prognosis. Among the ten models evaluated, the Habitat model constructed using the LightGBM algorithm showed the best performance in predicting Tim-3 expression status (training set vs. test set AUC 0.866 vs. 0.824).

Discussion

This study confirmed the importance of Tim-3 as a prognostic marker in HCC. The habitat radiomics model we developed effectively predicted intratumoral Tim-3 infiltration, providing valuable insights for the evaluation of ICI therapy in HCC patients.

Intratumoral and peritumoral radiomics of MRIs predicts pathologic complete response to neoadjuvant chemoimmunotherapy in patients with head and neck squamous cell carcinoma

BACKGROUND

For patients with locally advanced head and neck squamous cell carcinoma (HNSCC), combined programmed death receptor-1 inhibitor and chemotherapy improved response rate to neoadjuvant therapy. However, treatment response varies among patients. There is no tool to predict pathologic complete response (pCR) with high accuracy for now. To develop a tool based on radiomics features of MRI to predict pCR to neoadjuvant chemoimmunotherapy (NACI) may provide valuable assistance in treatment regimen determination for HNSCC.

METHODS

From January 2021 to April 2024, a total of 172 patients with HNSCC from three medical center, who received NACI followed by surgery, were included and allocated into a training set (n=84), an internal validation set (n=37) and an external validation set (n=51). Radiomics features were extracted from intratumoral and different peritumoral areas, and radiomics signature (Rad-score) for each area was constructed. A radiomics-clinical nomogram was developed based on Rad-scores and clinicopathological characteristics, tested in the validation sets, and compared with clinical nomogram and combined positive score (CPS) in predicting pCR.

RESULTS

The radiomics-clinical nomogram, incorporating peritumoral Rad-score, intratumoral Rad-score and CPS, achieved the highest accuracy with areas under the receiver operating characteristic curve of 0.904 (95% CI, 0.835 to 0.972) in the training cohort, 0.860 (95% CI, 0.722 to 0.998) in the internal validation cohort, and 0.849 (95% CI, 0.739 to 0.959) in the external validation cohort, respectively, which outperformed the clinical nomogram and CPS in predict pCR to NACI for HNSCC.

CONCLUSION

A nomogram developed based on intratumoral and peritumoral MRI radiomics features outperformed CPS, a widely employed biomarker, in predict pCR to NACI for HNSCC, which would provide incremental value in treatment regimen determination.

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy

BACKGROUND

Lenvatinib plus PD-1 inhibitors and interventional (LPI) therapy have demonstrated promising treatment effects in unresectable hepatocellular carcinoma (HCC). However, biomarkers for predicting the response to LPI therapy remain to be further explored. We aimed to develop a radiomics model to noninvasively predict the efficacy of LPI therapy.

METHODS

Clinical data of patients with HCC receiving LPI therapy were collected in our institution. The clinical model was built with clinical information. Nine machine learning classifiers were tested and the multilayer perceptron classifier with optimal performance was used as the radiomics model. The clinical-radiomics model was constructed by integrating clinical and radiomics scores through logistic regression analysis.

RESULTS

151 patients were enrolled in this study (2:1 randomization, 101 and 50 in the training and validation cohorts), of which three achieved complete response, 69 showed partial response, 46 showed stable disease, and 33 showed progressive disease. The objective response rate, disease control rate, and conversion resection rates were 47.7, 78.1 and 23.2%. 14 features were selected from the initially extracted 1223 for radiomics model construction. The area under the curves of the radiomics model (0.900 for training and 0.893 for validation) were comparable to that of the clinical-radiomics model (0.912 for training and 0.892 for validation), and both were superior to the clinical model (0.669 for training and 0.585 for validation). Meanwhile, the radiomics model can categorize participants into high-risk and low-risk groups for progression-free survival (PFS) and overall survival (OS) in the training (HR 1.913, 95% CI 1.121 to 3.265, p=0.016 for PFS; HR 4.252, 95% CI 2.051 to 8.816, p=0.001 for OS) and validation sets (HR 2.347, 95% CI 1.095 to 5.031, p=0.012 for PFS; HR 2.592, 95% CI 1.050 to 6.394, p=0.019 for OS).

CONCLUSION

The promising machine learning radiomics model was developed and validated to predict the efficacy of LPI therapy for patients with HCC and perform risk stratification, with comparable performance to clinical-radiomics model.

Platelet-to-lymphocyte ratio predicts tumor response and survival of patients with hepatocellular carcinoma undergoing immunotherapies

BACKGROUND

Lymphocyte-related factors were associated with survival outcome of different types of cancers. Nevertheless, the association between lymphocytes-related factors and tumor response of immunotherapy remains unclear.

METHODS

This is a retrospective study. Eligible participants included patients with unresectable or advanced hepatocellular carcinoma (HCC) who underwent immunotherapy as their first-line treatment. Radiological assessment of tumor response adhered to RECIST 1.1 and HCC-specific modified RECIST (mRECIST) criteria. Univariate and multivariate logistic analyses were employed to analyze clinical factors associated with tumor response. Kaplan-Meier survivial analysis were employed to compare progression-free survival (PFS) and overall survival (OS) across different clinical factors. Furthermore, patients who received treatment with either a combination of bevacizumab and anti-PD-1(L1) antibody (Beva group) or tyrosine-kinase inhibitor (TKI) and anti-PD-1 antibody (TKI group) were examined to explore the relation between clinical factors and tumor response.

RESULTS

A total of 208 patients were enrolled in this study. The median PFS and OS were 9.84 months and 24.44 months,respectively. An independent factor associated with a more favorable tumor response to immunotherapy was identified when PLR<100. Patients with PLR<100 had longer PFS than other patients, while OS showed no significant difference. Further analysis revealed that PLR exhibited superior prognostic value in patients of the Beva group as compared to those in the TKI group.

CONCLUSIONS

There exisits an association between PLR and tumor response as well as survival outcomes in patients receiving immunotherapy, particularly those treated with the combination of bevacizumab and anti-PD-1.

The MAPS-CRAFITY score: a novel efficacy predictive tool for unresectable hepatocellular carcinoma treated with targeted therapy plus immunotherapy

BACKGROUND

Body composition parameters (BCPs) are associated with mortality in patients with hepatocellular carcinoma (HCC). Our purpose was to develop a practical scoring model by BCP and the CRAFITY score to predict the overall survival (OS) and tumor response of patients with HCC who received targeted therapy plus immunotherapy.

METHODS

This retrospective study included 265 patients with HCC who received targeted therapy plus immunotherapy at 2 centers in China from August 2018 to February 2022. Univariate and multivariate Cox regression analyses were applied to analyze clinical factors and BCP. A scoring model based on independent risk factors was developed to predict OS and tumor response. Moreover, the model's prediction was further validated by an external cohort.

RESULTS

A total of 150 patients (55.5 ± 10.8 years) and 115 patients (55.0 ± 8.9 years) treated with lenvatinib or bevacizumab biosimilar plus anti-programmed death-1 (PD-1) antibody were included in training and validation cohorts, respectively. In the training cohort, independent predictive factors for OS included macrovascular invasion (p = 0.016), Child‒Pugh class (A vs. B, p = 0.001; A vs. C, p < 0.001), sarcopenia (p = 0.034), and the CRAFITY score (p = 0.011). Based on independent risk factors (MAcrovascular invasion, Child‒Pugh class, Sarcopenia, and the CRAFITY score) identified by multivariate analysis, a novel efficacy predictive tool named the MAPS-CRAFITY score was developed to predict OS. In all the training and validation cohorts, the OS differed significantly across the three groups based on the MAPS-CRAFITY score (< 2.1, 2.1-2.3, ≥ 2.4; p < 0.001). Moreover, the C-index of the MAPS-CRAFITY score was 0.720 and 0.761 in the training and validation cohorts, respectively. In both the validation and training cohorts, the MAPS-CRAFITY score was predictive of tumor response and disease control (p < 0.001). The AUCs of the MAPS-CRAFITY score for predicting disease control were 0.752 in the training cohort and 0.836 in the validation cohort.

CONCLUSIONS

The MAPS-CRAFITY score based on sarcopenia and the CRAFITY score is a reliable and practical tool for predicting the efficacy of targeted therapy plus immunotherapy in patients with unresectable HCC, and may help hepatologists and oncologists in clinical decision-making.