3D Quantitative tumour burden analysis in patients with hepatocellular carcinoma before TACE: comparing single-lesion vs. multi-lesion imaging biomarkers as predictors of patient survival

Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection

OBJECTIVES

This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

This was a single-center retrospective study enrolling patients who underwent resection for BCLC A and B HCC and preoperative contrast-enhanced MRI. Quantitative total tumor volume (cm3) and total tumor burden (TTB, %) were obtained using a DL automated segmentation tool. Radiologists' visual assessment was used to ensure the quality control of automated segmentation. The prognostic value of clinicopathological variables and tumor burden-related parameters for ER was determined by Cox regression analyses.

RESULTS

A total of 592 patients were included, with 525 and 67 patients assigned to BCLC A and B, respectively (2-year ER rate: 30.0% vs. 45.3%; hazard ratio (HR) = 1.8; p = 0.007). TTB was the most important predictor of ER (HR = 2.2; p < 0.001). Using 6.84% as the threshold of TTB, two ER risk strata were obtained in overall (p < 0.001), BCLC A (p < 0.001), and BCLC B (p = 0.027) patients, respectively. The BCLC B low-TTB patients had a similar risk for ER to BCLC A patients and thus were reassigned to a BCLC An stage; whilst the BCLC B high-TTB patients remained in a BCLC Bn stage. The 2-year ER rate was 30.5% for BCLC An patients vs. 58.1% for BCLC Bn patients (HR = 2.8; p < 0.001).

CONCLUSIONS

TTB determined by DL-based automated segmentation at MRI was a predictive biomarker for postoperative ER and facilitated refined subcategorization of patients within BCLC stages A and B.

CLINICAL RELEVANCE STATEMENT

Total tumor burden derived by deep learning-based automated segmentation at MRI may serve as an imaging biomarker for predicting early recurrence, thereby improving subclassification of Barcelona Clinic Liver Cancer A and B hepatocellular carcinoma patients after hepatectomy.

KEY POINTS

Total tumor burden (TTB) is important for Barcelona Clinic Liver Cancer (BCLC) staging, but is heterogenous. TTB derived by deep learning-based automated segmentation was predictive of postoperative early recurrence. Incorporating TTB into the BCLC algorithm resulted in successful subcategorization of BCLC A and B patients.

Predicting survival of patients with advanced hepatocellular carcinoma receiving combination targeted immunotherapy: an evaluation of volumetric imaging parameters

PURPOSE

To evaluate the potential of volumetric imaging in predicting survival of advanced hepatocellular carcinoma (HCC) patients receiving immunotherapy.

METHODS

Retrospective analysis included 40 patients with advanced HCC who received targeted immunotherapy. Baseline and follow-up contrast-enhanced abdominal computed tomography (CT) scans were analyzed. The largest tumor was chosen as the index lesion. Viable tumor volume (qViable) and percentage tumor viability (%Viability) were calculated. Response Evaluation Criteria in Solid Tumors (RECIST) and Tumor volume change after treatment (qRECIST) were measured. Associations with overall survival (OS) were assessed. Cox regression analysis assessed the association between variables and overall survival (OS). A new prognostic stratification system was attempted to categorize patients based on significant predictors of OS. Patients with a baseline %viability > 69% and %viability reduction ≥ 8% were classified as better prognosis. Patients were stratified into better, intermediate and worse prognosis groups based on baseline %viability > 69% and ≥ 8% %viability reduction (better prognosis); baseline %viability ≤ 69% and < 8% %viability reduction (worse prognosis); remainder were intermediate prognosis.

RESULTS

Patients with baseline %Viability > 69% and %Viability reduction ≥ 8% showed significantly higher OS. Multivariate analysis confirmed %Viability and %Viability reduction as significant predictors of OS. A prognostic stratification system using these parameters stratified patients into better, intermediate and worse prognosis groups, with the better prognosis showing highest OS. Most patients (97.5%) had stable disease by RECIST while the prognostic model re-classified 47.5% as better prognosis, 37.5% intermediate prognosis, and 15% worse prognosis.

CONCLUSION

Volumetric parameters of %Viability and %Viability reduction predict OS in HCC patients undergoing immunotherapy.

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.

Comparison of quantitative volumetric analysis and linear measurement for predicting the survival of Barcelona Clinic Liver Cancer 0- and A stage hepatocellular carcinoma after radiofrequency ablation

PURPOSE

The prognostic role of the tumor volume in patients with hepatocellular carcinoma (HCC) at the Barcelona Clinic Liver Cancer (BCLC) 0 and A stages remains unclear. This study aims to compare the volumetric measurement with linear measurement in early HCC burden profile and clarify the optimal cut-off value of the tumor volume.

METHODS

The consecutive patients diagnosed with HCC who underwent initial and curative-intent radiofrequency ablation (RFA) were included retrospectively. The segmentation was performed semi-automatically, and enhanced tumor volume (ETV) as well as total tumor volume (TTV) were obtained. The patients were categorized into high- and low-tumor burden groups according to various cutoff values derived from commonly used diameter values, X-tile software, and decision-tree analysis. The inter- and intra-reviewer agreements were measured using the intra-class correlation coefficient. Univariate and multivariate time-to-event Cox regression analyses were performed to identify the prognostic factors of overall survival.

RESULTS

A total of 73 patients with 81 lesions were analyzed in the whole cohort with a median follow-up of 31.0 (interquartile range: 16.0–36.3). In tumor segmentation, excellent consistency was observed in intra- and inter-reviewer assessments. There was a strong correlation between diameter-derived spherical volume and ETV as well as ETV and TTV. As opposed to all linear candidates and 4,188 mm3 (sphere equivalent to 2 cm in diameter), ETV >14,137 mm3 (sphere equivalent to 3 cm in diameter) or 23,000 mm3 (sphere equivalent to 3.5 cm in diameter) was identified as an independent risk factor of survival. Considering the value of hazard ratio and convenience to use, when ETV was at 23,000 mm3, it was regarded as the optimal volumetric cut-off value in differentiating survival risk.

CONCLUSION

The volumetric measurement outperforms linear measurement on tumor burden evaluation for survival stratification in patients at BCLC 0 and A stages HCC after RFA.

MR Imaging Volumetric Response after Yttrium-90 Radioembolization for Colorectal Liver Metastases: Predictability at Baseline and Correlation with Survival

PURPOSE

To prove the utility of magnetic resonance (MR) imaging response as a surrogate end point of treatment efficacy and survival after yttrium-90 transarterial radioembolization (TARE) for colorectal liver metastases (CRLMs), and to investigate whether outcomes can be predicted at baseline using MR imaging or clinical variables.

MATERIALS AND METHODS

A total of 50 (135) patients with TARE for CRLMs between August 2008 and January 2020 and peri-interventional MR imaging within defined timeframes were included for tumor segmentation. Pretreatment and posttreatment target tumor volumes were measured according to the volumetric Response Evaluation Criteria In Solid Tumors (vRECIST) and the quantitative European Association for the Study of the Liver (qEASL) criteria. Cox regression models were used to analyze the impact of MR morphologic response, vascularity at baseline, and clinical variables on patient survival. Logistic regression analyses were used to evaluate the predictors of MR morphologic response at baseline.

RESULTS

The median survival was 337 days (95% confidence interval [CI], 243-431). As opposed to the vRECIST, the application of the qEASL criteria 3 months after the treatment allowed for a significant (P < .05) separation of the survival curves for partial response, stable disease, and progressive disease with a median survival of 412 days (95% CI, 57-767) in responders. High tumor burden and technetium-99m lung shunt significantly decreased the probability of survival. MR morphologic response was not predictable at baseline using imaging or clinical data.

CONCLUSIONS

MR response according to the qEASL criteria outperformed the vRECIST in measuring the biologic impact of TARE and predicting patient survival. Baseline contrast enhancement did not predict MR response to treatment, which may reflect elevated dose requirements in tumors with a high proportion of viable tumor volume.

Analysis of Tumor Burden as a Biomarker for Patient Survival with Neuroendocrine Tumor Liver Metastases Undergoing Intra-Arterial Therapies: A Single-Center Retrospective Analysis

PURPOSE

To assess the value of quantitative analysis of tumor burden on baseline MRI for prediction of survival in patients with neuroendocrine tumor liver metastases (NELM) undergoing intra-arterial therapies.

MATERIALS AND METHODS

This retrospective single-center analysis included 122 patients with NELM who received conventional (n = 74) or drug-eluting beads, (n = 20) chemoembolization and radioembolization (n = 28) from 2000 to 2014. Overall tumor diameter (1D) and area (2D) of up to 3 largest liver lesions were measured on baseline arterially contrast enhanced MR images. Three-dimensional quantitative analysis was performed using the qEASL tool (IntelliSpace Portal Version 8, Philips) to calculate enhancing tumor burden (the ratio between enhancing tumor volume and total liver volume). Based on Q-statistics, patients were stratified into low tumor burden (TB) or high TB.

RESULTS

The survival curves were significantly separated between low TB and high TB groups for 1D (p < 0.001), 2D (p < 0.001) and enhancing TB (p = 0.008) measurements, with, respectively, 2.7, 2.6 and 2.2 times longer median overall survival (MOS) in the low TB group (p < 0.001, p < 0.001 and p = 0.008). Multivariate analysis showed that 1D, 2D, and enhancing TB were independent prognostic factors for MOS, with respective hazard ratios of 0.4 (95%CI: 0.2-0.6, p < 0.001), 0.4 (95%CI: 0.3-0.7, p < 0.001) and 0.5 (95%CI: 0.3-0.8, p = 0.003).

CONCLUSION

The overall tumor diameter, overall tumor area, and enhancing tumor burden are strong prognostic factors of overall survival in patients with neuroendocrine tumor liver metastases undergoing intra-arterial therapies.

Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI

Background Patients with intermediate- and advanced-stage hepatocellular carcinoma (HCC) represent a highly heterogeneous patient collective with substantial differences in overall survival. Purpose To evaluate enhancing tumor volume (ETV) and enhancing tumor burden (ETB) as new criteria within the Barcelona Clinic Liver Cancer (BCLC) staging system for optimized allocation of patients with intermediate- and advanced-stage HCC to undergo transarterial chemoembolization (TACE). Materials and Methods In this retrospective study, 682 patients with HCC who underwent conventional TACE or TACE with drug-eluting beads from January 2000 to December 2014 were evaluated. Quantitative three-dimensional analysis of contrast-enhanced MRI was performed to determine thresholds of ETV and ETB (ratio of ETV to normal liver volume). Patients with ETV below 65 cm³ or ETB below 4% were reassigned to BCLC B_n, whereas patients with ETV or ETB above the determined cutoffs were restratified or remained in BCLC C_n by means of stepwise verification of the median overall survival (mOS). Results This study included 494 patients (median age, 62 years [IQR, 56-71 years]; 401 men). Originally, 123 patients were classified as BCLC B with mOS of 24.3 months (95% CI: 21.4, 32.9) and 371 patients as BCLC C with mOS of 11.9 months (95% CI: 10.5, 14.8). The mOS of all included patients (including the BCLC B and C groups) was 15 months (95% CI: 12.3, 17.2). A total of 152 patients with BCLC C tumors were restratified into a new BCLC B_n class, in which the mOS was then 25.1 months (95% CI: 21.8, 29.7; P < .001). The mOS of the remaining patients (ie, BCLC C_n group) (n = 222; ETV ≥65 cm³ or ETB ≥4%) was 8.4 months (95% CI: 6.1, 11.2). Conclusion Substratification of patients with intermediate- and advanced-stage hepatocellular carcinoma according to three-dimensional quantitative tumor burden identified patients with a survival benefit from transarterial chemoembolization before therapy. © RSNA, 2022 Online supplemental material is available for this article.

Quantitative Imaging Biomarkers of the Whole Liver Tumor Burden Improve Survival Prediction in Metastatic Pancreatic Cancer

Simple Summary

Finding prognostic biomarkers and associated models with high accuracy in patients with pancreatic cancer remains a challenge. The aim of this study was to analyze whether the combination of quantitative imaging biomarkers based on geometric and radiomics analysis of whole liver tumor burden and established clinical parameters improves the prediction of survival in patients with metastatic pancreatic cancer. In this retrospective study a total of 75 patients with pancreatic cancer and liver metastases were analyzed. Segmentations of whole liver tumor burden from baseline contrast-enhanced CT images were used to derive different quantitative imaging biomarkers. For comparison, we chose two clinical prognostic models from the literature. We found that a combined clinical and imaging-based model has a significantly higher predictive performance to discriminate survival than the underlying clinical models alone (p < 0.003).

Abstract

Finding prognostic biomarkers with high accuracy in patients with pancreatic cancer (PC) remains a challenging problem. To improve the prediction of survival and to investigate the relevance of quantitative imaging biomarkers (QIB) we combined QIB with established clinical parameters. In this retrospective study a total of 75 patients with metastatic PC and liver metastases were analyzed. Segmentations of whole liver tumor burden (WLTB) from baseline contrast-enhanced CT images were used to derive QIBs. The benefits of QIBs in multivariable Cox models were analyzed in comparison with two clinical prognostic models from the literature. To discriminate survival, the two clinical models had concordance indices of 0.61 and 0.62 in a statistical setting. Combined clinical and imaging-based models achieved concordance indices of 0.74 and 0.70 with WLTB volume, tumor burden score (TBS), and bilobar disease being the three WLTB parameters that were kept by backward elimination. These combined clinical and imaging-based models have significantly higher predictive performance in discriminating survival than the underlying clinical models alone (p < 0.003). Radiomics and geometric WLTB analysis of patients with metastatic PC with liver metastases enhances the modeling of survival compared with models based on clinical parameters alone.

Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy

OBJECTIVES

To determine if three-dimensional whole liver and baseline tumor enhancement features on MRI can serve as staging biomarkers and help predict survival of patients with colorectal cancer liver metastases (CRCLM) more accurately than one-dimensional and non-enhancement-based features.

METHODS

This retrospective study included 88 patients with CRCLM, treated with transarterial chemoembolization or Y90 transarterial radioembolization between 2001 and 2014. Semi-automated segmentations of up to three dominant lesions were performed on pre-treatment MRI to calculate total tumor volume (TTV) and total liver volumes (TLV). Quantitative 3D analysis was performed to calculate enhancing tumor volume (ETV), enhancing tumor burden (ETB, calculated as ETV/TLV), enhancing liver volume (ELV), and enhancing liver burden (ELB, calculated as ELV/TLV). Overall and enhancing tumor diameters were also measured. A modified Kaplan-Meier method was used to determine appropriate cutoff values for each metric. The predictive value of each parameter was assessed by Kaplan-Meier survival curves and univariable and multivariable cox proportional hazard models.

RESULTS

All methods except whole liver (ELB, ELV) and one-dimensional/non-enhancement-based methods were independent predictors of survival. Multivariable analysis showed a HR of 2.1 (95% CI 1.3-3.4, p = 0.004) for enhancing tumor diameter, HR 1.7 (95% CI 1.1-2.8, p = 0.04) for TTV, HR 2.3 (95% CI 1.4-3.9, p < 0.001) for ETV, and HR 2.4 (95% CI 1.4-4.0, p = 0.001) for ETB.

CONCLUSIONS

Tumor enhancement of CRCLM on baseline MRI is strongly associated with patient survival after intra-arterial therapy, suggesting that enhancing tumor volume and enhancing tumor burden are better prognostic indicators than non-enhancement-based and one-dimensional-based markers.

KEY POINTS

• Tumor enhancement of colorectal cancer liver metastases on MRI prior to treatment with intra-arterial therapies is strongly associated with patient survival. • Three-dimensional, enhancement-based imaging biomarkers such as enhancing tumor volume and enhancing tumor burden may serve as the basis of a novel prognostic staging system for patients with liver-dominant colorectal cancer metastases.

Role of 3D quantitative tumor analysis for predicting overall survival after conventional chemoembolization of intrahepatic cholangiocarcinoma

This study was designed to assess 3D vs. 1D and 2D quantitative tumor analysis for prediction of overall survival (OS) in patients with Intrahepatic Cholangiocarcinoma (ICC) who underwent conventional transarterial chemoembolization (cTACE). 73 ICC patients who underwent cTACE were included in this retrospective analysis between Oct 2001 and Feb 2015. The overall and enhancing tumor diameters and the maximum cross-sectional and enhancing tumor areas were measured on baseline images. 3D quantitative tumor analysis was used to assess total tumor volume (TTV), enhancing tumor volume (ETV), and enhancing tumor burden (ETB) (ratio between ETV and liver volume). Patients were divided into low (LTB) and high tumor burden (HTB) groups. There was a significant separation between survival curves of the LTB and HTB groups using enhancing tumor diameter (p = 0.003), enhancing tumor area (p = 0.03), TTV (p = 0.03), and ETV (p = 0.01). Multivariate analysis showed a hazard ratio of 0.46 (95%CI: 0.27–0.78, p = 0.004) for enhancing tumor diameter, 0.56 (95% CI 0.33–0.96, p = 0.04) for enhancing tumor area, 0.58 (95%CI: 0.34–0.98, p = 0.04) for TTV, and 0.52 (95%CI: 0.30–0.91, p = 0.02) for ETV. TTV and ETV, as well as the largest enhancing tumor diameter and maximum enhancing tumor area, reliably predict the OS of patients with ICC after cTACE and could identify ICC patients who are most likely to benefit from cTACE.

Predicting Infiltrative Hepatocellular Carcinoma Patient Outcome Post-TACE: MR Bias Field Correction Effect on 3D-quantitative Image Analysis

Background and Aims: To investigate the impact of MR bias field correction on response determination and survival prediction using volumetric tumor enhancement analysis in patients with infiltrative hepatocellular carcinoma, after transcatheter arterial chemoembolization (TACE). Methods: This study included 101 patients treated with conventional or drug-eluting beads TACE between the years of 2001 and 2013. Semi-automated 3D quantification software was used to segment and calculate the enhancing tumor volume (ETV) of the liver with and without bias-field correction on multi-phasic contrast-enhanced MRI before and 1-month after initial TACE. ETV (expressed as cm3) at baseline imaging and the relative change in ETV (as % change, ETV%) before and after TACE were used to predict response and survival, respectively. Statistical survival analyses included Kaplan-Meier curve generation and Cox proportional hazards modeling. Q statistics were calculated and used to identify the best cut-off value for ETV to separate responders and non-responders (ETV cm3). The difference in survival was evaluated between responders and non-responders using Kaplan-Meier and Cox models. Results: MR bias field correction correlated with improved response calculation from baseline MR as well as survival after TACE; using a 415 cm3 cut-off for ETV at baseline (hazard ratio: 2.00, 95% confidence interval: 1.23-3.26, p=0.01) resulted in significantly improved response prediction (median survival in patients with baseline ETV <415 cm3: 19.66 months vs. ≥415 cm3: 9.21 months, p<0.001, log-rank test). A ≥41% relative decrease in ETV (hazard ratio: 0.58, 95%confidence interval: 0.37-0.93, p=0.02) was significant in predicting survival (ETV ≥41%: 19.20 months vs. ETV <41%: 8.71 months, p=0.008, log-rank test). Without MR bias field correction, response from baseline ETV could be predicted but survival after TACE could not. Conclusions: MR bias field correction improves both response assessment and accuracy of survival prediction using whole liver tumor enhancement analysis from baseline MR after initial TACE in patients with infiltrative hepatocellular carcinoma.

Post-TACE changes in ADC histogram predict overall and transplant-free survival in patients with well-defined HCC: a retrospective cohort with up to 10 years follow-up

OBJECTIVES

To evaluate the role of change in apparent diffusion coefficient (ADC) histogram after the first transarterial chemoembolization (TACE) in predicting overall and transplant-free survival in well-circumscribed hepatocellular carcinoma (HCC).

METHODS

Institution database was searched for HCC patients who got conventional TACE during 2005-2016. One hundred four patients with well-circumscribed HCC and complete pre- and post-TACE liver MRI were included. Volumetric MRI metrics including tumor volume, mean ADC, skewness, and kurtosis of ADC histograms were measured. Univariate and multivariable Cox models were used to test the independent role of change in imaging parameters to predict survival. P values < 0.05 were considered significant.

RESULTS

In total, 367 person-years follow-up data were analyzed. After adjusting for baseline liver function, tumor volume, and treatment modality, incremental percent change in ADC (ΔADC) was an independent predictor of longer overall and transplant-free survival (p = 0.009). Overall, a decrease in ADC-kurtosis (ΔkADC) showed a strong role in predicting longer survival (p = 0.021). Patients in the responder group (ΔADC ≥ 35%) had the best survival profile, compared with non-responders (ΔADC < 35%) (p < 0.001). ΔkADC, as an indicator of change in tissue homogeneity, could distinguish between poor and fair survival in non-responders (p < 0.001). It was not a measure of difference among responders (p = 0.244). Non-responders with ΔkADC ≥ 1 (homogeneous post-TACE tumor) had the worst survival outcome (HR = 5.70, p < 0.001), and non-responders with ΔkADC < 1 had a fair survival outcome (HR = 2.51, p = 0.029), compared with responders.

CONCLUSIONS

Changes in mean ADC and ADC kurtosis, as a measure of change in tissue heterogeneity, can be used to predict overall and transplant-free survival in well-circumscribed HCC, in order to monitor early response to TACE and identify patients with treatment failure and poor survival outcome.

KEY POINTS

• Changes in the mean and kurtosis of ADC histograms, as the measures of change in tissue heterogeneity, can be used to predict overall and transplant-free survival in patients with well-defined HCC. • A ≥ 35% increase in volumetric ADC after TACE is an independent predictor of good survival, regardless of the change in ADC histogram kurtosis. • In patients with < 35% ADC change, a decrease in ADC histogram kurtosis indicates partial response and fair survival, while ∆kurtosis ≥ 1 correlates with the worst survival outcome.

The relevance of CT-based geometric and radiomics analysis of whole liver tumor burden to predict survival of patients with metastatic colorectal cancer

OBJECTIVES

To investigate the prediction of 1-year survival (1-YS) in patients with metastatic colorectal cancer with use of a systematic comparative analysis of quantitative imaging biomarkers (QIBs) based on the geometric and radiomics analysis of whole liver tumor burden (WLTB) in comparison to predictions based on the tumor burden score (TBS), WLTB volume alone, and a clinical model.

METHODS

A total of 103 patients (mean age: 61.0 ± 11.2 years) with colorectal liver metastases were analyzed in this retrospective study. Automatic segmentations of WLTB from baseline contrast-enhanced CT images were used. Established biomarkers as well as a standard radiomics model building were used to derive 3 prognostic models. The benefits of a geometric metastatic spread (GMS) model, the Aerts radiomics prior model of the WLTB, and the performance of TBS and WLTB volume alone were assessed. All models were analyzed in both statistical and predictive machine learning settings in terms of AUC.

RESULTS

TBS showed the best discriminative performance in a statistical setting to discriminate 1-YS (AUC = 0.70, CI: [0.56, 0.90]). For the machine learning-based prediction for unseen patients, both a model of the GMS of WLTB (0.73, CI: [0.60, 0.84]) and the Aerts radiomics prior model (0.76, CI: [0.65, 0.86]) applied on the WLTB showed a numerically higher predictive performance than TBS (0.68, CI: [0.54, 0.79]), radiomics (0.65, CI: [0.55, 0.78]), WLTB volume alone (0.53, CI: [0.40. 0.66]), or the clinical model (0.56, CI: [0.43, 0.67]).

CONCLUSIONS

The imaging-based GMS model may be a first step towards a more fine-grained machine learning extension of the TBS concept for risk stratification in mCRC patients without the vulnerability to technical variance of radiomics.

KEY POINTS

• CT-based geometric distribution and radiomics analysis of whole liver tumor burden in metastatic colorectal cancer patients yield prognostic information. • Differences in survival are possibly attributable to the spatial distribution of metastatic lesions and the geometric metastatic spread analysis of all liver metastases may serve as robust imaging biomarker invariant to technical variation. • Imaging-based prediction models outperform clinical models for 1-year survival prediction in metastatic colorectal cancer patients with liver metastases.

Radiomics Analysis on Multiphase Contrast-Enhanced CT: A Survival Prediction Tool in Patients With Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization

Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68–0.79] and 0.70 [95% CI 0.62–0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58–0.70] and 0.65 [95% CI 0.55–0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance.

Comparing HCC arterial tumour vascularisation on baseline imaging and after lipiodol cTACE: how do estimations of enhancing tumour volumes differ on contrast-enhanced MR and CT?

OBJECTIVES

In this study, pre-treatment target lesion vascularisation in either contrast-enhanced (CE) CT or MRI and post-treatment lipiodol deposition in native CT scans were compared in HCC patients who underwent their first cTACE treatment. We analysed the impact of stratification according to cTACE selectivity on these correlations.

METHODS

Seventy-eight HCC patients who underwent their first cTACE procedure were retrospectively included. Pre-treatment tumour vascularisation in arterial contrast phase and post-treatment lipiodol deposition in native CT scans were evaluated using the qEASL (quantitative tumour enhancement) method. Correlations were analysed using scatter plots, the Pearson correlation coefficient (PCC) and linear regression analysis. Subgroup analysis was performed according to lobar, segmental and subsegmental execution of cTACE.

RESULTS

Arterial tumour volumes in both baseline CE CT (R² = 0.83) and CE MR (R² = 0.82) highly correlated with lipiodol deposition after cTACE. The regression coefficient between lipiodol deposition and enhancing tumour volume was 1.39 for CT and 0.33 for MR respectively, resulting in a ratio of 4.24. After stratification according to selectivity of cTACE, the regression coefficient was 0.94 (R² = 1) for lobar execution, 1.38 (R² = 0.96) for segmental execution and 1.88 (R² = 0.89) for subsegmental execution in the CE CT group.

CONCLUSIONS

Volumetric lipiodol deposition can be used as a reference to compare different imaging modalities in detecting vital tumour volumes. That approach proved CE MRI to be more sensitive than CE CT. Selectivity of cTACE significantly impacts the respective regression coefficients which allows for an innovative approach to the assessment of technical success after cTACE with a multitude of possible applications.

KEY POINTS

• Lipiodol deposition after cTACE highly correlates with pre-treatment tumour vascularisation and can be used as a reference to compare different imaging modalities in detecting vital tumour volumes. • Lipiodol deposition also correlates with the selectivity of cTACE and can therefore be used to quantify the technical success of the intervention.

Hepatocellular Carcinoma: Diagnosis, Treatment Algorithms, and Imaging Appearance after Transarterial Chemoembolization

Hepatocellular carcinoma (HCC) is a common cause of cancer-related death, with incidence increasing worldwide. Unfortunately, the overall prognosis for patients with HCC is poor and many patients present with advanced stages of disease that preclude curative therapies. Diagnostic and interventional radiologists play a key role in the management of patients with HCC. Diagnostic radiologists can use contrast-enhanced computed tomography (CT), magnetic resonance imaging, and ultrasound to diagnose and stage HCC, without the need for pathologic confirmation, by following established criteria. Once staged, the interventional radiologist can treat the appropriate patients with percutaneous ablation, transarterial chemoembolization, or radioembolization. Follow-up imaging after these liver-directed therapies for HCC can be characterized according to various radiologic response criteria; although, enhancement-based criteria, such as European Association for the Study of the Liver and modified Response Evaluation Criteria in Solid Tumors, are more reflective of treatment effect in HCC. Newer imaging technologies like volumetric analysis, dual-energy CT, cone beam CT and perfusion CT may provide additional benefits for patients with HCC.

Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib

Transcatheter arterial chemoembolization (TACE) and sorafenib combination treatment for unselected hepatocellular carcinoma (HCC) is controversial. We explored the potential of texture analysis for appropriate patient selection. There were 261 HCCs included (TACE group: n = 197; TACE plus sorafenib (TACE+Sorafenib) group n = 64). We applied a Gabor filter and wavelet transform with 3 band-width responses (filter 0, 1.0, and 1.5) to portal-phase computed tomography (CT) images of the TACE group. Twenty-one textural parameters per filter were extracted from the region of interests delineated around tumor outline. After testing survival correlations, the TACE group was subdivided according to parameter thresholds in receiver operating characteristic curves and compared to TACE+Sorafenib group survival. The Gabor-1-90 (filter 0) was most significantly correlated with TTP. The TACE group was accordingly divided into the TACE-1 (Gabor-1-90 ≤ 3.6190) and TACE-2 (Gabor-1-90 > 3.6190) subgroups; TTP was similar in the TACE-1 subgroup and TACE+Sorafenib group, but shorter in the TACE-2 subgroup. Only wavelet-3-D (filter 1.0) correlated with overall survival (OS), and was used for subgrouping. The TACE-5 (wavelet-3-D ≤ 12.2620) subgroup and the TACE+Sorafenib group showed similar OS, while the TACE-6 (wavelet-3-D > 12.2620) subgroup had shorter OS. Gabor-1-90 and wavelet-3-D were consistent.Independent of tumor number or size, CT textural parameters are correlated with TTP and OS. Patients with lower Gabor-1-90 (filter 0) and wavelet-3-D (filter 1.0) should be treated with TACE and sorafenib. Texture analysis holds promise for appropriate selection of HCCs for this combination therapy.

Intra-arterial therapies for liver cancer: assessing tumor response

INTRODUCTION

Intra-arterial therapies (IATs) play an integral role in the management of unresectable hepatocellular carcinoma and liver metastases. The ability to accurately assess tumor response to intra-arterial therapies is crucial for clinical management. Several one- and two-dimensional manual imaging-based response assessment techniques, based both on tumor size or enhancement, have shown to be highly subjective and merely surrogate for the actual tumor as a whole. Areas covered: Given the currently existing literature, we will discuss all available tumor assessment techniques and criteria for liver cancer with a strong emphasis on 3D quantitative imaging biomarkers of tumor response in this review. Expert commentary: The growing role of information technology in medicine has brought about the advent of software-assisted, segmentation-based assessment techniques that address the outstanding issues of a subjective reader and provide for more accurate assessment techniques for the locally treated lesions. Three-dimensional quantitative tumor assessment techniques are superior to one- and two-dimensional measurements. This allows for treatment alterations and more precise targeting, potentially resulting in improved patient outcome.

Renal Cell Carcinoma Metastatic to the Liver: Early Response Assessment after Intraarterial Therapy Using 3D Quantitative Tumor Enhancement Analysis

PURPOSE

Liver metastases from renal cell carcinoma (RCC) are not uncommon in the course of disease. However, data about tumor response to intraarterial therapy (IAT) are scarce. This study assessed whether changes of enhancing tumor volume using quantitative European Association for the Study of the Liver (qEASL) on magnetic resonance imaging (MRI) and computed tomography (CT) can evaluate tumor response and predict overall survival (OS) early after therapy.

METHODS AND MATERIALS

Fourteen patients with liver metastatic RCC treated with IAT (transarterial chemoembolization: n= 9 and yttrium-90: n= 5) were retrospectively included. All patients underwent contrast-enhanced imaging (MRI: n= 10 and CT: n= 4) 3 to 4 weeks pre- and posttreatment. Response to treatment was evaluated on the arterial phase using Response Evaluation Criteria in Solid Tumors (RECIST), World Health Organization, modified RECIST, EASL, tumor volume, and qEASL. Paired t test was used to compare measurements pre- and post-IAT. Patients were stratified into responders (≥65% decrease in qEASL) and nonresponders (<65% decrease in qEASL). OS was evaluated using Kaplan-Meier curves with log-rank test and the Cox proportional hazard model.

RESULTS

Mean qEASL (cm³) decreased from 93.5 to 67.2 cm³ (P= .004) and mean qEASL (%) from 63.1% to 35.6% (P= .001). No significant changes were observed using other response criteria. qEASL was the only significant predictor of OS when used to stratify patients into responders and nonresponders with median OS of 31.9 versus 11.1 months (hazard ratio [HR], 0.43; 95% confidence interval [CI], 0.19-0.97; P= .042) for qEASL (cm³) and 29.9 versus 10.2 months (HR, 0.09; 95% CI, 0.01-0.74; P= .025) for qEASL (%).

CONCLUSION

Three-dimensional (3D) quantitative tumor analysis is a reliable predictor of OS when assessing treatment response after IAT in patients with RCC metastatic to the liver. qEASL outperforms conventional non-3D methods and can be used as a surrogate marker for OS early after therapy.