Prediction of Therapeutic Response of Hepatocellular Carcinoma to Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings. AJR Am J Roentgenol. 2017;209:W211-W220. [PMID: 28813195 DOI: 10.2214/ajr.16.17398]

Artificial intelligence in interventional radiology: state of the art

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.

Radiomics-Based Prediction Model for Outcome of Radioembolization in Metastatic Colorectal Cancer

PURPOSE

To evaluate the benefit of a contrast-enhanced computed tomography (CT) radiomics-based model for predicting response and survival in patients with colorectal liver metastases treated with transarterial Yttrium-90 radioembolization (TARE).

MATERIALS AND METHODS

Fifty-one patients who underwent TARE were included in this single-center retrospective study. Response to treatment was assessed using the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) at 3-month follow-up. Patients were stratified as responders (complete/partial response and stable disease, n = 24) or non-responders (progressive disease, n = 27). Radiomic features (RF) were extracted from pre-TARE CT after segmentation of the liver tumor volume. A model was built based on a radiomic signature consisting of reliable RFs that allowed classification of response using multivariate logistic regression. Patients were assigned to high- or low-risk groups for disease progression after TARE according to a cutoff defined in the model. Kaplan-Meier analysis was performed to analyze survival between high- and low-risk groups.

RESULTS

Two independent RF [Energy, Maximal Correlation Coefficient (MCC)], reflecting tumor heterogeneity, discriminated well between responders and non-responders. In particular, patients with higher magnitude of voxel values in an image (Energy), and texture complexity (MCC), were more likely to fail TARE. For predicting treatment response, the area under the receiver operating characteristic curve of the radiomics-based model was 0.75 (95% CI 0.48-1). The high-risk group had a shorter overall survival than the low-risk group (3.4 vs. 6.4 months, p < 0.001).

CONCLUSION

Our CT radiomics model may predict the response and survival outcome by quantifying tumor heterogeneity in patients treated with TARE for colorectal liver metastases.

Lipiodol as a Predictive Indicator for Therapy Response to Transarterial Chemoembolization of Hepatocellular Carcinoma

Background: The predictive value of Lipiodol was evaluated for response evaluation of hepatocellular carcinoma (HCC) treated with conventional transarterial chemoembolization (cTACE) by analysis of the enhancement pattern during angiography and in postinterventional computed tomography (CT). Materials and Methods: This retrospective study included 30 patients (mean age 63 years, range: 36 to 82 years, 22 males) with HCC. Patients received three Lipiodol-based cTACE sessions, each followed by an unenhanced CT within 24-h. Contrast-enhanced magnetic resonance imaging (MRI) was acquired before and after the treatment to determine tumor response. Lipiodol enhancement pattern, tumor vascularization, and density were evaluated by angiography and CT. Initial tumor size and response to cTACE were analyzed by MRI according to modified response evaluation criteria in solid tumors (mRECIST) in a 4-week follow-up. Results: Analysis of HCC lesions (68 lesions in 30 patients) during cTACE revealed clear visibility and hypervascularization in angiography as a potential independent parameter able to predict tumor response. A significant correlation was found for response measurements by volume (p = 0.012), diameter (p = 0.006), and according to mRECIST (p = 0.039). The amount of Lipiodol and enhancement pattern in postinterventional CT did not correlate with therapy response. Measurements of Hounsfield unit values after cTACE do not allow sufficient prediction of the tumor response. Conclusion: Hypervascularized HCC lesions with clear visibility after Lipiodol administration in the angiography respond significantly better to cTACE compared to hypo- or nonvascularized lesions.

CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence

Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.

Development and Preliminary Validation of a Novel Convolutional Neural Network Model for Predicting Treatment Response in Patients with Unresectable Hepatocellular Carcinoma Receiving Hepatic Arterial Infusion Chemotherapy

The goal of this study was to evaluate the performance of a convolutional neural network (CNN) with preoperative MRI and clinical factors in predicting the treatment response of unresectable hepatocellular carcinoma (HCC) patients receiving hepatic arterial infusion chemotherapy (HAIC). A total of 191 patients with unresectable HCC who underwent HAIC in our hospital between May 2019 and March 2022 were retrospectively recruited. We selected InceptionV4 from three representative CNN models, AlexNet, ResNet, and InceptionV4, according to the cross-entropy loss (CEL). We subsequently developed InceptionV4 to fuse the information from qualified pretreatment MRI data and patient clinical factors. Radiomic information was evaluated based on several constant sequences, including enhanced T1-weighted sequences (with arterial, portal, and delayed phases), T2 FSE sequences, and dual-echo sequences. The performance of InceptionV4 was cross-validated in the training cohort (n = 127) and internally validated in an independent cohort (n = 64), with comparisons against single important clinical factors and radiologists in terms of receiver operating characteristic (ROC) curves. Class activation mapping was used to visualize the InceptionV4 model. The InceptionV4 model achieved an AUC of 0.871 (95% confidence interval [CI] 0.761-0.981) in the cross-validation cohort and an AUC of 0.826 (95% CI 0.682-0.970) in the internal validation cohort; these two models performed better than did the other methods (AUC ranges 0.783-0.873 and 0.708-0.806 for cross- and internal validations, respectively; P < 0.01). The present InceptionV4 model, which integrates radiomic information and clinical factors, helps predict the treatment response of unresectable HCC patients receiving HAIC treatment.

Application of Radiomics in the Efficacy Evaluation of Transarterial Chemoembolization for Hepatocellular Carcinoma: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES: This meta-analysis was aimed at evaluating the predictive value of radiomics in the context of transarterial chemoembolization (TACE) therapeutic response (TR) for hepatocellular carcinoma (HCC) and patients' survival status (SS) and providing favorable evidence for clinical application.

MATERIALS AND METHODS

We searched for literature in which radiomics was applied to assess the TR of TACE for HCC and the affected patients' survival status across PubMed, Embase, Cochrane Library and Web of Science until Jul 12, 2023. The quality of included literature was evaluated using a radiomics quality score (RQS) approach, and a meta-analysis was conducted using Stata15.0.

RESULTS

Twenty-four studies were included in the analysis. The meta-analysis revealed that the overall concordance-index (C-index) based on radiomics for predicting the TR and SS with TACE was 0.85 and 0.78, respectively. The combined radiomics-clinical model provided the best performance in evaluating the TR and SS associated with TACE. The C-index was 0.93 and 0.88 for TR and 0.84 and 0.80 for SS, in the training and validation sets, respectively. These values were higher than the 0.87 and 0.79 for TR and 0.79 and 0.70 for SS, respectively with the radiomics model, and 0.71 and 0.66 for TR and 0.72 and 0.66 for SS, respectively with the clinical model.

CONCLUSION

The radiomics prediction model for the efficacy of TACE in HCC showed a satisfactory prediction performance. The combined radiomics-clinic prediction model had the best performance.

Efficacy of Radiomics in Predicting Oncologic Outcome of Liver-Directed Combined Radiotherapy in Locally Advanced Hepatocellular Carcinoma

PURPOSE

We investigated whether radiomic features extracted from three-phase dynamic contrast-enhanced computed tomography (CECT) can be used to predict clinical outcomes, including objective treatment response (OR) and in-field failure-free survival rate (IFFR), in patients with hepatocellular carcinoma (HCC) who received liver-directed combined radiotherapy (LD-CRT).

METHODS

We included 409 patients, and they were randomly divided into training (n = 307) and validation (n = 102) cohorts. For radiomics models, we extracted 116 radiomic features from the region of interest on the CECT images. Significant clinical prognostic factors are identified to predict the OR and IFFR in the clinical models. We developed clinical models, radiomics models, and a combination of both features (CCR model).

RESULTS

Among the radiomic models evaluated for OR, the OR-PVP-Peri-1cm model showed favorable predictive performance with an area under the curve (AUC) of 0.647. The clinical model showed an AUC of 0.729, whereas the CCR model showed better performance (AUC 0.759). For the IFFR, the IFFR-PVP-Peri-1cm model showed an AUC of 0.673, clinical model showed 0.687, and the CCR model showed 0.736. We also developed and validated a prognostic nomogram based on CCR models.

CONCLUSION

In predicting the OR and IFFR in patients with HCC undergoing LD-CRT, CCR models performed better than clinical and radiomics models. Moreover, the constructed nomograms based on these models may provide valuable information on the prognosis of these patients.

Contrast-Enhanced CT Imaging Features Combined with Clinical Factors to Predict the Efficacy and Prognosis for Transarterial Chemoembolization of Hepatocellular Carcinoma

RATIONALE AND OBJECTIVES

Accurate prediction of treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) is critical for precision treatment. This study aimed to develop a comprehensive model (DLRC) that incorporates contrast-enhanced computed tomography (CECT) images and clinical factors to predict the response to TACE in patients with HCC.

MATERIALS AND METHODS

A total of 399 patients with intermediate-stage HCC were included in this retrospective study. Deep learning and radiomic signatures were established based on arterial phase CECT images, Correlation analysis and the least absolute shrinkage and selection (LASSO) regression analysis were applied for features selection. The DLRC model incorporating deep learning radiomic signatures and clinical factors was developed using multivariate logistic regression. The area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the models. Kaplan-Meier survival curves based on the DLRC were plotted to assess overall survival in the follow-up cohort (n = 261).

RESULTS

The DLRC model was developed using 19 quantitative radiomic features, 10 deep learning features, and 3 clinical factors. The AUC of the DLRC model was 0.937 (95% confidence interval [CI], 0.912-0.962) and 0.909 (95% CI, 0.850-0.968) in the training and validation cohorts, respectively, outperforming models established with two signatures or a single signature (p < 0.05). Stratified analysis showed that the DLRC was not statistically different between subgroups (p > 0.05), and the DCA confirmed the greater net clinical benefit. In addition, multivariable cox regression revealed that DLRC model outputs were independent risk factors for the overall survival (hazard ratios: 1.20, 95% CI: 1.03-1.40; p = 0.019).

CONCLUSION

The DLRC model exhibited a remarkable accuracy in predicting response to TACE, and it can be utilized as a potent tool for precision treatment.

A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy

OBJECTIVE

This study aimed to evaluate the predictive value of computed tomography (CT) texture features in the treatment response of patients with advanced pancreatic cancer (APC) receiving palliative chemotherapy.

METHODS

This study enrolled 84 patients with APC treated with first-line chemotherapy and conducted texture analysis on primary pancreatic tumors. 59 patients and 25 were randomly assigned to the training and validation cohorts at a ratio of 7:3. The treatment response to chemotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST1.1). The patients were divided into progressive and non-progressive groups. The least absolute shrinkage selection operator (LASSO) was applied for feature selection in the training cohort and a radiomics signature (RS) was calculated. A nomogram was developed based on a multivariate logistic regression model incorporating the RS and carbohydrate antigen 19-9 (CA19-9), and was internally validated using the C-index and calibration plot. We performed the decision curve analysis (DCA) and clinical impact curve analysis to reflect the clinical utility of the nomogram. The nomogram was further externally confirmed in the validation cohort.

RESULTS

The multivariate logistic regression analysis indicated that the RS and CA19-9 were independent predictors (P < 0.05), and a trend was found for chemotherapy between progressive and non-progressive groups. The nomogram incorporating RS, CA19-9 and chemotherapy showed favorable discriminative ability in the training (C-index = 0.802) and validation (C-index = 0.920) cohorts. The nomogram demonstrated favorable clinical utility.

CONCLUSION

The RS of significant texture features was significantly associated with the early treatment effect of patients with APC treated with chemotherapy. Based on the RS, CA19-9 and chemotherapy, the nomogram provided a promising way to predict chemotherapeutic effects for APC patients.

Computed tomography texture analysis combined with preoperative clinical factors serve as a predictor of early efficacy of transcatheter arterial chemoembolization in hepatocellular carcinoma

AIM

To investigate a pre-therapeutic radiomics nomogram to accurately predict hepatocellular carcinoma (HCC) lesion responses to transcatheter arterial chemoembolization (TACE).

METHODS

This retrospective study from January 2012 to 2022 included 92 TACE-treated patients who underwent liver contrast-enhanced CT scan 7 days before treatment, having complete clinical information. We extracted quantitative texture parameters and clinical factors for the largest tumors on the baseline arterial and portal venous phase CT images. An adaptive least absolute shrinkage and selection operator (LASSO)-penalized logistic regression identified independent predictors of tumor activity after TACE.

RESULTS

We fitted an adaptive LASSO regression model to narrow down the texture features and clinical risk factors of the tumor activity status. The selected texture features were used to construct radiomic scores (RadScore), which demonstrated superior performance in predicting tumor activity on both the training (area under the curve (AUC): 0.881, 95% CI: 0.799-0.963) and testing sets (AUC: 0.88, 95% CI: 0.726-1). A logistic regression-based nomogram was developed using RadScore and four selected clinical features. In the testing set, nomogram total points were significant predictors (P = 0.034), and the training set showed no departure from perfect fit (P = 0.833). Internal validation of the nomogram was obtained for the training (AUC: 0.91, 95% CI: 0.837-0.984) and testing (AUC: 0.889, 95% CI: 0.746-1) sets.

CONCLUSION

We propose a nomogram to predict the early response of HCC lesions to TACE treatment with high accuracy, which may serve as an additional criterion in multidisciplinary decision-making treatment.

Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography

This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Prediction of therapeutic response of unresectable hepatocellular carcinoma to hepatic arterial infusion chemotherapy based on pretherapeutic MRI radiomics and Albumin-Bilirubin score

Purpose

To construct and validate a combined nomogram model based on magnetic resonance imaging (MRI) radiomics and Albumin-Bilirubin (ALBI) score to predict therapeutic response in unresectable hepatocellular carcinoma (HCC) patients treated with hepatic arterial infusion chemotherapy (HAIC).

Methods

The retrospective study was conducted on 112 unresectable HCC patients who underwent pretherapeutic MRI examinations. Patients were randomly divided into training (n = 79) and validation cohorts (n = 33). A total of 396 radiomics features were extracted from the volume of interest of the primary lesion by the Artificial Kit software. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify optimal radiomic features. After feature selection, three models, including the clinical, radiomics, and combined models, were developed to predict the non-response of unresectable HCC to HAIC treatment. The performance of these models was evaluated by the receiver operating characteristic curve. According to the most efficient model, a nomogram was established, and the performance of which was also assessed by calibration curve and decision curve analysis. Kaplan–Meier curve and log-rank test were performed to evaluate the Progression-free survival (PFS).

Results

Using the LASSO regression, we ultimately selected three radiomics features from T2-weighted images to construct the radiomics score (Radscore). Only the ALBI score was an independent factor associated with non-response in the clinical model (P = 0.033). The combined model, which included the ALBI score and Radscore, achieved better performance in the prediction of non-response, with an AUC of 0.79 (95% CI 0.68–0.90) and 0.75 (95% CI 0.58–0.92) in the training and validation cohorts, respectively. The nomogram based on the combined model also had good discrimination and calibration (P = 0.519 for the training cohort and P = 0.389 for the validation cohort). The Kaplan–Meier analysis also demonstrate that the high-score patients had significantly shorter PFS than the low-score patients (P = 0.031) in the combined model, with median PFS 6.0 vs 9.0 months.

Conclusion

The nomogram based on the combined model consisting of MRI radiomics and ALBI score could be used as a biomarker to predict the therapeutic response of unresectable HCC after HAIC.

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.

Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma

Purpose

This study aims to develop a new model to more comprehensively and accurately predict the survival of patients with HCC after initial TACE.

Patients and Methods

The whole cohort (n = 102) was randomly divided into a training cohort and a validation cohort in the ratio of 8:2. The optimal radiomics signatures were screened using the least absolute shrinkage and selection operator algorithm (LASSO) regression for constructing the radscore to predict overall survival (OS). The C-index (95% confidence interval, CI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the models. The independent risk factors (hazard ratio, HR) for predicting OS were stratified by Kaplan–Meier (K-M) analysis and the Log rank test.

Results

The median OS was 439 days (95% CI: 215.795–662.205) in whole cohort, and in the training cohort and validation cohort, the median OS was 552 days (95% CI: 171.172–932.828), 395 days (95% CI: 309.415–480.585), respectively (P = 0.889). After multivariate cox regression, the combined radscore-clinical model was consisted of radscore (HR: 2.065, 95% CI: 1.285–3.316; P = 0.0029) and post-response (HR: 1.880, 95% CI: 1.310–2.697; P = 0.0007), both of which were independent risk factors for the OS. In the validation cohort, the efficacy of both the radscore (C-index: 0.769, 95% CI: 0.496–1.000) and combined model (C-index: 0.770, 95% CI: 0.581–0.806) were higher than that of the clinical model (C-index: 0.655, 95% CI: 0.508–0.802). The calibration curve of the combined model for predicting OS presented good consistency between observations and predictions in both the training cohort and validation cohort.

Conclusion

Noninvasive imaging has a good prediction performance of survival after initial TACE in patients with HCC. The combined model consisting of post-response and radscore may be able to better predict outcome.

In Vivo Comparison of Micro-Balloon Interventions (MBI) Advantage: A Retrospective Cohort Study of DEB-TACE Versus b-TACE and of SIRT Versus b-SIRT

PURPOSE

The purpose of this study was to evaluate in vivo the role of the micro-balloon by comparing trans-arterial chemoembolization (DEB-TACE) and selective internal radiotherapy (SIRT) procedures performed with and without balloon micro-catheter (b-DEB-TACE and DEB-TACE/SIRT and b-SIRT) for the treatment of hepatocellular carcinoma (HCC).

METHODS

The impact of a balloon micro-catheter on trans-arterial loco-regional treatment was analyzed using non-enhanced post-procedural cone-beam CT (Ne-CBCT) by comparing the attenuation values in the embolized area and the surrounding liver tissue before and after DEB-TACE versus b-DEB-TACE and by comparing 2D/3D dosimetry in single-photon emission computed tomography after SIRT versus b-SIRT, and by comparing the histological count of the beads following orthotopic liver transplantation in the DEB-TACE versus b-DEB-TACE subgroup.

RESULTS

We treated 84 HCC patients using trans-arterial loco-regional therapy. Fifty-three patients (26 DEB-TACE and 27 b-DEB-TACE) were analyzed in the TACE group. Contrast, signal-to-noise ratio, and contrast-to-noise ratio were all significantly higher in b-DEB-TACE subgroup than DEB-TACE (182.33 HU [CI95% 160.3-273.5] vs. 124 HU [CI95% 80.6-163.6]; 8.3 [CI95% 5.7-10.1] vs. 4.5 [CI95% 3.7-6.0]; 6.9 [CI95% 4.3-7.8] vs. 3.1 [CI95% 2.2-5.0] p < 0.05). Thirty-one patients (24 SIRT and 7 b-SIRT) were analyzed in the SIRT group. 2D dosimetry profile evaluation showed an activity intensity peak significantly higher in the b-SIRT than in the SIRT subgroup (987.5 ± 393.8 vs. 567.7 ± 302.2, p = 0.005). Regarding 3D dose analysis, the mean dose administered to the treated lesions was significantly higher in the b-SIRT than in the SIRT group (151.6 Gy ± 53.2 vs. 100.1 Gy ± 43.4, p = 0.01). In histological explanted liver analysis, there was a trend for higher intra-tumoral localization of embolic microspheres for b-DEB-TACE in comparison with DEB-TACE.

CONCLUSIONS

Due to the use of three different methods, the results of this study demonstrate in vivo, a better embolization profile of oncological intra-arterial interventions performed with balloon micro-catheter regardless of the embolic agent employed.

Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study

BACKGROUND AND OBJECTIVE

To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE).

METHODS

140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Response-related radiomics scores (Rad-scores) were constructed on T2-weighted images (T₂WI) and dynamic contrast-enhanced (DCE) imaging separately, and then integrated with conventional clinic-radiological variables into a logistic regression (LR) model for predicting tumor response. LR model was trained in 94 patients in center 1 and independently tested in 46 patients in center 2.

RESULTS

Among 4 MRI sequences, T₂WI achieved better performance than DCE (area under the curve [AUC] 0.754 vs 0.602 to 0.752). LR model by combining Rad-score on T₂WI with Barcelona Clinic Liver Cancer (BCLC) stage and albumin-bilirubin (ALBI) grade resulted in an AUC of 0.813 in training and 0.781 in test for predicting tumor response. In survival analysis, progression-free survival (PFS) and overall survival (OS) presented significant difference between LR-predicted responders and non-responders. The ALBI grade and BCLC stage were independent predictors of PFS; and LR-predicted response, ALBI grade, satellite node, and BCLC stage were independent predictors of OS. The resulting Cox model produced concordance-indexes of 0.705 and 0.736 for predicting PFS and OS, respectively.

CONCLUSIONS

The model combined MRI radiomics with clinical factors demonstrated favorable performance for predicting tumor response and clinical outcomes, thus may help personalized clinical management.

Artificial Intelligent Multi-Modal Point-of-Care System for Predicting Response of Transarterial Chemoembolization in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) ranks the second most lethal tumor globally and is the fourth leading cause of cancer-related death worldwide. Unfortunately, HCC is commonly at intermediate tumor stage or advanced tumor stage, in which only some palliative treatment can be used to offer a limited overall survival. Due to the high heterogeneity of the genetic, molecular, and histological levels, HCC makes the prediction of preoperative transarterial chemoembolization (TACE) efficacy and the development of personalized regimens challenging. In this study, a new multi-modal point-of-care system is employed to predict the response of TACE in HCC by a concept of integrating multi-modal large-scale data of clinical index and computed tomography (CT) images. This multi-modal point-of-care predicting system opens new possibilities for predicting the response of TACE treatment and can help clinicians select the optimal patients with HCC who can benefit from the interventional therapy.

Tumor enhancement ratio with unenhanced imaging is an independent prognostic factor for patients with hepatocellular carcinoma after transarterial chemoembolization

Objective

To investigative whether the odds tumor enhancement ratio (OTER) on cross-sectional imaging is a prognostic factor for hepatocellular carcinoma after transarterial chemoembolization (TACE).

Methods

This study involved 126 patients who underwent TACE from May 2015 to March 2019. The signal intensity/Hounsfield units (HU) was measured by placing regions of interest on the tumor and surrounding liver in unenhanced and arterial-phase contrast-enhanced cross-sectional images. The OTER was calculated as follows: OTER = (HU_TUMORart − HU_TUMORun)/ (HU_LIVERart − HU_LIVERun). Univariate analysis was performed to determine the factors associated with overall survival (OS). Variables with a P value of <0.10 were included in the multivariate Cox regression analysis.

Results

The median OS was 757 days. Tumors with a peripheral location, small size, and low OTER had better OS than those with a central location, large size, and high OTER. OS did not differ according to the extent of tumor involvement or tumor enhancement pattern. The OTER, tumor location, and size were included in the multivariate Cox regression analysis. A low OTER was the predictor of better OS.

Conclusion

A high OTER is a risk factor for poor OS in patients undergoing TACE. This should be taken into consideration before the procedure.

TwinLiverNet: Predicting TACE Treatment Outcome from CT scans for Hepatocellular Carcinoma using Deep Capsule Networks

Predicting response to treatment plays a key role to assist radiologists in hepato-cellular carcinoma (HCC) therapy planning. The most widely used treatment for unresectable HCC is the trans-arterial chemoembolization (TACE). A complete radiological response after the first TACE is a reliable predictor of treatment favourable outcome. However, visual inspection of contrast-enhanced CT scans is time-consuming, error prone and too operator-dependent. Thus, in this paper we propose TwinLiverNet: a deep neural network that is able to predict TACE treatment outcome through learning visual cue from CT scans. TwinLiverNet, specifically, integrates 3D convolutions and capsule networks and is designed to process simultaneously late arterial and delayed phases from contrast-enhanced CTs. Experimental results carried out on a dataset consisting of 126 HCC lesions show that TwinLiverNet reaches an average accuracy of 82% in predicting complete response to TACE treatment. Furthermore, combining multiple CT phases (specifically, late arterial and delayed ones) yields a performance increase of over 12 percent points. Finally, the introduction of capsule layers into the model avoids the model to overfit, while enhancing accuracy.Clinical relevance- TwinLiverNet supports radiologists in visual inspection of CT scans to assess TACE treatment outcome, while reducing inter-operator variability.

Variation in textural parameters of hepatic lesions during contrast medium injection

BACKGROUND

Textural parameters extracted using quantitative imaging techniques have been shown to have prognostic value for hepatocellular carcinoma (HCC).

PURPOSE

To evaluate whether the contrast medium timing of the image acquisition affects the reproducibility of textural parameters in HCC and hepatic tissue.

MATERIAL AND METHODS

This retrospective study included 17 patients with 37 HCC lesions. Perfusion computed tomography (CT) was obtained after 50 mL contrast medium injection. HCC lesions were segmented for analysis. The gray-level co-occurrence (GLCM) textural analysis parameters, homogeneity, energy, entropy, inertia, and correlation were calculated. Variation was quantified by calculating the SD of each parameter during respective perfusion series and the inter lesion variation as the SD among the lesions.

RESULTS

The average change in texture parameters in both HCC and hepatic tissue per second after injection was 0.01% to 0.3% of the respective texture parameter. In HCC, the average variation in homogeneity, energy, and entropy within each lesion after contrast medium injection was significantly less than the variation observed among the lesions (23% to 74%, P < 0.001). Significant differences in energy, entropy, inertia, and correlation between hepatic tissue and HCC were observed. However, when considering the intra-individual variation of hepatic tissue over time, only the HCC parameter energy was significantly outside that 95% confidence interval (P < 0.02).

CONCLUSION

The contrast medium timing does not affect the reproducibility of textural parameters in HCC and hepatic tissue. Thus, contrast medium timing should not be an issue at CT texture analysis of HCC.

Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma

BACKGROUND

Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology.

AIMS

To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation.

METHODS

We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies.

RESULTS

A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard.

CONCLUSIONS

Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.

Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study

Purpose

To investigate the potential of texture analysis and machine learning to predict treatment response to transarterial radioembolization (TARE) on pre-interventional cone-beam computed tomography (CBCT) images in patients with liver metastases.

Materials and Methods

In this IRB-approved retrospective single-center study 36 patients with a total of 104 liver metastases (56 % male, mean age 61.1 ± 13 years) underwent CBCT prior to TARE and follow-up imaging 6 months after therapy. Treatment response was evaluated according to RECIST version 1.1 and dichotomized into disease control (partial response/stable disease) versus disease progression (progressive disease). After target lesion segmentation, 104 radiomics features corresponding to seven different feature classes were extracted with the pyRadiomics package. After dimension reduction machine learning classifications were performed on a custom artificial neural network (ANN). Ten-fold cross validation on a previously unseen test data set was performed.

Results

The average administered cumulative activity from TARE was 1.6 Gbq (± 0.5 Gbq). At a mean follow-up of 5.9 ± 0.8 months disease control was achieved in 82 % of metastases. After dimension reduction, 15 of 104 (15 %) texture analysis features remained for further analysis. On a previously unseen set of liver metastases the Multilayer Perceptron ANN yielded a sensitivity of 94.2 %, specificity of 67.7 % and an area-under-the receiver operating characteristics curve of 0.85.

Conclusion

Our study indicates that texture analysis-based machine learning may has potential to predict treatment response to TARE using pre-treatment CBCT images of patients with liver metastases with high accuracy.

Toward Personalized Medicine in Radiotherapy of Hepatocellular Carcinoma: Emerging Radiomic Biomarker Candidates of Response and Toxicity

Radiology and radiotherapy are currently undergoing radical transformation with use of biomarkers and digital technologies such as artificial intelligence. These current and upcoming changes in radiology speak of an overarching new vision for personalized medicine. This is particularly evident in the case of radiotherapy of cancers, and of liver cancer in particular. The development of modern radiotherapy with stereotactic body radiotherapy allows targeted treatments to be delivered to the tumor site, limiting the dose to surrounding healthy organs, thus becoming a new therapeutic alternative for hepatocellular carcinoma and other liver tumors. However, not all patients have the same response to radiotherapy or display the same side-effect profile. Biomarkers of response and toxicity in liver radiotherapy would facilitate the vision and practice of personalized medicine. This expert review examines the available molecular, radiomic, and radiogenomic biomarker candidates for acute liver toxicity with potential use for prediction of radiotherapy-induced liver toxicity. To this end, I highlight for oncologists and life scientists that radiomics allows diagnostic images to be analyzed using computer algorithms to extract information imperceptible to the human eye and of relevance to forecasting clinical outcomes. This article underscores particularly (1) the microRNA-based biomarker candidates as among the most promising predictors of radiation-induced liver toxicity and (2) the texture features in radiomic analyses for response prediction. Radiotherapy of hepatocellular carcinoma is edging toward personalized medicine with emerging radiomic biomarker candidates. Future large-scale biomarker studies are called for to enable personalized medicine in liver cancers.

Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response

Radiomics refers to the process of conversion of conventional medical images into quantifiable data ("features") which can be further mined to reveal complex patterns and relationships between the voxels in the image. These high throughput features can potentially reflect the histology of biologic tissues at macroscopic and microscopic levels. Several studies have investigated radiomics of hepatocellular carcinoma (HCC) before and after treatment. HCC is a heterogeneous disease with diverse phenotypical and genotypical landscape. Due to this inherent heterogeneity, HCC lesions can manifest variable aggressiveness with different response to treatment options, including the newer targeted therapies. Hence, radiomics can be used as a potential tool to enable patient selection for therapies and to predict response to treatments and outcome. Additionally, radiomics may serve as a tool for earlier and more efficient assessment of response to treatment. Radiomics, radiogenomics, and radio-immunoprofiling and their potential roles in management of patients with HCC will be discussed and critically reviewed in this article.

Predicting the prognosis of hepatocellular carcinoma with the treatment of transcatheter arterial chemoembolization combined with microwave ablation using pretreatment MR imaging texture features

OBJECTIVE

To investigate the prognostic value of baseline magnetic resonance imaging (MRI) texture analysis of hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolization (TACE) and microwave ablation (MWA).

METHODS

MRI was performed on 102 patients with HCC before receiving TACE combined with MWA in this retrospective study. The best 10 texture features were screened as a feature group for each MRI sequence by MaZda software using mutual information coefficient (MI), nonlinear discriminant analysis (NDA) and other methods. The optimal feature group with the lowest misdiagnosis rate was achieved on one MRI sequence between two groups dichotomized by 3-year survival, which was used to optimize the significant texture features with the optimal cutoff values. The Cox proportional hazards model was generated for the significant texture features and clinical variables to determine the independent predictors of overall survival (OS). The predictive performance of the model was further evaluated by the area under the ROC curve (AUC). Kaplan-Meier and log-rank tests were performed for disease-free survival (DFS) and Local recurrence-free survival (LRFS).

RESULTS

The optimal feature group with the lowest misdiagnosis rate of 8.82% was obtained on T2WI using MI combined with NDA feature analysis. For Cox proportional hazards regression models, the independent prognostic factors associated with OS were albumin (P = 0.047), BCLC stage (P = 0.001), Correlat_{(1,- 1)T2} (P = 0.01) and SumEntrp_(3,0)T2 (P = 0.015), and the prediction efficiency of multivariate model is AUC = 0.876, 95%CI = 0.803-0.949. Kaplan-Meier analyses further demonstrated that BCLC (P < 0.001), Correlat_{(1,- 1)T2} (P = 0.023) and SumEntrp_(3,0)T2 (P < 0.001) were associated with DFS, and BCLC (P = 0.007) related to LRFS.

CONCLUSIONS

MR imaging texture features may be used to predict the prognosis of HCC treated with TACE combined with MWA.

Application of radiomics in hepatocellular carcinoma: A review

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with low 5-year survival rate. The high molecular heterogeneity in HCC poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC. However, current clinical practice based on single bioptic or archived tumor tissue has been deficient in identifying useful biomarkers. The concept of radiomics was first proposed in 2012 and is different from the traditional imaging analysis based on the qualitative or semi-quantitative analysis by radiologists. Radiomics refers to high-throughput extraction of large amounts number of high-dimensional quantitative features from medical images through machine learning or deep learning algorithms. Using the radiomics method could quantify tumoral phenotypes and heterogeneity, which may provide benefits in clinical decision-making at a lower cost. Here, we review the workflow and application of radiomics in HCC.

CT texture analysis of abdominal lesions – Part I.: Liver lesions

Artificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.

A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion

OBJECTIVE

A radiomics nomogram for pretreatment prediction of TACE refractoriness was developed and validated for hepatocellular carcinoma (HCC) without extrahepatic metastasis or macrovascular invasion.

MATERIALS AND METHODS

This study included 80 patients with HCC without extrahepatic metastasis or macrovascular involvement treated with TACE between July 2016 and November 2018. The datasets were divided into a training set (80%) and a test set (20%) for feature selection and tenfold cross-validation. Forty radiomic features were extracted from arterial-phase computed tomography (CT) using the Local Image Features Extraction software. The Lasso regression model was used for radiomics signature selection. The Lasso regression model was used for radiomics signature selection and the selected signatures were validated using the Mann-Whitney U-test. The radiomics nomogram was developed based on a multivariate logistic regression model incorporating the Rad-score, CT imaging factors, and clinical factors, and it was validated.

RESULTS

The Rad-score, which consists of the Gray-Level Zone Length Matrix (GLZLM)-Long-Zone Low Gray-Level Emphasis (LZLGE) and GLZLM-Gray-Level Non-Uniformity (GLNU), T-stage, log α-fetoprotein (AFP), and bilobar distribution were significantly associated with TACE refractoriness (p < 0.05). Predictors in the radiomics nomogram were the Rad-score and T-stage (Rad-score + T-stage), Rad-score and bilobar distribution (Rad-score + bilobar distribution), or Rad-score and logAFP (Rad-score + logAFP). The multivariate logistic regression model showed a good predictive performance (Rad-score + T-stage, AUC, 0.95; Rad-score + bilobar distribution, AUC 0.91; and Rad-score + logAFP, AUC, 0.91).

CONCLUSION

The radiomics nomogram could be used for the pretreatment prediction of TACE refractoriness.

Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma

Purpose

To investigate the role of contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics for pretherapeutic prediction of the response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).

Methods

One hundred and twenty-two HCC patients (objective response, n = 63; non-response, n = 59) who received CE-MRI examination before initial TACE were retrospectively recruited and randomly divided into a training cohort (n = 85) and a validation cohort (n = 37). All HCCs were manually segmented on arterial, venous and delayed phases of CE-MRI, and total 2367 radiomics features were extracted. Radiomics models were constructed based on each phase and their combination using logistic regression algorithm. A clinical-radiological model was built based on independent risk factors identified by univariate and multivariate logistic regression analyses. A combined model incorporating the radiomics score and selected clinical-radiological predictors was constructed, and the combined model was presented as a nomogram. Prediction models were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.

Results

Among all radiomics models, the three-phase radiomics model exhibited better performance in the training cohort with an area under the curve (AUC) of 0.838 (95% confidence interval (CI), 0.753 - 0.922), which was verified in the validation cohort (AUC, 0.833; 95% CI, 0.691 - 0.975). The combined model that integrated the three-phase radiomics score and clinical-radiological risk factors (total bilirubin, tumor shape, and tumor encapsulation) showed excellent calibration and predictive capability in the training and validation cohorts with AUCs of 0.878 (95% CI, 0.806 - 0.950) and 0.833 (95% CI, 0.687 - 0.979), respectively, and showed better predictive ability (P = 0.003) compared with the clinical-radiological model (AUC, 0.744; 95% CI, 0.642 - 0.846) in the training cohort. A nomogram based on the combined model achieved good clinical utility in predicting the treatment efficacy of TACE.

Conclusion

CE-MRI radiomics analysis may serve as a promising and noninvasive tool to predict therapeutic response to TACE in HCC, which will facilitate the individualized follow-up and further therapeutic strategies guidance in HCC patients.

Association between Texture Analysis Parameters and Molecular Biologic KRAS Mutation in Non-Mucinous Rectal Cancer

Purpose

To evaluate the association between magnetic resonance imaging (MRI)-based texture parameters and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in patients with non-mucinous rectal cancer.

Materials and Methods

Seventy-nine patients who had pathologically confirmed rectal non-mucinous adenocarcinoma with or without KRAS-mutation and had undergone rectal MRI were divided into a training (n = 46) and validation dataset (n = 33). A texture analysis was performed on the axial T2-weighted images. The association was statistically analyzed using the Mann-Whitney U test. To extract an optimal cut-off value for the prediction of KRAS mutation, a receiver operating characteristic curve analysis was performed. The cut-off value was verified using the validation dataset.

Results

In the training dataset, skewness in the mutant group (n = 22) was significantly higher than in the wild-type group (n = 24) (0.221 ± 0.283; -0.006 ± 0.178, respectively, p = 0.003). The area under the curve of the skewness was 0.757 (95% confidence interval, 0.606 to 0.872) with a maximum accuracy of 71%, a sensitivity of 64%, and a specificity of 78%. None of the other texture parameters were associated with KRAS mutation (p > 0.05). When a cut-off value of 0.078 was applied to the validation dataset, this had an accuracy of 76%, a sensitivity of 86%, and a specificity of 68%.

Conclusion

Skewness was associated with KRAS mutation in patients with non-mucinous rectal cancer.

Machine-learning analysis of contrast-enhanced computed tomography radiomics predicts patients with hepatocellular carcinoma who are unsuitable for initial transarterial chemoembolization monotherapy: A multicenter study

•

Patients who are unsuitable for chemoembolization could progress with extrahepatic spread or vascular invasion after initial chemoembolization monotherapy.

•

A radiomics signature based on the machine learning algorithm was identified.

•

The signature combined with clinicoradiologicial predictors could predict TACE-unsuitable patients.

•

The combined model showed improved predictive performance compared with the model without radiomics signature.

•

The combined model could stratify patients into three strata with a low, intermediate, or high risk in training and external testing sets.

Introduction

Due to the high heterogeneity of hepatocellular carcinoma (HCC), patients with non-advanced disease who are unsuitable for initial transarterial chemoembolization (TACE) monotherapy may have the potential to develop extrahepatic spread or vascular invasion. We aimed to develop and independently validate a radiomics-based model for predicting which patients will develop extrahepatic spread or vascular invasion after initial TACE monotherapy (EVIT).

Materials and methods

This retrospective study included 256 HCC patients (training set: n = 136; testing set: n = 120) who underwent TACE as initial therapy between April 2007 and June 2018. Clinicoradiological predictors were selected using multivariate logistic regression and a clinicoradiological model was constructed. The radiomic features were extracted from contrast-enhanced computed tomography (CT) images and a radiomics signature was constructed based on a machine learning algorithm. A combined model integrated clinicoradiological predictor and radiomics signature was developed. The predictive performance of the two models was evaluated and compared based on its discrimination, calibration, and clinical usefulness.

Results

In the training set, 34 (25.0%) patients were confirmed to have EVIT, whereas 26 (21.7%) patients in the testing set had EVIT. When the radiomics signature was added, the combined model showed improved discrimination performance compared to the clinicoradiological model (area under the curves [AUCs] 0.911 vs. 0.772 in the training set; AUCs 0.847 vs. 0.746 in the testing set) and could divide HCC patients into three strata of low, intermediate, or high risk in the two sets. Decision curve analysis demonstrated that the two models were clinically useful, and the combined model provided greater benefits for discriminating patients than the clinicoradiological model.

Conclusions

This study presents a model that integrates clinicoradiological predictors and CT-based radiomics signature that could provide a preoperative individualized prediction of EVIT in patients with HCC.

Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging

OBJECTIVES

Following the craze for radiomic features (RF), their lack of reliability raised the question of the generalizability of classification models. Inter-site harmonization of images therefore becomes a central issue. We compared RF harmonization processing designed to detect liver diseases in CT images.

METHODS

We retrospectively analyzed 76 multi-center portal CT series of non-diseased (NDL) and diseased liver (DL) patients. In each series, we positioned volumes of interest in spleen and liver, then extracted 9 RF (histogram and texture). We evaluated two RF harmonization approaches. First, in each series, we computed the Z-score of liver measurements based on those computed in the spleen. Second, we evaluated the ComBat method according to each imaging center; parameters were computed in the spleen and applied to the liver. We compared RF distributions and classification performances before/after harmonization. We classified NDL versus spleen and versus DL tissues.

RESULTS

The RF distributions were all different between liver and spleen (p < 0.05). The Z-score harmonization outperformed for the detection of liver versus spleen: AUC = 93.1% (p < 0.001). For the detection of DL versus NDL, in a case/control setting, we found no differences between the harmonizations: mean AUC = 73.6% (p = 0.49). Using the whole datasets, the performances were improved using ComBat (p = 0.05) AUC = 82.4% and degraded with Z-score AUC = 67.4% (p = 0.008).

CONCLUSIONS

Data harmonization requires to first focus on data structuring to not degrade the performances of subsequent classifications. Liver tissue classification after harmonization of spleen-based RF is a promising strategy for improving the detection of DL tissue.

KEY POINTS

• Variability of acquisition parameter makes radiomics of CT features non-reproducible. • Data harmonization can help circumvent the inter-site variability of acquisition protocols. • Inter-site harmonization must be carefully implemented and requires designing consistent data sets.

Radiomics of hepatocellular carcinoma

The diagnosis of hepatocellular carcinoma relies largely on non-invasive imaging, and is well suited for radiomics analysis. Radiomics is an emerging method for quantification of tumor heterogeneity by mathematically analyzing the spatial distribution and relationships of gray levels in medical images. The published studies on radiomics analysis of HCC provide encouraging data demonstrating potential utility for prediction of tumor biology, molecular profiles, post-therapy response, and outcome. The combination of radiomics data and clinical/laboratory information provides added value in many studies. Radiomics is a multi-step process that requires optimization and standardization, the development of semi-automated or automated segmentation methods, robust data quality control, and refinement of algorithms and modeling approaches for high-throughput data analysis. While radiomics remains largely in the research setting, the strong associations of predictive models and nomograms with certain pathologic, molecular, and immune markers with tumor aggressiveness and patient outcomes, provide great potential for clinical applications to inform optimized treatment strategies and patient prognosis.

Texture Analysis of Gray-Scale Ultrasound Images for Staging of Hepatic Fibrosis

Purpose

Materials and Methods

Results

Conclusion

Radiomic analysis of MRI to Predict Sustained Complete Response after Radiofrequency Ablation in Patients with Hepatocellular Carcinoma - A Pilot Study

OBJECTIVES

To investigate whether quantitative textural features, extracted from pretreatment MRI, can predict sustained complete response to radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC).

METHODS

In this IRB-approved study, patients were selected from a maintained six-year database of consecutive patients who underwent both pretreatment MRI imaging with a probable or definitive imaging diagnosis of HCC (LI-RADS 4 or 5) and loco-regional treatment with RFA. An experienced radiologist manually segmented the hepatic nodules in MRI arterial and equilibrium phases to obtain the volume of interest (VOI) for extraction of 107 quantitative textural features, including shape and first- and second-order features. Statistical analysis was performed to evaluate associations between textural features and complete response.

RESULTS

The study consisted of 34 patients with 51 treated hepatic nodules. Sustained complete response was achieved by 6 patients (4 with single nodule and 2 with multiple nodules). Of the 107 features from the arterial and equilibrium phases, 20 (18%) and 25 (23%) achieved AUC >0.7, respectively. The three best performing features were found in the equilibrium phase: Dependence Non-Uniformity Normalized and Dependence Variance (both GLDM class, with AUC of 0.78 and 0.76, respectively) and Maximum Probability (GLCM class, AUC of 0.76).

CONCLUSIONS

This pilot study demonstrates that a radiomic analysis of pre-treatment MRI might be useful in identifying patients with HCC who are most likely to have a sustained complete response to RFA. Second-order features (GLDM and GLCM) extracted from equilibrium phase obtained highest discriminatory performance.

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Purpose

Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors.

Methods

A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score.

Results

The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice.

Conclusion

Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-020-05142-w.

Response prediction of hepatocellular carcinoma undergoing transcatheter arterial chemoembolization: unlocking the potential of CT texture analysis through nested decision tree models

Objectives

To investigate if nested multiparametric decision tree models based on tumor size and CT texture parameters from pre-therapeutic imaging can accurately predict hepatocellular carcinoma (HCC) lesion response to transcatheter arterial chemoembolization (TACE).

Materials and methods

This retrospective study (January 2011–September 2017) included consecutive pre- and post-therapeutic dynamic CT scans of 37 patients with 92 biopsy-proven HCC lesions treated with drug-eluting bead TACE. Following manual segmentation of lesions according to modified Response Evaluation Criteria in Solid Tumors criteria on baseline arterial phase CT images, tumor size and quantitative texture parameters were extracted. HCCs were grouped into lesions undergoing primary TACE (VT-lesions) or repeated TACE (RT-lesions). Distinct multiparametric decision tree models to predict complete response (CR) and progressive disease (PD) for the two groups were generated. AUC and model accuracy were assessed.

Results

Thirty-eight of 72 VT-lesions (52.8%) and 8 of 20 RT-lesions (40%) achieved CR. Sixteen VT-lesions (22.2%) and 8 RT-lesions (40%) showed PD on follow-up imaging despite TACE treatment. Mean of positive pixels (MPP) was significantly higher in VT-lesions compared to RT-lesions (180.5 vs 92.8, p = 0.001). The highest AUC in ROC curve analysis and accuracy was observed for the prediction of CR in VT-lesions (AUC 0.96, positive predictive value 96.9%, accuracy 88.9%). Prediction of PD in VT-lesions (AUC 0.88, accuracy 80.6%), CR in RT-lesions (AUC 0.83, accuracy 75.0%), and PD in RT-lesions (AUC 0.86, accuracy 80.0%) was slightly inferior.

Conclusions

Nested multiparametric decision tree models based on tumor heterogeneity and size can predict HCC lesion response to TACE treatment with high accuracy. They may be used as an additional criterion in the multidisciplinary treatment decision-making process.

Key Points

• HCC lesion response to TACE treatment can be predicted with high accuracy based on baseline tumor heterogeneity and size.

• Complete response of HCC lesions undergoing primary TACE was correctly predicted with 88.9% accuracy and a positive predictive value of 96.9%.

• Progressive disease was correctly predicted with 80.6% accuracy for lesions undergoing primary TACE and 80.0% accuracy for lesions undergoing repeated TACE.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07511-3.

Texture analysis on bi-parametric MRI for evaluation of aggressiveness in patients with prostate cancer

PURPOSE

To evaluate the association between texture parameters based on bi-parametric MRI and Gleason score (GS) in patients with prostate cancer (PCa) and to evaluate diagnostic performance of any significant parameter for discriminating clinically significant cancer (CSC, GS ≥ 7) from non-CSC.

METHODS

A total of 116 patients who had been confirmed as prostate adenocarcinoma by radical prostatectomy or biopsy were divided into a training (n = 65) and a validation dataset (n = 51). All of the patients underwent preoperative 3T-MRI. Texture analysis was performed on axial T2WI and ADC maps (generated from b values, 0 and 1000 s/mm²) using dedicated software to cover the whole tumor volume. The correlation coefficient was calculated to evaluate the association between texture parameters and GS, and subsequent multiple regression analyses were applied for the significant parameters. To extract an optimal cut-off value for prediction of CSC, ROC curve analysis was performed.

RESULTS

In the training dataset, gray-level co-occurrence matrix (GLCM) entropy on ADC map was the only significant indicator for GS (coefficient of determination R², 0.4227, P = 0.0034). The AUC of GLCM entropy on ADC map was 0.825 (95% CI 0.711-0.907) with a maximum accuracy of 82%, a sensitivity of 86%, a specificity of 71%. When a cut-off value of 2.92 was applied to the validation dataset, it showed an accuracy of 92%, a sensitivity of 98%, and a specificity of 70%.

CONCLUSION

GLCM entropy on ADC map was associated with GS in patients with PCa and its estimated accuracy for discriminating CSC from non-CSC was 82%.

Impact of Bridging Locoregional Therapies for Hepatocellular Carcinoma on Post-transplant Clinical Outcome

Long waiting times due to ongoing organ shortage have led to increased utilization of locoregional therapies (LRTs) to bridge patients with hepatocellular carcinoma (HCC) to liver transplantation (LT). We performed this study to evaluate the impact of LRTs on post-LT outcomes. We conducted a retrospective study of patients who were transplanted for HCC at Stanford University Hospital between 2008 and 2018 (n = 302). We found that receipt of ≥5 LRTs was an independent and significant predictor of poor overall 5-year survival (58.3% vs. 83.3%; HR 2.26, p = .03), poor recurrence-free 5-year survival (51.9% vs. 80.4%; HR 2.12, p = .03), and was associated with higher rates of recurrence (25.0% vs. 7.4%, p = .001). Moreover, recurrent HCC was more likely to be the cause of death (58.3% vs. 41.7%, p = .04) in patients who received ≥5 LRTs. Also, patients who required ≥5 LRTs showed an overall lower rate of radiological complete response (46.9% vs. 97.8%, p = .001) and were more likely to have more advanced pathological stage tumors in the explant (65.6% vs. 29.6%, p < .001). In conclusion, receipt of ≥5 bridging LRTs prior to LT is associated with worse post-transplant clinical outcomes.

Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer

Conventional transarterial chemoembolization (cTACE) is a guideline-approved image-guided therapy option for liver cancer using the radiopaque drug-carrier and micro-embolic agent Lipiodol, which has been previously established as an imaging biomarker for tumor response. To establish automated quantitative and pattern-based image analysis techniques of Lipiodol deposition on 24 h post-cTACE CT as biomarker for treatment response. The density of Lipiodol deposits in 65 liver lesions was automatically quantified using Hounsfield Unit thresholds. Lipiodol deposition within the tumor was automatically assessed for patterns including homogeneity, sparsity, rim, and peripheral deposition. Lipiodol deposition was correlated with enhancing tumor volume (ETV) on baseline and follow-up MRI. ETV on baseline MRI strongly correlated with Lipiodol deposition on 24 h CT (p < 0.0001), with 8.22% ± 14.59 more Lipiodol in viable than necrotic tumor areas. On follow-up, tumor regions with Lipiodol showed higher rates of ETV reduction than areas without Lipiodol (p = 0.0475) and increasing densities of Lipiodol enhanced this effect. Also, homogeneous (p = 0.0006), non-sparse (p < 0.0001), rim deposition within sparse tumors (p = 0.045), and peripheral deposition (p < 0.0001) of Lipiodol showed improved response. This technical innovation study showed that an automated threshold-based volumetric feature characterization of Lipiodol deposits is feasible and enables practical use of Lipiodol as imaging biomarker for therapeutic efficacy after cTACE.

Critical review of HCC imaging in the multidisciplinary setting: treatment allocation and evaluation of response

Imaging has not only an established role in screening and diagnosis of hepatocellular carcinoma (HCC) in patients with chronic liver inflammatory diseases, but also a crucial importance for patient stratification and treatment allocation, as well as for assessing treatment response. In the setting of increasing therapeutic options for HCC, the Barcelona Clinic Liver Cancer (BCLC) system still remains the most appropriate way to select candidate cohorts for best treatments. This classification takes into account the imaging information on tumor burden and extension, liver function, and cancer-related symptoms, stratifying patients in five risk categories (Stages 0, A, B, C and D) associated with different treatment options. Still now, there are no clear roles for biomarkers use in treatment allocation. The increasing use of locoregional non-surgical therapies in the different stages is highly dependent on reliable evaluation of treatment response, in particular when they are used with curative intention or for downstaging at liver transplantation re-assessment. Moreover, objective response (OR) has emerged as an important imaging biomarker, providing information on tumor biology, which can contribute for further prognostic assessment. Current guidelines for OR assessment recommend only the measurement of viable tumor according to mRECIST criteria, with further classification into complete response, partial response, stable disease or progressive disease. Either computed tomography (CT) or magnetic resonance (MR) imaging can be used for this purpose, and the Liver Imaging Reporting and Data System (LI-RADS) committee has recently provided some guidance for reporting after locoregional therapies. Nevertheless, imaging pitfalls resulting from treatment-related changes can impact with the correct evaluation of treatment response, especially after transarterial radioembolization (TARE). Volume criteria and emerging imaging techniques might also contribute for a better refinement in the assessment of treatment response and monitoring. As the role of imaging deeply expands in the multidisciplinary assessment of HCC, our main objective in this review is to discuss state-of-the-art decision-making aspects for treatment allocation and provide guidance for treatment response evaluation.

Tumor Vascular Networks Depicted in Contrast-Enhanced Ultrasound Images as a Predictor for Transarterial Chemoembolization Treatment Response

Hepatocellular carcinoma (HCC) is prevalent worldwide. Among the various therapeutic options, transarterial chemoembolization (TACE) can be applied to the tumor vascular network by restricting the nutrients and oxygen supply to the tumor. Unique morphologic properties of this network may provide information predictive of future therapeutic responses, which would be significant for decision making during treatment planning. The extraction of morphologic features from the tumor vascular network depicted in abdominal contrast-enhanced ultrasound (CEUS) images faces several challenges, such as organ motion, limited resolution caused by clutter signal and segmentation of the vascular structures at multiple scales. In this study, we present an image processing and analysis approach for the prediction of HCC response to TACE treatment using clinical CEUS images and known pathologic responses. This method focuses on addressing the challenges of CEUS by incorporating a two-stage motion correction strategy, clutter signal removal, vessel enhancement at multiple scales and machine learning for predictive modeling. The morphologic features, namely, number of vessels (NV), number of bifurcations (NB), vessel to tissue ratio (VR), mean vessel length, tortuosity and diameter, from tumor architecture were quantified from CEUS images of 36 HCC patients before TACE treatment. Our analysis revealed that NV, NB and VR are the dominant features for the prediction of long-term TACE response. The model had an accuracy of 86% with a sensitivity and specificity of 89% and 82%, respectively. Reliable prediction of the TACE therapy response using CEUS-derived image features may help to provide personalized therapy planning, which will ultimately improve patient outcomes.

Diagnostic advances of artificial intelligence and radiomics in gastroenterology

Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.

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.

Evaluation of liver tumour response by imaging

The goal of assessing tumour response on imaging is to identify patients who are likely to benefit - or not - from anticancer treatment, especially in relation to survival. The World Health Organization was the first to develop assessment criteria. This early score, which assessed tumour burden by standardising lesion size measurements, laid the groundwork for many of the criteria that followed. This was then improved by the Response Evaluation Criteria in Solid Tumours (RECIST) which was quickly adopted by the oncology community. At the same time, many interventional oncology treatments were developed to target specific features of liver tumours that result in significant changes in tumours but have little effect on tumour size. New criteria focusing on the viable part of tumours were therefore designed to provide more appropriate feedback to guide patient management. Targeted therapy has resulted in a breakthrough that challenges conventional response criteria due to the non-linear relationship between response and tumour size, requiring the development of methods that emphasize the appearance of tumours. More recently, research into functional and quantitative imaging has created new opportunities in liver imaging. These results have suggested that certain parameters could serve as early predictors of response or could predict later tumour response at baseline. These approaches have now been extended by machine learning and deep learning. This clinical review focuses on the progress made in the evaluation of liver tumours on imaging, discussing the rationale for this approach, addressing challenges and controversies in the field, and suggesting possible future developments.

Equilibrium CT Texture Analysis for the Evaluation of Hepatic Fibrosis: Preliminary Evaluation against Histopathology and Extracellular Volume Fraction

Background: Evaluate equilibrium contrast-enhanced CT (EQ-CT) texture analysis (EQ-CTTA) against histologically-quantified fibrosis, serum-based enhanced liver fibrosis panel (ELF) and imaging-based extracellular volume fraction (ECV) in chronic hepatitis. Methods: This study was a re-analysis of image data from a previous prospective study. Pre- and equilibrium-phase post-IV contrast CT datasets were collected from patients with chronic hepatitis with contemporaneous liver biopsy and serum ELF measurement between April 2011 and July 2013. Biopsy samples were analysed to derive collagen proportionate area (CPA). EQ-CTTA was performed with a filtration histogram technique using texture analysis software, with texture quantification using statistical and histogram-based metrics (mean, skewness, standard deviation, entropy, etc.). Association between pre-contrast and EQ-CTTA against CPA, ECV and ELF was evaluated using Spearman’s rank correlation coefficient (r_s). Results: Complete datasets collected in 29 patients (16 male; 13 female), mean age (range): 49 (22–66 years). Liver ECV, CPA and ELF had a median (interquartile range) of 0.26 (0.24–0.29); 5.0 (3.0–13.7) and 9.71 (8.39–10.92). Difference in segment VII hepatic CTTA (medium texture scale) between EQ-CT and pre-contrast images was significantly and positively associated with ELF score (mean: r_s = 0.69, p < 0.001; skewness: r_s = 0.57, p = 0.007). Significant negative associations were observed between pre-contrast and EQ-CT whole hepatic CTTA (coarse texture scale) with CPA (pre-contrast, SD: r_s = −0.66, p < 0.001) and ECV (EQ-CT, entropy: r_s = −0.58, p = 0.006). Conclusions: Hepatic EQ-CTTA demonstrates significant association with validated markers of liver fibrosis, suggesting a role in non-invasive quantification of severity in diffuse fibrosis.

State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations

Radiomics is a new field in medical imaging with the potential of changing medical practice. Radiomics is characterized by the extraction of several quantitative imaging features which are not visible to the naked eye from conventional imaging modalities, and its correlation with specific relevant clinical endpoints, such as pathology, therapeutic response, and survival. Several studies have evaluated the use of radiomics in patients with hepatocellular carcinoma (HCC) with encouraging results, particularly in the pretreatment prediction of tumor biological characteristics, risk of recurrence, and survival. In spite of this, there are limitations and challenges to be overcome before the implementation of radiomics into clinical routine. In this article, we will review the concepts of radiomics and their current potential applications in patients with HCC. It is important that the multidisciplinary team involved in the treatment of patients with HCC be aware of the basic principles, benefits, and limitations of radiomics in order to achieve a balanced interpretation of the results toward a personalized medicine.

Predictive imaging for tumor response to drug-eluting microsphere transarterial chemoembolization in patients with BCLC-C advanced hepatocellular carcinoma

Drug-eluting microsphere transarterial chemoembolization (DEM-TACE) has been introduced to ensure more sustained and tumor-selective drug delivery for permanent embolization of HCC. The aim of this study was to determine the imaging characteristics that related to favourable treatment response in BCLC-C HCC patients treated with DEM-TACE. In total, 64 patients with BCLC-C HCC that treated with DEM-TACE using doxorubicin-eluted microspheres were retrospectively included. The images were assessed at baseline and at 4-12 weeks follow-up after receiving DEM-TACE. Pre- and post-procedural imaging characteristics were analysed by two independent radiologists and treatment response was evaluated using the modified Response Evaluation Criteria in Solid Tumors criteria. Multivariate analysis showed that vascular lake phenomenon (OR = 5.94, p = 0.03*), and homogeneous tumor enhancement (HTE) on cone-beam computed tomography (CBCT) during angiography (OR = 11.66, p < 0.001*) are associated with better radiological response. In contrast, residual tumor blush (OR = 0.11, p < 0.001*) is associated with worse radiological response. In conclusion, the initial tumor burden <50% (p = 0.012*) and HTE on CBCT (p = 0.040*) are good predictors for locoregional tumor control in patients with advanced HCCs, which can potentially improve patients' outcome.

Multiscale quantification of tumor microarchitecture for predicting therapy response using dynamic contrast-enhanced ultrasound imaging

Hepatocellular carcinoma (HCC) is the most common liver cancer with 1 million cases globally. A current clinical challenge is to determine which patients will respond to transarterial chemoembolization (TACE) as effective delivery of the embolic material may be influenced by the tumor vascular supply. The purpose of this study is to develop a novel image processing algorithm for improved quantification of tumor microvascular morphology features using contrast-enhanced ultrasound (CEUS) images and to predict the TACE response based on these biomarkers before treatment. A temporal sequence of CEUS images was corrected from rigid and non-rigid motion artifacts using affine and free form deformation models. Subsequently, a principal component analysis based singular value filter was applied to remove the clutter signal from each frame. A maximum intensity projection was created from high-resolution images. A multiscale vessel enhancement filter was first utilized to enhance the tubular structures as a preprocessing step before segmentation. Morphological image processing methods are used to extract the morphology features, namely, number of vessels (NV) and branching points (NB), vessel-to-tissue ratio (VR), and the mean vessel length (VL), tortuosity (VT), and diameter (VD) from the tumor vascular network. Finally, a support vector machine (SVM) is trained and validated using leave-one-out cross-validation technique. The proposed image analysis strategy was able to predict the patient outcome with 90% accuracy when the SVM was trained with the three features together (NB, NV, VR). Experimental results indicated that morphological features of tumor microvascular networks may be significant predictors for TACE response. Reliable prediction of the TACE therapy response may help provide effective therapy planning.