Efficacy and Interpretability Analysis of Noninvasive Imaging Based on Computed Tomography in Patients with Hepatocellular Carcinoma After Initial Transarterial Chemoembolization.

RATIONALE AND OBJECTIVES: The objective of this study is to accurately and timely assess the efficacy of patients with hepatocellular carcinoma (HCC) after the initial transarterial chemoembolization (TACE). MATERIALS AND METHODS: This retrospective study consisted of 279 patients with HCC in Center 1, who were split into training and validation cohorts in the ratio of 4:1, and 72 patients in Center 2 as an external testing cohort. Radiomics signatures both in the arterial phase and venous phase of contrast-enhanced computed tomography images were selected by univariate analysis, correlation analysis, and least absolute shrinkage and selection operator regression to build the predicting models. The clinical model and combined model were constructed by independent risk factors after univariate and multivariate logistic regression analysis. The biological interpretability of radiomics signatures correlating transcriptome sequencing data was explored using publicly available data sets. RESULTS: A total of 31 radiomics signatures in the arterial phase and 13 radiomics signatures in the venous phase were selected to construct Radscore_arterial and Radscore_venous, respectively, which were independent risk factors. After constructing the combined model, the area under the receiver operating characteristic curve in three cohorts was 0.865, 0.800, and 0.745, respectively. Through correlation analysis, 11 radiomics signatures in the arterial phase and 4 radiomics signatures in the venous phase were associated with 8 and 5 gene modules, respectively (All P < .05), which enriched some pathways closely related to tumor development and proliferation. CONCLUSION: Noninvasive imaging has considerable value in predicting the efficacy of patients with HCC after initial TACE. The biological interpretability of the radiological signatures can be mapped at the micro level.

Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma.

BACKGROUND: According to clinical practice guidelines, transarterial chemoembolization (TACE) is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma (HCC). Early prediction of treatment response can help patients choose a reasonable treatment plan. This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival. METHODS: A total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed. The tumor response was assessed by modified response evaluation criteria in solid tumors (mRECIST), and the response of the first TACE to each session and its correlation with overall survival were evaluated. The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator (LASSO), and four machine learning models were built with different types of regions of interest (ROIs) (tumor and corresponding tissues) and the model with the best performance was selected. The predictive performance was assessed with receiver operating characteristic (ROC) curves and calibration curves. RESULTS: Of all the models, the random forest (RF) model with peritumor (+10 mm) radiomic signatures had the best performance [area under ROC curve (AUC) = 0.964 in the training cohort, AUC = 0.949 in the validation cohort]. The RF model was used to calculate the radiomic score (Rad-score), and the optimal cutoff value (0.34) was calculated according to the Youden's index. Patients were then divided into a high-risk group (Rad-score > 0.34) and a low-risk group (Rad-score ≤ 0.34), and a nomogram model was successfully established to predict treatment response. The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves. Multivariate Cox regression identified six independent prognostic factors for overall survival, including male [hazard ratio (HR) = 0.500, 95% confidence interval (CI): 0.260-0.962, P = 0.038], alpha-fetoprotein (HR = 1.003, 95% CI: 1.002-1.004, P < 0.001), alanine aminotransferase (HR = 1.003, 95% CI: 1.001-1.005, P = 0.025), performance status (HR = 2.400, 95% CI: 1.200-4.800, P = 0.013), the number of TACE sessions (HR = 0.870, 95% CI: 0.780-0.970, P = 0.012) and Rad-score (HR = 3.480, 95% CI: 1.416-8.552, P = 0.007). CONCLUSIONS: The radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.

Efficacy of different strategies of responsive neurostimulation on seizure control and their association with acute neurophysiological effects in rats.

Responsive neurostimulation (RNS) has shown promising but limited efficacy in the treatment of drug-resistant epilepsy. The clinical utility of RNS is hindered by the incomplete understanding of the mechanism behind its therapeutic effects. Thus, assessing the acute effects of responsive stimulation (AERS) based on intracranial EEG recordings in the temporal lobe epilepsy rat model may provide a better understanding of the potential therapeutic mechanisms underlying the antiepileptic effect of RNS. Furthermore, clarifying the correlation between AERS and seizure severity may help guide the optimization of RNS parameter settings. In this study, RNS with high (130 Hz) and low frequencies (5 Hz) was applied to the subiculum (SUB) and CA1. To quantify the changes induced by RNS, we calculated the AERS during synchronization by Granger causality and analyzed the band power ratio in the classic power band after different stimulations were delivered in the interictal and seizure onset periods, respectively. This demonstrates that only targets combined with an appropriate stimulation frequency could be efficient for seizure control. High-frequency stimulation of CA1 significantly shortened the ongoing seizure duration, which may be causally related to increased synchronization after stimulation. Both high-frequency stimulation of the CA1 and low-frequency stimulation delivered to the SUB reduced seizure frequency, and the reduced seizure risk may correlate with the change in power ratio near the theta band. It indicated that different stimulations may control seizures in diverse manners, perhaps with disparate mechanisms. More focus should be placed on understanding the correlation between seizure severity and synchronization and rhythm around theta bands to simplify the process of parameter optimization.

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.

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.

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.