Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy

Abstract

This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann–Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038–0.063, AUC = 0.690–0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047–0.070, AUC = 0.699–0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028–0.074, AUC = 0.719–0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.

Delta-radiomics in cancer immunotherapy response prediction: A systematic review

“…Similarly, Li et al showed that DVintra_original_glszm_Zone Variance was an independent predictor for free-progression-survival in patients with advanced gastric cancer treated with ICIs [59] . This evidence was also observed with more complex treatment strategy, as in the study by Ho et al where delta radiomics was able to predict response in patients with hepatocellular carcinoma undergoing sequential trans-arterial chemoembolization plus stereotactic body radiotherapy plus immunotherapy [60] .…”

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

“…The R packages used include the following: base package for randomization and normalization; stats package for chi-squared test, Fisher's exact test, Mann-Whitney U test and Spearman's rank correlation coefficients; caret package for pair-wise correlations; glmnet package for logistic regression with the three-fold crossvalidation and LASSO penalty; and ROCR and cvAUC packages for ROC analysis and AUC calculation. A p-value of less than 0.05 represented statistical significance [12,39,[45][46][47][48].…”

“…The performance of the derived radiomics signature was evaluated in terms of accuracy, sensitivity and specificity. Additionally, the average AUC values of the training and testing cohorts were calculated [12,39,[45][46][47][48]. Figure 3 summarizes the feature selection, model development and statistical analysis processes.…”

“…When the absolute correlation coefficient of two features was greater than or equal to 0.4, the feature with the greater mean absolute correlation was removed. The model was then developed based on all remaining features [12,39,[45][46][47][48]. A correlation coefficient of 0.4 was selected as the threshold because previous radiomics studies used it to indicate moderate correlation with promising outcomes for feature reduction [49,50].…”

“…In addition to the model testing and bias minimization, the three-fold cross-validation was also responsible for identifying the optimal regularization parameter for LASSO (lambda). Finally, 1000 models were developed as a result of repeating the process of model training a 1000 times [12,39,[45][46][47][48].…”