Quantitative and qualitative evaluation of liver metastases with intraprocedural cone beam CT prior to transarterial radioembolization as a predictor of treatment response.

Purpose: To investigate, by quantitative and qualitative enhancement measurements, the correlation between tumor enhancement on cone beam computed tomography (CBCT) images and treatment response at 6 months in patients undergoing transarterial radioembolization (TARE) for liver metastases. Materials and Methods: 36 patients (56% male; median age 62.5 years) with 104 metastases were retrospectively included. Quantitative and qualitative enhancement of liver metastases were evaluated on CBCT images before TARE. Quantitative analysis consisted of lesion enhancement measurements (ROI HU lesion - ROI HU relative to inferior vena cava). Qualitative analysis consisted of subjective enhancement pattern analysis (diffuse, sparse, rim-like or non-enhancing). Morphologic tumor response was evaluated according to RECIST 1.1 criteria on follow-up CT or MR imaging. Results: At a mean follow up of 6.5 ± 3.7 months, progressive disease (PD) was found in 4 patients, partial response (PR) in 11 and stable disease (SD) in 21. Relative lesion enhancement was significantly different between these groups (-37.5±154.2 HU vs. 103.8±93.4 vs. 181±144 HU in PD vs. SD vs. PR group, respectively; p<0.01). ROC analysis of relative lesion enhancement to predict progressive disease showed an area under the curve of 0.86 (p<0.01). For qualitative lesion enhancement analysis, no difference between groups was found. Conclusion: Quantitative enhancement measurements derived from intraprocedural contrast enhanced CBCT may identify responders to TARE in patients with liver metastases.

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.