Analysis of treatment response based on 1.5T magnetic resonance imaging texture analysis in stereotactic body radiotherapy of hepatocellular carcinoma

Purpose: Magnetic Resonance Image (MRI)-guided stereotactic body radiotherapy (MRgSBRT) is a promising technique in the treatment of hepatocellular carcinoma (HCC). However, treatment response varies among patients. The aim of this study was to evaluate the treatment response using radiomics features extracted from 1.5 T MRI in HCC patients treated with SBRT. Materials and methods: 19 patients with biopsy-proven HCC who were treated with SBRT by 1.5 T MRI-guided were enrolled, all of whom were treated with 8-10 fractions with biological effective dose (BED) range of 95.2-107.1 Gy. We acquired images of pretreatment, delivered BEDs of 35-40 Gy, and delivered BEDs of 55-60 Gy. We combined three classic feature selection methods: least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and random forest (RF) to extract the intersection of radiomics features from the gross tumor volume (GTV) and features were averaged over the three fractions. We used the receiver operating characteristic (ROC) area under curve (AUC) obtained using leave-one-out cross-validation (LOOCV) to assess the predictive capacity. Results: Seven patients showed response to treatment based on post-treatment imaging studies. The optimum intersection of radiomics features selected by three methods was Neighborhood Grey Tone Difference Matrix (NGTDM) contrast and NGTDM strength. The logistic regression-based model achieved an AUC of 0.821 (95% confidence interval, 0.618-1). Conclusions: Radiomics features containing biological prognostic information extracted during MRgSBRT could predict tumor treatment response, facilitating stratification of high-risk patients and providing clinical application value for individualized care of patients with different response to treatment.

Automated summary

This study aimed to evaluate the treatment response in HCC patients treated with SBRT using radiomics features extracted from 1.5T MRI. The results showed that the logistic regression-based model achieved high predictive capacity, and radiomics features extracted during MRgSBRT could predict tumor treatment response.