Radiotherapy for liver tumors

Predictive Model of Liver Toxicity to Aid the Personalized Selection of Proton Versus Photon Therapy in Hepatocellular Carcinoma

PURPOSE

Our objective was to develop an externally validated model for predicting liver toxicity after radiation therapy in patients with hepatocellular carcinoma (HCC) that can integrate both photon and proton dose distributions with patient-specific characteristics.

METHODS AND MATERIALS

Training data consisted of all patients with HCC treated between 2008 and 2019 at our institution (n = 117, 60%/40% photon/proton). We developed a shallow convolutional neural network (CNN) to predict posttreatment liver dysfunction from the differential dose-volume histogram (DVH) and baseline liver metrics. To reduce bias and improve robustness, we used ensemble learning (CNNE). After a preregistered study analysis plan, we evaluated stability using internal bootstrap resampling and generalizability using a data set from a different institution (n = 88). Finally, we implemented a class activation map method to characterize the critical DVH subregions and benchmarked the model against logistic regression and XGBoost. The models were evaluated using the area under the receiver operating characteristic curve and area under the precision-recall curve.

RESULTS

The CNNE model showed similar internal performance and robustness compared with the benchmarks. CNNE exceeded the benchmark models in external validation, with an area under the receiver operating characteristic curve of 0.78 versus 0.55 to 0.70, and an area under the precision-recall curve of 0.6 versus 0.43 to 0.52. The model showed improved predictive power in the photon group, excellent specificity in both modalities, and high sensitivity in the photon high-risk group. Models built solely on DVHs confirm outperformance of the CNNE and indicate that the proposed structure efficiently abstracts features from both proton and photon dose distributions. The activation map method demonstrates the importance of the low-dose bath and its interaction with low liver function at baseline.

CONCLUSIONS

We developed and externally validated a patient-specific prediction model for hepatic toxicity based on the entire DVH and clinical factors that can integrate both photon and proton therapy cohorts. This model complements the new American Society for Radiation Oncology clinical practice guidelines and could support value-driven integration of proton therapy into the management of HCC.

Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy

PURPOSE

To stratify patients and aid clinical decision making, we developed machine learning models to predict treatment failure and radiation-induced toxicities after radiotherapy (RT) in patients with hepatocellular carcinoma across institutions.

MATERIALS AND METHODS

The models were developed using linear and nonlinear algorithms, predicting survival, nonlocal failure, radiation-induced liver disease, and lymphopenia from baseline patient and treatment parameters. The models were trained on 207 patients from Massachusetts General Hospital. Performance was quantified using Harrell's c-index, area under the curve (AUC), and accuracy in high-risk populations. Models' structures were optimized in a nested cross-validation approach to prevent overfitting. A study analysis plan was registered before external validation using 143 patients from MD Anderson Cancer Center. Clinical utility was assessed using net-benefit analysis.

RESULTS

The survival model stratified high-risk versus low-risk patients well in the external validation cohort (c-index = 0.75), better than existing risk scores. Predictions of 1-year survival and nonlocal failure were excellent (external AUC = 0.74 and 0.80, respectively), especially in the high-risk group (accuracy > 90%). Cause-of-death analysis showed differential modes of treatment failure in these cohorts and indicated that these models could be used to stratify RT patients for liver-sparing treatment regimen or combination approaches with systemic agents. Predictions of liver disease and lymphopenia were good but less robust (external AUC = 0.68 and 0.7, respectively), suggesting the need for more comprehensive consideration of dosimetry and better predictive biomarkers. The liver disease model showed excellent accuracy in the high-risk group (92%) and revealed possible interactions of platelet count with initial liver function.

CONCLUSION

Machine learning approaches can provide reliable outcome predictions in patients with hepatocellular carcinoma after RT in diverse cohorts across institutions. The excellent performance, particularly in high-risk patients, suggests novel strategies for patient stratification and treatment selection.

Intrafraction cone beam computed tomography verification of breath hold during liver stereotactic radiation therapy

INTRODUCTION

Intrafraction imaging is an Elekta feature that enables cone beam computed tomography (CBCT) acquisition simultaneously with treatment arc delivery. It has facilitated the introduction of breath-hold (BH) gated stereotactic body radiation therapy (SBRT) by enabling visualisation of tumour and organs at risk during treatment. The aims of this study were to assess BH reproducibility and use intrafraction CBCT (IF-CBCT) to quantify any variation in diaphragm position (diaphragmatic feathering) during the multiple BHs performed during each arc.

METHODS

IF-CBCTs for consecutive liver SBRT patients where BH was achieved using the Elekta Active Breathing Control (ABC) system were retrospectively evaluated. Average intrafraction couch shifts for deep-inspiration BH (DIBH) or end-expiration BH (EEBH) were recorded as an indication of reproducibility. Diaphragmatic feathering was quantified by measuring the difference between the most superior and inferior visible edges of the diaphragm on IF-CBCTs.

RESULTS

A total of 212 images from 30 patients were reviewed. Twenty-two (73.3%) patients were treated in EEBH. The mean intrafraction shift was similar between DIBH and EEBH groups with the largest mean shift of 0.22cm occurring in the superior-inferior direction. Mean diaphragmatic feathering was similar between the DIBH and EEBH groups, 0.09cm (0-0.44cm) and 0.14cm (0-1.89cm) respectively. A higher percentage of EEBH patients demonstrated no diaphragmatic feathering throughout treatment compared with DIBH patients (31.8% vs 25%).

CONCLUSION

The results of this study indicate that BH is reproducible in both DIBH and EEBH for liver SBRT treatment using the ABC system. Appropriate patient selection and BH coaching prior to CT simulation are critical to its success.

The impact of ionizing irradiation on liver detoxifying enzymes. A re-investigation

By looking at many studies describing the impact of ionizing irradiations in living mice on a few key detoxifying enzymes like catalase, superoxide dismutase, glutathione peroxidase, glutathione reductase and glutathione transferase, we noted conflicting evidences: almost all papers finalized to demonstrate the protective effects of natural or synthetic drugs against the damage by irradiations, described also a relevant inactivation of these enzymes in the absence of these compounds. Conversely, no inactivation and even enhanced activity has been noted under similar irradiation modality in all studies supporting the "adaptive response". Motivated by these curious discrepancies, we performed irradiation experiments on living mice, explanted mouse livers and liver homogenates observing that, in all conditions the activity of all these enzymes remained almost unchanged except for a slight increase found in explanted livers. Our results put a question about many previous scientific reports in this field.

Impact of potentially variable RBE in liver proton therapy

Currently, the relative biological effectiveness (RBE) is assumed to be constant with a value of 1.1 in proton therapy. Although trends of RBE variations are well known, absolute values in patients are associated with considerable uncertainties. This study aims to evaluate the impact of a variable proton RBE in proton therapy liver trials using different fractionation schemes. Sixteen liver cancer cases were evaluated assuming two clinical schedules of 40 Gy/5 fractions and 58.05 Gy/15 fractions. The linear energy transfer (LET) and physical dose distribution in patients were simulated using Monte Carlo. The variable RBE distribution was calculated using a phenomenological model, considering the influence of the LET, fraction size and α/β value. Further, models to predict normal tissue complication probability (NTCP) and tumor control probability (TCP) were used to investigate potential RBE effects on outcome predictions. Applying the variable RBE model to the 5 and 15 fractions schedules results in an increase in mean fraction-size equivalent dose (FED) to the normal liver of 5.0% and 9.6% respectively. For patients with a mean FED to the normal liver larger than 29.8 Gy, this results in a non-negligible increase in the predicted NTCP of the normal liver averaging 11.6%, ranging from 2.7% to 25.6%. On the other hand, decrease in TCP was less than 5% for both fractionation regimens for all patients when assuming a variable RBE instead of constant. Consequently, the difference in TCP between the two fractionation schedules did not change significantly assuming a variable RBE while the impact on the NTCP difference was highly case specific. In addition, both the NTCP and TCP decrease with increasing α/β value for both fractionation schemes, with the decreases being more pronounced when using a variable RBE compared to using RBE  =  1.1. Assuming a constant RBE of 1.1 most likely overestimates the therapeutic ratio in proton therapy for liver cancer, predominantly due to underestimation of the RBE-weighted dose to the normal liver. The impact of applying a variable RBE (as compared to RBE  =  1.1) on the NTCP difference of the two fractionation regimens is case dependent. A variable RBE results in a slight increase in TCP difference. Variations in patient radiosensitivity increase when using a variable RBE.

Liver-Directed Radiotherapy for Hepatocellular Carcinoma

BACKGROUND

The incidence of hepatocellular carcinoma (HCC) continues to increase world-wide. Many patients present with advanced disease with extensive local tumor or vascular invasion and are not candidates for traditionally curative therapies such as orthotopic liver transplantation (OLT) or resection. Radiotherapy (RT) was historically limited by its inability to deliver a tumoricidal dose; however, modern RT techniques have prompted renewed interest in the use of liver-directed RT to treat patients with primary hepatic malignancies.

SUMMARY

The aim of this review was to discuss the use of external beam RT in the treatment of HCC, with particular focus on the use of stereotactic body radiotherapy (SBRT). We review the intricacies of SBRT treatment planning and delivery. Liver-directed RT involves accurate target identification, precise and reproducible patient immobilization, and assessment of target and organ motion. We also summarize the published data on liver-directed RT, and demonstrate that it is associated with excellent local control and survival rates, particularly in patients who are not candidates for OLT or resection.

KEY MESSAGES

Modern liver-directed RT is safe and effective for the treatment of HCC, particularly in patients who are not candidates for OLT or resection. Liver-directed RT, including SBRT, depends on accurate target identification, precise and reproducible patient immobilization, and assessment of target and organ motion. Further prospective studies are needed to fully delineate the role of liver-directed RT in the treatment of HCC.