Dosimetric Analysis and Normal-Tissue Complication Probability Modeling of Child-Pugh Score and Albumin-Bilirubin Grade Increase After Hepatic Irradiation

Normal Tissue Complication Probability Modeling of Severe Radiation-Induced Lymphopenia Using Blood Dose for Patients With Hepatocellular Carcinoma

PURPOSE

This study aimed to develop a normal tissue complication probability (NTCP) model to estimate the risk of severe radiation-induced lymphopenia (SRIL; absolute lymphocyte count [ALC] < 500/μL) by using the blood dose of patients with hepatocellular carcinoma (HCC).

METHODS AND MATERIALS

We retrospectively collected data from 75 patients with HCC who received radiation therapy (RT) between 2015 and 2018. The hematological dose framework calculated blood dose-volume histograms (DVHs) using a predefined blood flow model, organ DVHs, the number of treatment fractions, and beam delivery time. A Lyman-Kutcher-Burman model with a generalized equivalent dose was used to establish the NTCP model, reflecting the whole-blood DVHs. Optimization of the Lyman-Kutcher-Burman parameters was conducted by minimizing a negative log-likelihood function.

RESULTS

There were 6, 4, 18, 33, and 14 patients in the groups with radiation-induced lymphopenia grades 0, 1, 2, 3, and 4, respectively. The median pre- and post-RT ALC values were 1410/μL (range, 520-3710/μL) and 470/μL (range, 60-1760/μL), respectively. There was a correlation between mean blood dose and ALC depletion (Pearson r = -0.664; P < .001). The average mean blood doses in each radiation-induced lymphopenia group were 2.90 Gy (95% CI, 1.96-3.85 Gy) for grade 0 to 1, 5.29 Gy (95% CI, 4.12-6.45 Gy) for grade 2, 8.81 Gy (95% CI, 7.55-10.07 Gy) for grade 3, and 11.69 Gy (95% CI, 9.82-17.57 Gy) for grade 4. When applying the developed NTCP model to predict SRIL, the area under the receiver operating characteristic curve and Brier score values were 0.89 and 0.12, respectively.

CONCLUSIONS

We developed the first NTCP model based on whole-blood DVHs for estimating SRIL after abdominal RT in patients with HCC. Our results showed a strong correlation between blood dose and ALC depletion, suggesting the potential to predict the risk of SRIL occurrence using blood dose.

Do We Have a Winner? Advocating for SBRT in HCC Management

•Stereotactic body radiotherapy (SBRT) is a safe and effective locoregional therapy for inoperable patients with HCC.•SBRT compares favorably with other local therapies in terms of local control, survival, morbidity, and cost-effectiveness.•SBRT should be considered and discussed in multidisciplinary management of appropriate HCC patients.•Advances in SBRT and novel combinations with systemic therapy may further widen the therapeutic index in HCC.

Comparing Predicted Toxicities between Hypofractionated Proton and Photon Radiotherapy of Liver Cancer Patients with Different Adaptive Schemes

With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients. Adaptive and non-adaptive IMRT and IMPT plans were optimized to 50 Gy (RBE = 1.1 for IMPT) in five fractions for 10 liver cancer patients, using the original MRI linac images and physician-drawn structures. Three liver NTCP models were used to predict radiation-induced liver disease, an increase in albumin-bilirubin level, and a Child-Pugh score increase of more than 2. Additionally, three duodenal NTCP models were used to predict gastric bleeding, gastrointestinal (GI) toxicity with grades >3, and duodenal toxicity grades 2-4. NTCPs were calculated for adaptive and non-adaptive IMRT and IMPT treatments. In general, IMRT showed higher NTCP values than IMPT and the differences were often significant. However, the differences between adaptive and non-adaptive treatment schemes were not significant, indicating that the NTCP benefit of adaptive treatment regimens is expected to be smaller than the expected difference between IMRT and IMPT.

Radiation dose is associated with improved local control for large, but not small, hepatocellular carcinomas

BACKGROUND

With advances in understanding liver tolerance, conformal techniques, image guidance, and motion management, dose-escalated radiotherapy has become a potential treatment for inoperable hepatocellular carcinoma (HCC). We aimed to evaluate the possible impact of biologically effective dose (BED) on local control and toxicity among patients with HCC.

METHODS AND MATERIALS

Patients treated at our institution from 2009 to 2018 were included in this retrospective analysis if they received definitive-intent radiotherapy with a nominal BED of at least 60 Gy. Patients were stratified into small and large tumors using a cutoff of 5 cm, based on our clinical practice. Toxicity was assessed using ALBI scores and rates of clinical liver function deterioration.

RESULTS

One hundred and twenty-eight patients were included, with a mean follow-up of 16 months. The majority of patients (90.5%) had a good performance status (ECOG 0-1), with Child-Pugh A (66.4%) and ALBI Grade 2 liver function at baseline (55.4%). Twenty (15.6%) patients had a local recurrence in the irradiated field during the follow-up period. Univariate and multivariate Cox proportional hazard analyses showed that only BED significantly predicted local tumor recurrence. Higher BED was associated with improved local control in tumors with equivalent diameters over 5 cm but not in smaller tumors. There was no difference in liver toxicity between the low and high-dose groups.

CONCLUSIONS

Higher radiotherapy dose is associated with improved local control in large tumors but not in tumors smaller than 5 cm in diameter. High-dose radiotherapy was not associated with increased liver toxicity.

The Effect of Stereotactic Body Radiation Therapy for Hepatocellular Cancer on Regional Hepatic Liver Function

PURPOSE

To investigate direct radiation dose-related and inflammation-mediated regional hepatic function losses after stereotactic body radiation therapy (SBRT) in patients with hepatocellular carcinoma (HCC) and poor liver function.

METHODS AND MATERIALS

Twenty-four patients with HCC enrolled on an IRB-approved adaptive SBRT trial had liver dynamic gadoxetic acid-enhanced magnetic resonance imaging and blood sample collections before and 1 month after SBRT. Gadoxetic acid uptake rate (k1) maps were quantified for regional hepatic function and coregistered to both 2-Gy equivalent dose and physical dose distributions. Regional k1 loss patterns from before to after SBRT were analyzed for effects of dose and patient using a mixed-effects model and logistic function and were associated with pretherapy liver-function albumin-bilirubin scores. Plasma levels of tumor necrosis factor α receptor 1 (TNFR1), an inflammation marker, were correlated with mean k1 losses in the lowest dose regions by Spearman rank correlation.

RESULTS

The whole group had a k1 loss rate of 0.4%/Gy (2-Gy equivalent dose); however, there was a significant random effect of patient in the mixed-effect model (P < .05). Patients with poor and good liver functions lost 50% of k1 values at 12.5 and 57.2 Gy and 33% and 16% of k1 values at the lowest dose regions (<5 Gy), respectively. The k1 losses at the lowest dose regions of individual patients were significantly correlated with their TNFR1 levels after SBRT (P < .02).

CONCLUSIONS

The findings suggest that regional hepatic function losses after SBRT in patients with HCC include both direct radiation dose-dependent and inflammation-mediated effects, which could influence how to manage these patients to preserve their liver function after SBRT.

Study of hepatic toxicity in small liver tumors after photon or proton therapy based on factors predicting the benefits of proton

OBJECTIVES

In a previous study of hepatic toxicity, the following three factors were identified to predict the benefits of proton beam therapy (PBT) for hepatocellular carcinomas (HCCs) with a maximum diameter of ≤5 cm and Child-pugh grade A (CP-A): number of tumors (1 vs ≥2), the location of tumors (hepatic hilum or others), and the sum of the diameters of lesions. The aim of this study is to analyze the association between these three factors and hepatic toxicity.

METHODS

We retrospectively reviewed patients of CP-A treated with PBT or photon stereotactic body radiotherapy (X-ray radiotherapy, XRT) for HCC ≤5 cm. For normal liver dose, the V5, V10, V20 (volumes receiving 5, 10, and 20 Gy at least), and the mean dose was evaluated. The albumin-bilirubin (ALBI) and CP score changes from the baseline were evaluated at 3 and 6 months after treatment.

RESULTS

In 89 patients (XRT: 48, PBT: 41), those with two or three (2-3) predictive factors were higher normal liver doses than with zero or one (0-1) factor. In the PBT group, the ALBI score worsened more in patients with 2-3 factors than those with 0-1 factor, at 3 months (median: 0.26 vs 0.02, p = 0.032) and at 6 months (median: 0.35 vs 0.10, p = 0.009). The ALBI score change in the XRT group and CP score change in either modality were not significantly different in the number of predictive factors.

CONCLUSION

The predictive factor numbers predicted the ALBI score change in PBT but not in XRT.

ADVANCES IN KNOWLEDGE

This study suggest that the number of predictive factors previously identified (0-1 vs 2-3) were significantly associated with dosimetric parameters of the normal liver in both modalities. In the proton group, the number of predictive factors was associated with a worsening ALBI score at 3 and 6 months, but these associations were not found in the photon SBRT group.

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.

Challenges and opportunities in stereotactic body proton radiotherapy of liver malignancies

Stereotactic body proton radiotherapy (SBPT) has the potential to be an effective tool for treating liver malignancies. While proton therapy enables near-zero exit dose and could improve normal tissue sparing, including liver and other surrounding structures, there are challenges in implementing the SBPT technique for proton therapy, including respiratory motion, range uncertainties, dose regimen, treatment planning, and image guidance. This article summarizes the technical and clinical challenges facing SBPT, along with the potential benefits of SBPT for liver malignancies. The clinical implementation of the technique is also described for the first six patients treated at the Johns Hopkins Proton Therapy Center using liver SBPT.

Potential benefits of using radioactive ion beams for range margin reduction in carbon ion therapy

Sharp dose gradients and high biological effectiveness make ions such as ¹²C an ideal tool to treat deep-seated tumors, however, at the same time, sensitive to errors in the range prediction. Tumor safety margins mitigate these uncertainties, but during the irradiation they lead to unavoidable damage to the surrounding healthy tissue. To fully exploit the Bragg peak benefits, a large effort is put into establishing precise range verification methods. Despite positron emission tomography being widely in use for this purpose in ¹²C therapy, the low count rates, biological washout, and broad activity distribution still limit its precision. Instead, radioactive beams used directly for treatment would yield an improved signal and a closer match with the dose fall-off, potentially enabling precise in vivo beam range monitoring. We have performed a treatment planning study to estimate the possible impact of the reduced range uncertainties, enabled by radioactive ¹¹C ions treatments, on sparing critical organs in tumor proximity. Compared to ¹²C treatments, (i) annihilation maps for ¹¹C ions can reflect sub- millimeter shifts in dose distributions in the patient, (ii) outcomes of treatment planning with ¹¹C significantly improve and (iii) less severe toxicities for serial and parallel critical organs can be expected.

A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy

Background

Imbalanced outcome is one of common characteristics of oncology datasets. Current machine learning approaches have limitation in learning from such datasets. Here, we propose to resolve this problem by utilizing a human-in-the-loop (HITL) approach, which we hypothesize will also lead to more accurate and explainable outcome prediction models.

Methods

A total of 119 HCC patients with 163 tumors were used in the study. 81 patients with 104 tumors from the University of Michigan Hospital treated with SBRT were considered as a discovery dataset for radiation outcomes model building. The external testing dataset included 59 tumors from 38 patients with SBRT from Princess Margaret Hospital. In the discovery dataset, 100 tumors from 77 patients had local control (LC) (96% of 104 tumors) and 23 patients had at least one grade increment of ALBI (I-ALBI) during six-month follow up (28% of 81 patients). Each patient had a total of 110 features, where 15 or 20 features were identified by physicians as expert knowledge features (EKFs) for LC or I-ALBI prediction. We proposed a HITL based Bayesian network (HITL-BN) approach to enhance the capability of selecting important features from imbalanced data in terms of accuracy and explainability through humans' participation by integrating feature importance ranking and Markov blanket algorithms. A pure data-driven Bayesian network (PD-BN) method was applied to the same discovery dataset of HCC patients as a benchmark.

Results

In the training and testing phases, the areas under receiver operating characteristic curves of the HITL-BN models for LC or I-ALBI prediction during SBRT are 0.85 (95% confidence interval: 0.75-0.95) or 0.89 (0.81-0.95) and 0.77 or 0.78, respectively. They significantly outperformed the during-treatment PD-BN model in predicting LC or I-ALBI based on the discovery cross-validation and testing datasets from the Delong tests.

Conclusion

By allowing the human expert to be part of the model building process, the HITL-BN approach yielded significantly improved accuracy as well as better explainability when dealing with imbalanced outcomes in the prediction of post-SBRT treatment response of HCC patients when compared to the PD-BN method.

Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients

PURPOSE

The aim of this study was to develop a normal tissue complication probability model using a machine learning approach (ML-based NTCP) to predict the risk of radiation-induced liver disease in hepatocellular carcinoma (HCC) patients.

MATERIALS AND METHODS

The study population included 201 HCC patients treated with radiotherapy. The patients' medical records were retrospectively reviewed to obtain the clinical and radiotherapy data. Toxicity was defined by albumin-bilirubin (ALBI) grade increase. The normal liver dose-volume histogram was reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose (2 Gy/fraction and α/β = 2). Three types of ML-based classification models were used, a penalized logistic regression (PLR), random forest (RF), and gradient-boosted tree (GBT) model. Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Internal validation was performed by 5-fold cross validation and external validation was done in 44 new patients.

RESULTS

Liver toxicity occurred in 87 patients (43.1%). The best individual model was the GBT model using baseline liver function, liver volume, and MLD as inputs and the best overall model was an ensemble of the PLR and GBT models. An AUROC of 0.82 with a standard deviation of 0.06 was achieved for the internal validation. An AUROC of 0.78 with a standard deviation of 0.03 was achieved for the external validation. The behaviors of the best GBT model were also in good agreement with the domain knowledge on NTCP.

CONCLUSION

We propose the methodology to develop an ML-based NTCP model to estimate the risk of ALBI grade increase.

A study on predicting cases that would benefit from proton beam therapy in primary liver tumors of less than or equal to 5 cm based on the estimated incidence of hepatic toxicity

•

An advantage of PBT is reducing the liver receiving low doses of radiation.

•

The factors predicting the benefit in PBT are different among NTCP models.

•

The tumor size, number, and location are useful in estimating the benefits of PBT.

Background

Materials and methods

Results

Conclusions

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.

Uniform versus non-uniform dose prescription for proton stereotactic body radiotherapy of liver tumors investigated by extensive motion-including treatment simulations

Compared to x-ray-based stereotactic body radiotherapy (SBRT) of liver cancer, proton SBRT may reduce the normal liver tissue dose. For an optimal trade-off between target and liver dose, a non-uniform dose prescription is often applied in x-ray SBRT, but lacks investigation for proton SBRT. Also, proton SBRT is prone to breathing-induced motion-uncertainties causing target mishit or dose alterations by interplay with the proton delivery. This study investigated non-uniform and uniform dose prescription in proton-based liver SBRT, including effects of rigid target motion observed during planning-4DCT and treatment. The study was based on 42 x-ray SBRT fractions delivered to 14 patients under electromagnetic motion-monitoring. For each patient, a non-uniform and uniform proton plan were made. The uniform plan was renormalized to be iso-toxic with the non-uniform plan using a NTCP model for radiation-induced liver disease. The motion data were used in treatment simulations to estimate the delivered target dose with rigid motion. Treatment simulations were performed with and without a repainting scheme designed to mitigate interplay effects. Including rigid motion, the achieved CTV mean dose after three fractions delivered without repainting was on average (±SD) 24.8 ± 8.4% higher and the D_98%was 16.2 ± 11.3% higher for non-uniform plans than for uniform plans. The interplay-induced increase in D_2%relative to the static plans was reduced from 3.2 ± 4.1% without repainting to -0.5 ± 1.7% with repainting for non-uniform plans and from 1.5 ± 2.0% to 0.1 ± 1.3% for uniform plans. Considerable differences were observed between estimated CTV doses based on 4DCT motion and intra-treatment motion. In conclusion, non-uniform dose prescription in proton SBRT may provide considerably higher tumor doses than uniform prescription for the same complication risk. Due to motion variability, target doses estimated from 4DCT motion may not accurately reflect the delivered dose. Future studies including modelling of deformations and associated range uncertainties are warranted to confirm the findings.

A Phase 2 Study of Image-Guided Proton Therapy for Operable or Ablation-Treatable Primary Hepatocellular Carcinoma

PURPOSE

Because most previous data on proton therapy for hepatocellular carcinoma (HCC) were retrospectively collected from inoperable or previously treated cases, our aim was to evaluate the outcome of image-guided proton therapy (IGPT) for operable or radiofrequency ablation-treatable primary HCC.

METHODS AND MATERIALS

This phase 2 study prospectively investigated the efficacy and safety of IGPT and quality of life (QoL) after IGPT for operable/ablatable HCC. The primary endpoint was overall survival, and the secondary endpoints were local control, incidence of grade ≥3 adverse events, and changes in QoL. Toxicities were evaluated with Common Terminology Criteria for Adverse Events, version 4.0. QoL scores were assessed with European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, version 3.0, and Quality of Life Questionnaire-Hepatocellular Carcinoma/Primary Liver Cancer Module. IGPT was performed using respiratory-gated techniques.

RESULTS

Forty-five patients (median age: 68 years; range, 36-80 years) were enrolled between June 2013 and February 2016; 38 were considered operable and 14 were indicated for radiofrequency ablation. The major underlying liver diseases were hepatitis B (n = 16), hepatitis C (n = 13), alcoholic hepatitis (n = 3), and nonalcoholic fatty liver disease (n = 13). The Child-Pugh score was A5 in 32 patients, A6 in 9 patients, and B7 in 4 patients. Thirty-seven patients with a peripherally located tumor were given 66 Gy relative biological effectiveness in 10 fractions, and 8 patients with a centrally located tumor received 72.6 Gy relative biological effectiveness in 22 fractions. The median follow-up period of surviving patients was 60 months (range, 42-75 months). Two- and 5-year overall survival rates were 84% (95% confidence interval [CI], 74%-95%) and 70% (95% CI, 56%-84%), respectively, and local control rates were 95% (95% CI, 89%-100%) and 92% (95% CI, 84%-100%), respectively. Grade 3 radiation-induced liver disease was observed in 1 patient. No significant changes were noted in QoL scores 1 year after treatment, except for body image.

CONCLUSIONS

Although the primary endpoint did not meet statistical significance as planned in the study design, IGPT is a safe and effective treatment for solitary primary HCC and may become a treatment option.

Toward Personalized Radiation Therapy of Liver Metastasis: Importance of Serial Blood Biomarkers

PURPOSE

To assess the added value of serial blood biomarkers in liver metastasis stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS

Eighty-nine patients were retrospectively included. Pre- and midtreatment blood samples were analyzed for potential biomarkers of the treatment response. Three biomarker classes were studied: gene mutation status, complete blood count, and inflammatory cytokine concentration in plasma. One-year local failure (LF) and 2-year overall survival (OS) were chosen as study end points. Multivariate logistic regression was used for response prediction. Added predictive benefit was assessed by quantifying the difference between the predictive performance of a baseline model (clinicopathologic and dosimetric predictors) and that of the biomarker-enhanced model, using three metrics: (1) likelihood ratio, (2) predictive variance, and (3) area under the receiver operating characteristic curve (AUC).

RESULTS

The most important predictors of LF were mutation in KRAS gene (hazard ratio [HR] = 2.92, 95% CI, [1.17 to 7.28], P = .02) and baseline and midtreatment concentration of plasma interleukin-6 (HR = 1.15 [1.04 to 1.26] and 1.06 [1.01 to 1.13], P = .01). Absolute lymphocyte count and platelet-to-lymphocyte ratio at baseline as well as neutrophil-to-lymphocyte ratio at baseline and before fraction 3 (HR = 1.33 [1.16 to 1.51] and 1.19 [1.09 to 1.30]) had the most significant association with OS (P = .0003). Addition of baseline GEN and inflammatory plasma cytokine biomarkers in predicting LF, respectively, increased AUC by 0.06 (from 0.73 to 0.79) and 0.07 (from 0.77 to 0.84). In predicting OS, inclusion of midtreatment complete blood count biomarkers increased AUC from 0.72 to 0.80, along with significant boosts in likelihood ratio and predictive variance.

CONCLUSION

Inclusion of serial blood biomarkers leads to significant gain in predicting response to liver metastasis stereotactic body radiation therapy and can guide treatment personalization.

Preoperative Combined Prediction Models Have Superior Capability in Predicting Survival as the Child-Pugh Grade in Patients with HCC after Interventional Embolotherapy

Background

It is of important clinical significance for hepatocellular carcinoma (HCC) patients to evaluate prognosis before interventional embolotherapy.

Methods

A total of 106 patients with HCC after interventional embolotherapy who had complete data with follow-up information until September 2019 were included in this study. These data were analyzed using SPSS Version 22.0 and R (version 3.6.1) statistical software.

Results

1) The diameter of the tumor, ascites, FIT, AFP, ALT, AST, GGT, and Child-Pugh score had the ability to predict the prognosis and survival of patients with HCC. Among these molecules, the predictive effectiveness (or the area under the receiver operating characteristic [ROC] curve) of GGT was the highest, although it was slightly lower than the predictive effectiveness of the Child-Pugh score, which is the gold standard for survival analysis. 2) Among survival analyses combining five molecular indicators, the predictive postoperative viability for combination 1 was the strongest with an area under the ROC curve (AUC) of 0.856 (0.779, 0.932), similar to the all-molecular combination (combination 16) with an AUC of 0.872 (0.798, 0.945), but much higher than that of the Child-Pugh score of 0.720 (0.616, 0.823) for HCC patients (all p<0.05). 3) Kaplan-Meier analyses showed that the 3-year cumulative survival rates were 55.3% for low-risk patients and 2.6% for high-risk patients.

Conclusion

A combined prediction model can determine the optimal combination of preoperative routine detection indices in patients with HCC intervention, and ROC curve analysis can quantify the efficacy of these indices in the survival and prognosis of HCC. Interestingly, combination 1 showed stronger predictive capability than the Child-Pugh score in predicting death risks for postoperative patients with HCC. When combination 1 has several missing clinical data, these combination prediction models (12, 3, 7, 13, 16) are also a replaceable choice. These findings may have important clinical significance in the formulation of individualized medical programs.

Do Biliary Complications after Proton Beam Therapy for Perihilar Hepatocellular Carcinoma Matter?

We aimed to evaluate the biliary complications and efficacy of proton beam therapy (PBT) for hepatocellular carcinoma (HCC). We retrospectively analyzed 167 patients who received PBT with ≥ 75 GyRBE of biological effective dose with 𝛼/β = 10 for primary HCC. The perihilar region was defined as a 1-cm area extending from the right, left, and common hepatic ducts, including the gallbladder and cystic duct. PBT-related biliary complications were defined as follows: significant elevation in bilirubin level to > 3.0 mg/dL; elevation to more than twice of the baseline level after the completion of PBT; or newly developed radiological biliary abnormalities, which were not caused by HCC progression, comorbidities, or other treatments. Eighty (47.9%) had perihilar HCC. PBT-related events occurred in seven (4.2%), three of whom had perihilar HCC. Radiologic biliary abnormalities developed in 12 patients (7.2%); however, no events were PBT-related. All patients who experienced PBT-related biliary complications had underlying liver cirrhosis. The albumin-bilirubin grade was identified as an independent factor associated with PBT-related biliary complications. PBT at the current dose showed a low rate of PBT-related biliary complications even for patients with perihilar HCC. PBT for HCC patients with risk factors requires attention to reduce PBT-related biliary complications.