Does Vascular Collapse Occur After Treatment of Hepatocellular Cancer With Stereotactic Body Radiation Therapy?

There is debate about why stereotactic body radiation therapy (SBRT) produces superior control of hepatocellular cancer (HCC) compared to fractionated treatment. Both preclinical and clinical evidence has been presented to support a "classic" biological explanation: the greater BED of SBRT produces more DNA damage and tumor cell kill. More recently, preclinical evidence has supported the concept of a "new biology", particularly radiation-induced vascular collapse, which increases hypoxia and free radical activation. This is hypothesized to cause much greater tumor cell death than was produced by the initial radiation-induced DNA damage to the tumor. We decided to investigate if vascular collapse occurs after standard SBRT for patients with HCC. Eight patients with 10 lesions underwent dynamic contrast enhanced MRI at the time of simulation and either 48 or 96 hours after the first fraction. Only three of 10 tumors showed a decrease in blood flow. These findings suggest that vascular collapse does not typically occur after SBRT for HCC.

Efficacy of a Second Course of Radiation for Patients With Metachronous Hepatocellular Carcinoma.

PURPOSE: Liver-directed radiation therapy is an effective treatment for hepatocellular carcinoma (HCC), but metachronous lesions develop outside the irradiated field in >50% of patients. We hypothesized that irradiation of these new lesions would produce an outcome like that of patients receiving a first course (C1) of treatment. METHODS AND MATERIALS: We included patients with HCC who received a second course (C2) of radiation therapy >1 month after C1. Toxicity was defined as Child-Pugh score increase ≥2 within 6 months posttreatment (binary model) and as the change in albumin-bilirubin during the year after treatment (longitudinal model). Overall survival (OS) and local failure (LF) were captured at the patient and lesion level, respectively; both were summarized with Kaplan-Meier estimates. Predictors of toxicity and OS were assessed using generalized linear mixed and Cox regression models, respectively. RESULTS: Of 340 patients with HCC, 47 underwent irradiation for metachronous HCC, receiving similar prescription dose in C1/C2. Median follow-up was 17 months after C1 and 15 months after C2. Twenty-two percent of patients experienced toxicity after C1, and 25% experienced toxicity after C2. Worse baseline albumin-bilirubin predicted toxicity in both binary (odds ratio, 2.40; 95% CI, 1.46-3.94; P = .0005) and longitudinal models (P < .005). Two-year LF rate was 11.2% after C1 and 8.3% after C2; tumor dose (hazard ratio [HR], 0.982; 95% CI, 0.969-0.995; P = .007) and tumor size (HR, 1.135; 95% CI, 1.068-1.206; P < .005) predicted LF. Two-year OS was 46.0% after C1 and 42.6% after C2; tumor dose (HR, 0.986; 95% CI, 0.979-0.992; P < .005) and tumor size (HR, 1.049; 95% CI, 1.010-1.088; P = .0124) predicted OS. Reirradiation was not associated with toxicity (P > .7), LF (P = .79), or OS (P = .39). CONCLUSIONS: In this largest series in the Western hemisphere, we demonstrate that irradiation for metachronous HCC offers low rates of LF with acceptable toxicity and OS like that of patients receiving a C1. These findings support judicious selection of patients for reirradiation in metachronous HCC.

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.

Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment.

Purpose: Parametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary pathologies on a patient-specific basis. As one of the first studies to implement such a technique in the radiation oncology clinic, the goal of this work was to assess the feasibility for PRM analysis to identify pulmonary abnormalities in patients with lung cancer before radiation therapy (RT). Methods and Materials: High-resolution, paired inspiration and expiration CT scans were acquired from 23 patients with lung cancer as part of routine treatment planning CT acquisition. When applied to the paired CT scans, PRM analysis classifies lung parenchyma, on a voxel-wise basis, as normal, small airways disease (SAD), emphysema, or parenchymal disease (PD). PRM classifications were quantified as a percent of total lung volume and were evaluated globally and regionally within the lung. Results: PRM analysis of pre-RT CT scans was successfully implemented using a workflow that produced patient-specific maps and quantified specific phenotypes of pulmonary abnormalities. Through this study, a large prevalence of SAD and PD was demonstrated in this lung cancer patient population, with global averages of 10% and 17%, respectively. Moreover, PRM-classified normal and SAD in the region with primary tumor involvement were found to be significantly different from global lung values. When present, elevated levels of PD and SAD abnormalities tended to be pervasive in multiple regions of the lung, indicating a large burden of underlying disease. Conclusions: Pulmonary abnormalities, as detected by PRM, were characterized in patients with lung cancer scheduled for RT. Although further study is needed, PRM is a highly accessible CT-based imaging technique that has the potential to identify local lung abnormalities associated with chronic obstructive pulmonary disease and interstitial lung disease. Further investigation in the radiation oncology setting may provide strategies for tailoring RT planning and risk assessment based on pre-existing PRM-based pathology.

Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy.

Subtle differences in a patient's genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient's dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients' specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT.

Feasibility of function-guided lung treatment planning with parametric response mapping.

PURPOSE: Recent advancements in functional lung imaging have been developed to improve clinicians' knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)-based functional lung imaging method that utilizes a voxel-wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning. METHODS AND MATERIALS: A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric-modulated arc therapy and PRM-guided treatment plans were designed for each patient. RESULTS: PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM-guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity. CONCLUSIONS: PRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM-guided treatment planning.

The Potential for Midtreatment Albumin-Bilirubin (ALBI) Score to Individualize Liver Stereotactic Body Radiation Therapy.

PURPOSE: Our individualized functional response adaptive approach to liver stereotactic body radiation therapy (SBRT) with assessment of indocyanine green (ICG) retention at baseline and midtreatment to detect subclinical changes in liver function, permitting dose adjustment, has decreased toxicity while preserving efficacy. We hypothesized that assessment of the albumin-bilirubin (ALBI) score at baseline and midtreatment would allow for more practical identification of patients at risk for treatment-related toxicity (TRT). METHODS AND MATERIALS: Patients with hepatocellular carcinoma were treated on 3 prospective institutional review board-approved trials using baseline and midtreatment ICG to deliver individualized functional response adaptive liver SBRT. Patients received 3 or 5 fractions, with fraction 3 followed by a 1-month treatment break. TRT was a ≥2-point rise in Child-Pugh score within 6 months of SBRT. Logistic regression was used to estimate odds ratios (ORs) and confidence intervals (CIs) for assessment of TRT. Area under the receiver operating curve was used to compare predictive ability across models. RESULTS: In total, 151 patients underwent 166 treatments. Baseline Child-Pugh class and ALBI grade were A (66.9%), B (31.3%), or C (1.8%) and 1 (25.9%), 2 (65.7%), or 3 (8.4%), respectively. Thirty-five patients (20.3%) experienced TRT. On univariate analysis, baseline ALBI (OR, 1.8; 95% CI, 1.24-2.62; P = .02), baseline ICG (OR, 1.66; 95% CI, 1.17-2.35; P = .04), and change in ALBI (OR, 3.07; 95% CI, 1.29-7.32; P = .003) were associated with increased odds of TRT. ALBI-centric models performed similarly to ICG-centric models on multivariate analyses predicting toxicity (area under the receiver operating curve of 0.79 for both). In a model incorporating baseline and midtreatment change in ALBI and ICG, both ALBI values were statistically significantly associated with toxicity, whereas ICG values were not. CONCLUSIONS: Incorporation of midtreatment change in ALBI in addition to baseline ALBI improves the ability to predict TRT in patients with hepatocellular carcinoma receiving SBRT. Our findings suggest that functional response adaptive treatment could be implemented in a practical manner because the ALBI score is easily obtained from standard laboratory values.

Circulating microRNAs as biomarkers of radiation-induced cardiac toxicity in non-small-cell lung cancer.

PURPOSE: Radiation-induced cardiac toxicity (RICT) is an increasingly well-appreciated source of morbidity and mortality in patients receiving thoracic radiotherapy (RT). Currently available methods to predict RICT are suboptimal. We investigated circulating microRNAs (c-miRNAs) as potential biomarkers of RICT in patients undergoing definitive RT for non-small-cell lung cancer (NSCLC). METHODS: Data from 63 patients treated on institutional trials were analyzed. Prognostic models of grade 3 or greater (G3 +) RICT based on pre-treatment c-miRNA levels ('c-miRNA'), mean heart dose (MHD) and pre-existing cardiac disease (PCD) ('clinical'), and a combination of these ('c-miRNA + clinical') were developed. Elastic net Cox regression and full cross validation were used for variable selection, model building, and model evaluation. Concordance statistic (c-index) and integrated Brier score (IBS) were used to evaluate model performance. RESULTS: MHD, PCD, and serum levels of 14 c-miRNA species were identified as jointly prognostic for G3 + RICT. The 'c-miRNA and 'clinical' models yielded similar cross-validated c-indices (0.70 and 0.72, respectively) and IBSs (0.26 and 0.28, respectively). However, prognostication was not improved by combining c-miRNA and clinical factors (c-index 0.70, IBS 0.28). The 'c-miRNA' and 'clinical' models were able to significantly stratify patients into high- and low-risk groups of developing G3 + RICT. Chi-square testing demonstrated a marginally significantly higher prevalence of PCD in patients with high- compared to low-risk c-miRNA profile (p = 0.09), suggesting an association between some c-miRNAs and PCD. CONCLUSIONS: We identified a pre-treatment c-miRNA signature prognostic for G3 + RICT. With further development, pre- and mid-treatment c-miRNA profiling could contribute to patient-specific dose selection and treatment adaptation.

Potential for dose-escalation and reduction of risk in pancreatic cancer using IMRT optimization with lexicographic ordering and gEUD-based cost functions.

Radiotherapy for pancreatic cancer is limited by the tolerance of local organs at risk (OARs) and frequent overlap of the planning target volume (PTV) and OAR volumes. Using lexicographic ordering (LO), a hierarchical optimization technique, with generalized equivalent uniform dose (gEUD) cost functions, we studied the potential of intensity modulated radiation therapy (IMRT) to increase the dose to pancreatic tumors and to areas of vascular involvement that preclude surgical resection [surgical boost volume (SBV)]. We compared 15 forward planned three-dimensional conformal (3DCRT) and IMRT treatment plans for locally advanced unresectable pancreatic cancer. We created IMRT plans optimized using LO with gEUD-based cost functions that account for the contribution of each part of the resulting inhomogeneous dose distribution. LO-IMRT plans allowed substantial PTV dose escalation compared with 3DCRT; median increase from 52 Gy to 66 Gy (a=-5,p<0.005) and median increase from 50 Gy to 59 Gy (a=-15,p<0.005). LO-IMRT also allowed increases to 85 Gy in the SBV, regardless of a value, along with significant dose reductions in OARs. We conclude that LO-IMRT with gEUD cost functions could allow dose escalation in pancreas tumors with concomitant reduction in doses to organs at risk as compared with traditional 3DCRT.

The prediction of radiation-induced liver dysfunction using a local dose and regional venous perfusion model.

We have shown that high dose conformal radiation combined with chemotherapy appears to prolong the survival of patients with unresectable intrahepatic cancers. The ability to safely deliver higher doses is primarily limited by the development of radiation-induced liver disease, characterized by venous occlusion. In this study, we investigated whether portal venous perfusion measured prior to the end of radiation therapy (RT) together with dose could predict liver venous perfusion dysfunction after treatment. Ten patients with unresectable intrahepatic cancer participated in an IRB-approved computer tomography (CT) perfusion study. Hepatic arterial and portal vein perfusion distributions were estimated by using dynamic contrast enhanced CT and the single compartmental model. Scans were obtained at four time points: prior to treatment, after 15 and 30 fractions of 1.5 Gy treatments, and one month following the completion of RT. Multivariant linear regression was used to determine covariances among the first three time point measurements plus dose for prediction of the post RT measurement. The reduction in the regional venous perfusion one month following RT was predicted by the local accumulated dose and the change in the regional venous perfusion after -30 fractions (F=90.6,p <0.000 01). Each Gy produced an approximately 1.2% of reduction in the venous perfusion. This local dose and venous perfusion model has the potential to predict individual sensitivity to radiation. This is the first step toward developing a method to deliver higher and potentially more curative radiation doses to the patients who can safely receive these higher doses.