Feasibility of 4D perfusion CT imaging for the assessment of liver treatment response following SBRT and sorafenib

Response Evaluation Using Contrast-Enhanced Ultrasound for Unresectable Advanced Hepatocellular Carcinoma Treated With Tyrosine Kinase Inhibitors Plus Anti-PD-1 Antibody Therapy

OBJECTIVE

The aim of the work described here was to evaluate the role of contrast-enhanced ultrasound (CEUS) in response evaluation for unresectable advanced hepatocellular carcinoma (HCC) treated with tyrosine kinase inhibitors (TKIs) plus anti-programmed cell death protein-1 (PD-1) antibody therapy.

METHODS

A prospective cohort of consecutive patients with HCC who received combined TKI/anti-PD-1 antibody treatment for unresectable HCC between January 2022 and October 2022 was included in this study. The patients underwent unenhanced ultrasound (US) and CEUS examinations before treatment and at follow-up. Changes in the largest diameters of the target tumor on unenhanced US and the largest diameters of the enhancing target tumors on CEUS were evaluated. Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with unenhanced US and magnetic resonance imaging/computed tomography (MRI/CT) and modified RECIST (mRECIST) with CEUS and CEMRI/CT were used to assess treatment response.

RESULTS

A total of 24 HCC patients (23 men and 1 woman; mean age: 56.5 ± 8.5 y; Barcelona Clinic Liver Cancer stage C, 62.5%; 29 intrahepatic target tumors) were studied. Calculations of degree of necrosis in the target tumors revealed no significant differences between CEUS and CEMRI/CT (44.5 ± 36.2% vs. 45.3 ± 36.8%, p = 0.862). As for the differentiation of responders from non-responders, the agreement between RECIST version 1.1 of unenhanced US and mRECIST-CEUS was poor (κ coefficient = 0.233). Meanwhile, there was a high degree of concordance between mRECIST-CEUS and mRECIST-CEMRI/CT (κ coefficient = 0.812).

CONCLUSION

CEUS proved to be superior to baseline US and is comparable to CEMRI/CT in defining treatment outcome for combined TKI/anti-PD-1 antibody therapy.

Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures

Purpose

Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms.

Materials and Methods

Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses.

Results

Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach.

Conclusion

This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs.

Contrast-enhanced ultrasonography for blood flow detection in hepatocellular carcinoma during lenvatinib therapy

PURPOSE

Blood flow reduction after initiation of lenvatinib therapy may not always indicate tumor necrosis. This study aimed to compare the blood flow detectability of contrast-enhanced ultrasonography (CEUS), contrast-enhanced computed tomography (CT), and contrast-enhanced magnetic resonance imaging (MRI) in hepatocellular carcinoma (HCC) during lenvatinib therapy.

METHODS

A total of 12 cases underwent CEUS and contrast-enhanced CT/MRI within 2 weeks during lenvatinib therapy. Vascularity on CEUS and CT/MRI was compared.

RESULTS

At the time of CEUS examination, the median period from the start of lenvatinib was 227 ± 210 (31-570) days. CEUS showed hyperenhancement in eight cases (66.7%), hypoenhancement in two cases (16.7%), and no enhancement in one case (8.3%), while CT/MRI showed hyperenhancement in one case (8.3%), ring enhancement in three cases (25.0%), and hypoenhancement in eight cases (66.7%) (p = 0.007). Transarterial chemoembolization (n = 3), radiofrequency ablation (n = 2), and stereotactic body radiation therapy (n = 2) were performed after blood flow detection by CEUS.

CONCLUSIONS

The viability of the HCC should be confirmed using CEUS when contrast-enhanced CT/MRI reveals lesion hypoenhancement during lenvatinib therapy.

DCE-MRI assessment of response to neoadjuvant SABR in early stage breast cancer: Comparisons of single versus three fraction schemes and two different imaging time delays post-SABR

PURPOSE

To determine the effect of dose fractionation and time delay post-neoadjuvant stereotactic ablative radiotherapy (SABR) on dynamic contrast-enhanced (DCE)-MRI parameters in early stage breast cancer patients.

MATERIALS AND METHODS

DCE-MRI was acquired in 17 patients pre- and post-SABR. Five patients were imaged 6-7 days post-21 Gy/1fraction (group 1), six 16-19 days post-21 Gy/1fraction (group 2), and six 16-18 days post-30 Gy/3 fractions every other day (group 3). DCE-MRI scans were performed using half the clinical dose of contrast agent. Changes in the surrounding tissue were quantified using a signal-enhancement threshold metric that characterizes changes in signal-enhancement volume (SEV). Tumour response was quantified using Ktrans and ve (Tofts model) pre- and post-SABR. Significance was assessed using a Wilcoxin signed-rank test.

RESULTS

All group 1 and 4/6 group 2 patients' SEV increased post-SABR. All group 3 patients' SEV decreased. The mean Ktrans increased for group 1 by 76% (p = 0.043) while group 2 and 3 decreased 15% (p = 0.028) and 34% (p = 0.028), respectively. For ve, there was no significant change in Group 1 (p = 0.35). Groups 2 showed an increase of 24% (p = 0.043), and Group 3 trended toward an increase (23%, p = 0.08).

CONCLUSION

Kinetic parameters measured 2.5 weeks post-SABR in both single fraction and three fraction groups were indicative of response but only the single fraction protocol led to enhancement in the surrounding tissue. Our results also suggest that DCE-MRI one-week post-SABR may be too early for response assessment, at least for single fraction SABR, whereas 2.5 weeks appears sufficiently long to minimize confounding acute effects.

The transformation of radiation oncology using real-time magnetic resonance guidance: A review

Radiation therapy (RT) is an essential component of effective cancer care and is used across nearly all cancer types. The delivery of RT is becoming more precise through rapid advances in both computing and imaging. The direct integration of magnetic resonance imaging (MRI) with linear accelerators represents an exciting development with the potential to dramatically impact cancer research and treatment. These impacts extend beyond improved imaging and dose deposition. Real-time MRI-guided RT is actively transforming the work flows and capabilities of virtually every aspect of RT. It has the opportunity to change entirely the delivery methods and response assessments of numerous malignancies. This review intends to approach the topic of MRI-based RT guidance from a vendor neutral and international perspective. It also aims to provide an introduction to this topic targeted towards oncologists without a speciality focus in RT. Speciality implications, areas for physician education and research opportunities are identified as they are associated with MRI-guided RT. The uniquely disruptive implications of MRI-guided RT are discussed and placed in context. We further aim to describe and outline important future changes to the speciality of radiation oncology that will occur with MRI-guided RT. The impacts on RT caused by MRI guidance include target identification, RT planning, quality assurance, treatment delivery, training, clinical workflow, tumour response assessment and treatment scheduling. In addition, entirely novel research areas that may be enabled by MRI guidance are identified for future investigation.

Unified platform for multimodal voxel-based analysis to evaluate tumour perfusion and diffusion characteristics before and after radiation treatment evaluated in metastatic brain cancer

OBJECTIVE:

Early changes in tumour behaviour following stereotactic radiosurgery) are potential biomarkers of response. To-date quantitative model-based measures of dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI parameters have shown widely variable findings, which may be attributable to variability in image acquisition, post-processing and analysis. Big data analytic approaches are needed for the automation of computationally intensive modelling calculations for every voxel, independent of observer interpretation.

METHODS:

This unified platform is a voxel-based, multimodality architecture that brings complimentary solute transport processes such as perfusion and diffusion into a common framework. The methodology was tested on synthetic data and digital reference objects and consequently evaluated in patients who underwent volumetric DCE-CT, DCE-MRI and DWI-MRI scans before and after treatment. Three-dimensional pharmacokinetic parameter maps from both modalities were compared as well as the correlation between apparent diffusion coefficient (ADC) values and the extravascular, extracellular volume (Ve). Comparison of histogram parameters was done via Bland-Altman analysis, as well as Student's t-test and Pearson's correlation using two-sided analysis.

RESULTS:

System testing on synthetic Tofts model data and digital reference objects recovered the ground truth parameters with mean relative percent error of 1.07 × 10-7 and 5.60 × 10-4 respectively. Direct voxel-to-voxel Pearson's analysis showed statistically significant correlations between CT and MR which peaked at Day 7 for Ktrans (R = 0.74, p <= 0.0001). Statistically significant correlations were also present between ADC and Ve derived from both DCE-MRI and DCE-CT with highest median correlations found at Day 3 between median ADC and Ve,MRI values (R = 0.6, p < 0.01) The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxelwise T10 maps (R = 0.575, p < 0.001) instead of assigning a fixed T10 value.

CONCLUSION:

The unified implementation of multiparametric transport modelling allowed for more robust and timely observer-independent data analytics. Utility of a common analysis platform has shown higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

ADVANCES IN KNOWLEDGE:

Utility of a common analysis platform has shown statistically higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

Stereotactic body radiation therapy in primary hepatocellular carcinoma: current status and future directions

Stereotactic body radiation therapy (SBRT) is a form of radiation therapy that has been used in the treatment of primary hepatocellular carcinoma (HCC) over the past decade. To evaluate the clinical efficacy of SBRT in primary HCC, a literature search was conducted to identify original research articles published from January 2000 through January 2018 in PubMed on SBRT in HCC. All relevant studies published from 2004 to 2018 were included. Prospective studies demonstrated 2-year local control (LC) rates ranging from 64-95% and overall survival (OS) rates ranging from 34% (2-year) to 65% (3-year). Retrospective studies demonstrated 2-year LC rates of 44-90% and 2-year OS rates of 24-67%. Reported toxicities in primary HCC patients vary but SBRT appears to be relatively well tolerated. Studies comparing SBRT to radiofrequency ablation (RFA) are few, but they suggest SBRT may be more effective than RFA in specific primary HCC populations. Additionally, SBRT appears to increase the efficacy of both transarterial chemoembolization (TACE) and sorafenib in selected primary HCC populations.

Imaging evaluation of sorafenib for treatment of advanced hepatocellular carcinoma

Sorafenib, which is a novel targeted agent, plays an important role in treating advanced hepatocellular carcinoma (HCC) through its antiangiogenic and antiproliferative effects. However, conventional morphology-based radiographic evaluation systems may underestimate the efficacy of sorafenib in HCC due to a lack of apparent tumor shrinkage or altered tumor morphology in many cases. This calls for the development of more accurate imaging methods for evaluating sorafenib. The introduction of tumor burden measurements based on viability and other evolving imaging approaches for assessing therapeutic effects are promising for overcoming some of the limitations of the morphology-based criteria. In this review, we summarize various imaging methods that are used to assess treatment responses of advanced HCC to sorafenib. Imaging markers predictive of prognosis in advanced HCC after treatment with sorafenib are also included and discussed.

Joint Head and Neck Radiotherapy-MRI Development Cooperative, Ger RB, Mohamed ASR, Awan MJ, Ding Y, Li K, Fave XJ, Beers AL, Driscoll B, Elhalawani H, Hormuth DA, Houdt PJV, He R, Zhou S, Mathieu KB, Li H, Coolens C, Chung C, Bankson JA, Huang W, Wang J, Sandulache VC, Lai SY, Howell RM, Stafford RJ, Yankeelov TE, Heide UAVD, Frank SJ, Barboriak DP, Hazle JD, Court LE, Kalpathy-Cramer J, Fuller CD. A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff's alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.

Diagnostic value of dynamic contrast-enhanced CT with perfusion imaging in the quantitative assessment of tumor response to sorafenib in patients with advanced hepatocellular carcinoma: A feasibility study

PURPOSE

To investigate the feasibility of perfusion-CT (p-CT) measurements in quantitative assessment of hemodynamic changes related to sorafenib in patients with advanced hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Twenty-two patients with advanced HCC underwent p-CT study (256-MDCT scanner) before and 2 months after sorafenib administration. Dedicated perfusion software generated a quantitative map of arterial and portal perfusion and calculated the following perfusion parameters in target liver lesion: hepatic perfusion (HP), time-to-peak (TTP), blood volume (BV), arterial perfusion (AP), and hepatic perfusion index (HPI). After the follow-up scan, patients were categorized as responders and non-responders, according to mRECIST. Perfusion values were analyzed and compared in HCC lesions and in the cirrhotic parenchyma (n=22), such as between baseline and follow-up in progressors and non-progressors.

RESULTS

Before treatment, all mean perfusion values were significantly higher in HCC lesions than in the cirrhotic parenchyma (HP 47.8±17.2 vs 13.3±6.3mL/s per 100g; AP 47.9±18.1 vs 12.9±10.7mL/s; p<0.001). The group that responded to sorafenib (n=17) showed a significant reduction of values in HCC target lesions after therapy (HP 29.2±23.3 vs 48.1±15.1; AP 29.4±24.6 vs 49.2±17.4; p<0.01), in comparison with the non-responder group (n=5) that demonstrated no significant variation before and after treatment of HP (46.9±25.1 vs 46.7±24.1) and AP (43.4±21.7 vs 43.5±24.6). Among the responder group, HP percentage variation (Δ) in target lesions, during treatment, showed a significantly different (p=0.04) ΔHP in the group with complete response (79%) compared to the group with partial response or stable disease (16%).

CONCLUSIONS

p-CT technique can be used for HCC quantitative assessment of changes related to anti-angiogenic therapy. Identification of response predictors might help clinicians in selection of patients who may benefit from targeted-therapy allowing for optimization of individualized treatment.