Magnetic resonance imaging for prostate bed radiotherapy planning: An inter- and intra-observer variability study

Intra-fractional geometric and dose/volume metric variations of magnetic resonance imaging-guided stereotactic radiotherapy of prostate bed after radical prostatectomy

Background and purpose

Magnetic Resonance Imaging (MRI)-guided Stereotactic body radiotherapy (SBRT) treatment to prostate bed after radical prostatectomy has garnered growing interests. The aim of this study is to evaluate intra-fractional anatomic and dose/volume metric variations for patients receiving this treatment.

Materials and methods

Nineteen patients who received 30-34 Gy in 5 fractions on a 0.35T MR-Linac were included. Pre- and post-treatment MRIs were acquired for each fraction (total of 75 fractions). The Clinical Target Volume (CTV), bladder, rectum, and rectal wall were contoured on all images. Volumetric changes, Hausdorff distance, Mean Distance to Agreement (MDA), and Dice similarity coefficient (DSC) for each structure were calculated. Median value and Interquartile range (IQR) were recorded. Changes in target coverage and Organ at Risk (OAR) constraints were compared and evaluated using Wilcoxon rank sum tests at a significant level of 0.05.

Results

Bladder had the largest volumetric changes, with a median volume increase of 48.9 % (IQR 28.9-76.8 %) and a median MDA of 5.1 mm (IQR 3.4-7.1 mm). Intra-fractional CTV volume remained stable with a median volume change of 1.2 % (0.0-4.8 %). DSC was 0.97 (IQR 0.94-0.99). For the dose/volume metrics, there were no statistically significant changes observed except for an increase in bladder hotspot and a decrease of bladder V32.5 Gy and mean dose. The CTV V95% changed from 99.9 % (IQR 98.8-100 %) to 99.6 % (IQR 93.9-100 %).

Conclusion

Despite intra-fractional variations of OARs, CTV coverage remained stable during MRI-guided SBRT treatments for the prostate bed.

Development of Prostate Bed Delineation Consensus Guidelines for Magnetic Resonance Image-Guided Radiotherapy and Assessment of Its Effect on Interobserver Variability

PURPOSE

The use of magnetic resonance imaging (MRI) in radiotherapy planning is becoming more widespread, particularly with the emergence of MRI-guided radiotherapy systems. Existing guidelines for defining the prostate bed clinical target volume (CTV) show considerable heterogeneity. This study aimed to establish baseline interobserver variability (IOV) for prostate bed CTV contouring on MRI, develop international consensus guidelines, and evaluate its effect on IOV.

METHODS AND MATERIALS

Participants delineated the CTV on 3 MRI scans, obtained from the Elekta Unity MR-Linac, as per their normal practice. Radiation oncologist contours were visually examined for discrepancies, and interobserver comparisons were evaluated against simultaneous truth and performance level estimation (STAPLE) contours using overlap metrics (Dice similarity coefficient and Cohen's kappa), distance metrics (mean distance to agreement and Hausdorff distance), and volume measurements. A literature review of postradical prostatectomy local recurrence patterns was performed and presented alongside IOV results to the participants. Consensus guidelines were collectively constructed, and IOV assessment was repeated using these guidelines.

RESULTS

Sixteen radiation oncologists' contours were included in the final analysis. Visual evaluation demonstrated significant differences in the superior, inferior, and anterior borders. Baseline IOV assessment indicated moderate agreement for the overlap metrics while volume and distance metrics demonstrated greater variability. Consensus for optimal prostate bed CTV boundaries was established during a virtual meeting. After guideline development, a decrease in IOV was observed. The maximum volume ratio decreased from 4.7 to 3.1 and volume coefficient of variation reduced from 40% to 34%. The mean Dice similarity coefficient rose from 0.72 to 0.75 and the mean distance to agreement decreased from 3.63 to 2.95 mm.

CONCLUSIONS

Interobserver variability in prostate bed contouring exists among international genitourinary experts, although this is lower than previously reported. Consensus guidelines for MRI-based prostate bed contouring have been developed, and this has resulted in an improvement in contouring concordance. However, IOV persists and strategies such as an education program, development of a contouring atlas, and further refinement of the guidelines may lead to additional improvements.

Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer

Purpose

The aim of this study was to propose and evaluate a novel three-dimensional (3D) V-Net and two-dimensional (2D) U-Net mixed (VUMix-Net) architecture for a fully automatic and accurate gross tumor volume (GTV) in esophageal cancer (EC)–delineated contours.

Methods

We collected the computed tomography (CT) scans of 215 EC patients. 3D V-Net, 2D U-Net, and VUMix-Net were developed and further applied simultaneously to delineate GTVs. The Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95HD) were used as quantitative metrics to evaluate the performance of the three models in ECs from different segments. The CT data of 20 patients were randomly selected as the ground truth (GT) masks, and the corresponding delineation results were generated by artificial intelligence (AI). Score differences between the two groups (GT versus AI) and the evaluation consistency were compared.

Results

In all patients, there was a significant difference in the 2D DSCs from U-Net, V-Net, and VUMix-Net (p=0.01). In addition, VUMix-Net showed achieved better 3D-DSC and 95HD values. There was a significant difference among the 3D-DSC (mean ± STD) and 95HD values for upper-, middle-, and lower-segment EC (p<0.001), and the middle EC values were the best. In middle-segment EC, VUMix-Net achieved the highest 2D-DSC values (p<0.001) and lowest 95HD values (p=0.044).

Conclusion

The new model (VUMix-Net) showed certain advantages in delineating the GTVs of EC. Additionally, it can generate the GTVs of EC that meet clinical requirements and have the same quality as human-generated contours. The system demonstrated the best performance for the ECs of the middle segment.

A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy

Background and purpose.Accurate volume delineation plays an essential role in radiotherapy. Contouring is a potential source of uncertainties in radiotherapy treatment planning that could affect treatment outcomes. Therefore, reducing the degree of contouring uncertainties is crucial. The role of utilized imaging modality in the organ delineation uncertainties has been investigated. This systematic review explores the influential factors on inter-and intra-observer uncertainties of target volume and organs at risk (OARs) delineation focusing on the used imaging modality for these uncertainties reduction and the reported subsequent histopathology and follow-up assessment.Methods and materials.An inclusive search strategy has been conducted to query the available online databases (Scopus, Google Scholar, PubMed, and Medline). 'Organ at risk', 'target', 'delineation', 'uncertainties', 'radiotherapy' and their relevant terms were utilized using every database searching syntax. Final article extraction was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Included studies were limited to the ones published in English between 1995 and 2020 and that just deal with computed tomography (CT) and magnetic resonance imaging (MRI) modalities.Results.A total of 923 studies were screened and 78 were included of which 31 related to the prostate 20 to the breast, 18 to the head and neck, and 9 to the brain tumor site. 98% of the extracted studies performed volumetric analysis. Only 24% of the publications reported the dose deviations resulted from variation in volume delineation Also, heterogeneity in studied populations and reported geometric and volumetric parameters were identified such that quantitative synthesis was not appropriate.Conclusion.This review highlightes the inter- and intra-observer variations that could lead to contouring uncertainties and impede tumor control in radiotherapy. For improving volume delineation and reducing inter-observer variability, the implementation of well structured training programs, homogeneity in following consensus and guidelines, reliable ground truth selection, and proper imaging modality utilization could be clinically beneficial.

Prostate Bed Delineation Guidelines for Postoperative Radiation Therapy: On Behalf Of The Francophone Group of Urological Radiation Therapy

PURPOSE

Prostate bed (PB) irradiation is considered the standard postoperative treatment after radical prostatectomy (RP) for tumors with high-risk features or persistent prostate-specific antigen, or for salvage treatment in case of biological relapse. Four consensus guidelines have been published to standardize practices and reduce the interobserver variability in PB delineation but with discordant recommendations. To improve the reproducibility in the PB delineation, the Francophone Group of Urological Radiotherapy (Groupe Francophone de Radiothérapie Urologique [GFRU]) worked to propose a new and more reproducible consensus guideline for PB clinical target volume (CTV) definition.

METHODS AND MATERIALS

A 4-step procedure was used. First, a group of 10 GFRU prostate experts evaluated the 4 existing delineation guidelines for postoperative radiation therapy (European Organization for Research and Treatment of Cancer; the Faculty of Radiation Oncology Genito-Urinary Group; the Radiation Therapy Oncology Group; and the Princess Margaret Hospital) to identify divergent issues. Second, data sets of 50 magnetic resonance imaging studies (25 after RP and 25 with an intact prostate gland) were analyzed to identify the relevant anatomic boundaries of the PB. Third, a literature review of surgical, anatomic, histologic, and imaging data was performed to identify the relevant PB boundaries. Fourth, a final consensus on PB CTV definition was reached among experts.

RESULTS

Definitive limits of the PB CTV delineation were defined using easily visible landmarks on computed tomography scans (CT). The purpose was to ensure a better reproducibility of PB definition for any radiation oncologist even without experience in postoperative radiation therapy.

CONCLUSIONS

New recommendations for PB delineation based on simple anatomic boundaries and available as a CT image atlas are proposed by the GFRU. Improvement in uniformity in PB CTV definition and treatment homogeneity in the context of clinical trials are expected.

Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks

Background

This study aims to construct and validate a model based on convolutional neural networks (CNNs), which can fulfil the automatic segmentation of clinical target volumes (CTVs) of breast cancer for radiotherapy.

Methods

In this work, computed tomography (CT) scans of 110 patients who underwent modified radical mastectomies were collected. The CTV contours were confirmed by two experienced oncologists. A novel CNN was constructed to automatically delineate the CTV. Quantitative evaluation metrics were calculated, and a clinical evaluation was conducted to evaluate the performance of our model.

Results

The mean Dice similarity coefficient (DSC) of the proposed model was 0.90, and the 95th percentile Hausdorff distance (95HD) was 5.65 mm. The evaluation results of the two clinicians showed that 99.3% of the chest wall CTV slices could be accepted by clinician A, and this number was 98.9% for clinician B. In addition, 9/10 of patients had all slices accepted by clinician A, while 7/10 could be accepted by clinician B. The score differences between the AI (artificial intelligence) group and the GT (ground truth) group showed no statistically significant difference for either clinician. However, the score differences in the AI group were significantly different between the two clinicians. The Kappa consistency index was 0.259. It took 3.45 s to delineate the chest wall CTV using the model.

Conclusion

Our model could automatically generate the CTVs for breast cancer. AI-generated structures of the proposed model showed a trend that was comparable, or was even better, than those of human-generated structures. Additional multicentre evaluations should be performed for adequate validation before the model can be completely applied in clinical practice.

Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

Imaging for Target Delineation and Treatment Planning in Radiation Oncology: Current and Emerging Techniques

Imaging in radiation oncology has a wide range of applications. It is necessary not only for tumor staging and treatment response assessment after therapy but also for the treatment planning process, including definition of target and organs at risk, as well as treatment plan calculation. This article provides a comprehensive overview of the main imaging modalities currently used for target delineation and treatment planning and gives insight into new and promising techniques.

Does magnetic resonance imaging improve soft tissue sarcoma contouring for radiotherapy?

Objective:

Soft tissue sarcomas (STS) are a rare, heterogeneous tumour group. Radiotherapy improves local control. CT is used to plan radiotherapy, but has poor soft tissue definition. MRI has superior soft tissue definition. Contour variation amongst oncologists is an important factor in treatment failure. This study is the first to directly compare STS tumour contouring using CT vs MRI.

Methods:

Planning CT and T₂ weighted MR images of eight patients with STS were distributed to four oncologists. Gross tumour volume was contoured on both imaging modalities using in-house software. Images were recontoured 6 weeks later. The mean distance to agreement (DTA), standard deviation of the DTA, dice similarity coefficient (DSC) and contour volume were calculated for each oncologist and compared to a median contour volume. Results for CT and MRI were compared using a pairwise Student's t-test.

Results:

When comparing MRI to CT, tumour volumes were significantly smaller, with a difference of 21.4 cm³ across all patients (p = 0.008). There was not a statistically significant difference in the mean distance to agreement or dice similarity coefficient, but the standard deviation of the DTA showed a statistically significant improvement ( p = 0.04). For intraobserver variation, there was no statistically significant improvement using MRI vs CT.

Conclusion:

Oncologists contour smaller tumour volumes using MRI, with reduced interobserver variation. Improving the reliability and consistency of contouring is needed for improved quality assurance.

Advances in knowledge:

With further experience, the use of MRI in STS radiotherapy planning may reduce variation between oncologists and contribute to improved local control and reduced treatment toxicities.

Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning

PURPOSE

To train and evaluate a very deep dilated residual network (DD-ResNet) for fast and consistent auto-segmentation of the clinical target volume (CTV) for breast cancer (BC) radiotherapy with big data.

METHODS

DD-ResNet was an end-to-end model enabling fast training and testing. We used big data comprising 800 patients who underwent breast-conserving therapy for evaluation. The CTV were validated by experienced radiation oncologists. We performed a fivefold cross-validation to test the performance of the model. The segmentation accuracy was quantified by the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). The performance of the proposed model was evaluated against two different deep learning models: deep dilated convolutional neural network (DDCNN) and deep deconvolutional neural network (DDNN).

RESULTS

Mean DSC values of DD-ResNet (0.91 and 0.91) were higher than the other two networks (DDCNN: 0.85 and 0.85; DDNN: 0.88 and 0.87) for both right-sided and left-sided BC. It also has smaller mean HD values of 10.5 mm and 10.7 mm compared with DDCNN (15.1 mm and 15.6 mm) and DDNN (13.5 mm and 14.1 mm). Mean segmentation time was 4 s, 21 s and 15 s per patient with DDCNN, DDNN and DD-ResNet, respectively. The DD-ResNet was also superior with regard to results in the literature.

CONCLUSIONS

The proposed method could segment the CTV accurately with acceptable time consumption. It was invariant to the body size and shape of patients and could improve the consistency of target delineation and streamline radiotherapy workflows.

The influence of the image registration method on the adaptive radiotherapy. A proof of the principle in a selected case of prostate IMRT

PURPOSE

To analyse the influence of the image registration method on the adaptive radiotherapy of an IMRT prostate treatment, and to compare the dose accumulation according to 3 different image registration methods with the planned dose.

MATERIAL AND METHODS

The IMRT prostate patient was CT imaged 3 times throughout his treatment. The prostate, PTV, rectum and bladder were segmented on each CT. A Rigid, a deformable (DIR) B-spline and a DIR with landmarks registration algorithms were employed. The difference between the accumulated doses and planned doses were evaluated by the gamma index. The Dice coefficient and Hausdorff distance was used to evaluate the overlap between volumes, to quantify the quality of the registration.

RESULTS

When comparing adaptive vs no adaptive RT, the gamma index calculation showed large differences depending on the image registration method (as much as 87.6% in the case of DIR B-spline). The quality of the registration was evaluated using an index such as the Dice coefficient. This showed that the best result was obtained with DIR with landmarks compared with the rest and it was always above 0.77, reported as a recommended minimum value for prostate studies in a multi-centre review.

CONCLUSIONS

Apart from showing the importance of the application of an adaptive RT protocol in a particular treatment, this work shows that the election of the registration method is decisive in the result of the adaptive radiotherapy and dose accumulation.

MRI Reduces Variation of Contouring for Boost Clinical Target Volume in Breast Cancer Patients Without Surgical Clips in the Tumour Bed

BACKGROUND

Omitting the placement of clips inside tumour bed during breast cancer surgery poses a challenge for delineation of lumpectomy cavity clinical target volume (CTVLC). We aimed to quantify inter-observer variation and accuracy for CT- and MRI-based segmentation of CTVLC in patients without clips.

PATIENTS AND METHODS

CT- and MRI-simulator images of 12 breast cancer patients, treated by breast conserving surgery and radiotherapy, were included in this study. Five radiation oncologists recorded the cavity visualization score (CVS) and delineated CTVLC on both modalities. Expert-consensus (EC) contours were delineated by a senior radiation oncologist, respecting opinions of all observers. Inter-observer volumetric variation and generalized conformity index (CIgen) were calculated. Deviations from EC contour were quantified by the accuracy index (AI) and inter-delineation distances (IDD).

RESULTS

Mean CVS was 3.88 +/- 0.99 and 3.05 +/- 1.07 for MRI and CT, respectively (p = 0.001). Mean volumes of CTVLC were similar: 154 +/- 26 cm3 on CT and 152 +/- 19 cm3 on MRI. Mean CIgen and AI were superior for MRI when compared with CT (CIgen: 0.74 +/- 0.07 vs. 0.67 +/- 0.12, p = 0.007; AI: 0.81 +/- 0.04 vs. 0.76 +/- 0.07; p = 0.004). CIgen and AI increased with increasing CVS. Mean IDD was 3 mm +/- 1.5 mm and 3.6 mm +/- 2.3 mm for MRI and CT, respectively (p = 0.017).

CONCLUSIONS

When compared with CT, MRI improved visualization of post-lumpectomy changes, reduced interobserver variation and improved the accuracy of CTVLC contouring in patients without clips in the tumour bed. Further studies with bigger sample sizes are needed to confirm our findings.

Target volume definition for post prostatectomy radiotherapy: Do the consensus guidelines correctly define the inferior border of the CTV?

AIM

We compare urethrogram delineation of the caudal aspect of the anastomosis to the recommended guidelines of post prostatectomy radiotherapy.

BACKGROUND

Level one evidence has established the indications for, and importance of, adjuvant radiotherapy following radical prostatectomy. Several guidelines have recently addressed delineation of the prostate bed target volume including identification of the vesico-urethral anastomosis, taken as the first CT slice caudal to visible urine in the bladder neck. The inferior border of clinical target volume is then variably defined 5-12 mm below this anastomosis or 15 mm cranial to the penile bulb.

METHODS AND MATERIALS

Thirty-three patients who received adjuvant radiotherapy following radical prostatectomy were reviewed. All underwent planning CT with urethrogram. The authors (MM, JC) independently identified the CT slice caudal to the last slice showing urine in the bladder neck (called the CT Reference Slice), and measured the distance between this and the tip of the urethrogram cone. Five patients also had a diagnostic MRI at the time of CT planning to better visualize the anatomy.

RESULTS

Sixty-six readings were obtained. The mean distance between the Bladder CT Reference Slice and the most cranial urethrogram contrast slice was 16.1 mm (MM 16.4 mm, JC 15.8 mm), range: 6.8-34.2 mm. The mean distance between the urethrogram tip and the ischial tuberosities was 19.9 mm (range 12.5-29.8 mm). The mean distance between the CT Reference Slice and the ischial tuberosities was 36.9 mm (range 28.3-52.4 mm).

CONCLUSIONS

Guidelines for prostate bed radiation post prostatectomy have been developed after publication of the trials proving benefit of such treatment, and are thus untested. The anastomosis is a frequent site of local relapse but is variably defined by the existing guidelines, none of which take into account anatomic patient variation and all of which are at variance with urethrogram data. We recommend the use of planning urethrogram to better delineate the vesico-urethral junction and minimize the potential for geographic misses.

Impact of 18F-Fluciclovine PET on Target Volume Definition for Postprostatectomy Salvage Radiotherapy: Initial Findings from a Randomized Trial

The purpose of this study was to evaluate the role of the synthetic amino acid PET radiotracer ¹⁸F-fluciclovine in modifying the defined clinical and treatment-planning target volumes in postprostatectomy patients undergoing salvage radiotherapy and to evaluate the resulting dosimetric consequences to surrounding organs at risk. Methods: Ninety-six patients were enrolled in a randomized, prospective intention-to-treat clinical trial for potential salvage radiotherapy for recurrent prostate cancer after prostatectomy. The initial treatment plan was based on the results from conventional abdominopelvic CT and MRI. The 45 patients in the experimental arm also underwent abdominopelvic ¹⁸F-fluciclovine PET/CT, and the images were registered with the conventional images to determine whether the results would modify the initial treatment plan. The 51 patients in the control arm did not undergo ¹⁸F-fluciclovine PET/CT. For each patient, the clinical and treatment-planning target volumes that would have been treated before ¹⁸F-fluciclovine registration were compared with those after registration. For organs at risk (rectum, bladder, and penile bulb), the volumes receiving 40 Gy and 65 Gy before registration were compared with those after registration. Statistical comparisons were made using the paired t test. Acute genitourinary and gastrointestinal toxicity as defined by the Radiation Therapy Oncology Group was compared between the control and experimental arms using the χ² test. Results: In 24 cases, radiotherapy was planned to a clinical target volume consisting of the prostate bed alone (CTV) (64.8-66.6 Gy). In 21 cases, radiotherapy was planned to a clinical target volume consisting of the pelvis (CTV1) (45.0 Gy) followed by a boost to the prostate bed (CTV2) (19.8-25.2 Gy). In each case, the respective treatment-planning target volume expansion (PTV, PTV1, or PTV2) was 0.8 cm (0.6 cm posterior). With the exception of PTV2, all postregistration volumes were significantly larger than the corresponding preregistration volumes. Analysis of the rectum, bladder, and penile bulb volumes receiving 40 Gy and 60 Gy demonstrated that only the penile bulb volumes were significantly higher after registration. No significant differences in acute genitourinary or gastrointestinal toxicity were observed. Conclusion: Including information from ¹⁸F-fluciclovine PET in the treatment-planning process led to significant differences in the defined target volume, with higher doses to the penile bulb but no significant differences in rectal or bladder dose or in acute genitourinary or gastrointestinal toxicity. Longer follow-up is needed to determine the impact of ¹⁸F-fluciclovine PET on cancer control and late toxicity endpoints.

Uncertainties in target volume delineation in radiotherapy - are they relevant and what can we do about them?

Background

Modern radiotherapy techniques enable delivery of high doses to the target volume without escalating dose to organs at risk, offering the possibility of better local control while preserving good quality of life. Uncertainties in target volume delineation have been demonstrated for most tumour sites, and various studies indicate that inconsistencies in target volume delineation may be larger than errors in all other steps of the treatment planning and delivery process. The aim of this paper is to summarize the degree of delineation uncertainties for different tumour sites reported in the literature and review the effect of strategies to minimize them.

Conclusions

Our review confirmed that interobserver variability in target volume contouring represents the largest uncertainty in the process for most tumour sites, potentially resulting in a systematic error in dose delivery, which could influence local control in individual patients. For most tumour sites the optimal combination of imaging modalities for target delineation still needs to be determined. Strict use of delineation guidelines and protocols is advisable both in every day clinical practice and in clinical studies to diminish interobserver variability. Continuing medical education of radiation oncologists cannot be overemphasized, intensive formal training on interpretation of sectional imaging should be included in the program for radiation oncology residents.

A feasibility study evaluating the relationship between dose and focal liver reaction in stereotactic ablative radiotherapy for liver cancer based on intensity change of Gd-EOB-DTPA-enhanced magnetic resonance images

PURPOSE

In order to evaluate the relationship between the dose to the liver parenchyma and focal liver reaction (FLR) after stereotactic ablative body radiotherapy (SABR), we suggest a novel method using a three-dimensional dose distribution and change in signal intensity of gadoxetate disodium-gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) hepatobiliary phase images.

MATERIALS AND METHODS

In our method, change of the signal intensity between the pretreatment and follow-up hepatobiliary phase images of Gd-EOB-DTPA-enhanced MRI was calculated and then threshold dose (TD) for developing FLR was obtained from correlation of dose with the change of the signal intensity. For validation of the method, TDs for six patients, who had been treated for liver cancer with SABR with 45-60 Gy in 3 fractions, were calculated using the method, and we evaluated concordance between volume enclosed by isodose of TD by the method and volume identified as FLR by a physician.

RESULTS

The dose to normal liver was correlated with change in signal intensity between pretreatment and follow-up MRI with a median R(2) of 0.935 (range, 0.748 to 0.985). The median TD by the method was 23.5 Gy (range, 18.3 to 39.4 Gy). The median value of concordance was 84.5% (range, 44.7% to 95.9%).

CONCLUSION

Our method is capable of providing a quantitative evaluation of the relationship between dose and intensity changes on follow-up MRI, as well as determining individual TD for developing FLR. We expect our method to provide better information about the individual relationship between dose and FLR in radiotherapy for liver cancer.