J Ford

Apparent diffusion coefficient (ADC) change on repeated diffusion-weighted magnetic resonance imaging during radiochemotherapy for non-small cell lung cancer: A pilot study

OBJECTIVES Serial diffusion-weighted magnetic resonance imaging (DW-MRI) during radiochemotherapy of non-small cell lung cancer (NSCLC) is analyzed to investigate the apparent diffusion coefficient (ADC) as a potential biomarker for tumor response. METHODS Ten patients underwent DW-MRI prior to and at three and six weeks during radiochemotherapy. Three methods of contouring primary tumors (PT) were performed to evaluate the impact of tumor heterogeneity on ADC values: PTT: whole tumor volume; PTT-N: PTT-necrosis; PTL: small volume of presumed active tumor with low ADC value. Pretreatment and during-treatment absolute ADC values and ADC value changes were analyzed for PT and involved lymph nodes (LN). RESULTS ADC values for PTT, PTT-N, PTL and LN increased by 8-14% (PT) and 15% (LN) at three weeks, and 19-26% and 23% at 6 weeks post initial treatment (p=0.04-0.002). Average percent ADC value increase was smaller than tumor volume regression (p=0.06-0.0005). Patients with overall survival <12 months had a lower increase of ADC values compared to longer surviving patients (p=0.008 for PTT). CONCLUSIONS Significant ADC value increases during radiochemotherapy for non-small cell lung cancer were observed. ADC value change during treatment appears to be an independent marker of patient outcome and warrants further investigation.

Estimation of optimal b-value sets for obtaining apparent diffusion coefficient free from perfusion in non-small cell lung cancer

The purpose of this study was to determine optimal sets of b-values in diffusion-weighted MRI (DW-MRI) for obtaining monoexponential apparent diffusion coefficient (ADC) close to perfusion-insensitive intravoxel incoherent motion (IVIM) model ADC (ADCIVIM) in non-small cell lung cancer. Ten subjects had 40 DW-MRI scans before and during radiotherapy in a 1.5 T MRI scanner. Respiratory triggering was applied to the echo-planar DW-MRI with TR ≈ 4500 ms, TE  =  74 ms, eight b-values of 0-1000 μs μm(-2), pixel size  =  1.98 × 1.98 mm(2), slice thickness  =  6 mm, interslice gap  =  1.2 mm, 7 axial slices and total acquisition time ≈6 min. One or more DW-MRI scans together covered the whole tumour volume. Monoexponential model ADC values using various b-value sets were compared to reference-standard ADCIVIM values using all eight b-values. Intra-scan coefficient of variation (CV) of active tumour volumes was computed to compare the relative noise in ADC maps. ADC values for one pre-treatment DW-MRI scan of each of the 10 subjects were computed using b-value pairs from DW-MRI images synthesized for b-values of 0-2000 μs μm(-2) from the estimated IVIM parametric maps and corrupted by various Rician noise levels. The square root of mean of squared error percentage (RMSE) of the ADC value relative to the corresponding ADCIVIM for the tumour volume of the scan was computed. Monoexponential ADC values for the b-value sets of 250 and 1000; 250, 500 and 1000; 250, 650 and 1000; 250, 800 and 1000; and 250-1000 μs μm(-2) were not significantly different from ADCIVIM values (p > 0.05, paired t-test). Mean error in ADC values for these sets relative to ADCIVIM were within 3.5%. Intra-scan CVs for these sets were comparable to that for ADCIVIM. The monoexponential ADC values for other sets-0-1000; 50-1000; 100-1000; 500-1000; and 250 and 800 μs μm(-2) were significantly different from the ADCIVIM values. From Rician noise simulation using b-value pairs, there was a wide range of acceptable b-value pairs giving small RMSE of ADC values relative to ADCIVIM. The pairs for small RMSE had lower b-values as the noise level increased. ADC values of a two b-value set-250 and 1000 μs μm(-2), and all three b-value sets with 250, 1000 μs μm(-2) and an intermediate value approached ADCIVIM, with relative noise comparable to that of ADCIVIM. These sets may be used in lung tumours using comparatively short scan and post-processing times. Rician noise simulation suggested that the b-values in the vicinity of these experimental best b-values can be used with error within an acceptable limit. It also suggested that the optimal sets will have lower b-values as the noise level becomes higher.

Daily Tracking of Glioblastoma Resection Cavity, Cerebral Edema, and Tumor Volume with MRI-Guided Radiation Therapy

Radiation therapy (RT) plays a critical role in the treatment of glioblastoma. Studies of brain imaging during RT for glioblastoma have demonstrated changes in the brain during RT. However, frequent or daily utilization of standalone magnetic resonance imaging (MRI) scans during RT have limited feasibility. The recent release of the tri-cobalt-60 MRI-guided RT (MR-IGRT) device (ViewRay MRIdian, Cleveland, OH) allows for daily brain MRI for the RT setup. Daily MRI of three postoperative patients undergoing RT and temozolomide for glioblastoma over a six-week course allowed for the identification of changes to the cavity, edema, and visible tumor on a daily basis. The volumes and dimensions of the resection cavities, edema, and T2-hyperintense tumor were measured. A general trend of daily decreases in cavity measurements was observed in all patients. For the one patient with edema, a trend of daily increases followed by a trend of daily decreases were observed. These results suggest that daily MRI could be used for onboard resimulation and adaptive RT for future fluctuations in the sizes of brain tumors, cavities, or cystic components. This could improve tumor targeting and reduce RT of healthy brain tissue.

SU-F-R-35: Repeatability of Texture Features in T1- and T2-Weighted MR Images

PURPOSE To evaluate repeatability of lung tumor texture features from inspiration/expiration MR image pairs for potential use in patient specific care models and applications. Repeatability is a desirable and necessary characteristic of features included in such models. METHODS T1-weighted Volumetric Interpolation Breath-Hold Examination (VIBE) and/or T2-weighted MRI scans were acquired for 15 patients with non-small cell lung cancer before and during radiotherapy for a total of 32 and 34 same session inspiration-expiration breath-hold image pairs respectively. Bias correction was applied to the VIBE (VIBE_BC) and T2-weighted (T2_BC) images. Fifty-nine texture features at five wavelet decomposition ratios were extracted from the delineated primary tumor including: histogram(HIST), gray level co-occurrence matrix(GLCM), gray level run length matrix(GLRLM), gray level size zone matrix(GLSZM), and neighborhood gray tone different matrix (NGTDM) based features. Repeatability of the texture features for VIBE, VIBE_BC, T2-weighted, and T2_BC image pairs was evaluated by the concordance correlation coefficient (CCC) between corresponding image pairs, with a value greater than 0.90 indicating repeatability. RESULTS For the VIBE image pairs, the percentage of repeatable texture features by wavelet ratio was between 20% and 24% of the 59 extracted features; the T2-weighted image pairs exhibited repeatability in the range of 44-49%. The percentage dropped to 10-20% for the VIBE_BC images, and 12-14% for the T2_BC images. In addition, five texture features were found to be repeatable in all four image sets including two GLRLM, two GLZSM, and one NGTDN features. No single texture feature category was repeatable among all three image types; however, certain categories performed more consistently on a per image type basis. CONCLUSION We identified repeatable texture features on T1- and T2-weighted MRI scans. These texture features should be further investigated for use in specific applications such as tissue classification and changes during radiation therapy utilizing a standard imaging protocol. Authors have the following disclosures: a research agreement with Philips Medical systems (Hugo, Weiss), a license agreement with Varian Medical Systems (Hugo, Weiss), research grants from the National Institute of Health (Hugo, Weiss), UpToDate royalties (Weiss), and none(Mahon, Ford, Karki). Authors have no potential conflicts of interest to disclose.

SU-F-R-57: Validation of Quantitative Radiomic Texture Features for Oncologic MRI: A Simulation Study

PURPOSE Radiomic texture features extracted from diverse MRI modalities have been investigated regarding their predictive and prognostic values in a variety of cancers. However, their validity has not yet been fully assessed. With the aid of a digital MRI phantom, the objective of this study was to examine the validity and reliability of MRI texture metrics. METHODS MR signal of the employed digital phantom was simulated in a multiple coil setting with realistic acquisition noise. Three iterative algorithms based respectively on conjugate gradient (CG), total variation (TV), and wavelet regularization (WL) were used for image reconstruction. For each algorithm, 80 independent simulations were carried out with different levels of noise perturbation. 22 features related to grey-level co-occurrence matrices (GLCOM), zone size matrices (GLZSM), and neighborhood difference matrices (GLNDM) were evaluated for each resultant image within two ROIs featuring heterogeneous patterns of signal intensity on the ground truth image. Texture features extracted from these simulated images were compared to those from the ground truth image, and differences were identified. RESULTS In comparison to the ground truth data, texture features appearing on images reconstructed with CG, TV, and WL from signal with no noise perturbation varied with a range of 0.18-3.3×104 %, 0.57-3.6×104 %, and 0.84-3.5×104 %, respectively while varying more significantly for noisy data with largest variation of 2.6×105 % for CG, 2.2×105 % for TV, and 2.8×105 % for WL. Texture differences between ROIs also revealed considerable extent of variation from those on the ground truth image with a range of 1.91-4.9×104 % for CG-based data, 27.50-3.0×104 % for TV-based, and 11.99-3.2×104 % for WL-based. CONCLUSION Variability of texture appearance on MRI with respect to the choice of reconstruction algorithm and noise level is significant and feature-dependent. Certain texture features may be preserved by MR imaging; however adequate precautions need to be taken on their validity and reliability.

Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy

In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. “Radiomics”, as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.

Magnetic Resonance-guided External Beam Radiation and Brachytherapy for a Patient with Intact Cervical Cancer

Radiation treatment verification has improved significantly over the past decades. The field has moved from film X-rays and skin marks to fiducial tracking and daily cone beam computed tomography (CBCT) for tumor localization. We now have the ability to perform daily on-board magnetic resonance imaging (MRI), which provides superior soft tissue contrast compared to computed tomography (CT). In the management of cervical cancer, the brachytherapy literature has demonstrated that MRI allows for better delineation of the high-risk clinical target volume (HR-CTV) and the use of MRI-guided brachytherapy has translated into improved treatment outcomes. Consensus contouring guidelines for intensity modulated radiation therapy (IMRT) for cervical cancer advise including the whole uterus in the target volume and adding large planning target volume (PTV) margins to account for inter-fractional uterine motion and target motion resulting from variable rectal and bladder filling. MRI-guided radiation therapy (MRgRT) systems enable the possibility to precisely delineate the target volume on a daily basis and to perform truly adaptive delivery. This advancement in technology provides the opportunity to explore how external beam treatment volumes could be safely reduced for better sparing of pelvic organs for the benefit of our patients with cervical cancer. We describe the MR-guided definitive external beam radiation therapy and brachytherapy for a 32-year-old woman with intact cervical cancer. We contoured the uterus, bladder, rectum, and gross tumor volume (GTV) on each of her 25 set-up MRIs. We demonstrate a steady reduction in the GTV and increased displacement of the uterus and GTV as the GTV decreased in size. The findings presented suggest that cervical cancer could greatly benefit from an adaptive MRgRT approach.

Analysis of Magnetic Resonance Image Signal Fluctuations Acquired During MR-Guided Radiotherapy

Magnetic resonance-guided radiotherapy (MRgRT) is a new and evolving treatment modality that allows unprecedented visualization of the tumor and surrounding anatomy. MRgRT includes daily 3D magnetic resonance imaging (MRI) for setup and rapidly repeated near real-time MRI scans during treatment for target tracking. One of the more exciting potential benefits of MRgRT is the ability to analyze serial MRIs to monitor treatment response or predict outcomes. A typical radiation treatment (RT) over the span of 10-15 minutes on the MRIdian system (ViewRay, Cleveland, OH) yields thousands of “cine” images, each acquired in 250 ms. This unique data allows for a glimpse in image intensity changes during RT delivery. In this report, we analyze cine images from a single fraction RT of a glioblastoma patient on the ViewRay platform in order to characterize the dynamic signal changes occurring during RT therapy. The individual frames in the cines were saved into DICOM format and read into an MIM image analysis platform (MIM Software, Cleveland, OH) as a time series. The three possible states of the three Cobalt-60 radiation sources—OFF, READY, and ON—were also recorded. An in-house Java plugin for MIM was created in order to perform principal component analysis (PCA) on each of the datasets. The analysis resulted in first PC, related to monotonous signal increase over the course of the treatment fraction. We found several distortion patterns in the data that we postulate result from the perturbation of the magnetic field due to the moving metal parts in the platform while treatment was being administered. The largest variations were detected when all Cobalt-60 sources were OFF. During this phase of the treatment, the gantry and multi-leaf collimators (MLCs) are moving. Conversely, when all Cobalt-60 sources were in the ON position, the image signal fluctuations were minimal, relating to very little mechanical motion. At this phase, the gantry, the MLCs, and sources are fixed in their positions. These findings were confirmed in a study with the daily quality assurance (QA) phantom. While the identified variations were not related to physiological processes, our findings confirm the sensitivity of the developed approach to identify very small fluctuations. Relating these variations to the physical changes that occur during treatment shows the methodical ability of the technique to uncover their underlying sources.

SU-D-207A-03: Potential Role of BOLD MRI in Discrimination of Aggressive Tumor Habitat in Prostate Cancer

PURPOSE To determine whether blood oxygenation level dependent (BOLD) MRI signal measured in prostate cancer patients, in addition to quantitative diffusion and perfusion parameters from multiparametric (mp)MRI exams, can help discriminate aggressive and/or radioresistant lesions. METHODS Several ongoing clinical trials in our institution require mpMRI exam to determine eligibility (presence of identifiable tumor lesion on mpMRI) and prostate volumes for dose escalation. Upon consent, patients undergo fiducial markers placement and a T2*-weighted imaging at the time of CT sim to facilitate the fusion. In a retrospective analysis eleven clinical trial patients were identified who had undergone mpMRI on GE 3T magnet, followed by T2*-weighted imaging (time-period mean±SD = 48±20 days) using a consistent protocol (gradient echo, TR/TE=30/11.8ms, flip angle=12, matrix=256×256×75, voxel size=1.25×1.25×2.5mm). ROIs for prostate tumor lesions were automatically determined using ADC threshold ≤1200 µm2/s. Although the MR protocol was not intended for BOLD analysis, we utilized the T2*-weighted signal normalized to that in nearby muscle; likewise, T2-weighted lesion signal was normalized to muscle, following rigid registration of the T2 to T2* images. The ratio of these normalized signals, T2*/T2, is a measure of BOLD effect in the prostate tumors. Perfusion parameters (Ktrans, ve, kep) were also calculated. RESULTS T2*/T2 (mean±SE) was found to be substantially lower for Gleason score (GS) 8&9 (0.82±0.04) compared to GS 7 (1.08±0.07). A k-means cluster analysis of T2*/T2 versus kep = Ktrans/ve revealed two distinct clusters, one with higher T2*/T2 and lower kep, containing only GS 7 lesions, and another with lower T2*/T2 and higher kep, associated with tumor aggressiveness. This latter cluster contained all GS 8&9 lesions, as well as some GS 7. CONCLUSION BOLD MRI, in addition to ADC and kep, may play a role (perhaps orthogonal to Gleason score) in identifying prostate lesions that would benefit from more aggressive radiotherapy.

SU-F-J-84: Comparison of Quantitative Deformable Image Registration Evaluation Tools: Application to Prostate IGART

PURPOSE Errors in displacement vector fields (DVFs) generated by Deformable Image Registration (DIR) algorithms can give rise to significant uncertainties in contour propagation and dose accumulation in Image-Guided Adaptive Radiotherapy (IGART). The purpose of this work is to assess the accuracy of two DIR algorithms using a variety of quality metrics for prostate IGART. METHODS Pelvic CT images were selected from an anonymized database of nineteen prostate patients who underwent 8-12 serial scans during radiotherapy. Prostate, bladder, and rectum were contoured on 34 image-sets for three patients by the same physician. The planning CT was deformably-registered to daily CT using three variants of the Small deformation Inverse Consistent Linear Elastic (SICLE) algorithm: Grayscale-driven (G), Contour-driven (C, which utilizes segmented structures to drive DIR), combined (G+C); and also grayscale ITK demons (Gd). The accuracy of G, C, G+C SICLE and Gd registrations were evaluated using a new metric Edge Gradient Distance to Agreement (EGDTA) and other commonly-used metrics such as Pearson Correlation Coefficient (PCC), Dice Similarity Index (DSI) and Hausdorff Distance (HD). RESULTS C and G+C demonstrated much better performance at organ boundaries, revealing the lowest HD and highest DSI, in prostate, bladder and rectum. G+C demonstrated the lowest mean EGDTA (1.14 mm), which corresponds to highest registration quality, compared to G and C DVFs (1.16 and 2.34 mm). However, demons DIR showed the best overall performance, revealing lowest EGDTA (0.73 mm) and highest PCC (0.85). CONCLUSION As expected, both C- and C+G SICLE more accurately reproduce manually-contoured target datasets than G-SICLE or Gd using HD and DSI metrics. In general, the Gd appears to have difficulty reproducing large daily position and shape changes in the rectum and bladder. However, Gd outperforms SICLE in terms of EGDTA and PCC metrics, possibly at the expense of topological quality of the estimated DVFs.