Initial experience with an electron FLASH research extension (FLEX) for the Clinac system.

PURPOSE: Radiotherapy delivered at ultra-high-dose-rates (≥40 Gy/s), that is, FLASH, has the potential to effectively widen the therapeutic window and considerably improve the care of cancer patients. The underlying mechanism of the FLASH effect is not well understood, and commercial systems capable of delivering such dose rates are scarce. The purpose of this study was to perform the initial acceptance and commissioning tests of an electron FLASH research product for preclinical studies. METHODS: A linear accelerator (Clinac 23EX) was modified to include a non-clinical FLASH research extension (the Clinac-FLEX system) by Varian, a Siemens Healthineers company (Palo Alto, CA) capable of delivering a 16 MeV electron beam with FLASH and conventional dose rates. The acceptance, commissioning, and dosimetric characterization of the FLEX system was performed using radiochromic film, optically stimulated luminescent dosimeters, and a plane-parallel ionization chamber. A radiation survey was conducted for which the shielding of the pre-existing vault was deemed sufficient. RESULTS: The Clinac-FLEX system is capable of delivering a 16 MeV electron FLASH beam of approximately 1 Gy/pulse at isocenter and reached a maximum dose rate >3.8 Gy/pulse near the upper accessory mount on the linac gantry. The percent depth dose curves of the 16 MeV FLASH and conventional modes for the 10 × 10 cm2 applicator agreed within 0.5 mm at a range of 50% of the maximum dose. Their respective profiles agreed well in terms of flatness but deviated for field sizes >10 × 10 cm2 . The output stability of the FLASH system exhibited a dose deviation of <1%. Preliminary cell studies showed that the FLASH dose rate (180 Gy/s) had much less impact on the cell morphology of 76N breast normal cells compared to the non-FLASH dose rate (18 Gy/s), which induced large-size cells. CONCLUSION: Our studies characterized the non-clinical Clinac-FLEX system as a viable solution to conduct FLASH research that could substantially increase access to ultra-high-dose-rate capabilities for scientists.

Technical Note: Using virtual noncontrast images from dual-energy CT to eliminate the need of precontrast CT for x-ray radiation treatment planning of abdominal tumors†.

PURPOSE: Radiation therapy (RT) planning frequently utilizes contrast-enhanced CT. However, dose calculations should not be performed on a contrast-enhanced CT because the patient will not receive bolus during treatment. It is typical to acquire CT twice during RT simulation: once before injection of bolus and once after. The registration between these datasets introduces errors. In this work, we investigate the use of virtual noncontrast images (VNC) derived from dual-energy CT (DECT) to eliminate the precontrast CT and the registration error. METHODS: CT datasets, including conventional 120 kVp pre- and postcontrast CTs and postcontrast DECT, acquired for ten pancreatic cancer patients were evaluated. The DECTs were acquired simultaneously using a dual source (DS) CT simulator. VNC and virtual mono-energetic images (VMI) were derived from DECTs. Gross tumor volumes (GTV), planning target volumes (PTV), and organs at risks (OAR) were delineated on the postcontrast CT and then populated to the precontrast CT and the VNC. An IMRT plan (50.4 Gy in 28 fractions) was then optimized on the precontrast CT. Dose distributions were recalculated on the VNC images. Contours from the pre- and postcontrast CTs and the dose distributions based on both were compared. RESULTS: On average, the distance of centroids of the populated duodenum contours on precontrast CT differed by 6.0 ± 4.0 mm from those on postcontrast CTs. The dose distributions on the precontrast CT and VNC were almost identical. The PTV mean and maximum doses differed by 0.1% and 0.2% between the two plans, respectively. CONCLUSION: The VNC derived from DECT can be used to replace the conventional precontrast CT scan for RT planning, eliminating the need for an additional precontrast CT scan and eliminating the registration errors. Thus, VNC can become an important asset to the future of RT.

Improving Structure Delineation for Radiation Therapy Planning Using Dual-Energy CT.

PURPOSE: We present the advantages of using dual-energy CT (DECT) for radiation therapy (RT) planning based on our clinical experience. METHODS: DECT data acquired for 20 representative patients of different tumor sites and/or clinical situations with dual-source simultaneous scanning (Drive, Siemens) and single-source sequential scanning (Definition, Siemens) using 80 and 140-kVp X-ray beams were analyzed. The data were used to derive iodine maps, fat maps, and mono-energetic images (MEIs) from 40 to 190 keV to exploit the energy dependence of X-ray attenuation. The advantages of using these DECT-derived images for RT planning were investigated. RESULTS: When comparing 40 keV MEIs to conventional 120-kVp CT, soft tissue contrast between the duodenum and pancreatic head was enhanced by a factor of 2.8. For a cholangiocarcinoma patient, contrast between tumor and surrounding tissue was increased by 96 HU and contrast-to-noise ratio was increased by up to 60% for 40 keV MEIs compared to conventional CT. Simultaneous dual-source DECT also preserved spatial resolution in comparison to sequential DECT as evidenced by the identification of vasculature in a pancreas patient. Volume of artifacts for five patients with titanium implants was reduced by over 95% for 190 keV MEIs compared to 120-kVp CT images. A 367-cm3 region of photon starvation was identified by low CT numbers in the soft tissue of a mantle patient in a conventional CT scan but was eliminated in a 190 keV MEI. Fat maps enhanced image contrast as demonstrated by a meningioma patient. CONCLUSION: The use of DECT for RT simulation offers clinically meaningful advantages through improved simulation workflow and enhanced structure delineation for RT planning.

Auto-segmentation of pancreatic tumor in multi-parametric MRI using deep convolutional neural networks.

PURPOSE: The recently introduced MR-Linac enables MRI-guided Online Adaptive Radiation Therapy (MRgOART) of pancreatic cancer, for which fast and accurate segmentation of the gross tumor volume (GTV) is essential. This work aims to develop an algorithm allowing automatic segmentation of the pancreatic GTV based on multi-parametric MRI using deep neural networks. METHODS: We employed a square-window based convolutional neural network (CNN) architecture with three convolutional layer blocks. The model was trained using about 245,000 normal and 230,000 tumor patches extracted from 37 DCE MRI sets acquired in 27 patients with data augmentation. These images were bias corrected, intensity standardized, and resampled to a fixed voxel size of 1 × 1 × 3 mm3. The trained model was tested on 19 DCE MRI sets from another 13 patients, and the model-generated GTVs were compared with the manually segmented GTVs by experienced radiologist and radiation oncologists based on Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and Mean Surface Distance (MSD). RESULTS: The mean values and standard deviations of the performance metrics on the test set were DSC = 0.73 ± 0.09, HD = 8.11 ± 4.09 mm, and MSD = 1.82 ± 0.84 mm. The interobserver variations were estimated to be DSC = 0.71 ± 0.08, HD = 7.36 ± 2.72 mm, and MSD = 1.78 ± 0.66 mm, which had no significant difference with model performance at p values of 0.6, 0.52, and 0.88, respectively. CONCLUSION: We developed a CNN-based model for auto-segmentation of pancreatic GTV in multi-parametric MRI. Model performance was comparable to expert radiation oncologists. This model provides a framework to incorporate multimodality images and daily MRI for GTV auto-segmentation in MRgOART.

Estimation of changing gross tumor volume from longitudinal CTs during radiation therapy delivery based on a texture analysis with classifier algorithms: a proof-of-concept study.

BACKGROUND: Adaptive radiation therapy (ART) is moving into the clinic rapidly. Capability of delineating the tumor change as a result of treatment response during treatment delivery is essential for ART. During image-guided radiation therapy (IGRT), a CT or cone-beam CT is taken at the time of daily setup and the tumor is not visible by eye in regions of soft tissue due to low contrast. The scope of this paper is to develop a method using a classifier trained on non-contrast CT textures, to estimate the gross tumor volume (GTV) of the day (GTVd) from daily (longitudinal) CTs acquired during the course of IGRT when the tumor is not visible. METHODS: CT textures from daily diagnostic-quality CTs routinely acquired during IGRT using an in-room CT were analyzed. Pretreatment GTV was delineated from pre-RT diagnostic images and populated to the first daily CT. Maps of first-order textures (mean, SD, entropy, skewness and kurtosis) and short-range second-order textures were created from the first daily CT. The classifier was trained to sort voxels into GTV and surrounding tissue on subsequent daily CTs over the course of RT. Optimum combinations of textures was defined by repeating the training process with all possible texture combinations. The trained classifier was used to identify voxels belonging to the GTVd, based on the CT of the day. Posttreatment GTV delineated from the post-RT follow-up images was populated to the last daily CT and used to validate the last GTVd delineated by the classifier. To demonstrate the concept, the method was described using three representative treatment sites, e.g., lung, breast and pancreatic tumors. RESULTS: Comparing the classifier map generated from a new CT to the initial training CT, the dice coefficient (DC) for GTV in lung is 83% on the eighth treatment and 84% on the last. The DC for the breast GTV is 56% mid-treatment and 65% at the last treatment. In the case of the pancreas with the least in organ tissue contrast, the DC for 4 cases ranges from 21% to 77% for the last treatment compared with the post-RT diagnostic CT. The Housdorff distance (HD) ranged from 2.9 to 5.9 mm with the mean GTV RECIST dimension of 22.75 mm long by 14.7 mm short. CONCLUSIONS: It is feasible to estimate the general region of the GTV of the day from the daily CT acquired during RT, based on CT textures, using a trained voxel classifier algorithm. The obtained GTV may be used as a starting point for an accurate GTV delineation in online adaptive replanning. Further study with larger patient datasets is required to improve the robustness of the algorithms.

A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer.

Changes of radiomic features over time in longitudinal images, delta radiomics, can potentially be used as a biomarker to predict treatment response. This study aims to develop a delta-radiomic process based on machine learning by (1) acquiring and registering longitudinal images, (2) segmenting and populating regions of interest (ROIs), (3) extracting radiomic features and calculating their changes (delta-radiomic features, DRFs), (4) reducing feature space and determining candidate DRFs showing treatment-induced changes, and (5) creating outcome prediction models using machine learning. This process was demonstrated by retrospectively analyzing daily non-contrast CTs acquired during routine CT-guided-chemoradiation therapy for 90 pancreatic cancer patients. A total of 2520 CT sets (28-daily-fractions-per-patient) along with their pathological response were analyzed. Over 1300 radiomic features were extracted from the segmented ROIs. Highly correlated DRFs were ruled out using Spearman correlations. Correlation between the selected DRFs and pathological response was established using linear-regression-models. T test and linear-mixed-effects-models were used to determine which DRFs changed significantly compared with first fraction. A Bayesian-regularization-neural-network was used to build a response prediction model. The model was trained using 50 patients and leave-one-out-cross-validation. Performance was judged using the area-under-ROC-curve. External independent validation was done using data from the remaining 40 patients. The results show that 13 DRFs passed the tests and demonstrated significant changes following 2-4 weeks of treatment. The best performing combination differentiating good versus bad responders (CV-AUC = 0.94) was obtained using normalized-entropy-to-standard-deviation-difference-(NESTD), kurtosis, and coarseness. With further studies using larger data sets, delta radiomics may develop into a biomarker for early prediction of treatment response.

Serial T2-Weighted Magnetic Resonance Images Acquired on a 1.5 Tesla Magnetic Resonance Linear Accelerator Reveal Radiomic Feature Variation in Organs at Risk: An Exploratory Analysis of Novel Metrics of Tissue Response in Prostate Cancer.

"Delta-radiomics" investigates variations in quantitative image metrics over time and can yield important clinical information. We hypothesized that in patients undergoing active radiation therapy (RT) for prostate cancer (PCa), there would exist observable variation in the quantitative metrics that describe the T2-weighted (T2W) intensity histogram in the prostate and surrounding organs at risk (OAR) over time. We investigated the feasibility of acquisition and subsequent analysis of the delta-radiomic profiles of these regions of interest (ROI) in serial T2W magnetic resonance (MR) images obtained on a 1.5 Tesla (T) Magnetic Resonance Linear Accelerator (MRL). Principally, we sought to illustrate the significance of longitudinal radiomic data acquisition for tissue response monitoring and provide a framework for future hypothesis driven research. Patients with PCa undergoing treatment with RT were compiled from an ongoing prospective observational imaging trial using a 1.5 T MRL (NCT30500081). Contiguous axial slices of prostate parenchyma were contoured and temporally normalized to sections of Sartorius muscle which served as a control. Similarly, contiguous sections of rectal and bladder wall adjacent to the prostate were contoured and temporally normalized to regions of these organs further removed from the planning target volume (PTV). First order statistical descriptors of the T2W intensity histogram were extracted and evaluated for changes over time using linear mixed effects regression modeling and post-hoc contrasts. Benjamini-Hochberg corrections were employed to reduce the effects of multiple testing and control for the false discovery rate (FDR). Four patients with a median age of 69 comprised this exploratory cohort. One patient had low-risk disease, two had intermediate (one favorable, one unfavorable), and one had high risk disease. Three out of four patients underwent definitive radiation to 75.6 Gray (Gy) in 42 fractions and one received hypofractionated therapy to a total dose of 70 Gy over 28 fractions, and all received treatment on a conventional linear accelerator. The most significant acute toxicity event was grade 2 GU dysfunction observed in two patients. Follow up ranged from 1 month to 10 months post treatment, and no long-term complications were reported in patients who completed treatment at least one month prior. Bladder wall adjacent to the prostate demonstrated significant variation in the mean and median metric values after the first week of treatment. In addition, rectal wall adjacent to the prostate exhibited significant variation in the mean, median, and standard deviation metric values by the second week of treatment. No significant variation in any radiomic feature was observed in the Sartorius control. This exploratory study is one of the earliest examining the delta-radiomic characteristics of the T2W intensity histogram in OAR extracted from images acquired on a 1.5 T MRL in patients actively being treated with RT for PCa. We demonstrated a feasible approach to longitudinal radiomic data acquisition providing limitless opportunity for future research. Analysis of the delta-radiomic profiles in OAR revealed significant variation in metrics after only one week of RT in bladder and rectal wall adjacent to the prostate. These findings must be further investigated and validated with expanded data sets with long-term follow up and correlation to clinical outcomes including toxicity and tumor control.

Technical Note: Enhancing soft tissue contrast and radiation-induced image changes with dual-energy CT for radiation therapy.

PURPOSE: The purpose of this work is to investigate the use of low-energy monoenergetic decompositions obtained from dual-energy CT (DECT) to enhance image contrast and the detection of radiation-induced changes of CT textures in pancreatic cancer. METHODS: The DECT data acquired for 10 consecutive pancreatic cancer patients during routine nongated CT-guided radiation therapy (RT) using an in-room CT (Definition AS Open, Siemens Healthcare, Malvern, PA) were analyzed. With a sequential DE protocol, the scanner rapidly performs two helical acquisitions, the first at a tube voltage of 80 kVp and the second at a tube voltage of 140 kVp. Virtual monoenergetic images across a range of energies from 40 to 140 keV were reconstructed using an image-based material decomposition. Intravenous (IV) bolus-free contrast enhancement in pancreas patient tumors was measured across a spectrum of monoenergies. For treatment response assessment, the changes in CT histogram features (including mean CT number (MCTN), entropy, kurtosis) in pancreas tumors were measured during treatment. The results from the monoenergetic decompositions were compared to those obtained from the standard 120 kVp CT protocol for the same subjects. RESULTS: Data of monoenergetic decompositions of the 10 patients confirmed the expected enhancement of soft tissue contrast as the energy is decreased. The changes in the selected CT histogram features in the pancreas during RT delivery were amplified with the low-energy monoenergetic decompositions, as compared to the changes measured from the 120 kVp CTs. For the patients studied, the average reduction in the MCTN in pancreas from the first to the last (the 28th) treatment fraction was 4.09 HU for the standard 120 kVp and 11.15 HU for the 40 keV monoenergetic decomposition. CONCLUSIONS: Low-energy monoenergetic decompositions from DECT substantially increase soft tissue contrast and increase the magnitude of radiation-induced changes in CT histogram textures during RT delivery for pancreatic cancer. Therefore, quantitative DECT may assist the detection of early RT response.

Correlation of ADC With Pathological Treatment Response for Radiation Therapy of Pancreatic Cancer.

PURPOSE: To investigate the feasibility of using apparent diffusion coefficient (ADC) to assesspathological treatment response in pancreatic ductal adenocarcinoma (PDAC) following neoadjuvant chemoradiation (nCR). MATERIALS/METHODS: MRI and pathological data collected for 25patients with resectable and borderline resectable PDAC following nCR were retrospectively analyzed. Pre- and post-nCR mean ADC values in the tumors were compared using Wilcoxon matched pairs test. Correlation of pathological treatment response and ADC values was assessed using Pearson's correlation coefficient test and receiver-operating-curve (ROC) analysis. RESULTS: The average mean and standard deviation (SD) of the ADC values for all the patients analyzed were significantly higher in post-nCR (1.667±0.161×10-3) compared with those prior to nCR (1.395±0.136×10-3 mm2/sec), (P<0.05). The mean ADC values after nCR were significantly correlated with the pathological responses (r=-0.5172); P=0.02. The area under the curve (AUC) of the ADC values for differentiating G1, G2 and G3 pathological responses, using ROC analysis, was found to be 0.6310 and P=0.03. CONCLUSION: Changes of pre- and post-treatment ADC values significantly correlated with pathological treatment response for PDAC patients treated with chemoradiation therapy, indicating that the ADC could be used to assesstreatment response for PDAC.

Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study.

PURPOSE: In an effort for early assessment of treatment response, we investigate radiation induced changes in quantitative CT features of tumor during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. METHODS: Diagnostic-quality CT data acquired daily during routine CT-guided CRT using a CT-on-rails for 20 pancreatic head cancer patients were analyzed. On each daily CT, the pancreatic head, the spinal cord and the aorta were delineated and the histograms of CT number (CTN) in these contours were extracted. Eight histogram-based radiomic metrics including the mean CTN (MCTN), peak position, volume, standard deviation (SD), skewness, kurtosis, energy and entropy were calculated for each fraction. Paired t-test was used to check the significance of the change of specific metric at specific time. GEE model was used to test the association between changes of metrics over time for different pathology responses. RESULTS: In general, CTN histogram in the pancreatic head (but not in spinal cord) changed during the CRT delivery. Changes from the 1st to the 26th fraction in MCTN ranged from -15.8 to 3.9 HU with an average of -4.7 HU (p<0.001). Meanwhile the volume decreased, the skewness increased (less skewed), and the kurtosis decreased (less peaked). The changes of MCTN, volume, skewness, and kurtosis became significant after two weeks of treatment. Patient pathological response is associated with the changes of MCTN, SD, and skewness. In cases of good response, patients tend to have large reductions in MCTN and skewness, and large increases in SD and kurtosis. CONCLUSIONS: Significant changes in CT radiomic features, such as the MCTN, skewness, and kurtosis in tumor were observed during the course of CRT for pancreas cancer based on quantitative analysis of daily CTs. These changes may be potentially used for early assessment of treatment response and stratification for therapeutic intensification.