Dennis Mackin

Measuring Computed Tomography Scanner Variability of Radiomics Features.

ObjectivesThe purpose of this study was to determine the significance of interscanner variability in CT image radiomics studies. Materials and MethodsWe compared the radiomics features calculated for non–small cell lung cancer (NSCLC) tumors from 20 patients with those calculated for 17 scans of a specially designed radiomics phantom. The phantom comprised 10 cartridges, each filled with different materials to produce a wide range of radiomics feature values. The scans were acquired using General Electric, Philips, Siemens, and Toshiba scanners from 4 medical centers using their routine thoracic imaging protocol. The radiomics feature studied included the mean and standard deviations of the CT numbers as well as textures derived from the neighborhood gray-tone difference matrix. To quantify the significance of the interscanner variability, we introduced the metric feature noise. To look for patterns in the scans, we performed hierarchical clustering for each cartridge. ResultsThe mean CT numbers for the 17 CT scans of the phantom cartridges spanned from −864 to 652 Hounsfield units compared with a span of −186 to 35 Hounsfield units for the CT scans of the NSCLC tumors, showing that the phantoms dynamic range includes that of the tumors. The interscanner variability of the feature values depended on both the cartridge material and the feature, and the variability was large relative to the interpatient variability in the NSCLC tumors for some features. The feature interscanner noise was greatest for busyness and least for texture strength. Hierarchical clustering produced different clusters of the phantom scans for each cartridge, although there was some consistent clustering by scanner manufacturer. ConclusionsThe variability in the values of radiomics features calculated on CT images from different CT scanners can be comparable to the variability in these features found in CT images of NSCLC tumors. These interscanner differences should be considered, and their effects should be minimized in future radiomics studies.

Imaging of prompt gamma rays emitted during delivery of clinical proton beams with a Compton camera: Feasibility studies for range verification

The purpose of this paper is to evaluate the ability of a prototype Compton camera (CC) to measure prompt gamma rays (PG) emitted during delivery of clinical proton pencil beams for prompt gamma imaging (PGI) as a means of providing in vivo verification of the delivered proton radiotherapy beams. A water phantom was irradiated with clinical 114 MeV and 150 MeV proton pencil beams. Up to 500 cGy of dose was delivered per irradiation using clinical beam currents. The prototype CC was placed 15u2009cm from the beam central axis and PGs from 0.2 MeV up to 6.5 MeV were measured during irradiation. From the measured data (2D) images of the PG emission were reconstructed. (1D) profiles were extracted from the PG images and compared to measured depth dose curves of the delivered proton pencil beams. The CC was able to measure PG emission during delivery of both 114 MeV and 150 MeV proton beams at clinical beam currents. 2D images of the PG emission were reconstructed for single 150 MeV proton pencil beams as well as for a 5u2009u2009u2009×u2009u2009u20095u2009cm mono-energetic layer of 114 MeV pencil beams. Shifts in the Bragg peak (BP) range were detectable on the 2D images. 1D profiles extracted from the PG images show that the distal falloff of the PG emission profile lined up well with the distal BP falloff. Shifts as small as 3u2009mm in the beam range could be detected from the 1D PG profiles with an accuracy of 1.5u2009mm or better. However, with the current CC prototype, a dose of 400 cGy was required to acquire adequate PG signal for 2D PG image reconstruction. It was possible to measure PG interactions with our prototype CC during delivery of proton pencil beams at clinical dose rates. Images of the PG emission could be reconstructed and shifts in the BP range were detectable. Therefore PGI with a CC for in vivo range verification during proton treatment delivery is feasible. However, improvements in the prototype CC detection efficiency and reconstruction algorithms are necessary to make it a clinically viable PGI system.

Evaluation of a stochastic reconstruction algorithm for use in Compton camera imaging and beam range verification from secondary gamma emission during proton therapy.

In this paper, we study the feasibility of using the stochastic origin ensemble (SOE) algorithm for reconstructing images of secondary gammas emitted during proton radiotherapy from data measured with a three-stage Compton camera. The purpose of this study was to evaluate the quality of the images of the gamma rays emitted during proton irradiation produced using the SOE algorithm and to measure how well the images reproduce the distal falloff of the beam. For our evaluation, we performed a Monte Carlo simulation of an ideal three-stage Compton camera positioned above and orthogonal to a proton pencil beam irradiating a tissue phantom. Scattering of beam protons with nuclei in the phantom produces secondary gamma rays, which are detected by the Compton camera and used as input to the SOE algorithm. We studied the SOE reconstructed images as a function of the number of iterations, the voxel probability parameter, and the number of detected gammas used by the SOE algorithm. We quantitatively evaluated the capabilities of the SOE algorithm by calculating and comparing the normalized mean square error (NMSE) of SOE reconstructed images. We also studied the ability of the SOE reconstructed images to predict the distal falloff of the secondary gamma production in the irradiated tissue. Our results show that the images produced with the SOE algorithm converge in ~10,000 iterations, with little improvement to the image NMSE for iterations above this number. We found that the statistical noise of the images is inversely proportional to the ratio of the number of gammas detected to the SOE voxel probability parameter value. In our study, the SOE predicted distal falloff of the reconstructed images agrees with the Monte Carlo calculated distal falloff of the gamma emission profile in the phantom to within ±0.6 mm for the positions of maximum emission (100%) and 90%, 50% and 20% distal falloff of the gamma emission profile. We conclude that the SOE algorithm is an effective method for reconstructing images of a proton pencil beam from the data collected by an ideal Compton camera and that these images accurately model the distal falloff of secondary gamma emission during proton irradiation.

Measurement of characteristic prompt gamma rays emitted from oxygen and carbon in tissue-equivalent samples during proton beam irradiation

The purpose of this work was to characterize how prompt gamma (PG) emission from tissue changes as a function of carbon and oxygen concentration, and to assess the feasibility of determining elemental concentration in tissues irradiated with proton beams. For this study, four tissue-equivalent water-sucrose samples with differing densities and concentrations of carbon, hydrogen, and oxygen were irradiated with a 48 MeV proton pencil beam. The PG spectrum emitted from each sample was measured using a high-purity germanium detector, and the absolute detection efficiency of the detector, average beam current, and delivered dose distribution were also measured. Changes to the total PG emission from (12)C (4.44 MeV) and (16)O (6.13 MeV) per incident proton and per Gray of absorbed dose were characterized as a function of carbon and oxygen concentration in the sample. The intensity of the 4.44 MeV PG emission per incident proton was found to be nearly constant for all samples regardless of their carbon concentration. However, we found that the 6.13 MeV PG emission increased linearly with the total amount (in grams) of oxygen irradiated in the sample. From the measured PG data, we determined that 1.64xa0×xa010(7)xa0oxygen PGs were emitted per gram of oxygen irradiated per Gray of absorbed dose delivered with a 48 MeV proton beam. These results indicate that the 6.13 MeV PG emission from (16)O is proportional to the concentration of oxygen in tissue irradiated with proton beams, showing that it is possible to determine the concentration of oxygen within tissues irradiated with proton beams by measuring (16)O PG emission.

Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels

Purpose: Many radiomics features were originally developed for non‐medical imaging applications and therefore original assumptions may need to be reexamined. In this study, we investigated the impact of slice thickness and pixel spacing (or pixel size) on radiomics features extracted from Computed Tomography (CT) phantom images acquired with different scanners as well as different acquisition and reconstruction parameters. The dependence of CT texture features on gray‐level discretization was also evaluated. Methods and materials: A texture phantom composed of 10 different cartridges of different materials was scanned on eight different CT scanners from three different manufacturers. The images were reconstructed for various slice thicknesses. For each slice thickness, the reconstruction Field Of View (FOV) was varied to render pixel sizes ranging from 0.39 to 0.98 mm. A fixed spherical region of interest (ROI) was contoured on the images of the shredded rubber cartridge and the 3D printed, 20% fill, acrylonitrile butadiene styrene plastic cartridge (ABS20) for all phantom imaging sets. Radiomic features were extracted from the ROIs using an in‐house program. Features categories were: shape (10), intensity (16), GLCM (24), GLZSM (11), GLRLM (11), and NGTDM (5), fractal dimensions (8) and first‐order wavelets (128), for a total of 213 features. Voxel‐size resampling was performed to investigate the usefulness of extracting features using a suitably chosen voxel size. Acquired phantom image sets were resampled to a voxel size of 1 × 1 × 2 mm3 using linear interpolation. Image features were therefore extracted from resampled and original datasets and the absolute value of the percent coefficient of variation (%COV) for each feature was calculated. Based on the %COV values, features were classified in 3 groups: (1) features with large variations before and after resampling (%COV >50); (2) features with diminished variation (%COV <30) after resampling; and (3) features that had originally moderate variation (%COV <50%) and were negligibly affected by resampling. Group 2 features were further studied by modifying feature definitions to include voxel size. Original and voxel‐size normalized features were used for interscanner comparisons. A subsequent analysis investigated feature dependency on gray‐level discretization by extracting 51 texture features from ROIs from each of the 10 different phantom cartridges using 16, 32, 64, 128, and 256 gray levels. Results: Out of the 213 features extracted, 150 were reproducible across voxel sizes, 42 improved significantly (%COV <30, Group 2) after resampling, and 21 had large variations before and after resampling (Group 1). Ten features improved significantly after definition modification effectively removed their voxel‐size dependency. Interscanner comparison indicated that feature variability among scanners nearly vanished for 8 of these 10 features. Furthermore, 17 out of 51 texture features were found to be dependent on the number of gray levels. These features were redefined to include the number of gray levels which greatly reduced this dependency. Conclusion: Voxel‐size resampling is an appropriate pre‐processing step for image datasets acquired with variable voxel sizes to obtain more reproducible CT features. We found that some of the radiomics features were voxel size and gray‐level discretization‐dependent. The introduction of normalizing factors in their definitions greatly reduced or removed these dependencies.

Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?

PURPOSEnIncreasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone-beam CT (CBCT) images taken using medical Linac onboard-imaging systems in order to track them through treatment.nnnMETHODSnTest-retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rank correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test-retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic-motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test-retest image set.nnnRESULTSnOf the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4-13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturer-protocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6-8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray-level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray-level run emphasis, gray-level nonuniformity, sum-average, information measure correlation, scaled mean, and entropy) from the center-slice measurements were considered reproducible.nnnCONCLUSIONSnSome radiomics features are robust to the noise and poor image quality of CBCT images when the imaging protocol is consistent, relative changes in the features are used, and patients are limited to those with less than 1 cm of motion.

Towards Effective and Efficient Patient-Specific Quality Assurance for Spot Scanning Proton Therapy

An intensity-modulated proton therapy (IMPT) patient-specific quality assurance (PSQA) program based on measurement alone can be very time consuming due to the highly modulated dose distributions of IMPT fields. Incorporating independent dose calculation and treatment log file analysis could reduce the time required for measurements. In this article, we summarize our effort to develop an efficient and effective PSQA program that consists of three components: measurements, independent dose calculation, and analysis of patient-specific treatment delivery log files. Measurements included two-dimensional (2D) measurements using an ionization chamber array detector for each field delivered at the planned gantry angles with the electronic medical record (EMR) system in the QA mode and the accelerator control system (ACS) in the treatment mode, and additional measurements at depths for each field with the ACS in physics mode and without the EMR system. Dose distributions for each field in a water phantom were calculated independently using a recently developed in-house pencil beam algorithm and compared with those obtained using the treatment planning system (TPS). The treatment log file for each field was analyzed in terms of deviations in delivered spot positions from their planned positions using various statistical methods. Using this improved PSQA program, we were able to verify the integrity of the data transfer from the TPS to the EMR to the ACS, the dose calculation of the TPS, and the treatment delivery, including the dose delivered and spot positions. On the basis of this experience, we estimate that the in-room measurement time required for each complex IMPT case (e.g., a patient receiving bilateral IMPT for head and neck cancer) is less than 1 h using the improved PSQA program. Our experience demonstrates that it is possible to develop an efficient and effective PSQA program for IMPT with the equipment and resources available in the clinic.

Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer

Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non–small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment radiomics features, and delta-radiomics features. All of the radiomics features changed significantly during radiation therapy. For overall survival and distant metastases, pretreatment compactness improved the c-index. For local recurrence, pretreatment imaging features were not prognostic, while texture-strength measured at the end of treatment significantly stratified high- and low-risk patients. These results suggest radiomics features change due to radiation therapy and their values at the end of treatment may be indicators of tumor response.

Improving spot-scanning proton therapy patient specific quality assurance with HPlusQA, a second-check dose calculation engine.

PURPOSEnThe purpose of this study was to validate the use of HPlusQA, spot-scanning proton therapy (SSPT) dose calculation software developed at The University of Texas MD Anderson Cancer Center, as second-check dose calculation software for patient-specific quality assurance (PSQA). The authors also showed how HPlusQA can be used within the current PSQA framework.nnnMETHODSnThe authors compared the dose calculations of HPlusQA and the Eclipse treatment planning system with 106 planar dose measurements made as part of PSQA. To determine the relative performance and the degree of correlation between HPlusQA and Eclipse, the authors compared calculated with measured point doses. Then, to determine how well HPlusQA can predict when the comparisons between Eclipse calculations and the measured dose will exceed tolerance levels, the authors compared gamma index scores for HPlusQA versus Eclipse with those of measured doses versus Eclipse. The authors introduce the αβγ transformation as a way to more easily compare gamma scores.nnnRESULTSnThe authors compared measured and calculated dose planes using the relative depth, z∕R × 100%, where z is the depth of the measurement and R is the proton beam range. For relative depths than less than 80%, both Eclipse and HPlusQA calculations were within 2 cGy of dose measurements on average. When the relative depth was greater than 80%, the agreement between the calculations and measurements fell to 4 cGy. For relative depths less than 10%, the Eclipse and HPlusQA dose discrepancies showed a negative correlation, -0.21. Otherwise, the correlation between the dose discrepancies was positive and as large as 0.6. For the dose planes in this study, HPlusQA correctly predicted when Eclipse had and had not calculated the dose to within tolerance 92% and 79% of the time, respectively. In 4 of 106 cases, HPlusQA failed to predict when the comparison between measurement and Eclipses calculation had exceeded the tolerance levels of 3% for dose and 3 mm for distance-to-agreement.nnnCONCLUSIONSnThe authors found HPlusQA to be reasonably effective (79% ± 10%) in determining when the comparison between measured dose planes and the dose planes calculated by the Eclipse treatment planning system had exceeded the acceptable tolerance levels. When used as described in this study, HPlusQA can reduce the need for patient specific quality assurance measurements by 64%. The authors believe that the use of HPlusQA as a dose calculation second check can increase the efficiency and effectiveness of the QA process.

Harmonizing the pixel size in retrospective computed tomography radiomics studies

Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination. Intra-patient agreement and inter-patient agreement were compared using the overall concordance correlation coefficient (OCCC). To further evaluate the corrections, hierarchical clustering was used to identify patient scans before and after correction. To assess the general applicability of the corrections, they were applied to 17 CT scans of a radiomics phantom. The reduction in the inter-scanner variability relative to non–small cell lung cancer patient scans was quantified. The variation in pixel sizes caused the intra-patient variability to be large (OCCC <95%) relative to the inter-patient variability in 79% of the features. However, with the resampling and filtering corrections, the intra-patient variability was relatively large in only 10% of the features. With the filtering correction, 8 of 8 patients were correctly clustered, in contrast to only 2 of 8 without the correction. In the phantom study, resampling and filtering the images of a rubber particle cartridge substantially reduced variability in 61% of the radiomics features and substantially increased variability in only 6% of the features. Surprisingly, resampling without filtering tended to increase the variability. In conclusion, applying a correction based on resampling and Butterworth low-pass filtering in the frequency domain effectively reduced variability in CT radiomics features caused by variations in pixel size. This correction may also reduce the variability introduced by other CT scan acquisition parameters.