Transferability of patients for radiation treatment between unmatched machines.

PURPOSE: The feasibility of transferring patients between unmatched machines for a limited number of treatment fractions was investigated for three-dimensional conformal radiation therapy (3DCRT) and volumetric modulated arc therapy (VMAT) treatments. METHODS: Eighty patient-plans were evaluated on two unmatched linacs: Elekta Versa HD and Elekta Infinity. Plans were equally divided into pelvis 3DCRT, prostate VMAT, brain VMAT, and lung VMAT plans. While maintaining the number of monitor units (MUs), plans were recalculated on the machine not originally used for treatment. Relative differences in dose were calculated between machines for the target volume and organs at risk (OARs). Differences in mean dose were assessed with paired t-tests (p < 0.05). The number of interchangeable fractions allowable before surpassing a cumulative ±5% difference in dose was determined. Additionally, patient-specific quality assurance (PSQA) measurements using ArcCHECK for both machines were compared with distributions calculated on the machine originally used for treatment using gradient compensation (GC) with 2%/2-mm criteria. RESULTS: Interchanging the two machines for pelvic 3DCRT and VMAT (prostate, brain, and lung) plans resulted in an average change in target mean dose of 0.9%, -0.5%, 0.6%, 0.5%, respectively. Based on the differences in dose to the prescription point when changing machines, statistically, nearly one-fourth of the prescribed fractions could be transferred between linacs for 3DCRT plans. While all of the prescribed fractions could typically be transferred among prostate VMAT plans, a rather large number of treatment fractions, 31% and 38%, could be transferred among brain and lung VMAT plans, respectively, without exceeding a ±5% change in the prescribed dose for two Elekta machines. Additionally, the OAR dosage was not affected within the given criterion with change of machine. CONCLUSIONS: Despite small differences in calculated dose, transferring patients between two unmatched Elekta machines with similar multileaf collimator (MLC)-head for target coverage and minimum changes in OAR dose is possible for a limited number of fractions (≤3) to improve clinical flexibility and institutional throughput along with patient satisfaction. A similar study could be carried out for other machines for operational throughput.

Baseline Computed Tomography Radiomic and Genomic Assessment of Head and Neck Squamous Cell Carcinoma.

PURPOSE: To determine the relationship between computed tomography (CT) radiomic features and gene expression levels in head and neck squamous cell carcinoma (HNSCC). METHODS: This retrospective study included 66 patients with HNSCC primary lesions (36 oropharyngeal, 6 hypopharyngeal, 10 laryngeal, 14 oral cavity). Gene expression information for 6 targetable genes (fibroblast growth factor receptor [FGFR]1, epidermal growth factor receptor [EGFR], FGFR2, FGFR3, EPHA2, PIK3CA) was obtained via Agilent microarrays from samples collected between 1997 and 2010. Pretreatment contrast-enhanced soft tissue neck CT scans were reviewed, and 142 radiomics features were derived. R was used to calculate Pearson correlation coefficients were calculated between gene expression levels and each radiomic feature. P values were adjusted using the false discovery rate (FDR) method. RESULTS: There were significant correlations between FGFR1 and 5 gray level cooccurrence matrix (GLCM) features with FDR-adjusted P values less than 0.05: inertia (r = 0.366, FDR-adjusted P = 0.006), absolute value (r = 0.31, FDR-adjusted P = 0.024), contrast (r = 0.366, FDR-adjusted P = 0.006), difference average (r = 0.31, FDR-adjusted P = 0.024), and difference variance (r = 0.37, FDR-adjusted P = 0.005). There was 1 correlated feature for FGFR2 with an FDR-adjusted P value less than 0.05: fractal dimension box-coarse (r = 0.33, FDR-adjusted P = 0.018). There was 1 correlated feature for EPHA2 with an FDR-adjusted P value less than 0.05: GLCM entropy (r = -0.28, FDR-adjusted P = 0.049). Six of the 7 features that showed significant correlation belonged to the GLCM class of features. CONCLUSIONS: The CT radiomic features demonstrate correlations with FGFR1 status in HNSCC and should be further investigated for their potential to predict FGFR1 status.

A Dosimetric Parameter Reference Look-Up Table for GRID Collimator-Based Spatially Fractionated Radiation Therapy.

Computations of heterogeneity dose parameters in GRID therapy remain challenging in many treatment planning systems (TPS). To address this difficulty, we developed reference dose tables for a standard GRID collimator and validate their accuracy. The .decimal Inc. GRID collimator was implemented within the Eclipse TPS. The accuracy of the dose calculation was confirmed in the commissioning process. Representative sets of simulated ellipsoidal tumours ranging from 6-20 cm in diameter at a 3-cm depth; 16-cm ellipsoidal tumours at 3, 6, and 10 cm in depth were studied. All were treated with 6MV photons to a 20 Gy prescription dose at the tumour center. From these, the GRID therapy dosimetric parameters (previously recommended by the Radiosurgery Society white paper) were derived. Differences in D5 through D95 and EUD between different tumour sizes at the same depth were within 5% of the prescription dose. PVDR from profile measurements at the tumour center differed from D10/D90, but D10/D90 variations for the same tumour depths were within 11%. Three approximation equations were developed for calculating EUDs of different prescription doses for three radiosensitivity levels for 3-cm deep tumours. Dosimetric parameters were consistent and predictable across tumour sizes and depths. Our study results support the use of the developed tables as a reference tool for GRID therapy.

Radiomics-based assessment of idiopathic pulmonary fibrosis is associated with genetic mutations and patient survival.

Purpose: The purpose of our study was to combine differences in radiomic features extracted from lung regions in the computed tomography (CT) scans of patients diagnosed with idiopathic pulmonary fibrosis (IPF) to identify associations with genetic variations and patient survival. Approach: A database of CT scans and genomic data from 169 patients diagnosed with IPF was collected retrospectively. Six region-of-interest pairs (three per lung, positioned posteriorly, anteriorly, and laterally) were placed in each of three axial CT sections for each patient. Thirty-one features were used in logistic regression to classify patients' genetic mutation status; classification performance was evaluated through the area under the receiver operating characteristic (ROC) curve [average area under the ROC curve (AUC)]. Kaplan-Meier (KM) survival curve models quantified the ability of each feature to differentiate between survival curves based on feature-specific thresholds. Results: Nine first-order texture features and one fractal feature were correlated with TOLLIP-1 (rs4963062) mutations (AUC: 0.54 to 0.74), and five Laws' filter features were correlated with TOLLIP-2 (rs5743905) mutations (AUC: 0.53 to 0.70). None of the features analyzed were found to be correlated with MUC5B mutations. First-order and fractal features demonstrated the greatest discrimination between KM curves. Conclusions: A radiomics approach for the correlation of patient genetic mutations with image texture features has potential as a biomarker. These features also may serve as prognostic indicators using a survival curve modeling approach in which the combination of radiomic features and genetic mutations provides an enhanced understanding of the interaction between imaging phenotype and patient genotype on the progression and treatment of IPF.

Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis.

Purpose: While radiomics feature values can differ when extracted using different radiomics software, the effects of these variations when applied to a particular clinical task are currently unknown. The goal of our study was to use various radiomics software packages to classify patients with radiation pneumonitis (RP) and to quantify the variation in classification ability among packages. Approach: A database of serial thoracic computed tomography scans was obtained from 105 patients with esophageal cancer. Patients were treated with radiation therapy (RT), resulting in 20 patients developing RP grade ≥ 2 . Regions of interest (ROIs) were randomly placed in the lung volume of the pre-RT scan within high-dose regions ( ≥ 30 Gy ), and corresponding ROIs were anatomically matched in the post-RT scan. Three radiomics packages were compared: A1 (in-house), IBEX v1.0 beta, and PyRadiomics v.2.0.0. Radiomics features robust to deformable registration and common among radiomics packages were calculated: four first-order and four gray-level co-occurrence matrix features. Differences in feature values between time points were calculated for each feature, and logistic regression was used in conjunction with analysis of variance to classify patients with and without RP ( p < 0.006 ). Classification ability for each package was assessed using receiver operating characteristic (ROC) analysis and compared using the area under the ROC curve (AUC). Results: Of the eight radiomics features, five were significantly correlated with RP status for all three packages, whereas one feature was not significantly correlated with RP for all three packages. The remaining two features differed in whether or not they were significantly associated with RP status among the packages. Seven of the eight features agreed among the packages in whether the AUC value was significantly > 0.5 . Conclusions: Radiomics features extracted using different software packages can result in differences in classification ability.

Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features.

Purpose: The goal of this study was to quantify the effects of iterative reconstruction on radiomics features of normal anatomic structures on head and neck computed tomography (CT) scans. Methods: Regions of interest (ROI) containing five different tissue types and an ROI containing only air were extracted from CT scans of the head and neck from 108 patients. Each scan was reconstructed using three different iDose 4 reconstruction levels (2, 4, and 6) in addition to bone, thin slice (1-mm slice thickness), and thin-bone reconstructions. From each ROI in all reconstructions, 142 radiomic features were calculated. For each of the six ROIs, features were compared between combinations of iDose levels (2v4, 4v6, and 2v6) with a threshold of α = 0.05 after correcting for multiple comparisons ( p < 0.00006 ). Features from iDose 4 - 2 reconstructions were also compared to bone, thin slice, and thin-bone reconstructions. Spearman's rank correlation coefficient, ρ , quantified the relative feature value agreement across iDose 4 reconstructions. Results: When comparing radiomics features across the three iDose 4 reconstruction levels, over half of all features reflected significant differences for all tissue types, while no features demonstrated significant differences when extracted from air ROIs. When assessing feature value agreement, at least 97% of features reflected excellent agreement ( ρ > 0.9 ) when comparing the three iDose levels for all ROIs. When comparing iDose 4 - 2 to bone, thin slice, and thin-bone reconstructions, more than half of all features demonstrated significant differences for all ROIs and 89 % of features reflected excellent agreement for all ROIs. Conclusion: Many radiomics features are dependent on the iterative reconstruction level, and the magnitude of this dependency is affected by the tissue from which features are extracted. For studies using images reconstructed using varying iDose 4 reconstruction levels, features robust to these should be used.

Harmonization of radiomic feature variability resulting from differences in CT image acquisition and reconstruction: assessment in a cadaveric liver.

Studies investigating the effects of computed tomography (CT) image acquisition and reconstruction parameters have mostly been limited to non-human phantoms to limit exposure to patients. This study investigates these variations using a cadaveric liver and determines harmonization methods to mitigate these variations. A reference CT scan of a cadaveric liver was acquired along with 16 modified scans. Modified scans were obtained with altered image acquisition and reconstruction parameters. In each slice, the liver was segmented and used to calculate 142 features. Student's t-tests assessed differences between reference and modified scans for each feature after correcting for multiple comparisons. Features were harmonized between reference and modified scans using histogram normalization, pixel resampling, Butterworth filtering, resampling and filtering combined, and ComBat harmonization. The number of features reflecting significant differences before and after harmonization were compared across imaging parameters. Reducing the field-of-view (FOV) and using coronal instead of axial scans resulted in the greatest number of features reflecting significant differences (67.6%, and 35.9%, respectively) and resulted in the greatest median relative change in feature values (25.4% and 18.2%, respectively). Changes in tube voltage, pitch, and slice interval resulted in the smallest number of features reflecting significance (0.7%) with median relative changes in feature <2%. Histogram normalization reduced or maintained the number of significantly different features for all scans, while ComBat reduced the number of significantly different features to zero for all scans. The remaining harmonization methods had mixed effects: resampling reduced the number of features reflecting significant differences for half of the imaging parameters, while filtering alone and filtering combined with resampling both reduced the number of features reflecting significance for 10 of the 16 parameters. The dependence of radiomic features on image acquisition and reconstruction parameters varies in a cadaveric liver; however, various harmonization methods have shown promise in mitigating these dependencies, particularly ComBat.

Variation in algorithm implementation across radiomics software.

Given the increased need for consistent quantitative image analysis, variations in radiomics feature calculations due to differences in radiomics software were investigated. Two in-house radiomics packages and two freely available radiomics packages, MaZda and IBEX, were utilized. Forty 256 × 256 - pixel regions of interest (ROIs) from 40 digital mammograms were studied along with 39 manually delineated ROIs from the head and neck (HN) computed tomography (CT) scans of 39 patients. Each package was used to calculate first-order histogram and second-order gray-level co-occurrence matrix (GLCM) features. Friedman tests determined differences in feature values across packages, whereas intraclass-correlation coefficients (ICC) quantified agreement. All first-order features computed from both mammography and HN cases (except skewness in mammography) showed significant differences across all packages due to systematic biases introduced by each package; however, based on ICC values, all but one first-order feature calculated on mammography ROIs and all but two first-order features calculated on HN CT ROIs showed excellent agreement, indicating the observed differences were small relative to the feature values but the bias was systematic. All second-order features computed from the two databases both differed significantly and showed poor agreement among packages, due largely to discrepancies in package-specific default GLCM parameters. Additional differences in radiomics features were traced to variations in image preprocessing, algorithm implementation, and naming conventions. Large variations in features among software packages indicate that increased efforts to standardize radiomics processes must be conducted.

An analysis of knowledge-based planning for stereotactic body radiation therapy of the spine.

PURPOSE: Planning for spine stereotactic body radiation therapy (SBRT) is time consuming, and differences in planner experience and technique result in discrepancies in plan quality between facilities. Here, knowledge-based planning is analyzed to determine if it may be effective in improving the quality and efficiency of spine SBRT planning. MATERIALS AND METHODS: Thirty-eight spine SBRT cases were collected from the University of Michigan database and inverse planned to deliver 3 10-Gy fractions to the planning target volume (PTV). These plans were used to train a knowledge-based model (model A) using RapidPlan (Varian Medical Systems). The model was evaluated for outliers and validated in 10 independent cases. Each of these cases was manually planned to compare the quality of the model-generated plans with the manual plans. To further test the robustness of the software, 2 additional models (models B and C) were created with intentional outliers resulting from inconsistent contouring. RESULTS: Using models A, B, and C, all 10 generated plans met all dose objectives for modeled organs at risk (OARs) (spinal cord, cord planning risk volume, and esophagus) without user intervention. The target coverage and OAR dose sparing was improved or equivalent to manual planning by an expert dosimetrist; however, manually created plans typically required 1 to 1.5 hours to produce and model-generated plans required only 10 to 15 minutes with minimal human intervention to meet all dose objectives. CONCLUSIONS: The clinical quality of plans produced by RapidPlan were found to improve on or be similar to the manually created plans in terms of normal tissue objectives and PTV dose coverage and could be produced in a fraction of the time. RapidPlan is a robust technique that can improve planning efficiency in spine SBRT while maintaining or potentially improving plan quality and standardization across planners and centers.