Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that ...imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables-HPV status and tumor volume-were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Radiomics has shown promise in improving models for predicting patient outcomes. However, to maximize the information gain of the radiomics features, especially in larger patient cohorts, the ...variability in radiomics features owing to differences between scanners and scanning protocols must be accounted for. To this aim, the imaging variability of radiomics feature values was evaluated on 100 computed tomography scanners at 35 clinics by imaging a radiomics phantom using a controlled protocol and the commonly used chest and head protocols of the local clinic. We used a linear mixed-effects model to determine the degree to which the manufacturer and individual scanners contribute to the overall variability. Using a controlled protocol reduced the overall variability by 57% and 52% compared to the local chest and head protocols respectively. The controlled protocol also reduced the relative contribution of the manufacturer to the total variability. For almost all variabilities (manufacturer, scanner, and residual with different preprocesssing), the controlled protocol scans had a significantly smaller variability than the local protocol scans did. For most radiomics features, the imaging variability was small relative to the inter-patient feature variability in non-small cell lung cancer and head and neck squamous cell carcinoma patient cohorts. From this study, we conclude that using controlled scans can reduce the variability in radiomics features, and our results demonstrate the importance of using controlled protocols in prospective radiomics studies.
Radiomics is one such "big data" approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a ...non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between "favorable" and "unfavorable" clusters were noted.
•Head and neck patients are often affected by streak and beam hardening artifacts, impacting their inclusion in studies.•Streak artifacts impact the majority of radiomics features’ values.•Contours ...of structures can abut bone without affecting most radiomics features’ values if needed.•Most features were robust with up to 50% of the original tumor volume removed.•More patients’ head and neck CTs can be used in radiomics studies by simply removing slices affected by streak artifacts.
Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies. The robustness of feature values was tested by removing slices of the gross tumor volume (GTV) on computed tomography images from 30 patients with head and neck cancer; these images did not have streak artifacts or had artifacts far from the GTV. The range of each feature value over a percentage of the GTV was compared to the inter-patient variability at full volume. To determine the effects of beam-hardening artifacts, we scanned a phantom with 5 cartridges of different materials encased in polystyrene buildup. A cylindrical hole through the cartridges contained either a rod of polylactic acid to simulate water or a rod of polyvinyl chloride to simulate bone. A region of interest was drawn in each cartridge flush with the rod. Most features were robust with up to 50% of the original GTV removed. Most feature values did not significantly differ when measured with the polylactic acid rod or the polyvinyl chloride rod. Of those that did, the size of the difference did not exceed the inter-patient standard deviation in most cases. We conclude that simply removing slices affected by streak artifacts can enable these scans to be included in radiomics studies and that contours of structures can abut bone without being affected by beam hardening if needed.
Oligometastatic non-small cell lung cancer (NSCLC) is a heterogeneous condition with few known risk stratification factors. A quantitative imaging feature (QIF) on positron emission tomography (PET), ...gray-level co-occurrence matrix energy, has been linked with outcome of nonmetastatic NSCLC. We hypothesized that GLCM energy would enhance the ability of models comprising standard clinical prognostic factors (CPFs) to stratify oligometastatic patients based on overall survival (OS).
We assessed 79 patients with oligometastatic NSCLC (≤3 metastases) diagnosed in 2007–2015. The primary and largest metastases at diagnosis were delineated on pretreatment scans with GLCM energy extracted using imaging biomarker explorer (IBEX) software. Iterative stepwise elimination feature selection based on the Akaike information criterion identified the optimal model comprising CPFs for predicting OS in a multivariate Cox proportional hazards model. GLCM energy was tested for improving prediction accuracy.
Energy was a significant predictor of OS (P = 0.028) in addition to the selected CPFs. The c-indexes for the CPF-only and CPF + Energy models were 0.720 and 0.739.
Incorporating Energy strengthened a CPF model for predicting OS. These findings support further exploration of QIFs, including markers of the primary tumor vs. those of the metastatic sites.
Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's graphical user interface (GUI) and to ...demonstrate how IBEX calculated features have been used in clinical studies. IBEX allows for the import of DICOM images with DICOM radiation therapy structure files or Pinnacle files. Once the images are imported, IBEX has tools within the Data Selection GUI to manipulate the viewing of the images, measure voxel values and distances, and create and edit contours. IBEX comes with 27 preprocessing and 132 feature choices to design feature sets. Each preprocessing and feature category has parameters that can be altered. The output from IBEX is a spreadsheet that contains: 1) each feature from the feature set calculated for each contour in a data set, 2) image information about each contour in a data set, and 3) a summary of the preprocessing and features used with their selected parameters. Features calculated from IBEX have been used in studies to test the variability of features under different imaging conditions and in survival models to improve current clinical models.
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•Gamma Knife Icon auto-corrects the shots according to treatment position.•For robust planning, it is better to use only uniform shots.•When delivery dose differs too much, re-adjust ...position or re-plan is recommended.
To verify whether Icon automatic correction is robust in preserving plan quality.
An end-to-end phantom was used to verify Icon’s correction accuracy qualitatively. For quantitative assessment, two plans, a composite- and a uniform-shot-only, were created for an elliptical- (E) and a sausage-shaped (S) lesion inside a PseudoPatient head phantom with a film insert. The phantom was irradiated in the planned and three other positions under each plan: 14° pitch (B); 14° rotation + 8° pitch (C); 95° rotation + 4-cm shift (D).
Icon accurately corrects the locations of the shots. For the uniform-shot plans: all gamma index passing rates were >97%, and the differences between the planned and the delivery doses (minimum, maximum, and mean) were all ≤0.1 Gy. For the composite-shot plans, however, the dose differences increased as the phantom was shifted through positions B-D, with a gamma index passing rate of 61% for lesion-E in position D, and 92%, 79%, and 45% for lesion-S in positions B, C, and D, respectively.
Plans using only uniform shots are more robust to deviations in treatment position. The tolerance for such deviations may be lower for plans using composite shots.
Purpose:
The authors investigated how the characteristics of the detectors used in a three-stage Compton camera (CC) affect the CC's ability to accurately measure the emission distribution and energy ...spectrum of prompt gammas (PG) emitted by nuclear de-excitations during proton therapy. The detector characteristics they studied included the material (high-purity germanium HPGe and cadmium zinc telluride CZT), Doppler broadening (DB), and resolution (lateral, depth, and energy).
Methods:
The authors simulated three-stage HPGe and CZT CCs of various configurations, detecting gammas from point sources with energies ranging from 0.511 to 7.12 MeV. They also simulated a proton pencil beam irradiating a tissue target to study how the detector characteristics affect the PG data measured by CCs in a clinical proton therapy setting. They used three figures of merit: the distance of closest approach (DCA) and the point of closest approach (PCA) between the measured and actual position of the PG emission origin, and the calculated energy resolution.
Results:
For CCs with HPGe detectors, DB caused the DCA to be greater than 3 mm for 14% of the 6.13 MeV gammas and 20% of the 0.511 MeV gammas. For CCs with CZT detectors, DB caused the DCA to be greater than 3 mm for 18% of the 6.13 MeV gammas and 25% of the 0.511 MeV gammas. The full width at half maximum (FWHM) of the PCA in the
$\hat z$
z
̂
direction for HPGe and CZT detectors ranged from 1.3 to 0.4 mm for gammas with incident energy ranging from 0.511 to 7.12 MeV. For CCs composed of HPGe detectors, the resolution of incident gamma energy calculated by the CC ranged from 6% to 1% for gammas with true incident energies from 0.511 to 7.12 MeV. For CCs composed of CZT detectors, the resolution of gamma energy calculated by the CC ranged from 10% to 1% for gammas with true incident energies from 0.511 to 7.12 MeV. For HPGe and CZT CCs in which all detector effect were included, the DCA was less than 3 mm for 75% and 68% of the detected gammas, respectively, and restricting gammas to those having energy greater than 2.0 MeV increased these percentages to 83% and 77% for HPGe and CZT, respectively. Distributions of the true gamma origins and the PCA after detector characteristics had been included showed good agreement on beam range and some loss of resolution for the lateral profile of the PG emission. Characteristic energy lines were evident in the calculated gamma energy spectrum.
Conclusions:
The authors found the following: (1) DB is the dominant source of spatial and energy resolution loss in the CCs at all energy levels; (2) the largest difference in the spatial resolution of HPGe and CZT CCs is that the spatial resolution distributions of CZT have broader tails. The differences in the FWHM of these distributions are small; (3) the energy resolution of both HPGe and CZT three-stage CCs is adequate for PG spectroscopy; and (4) restricting the gammas to those having energy greater than 2.0 MeV can improve the achievable image resolution.
To investigate the inter‐ and intra‐fraction motion associated with the use of a low‐cost tape immobilization technique as an alternative to thermoplastic immobilization masks for whole‐brain ...treatments. The results of this study may be of interest to clinical staff with severely limited resources (e.g., in low‐income countries) and also when treating patients who cannot tolerate standard immobilization masks. Setup reproducibility of eight healthy volunteers was assessed for two different immobilization techniques. (a) One strip of tape was placed across the volunteer's forehead and attached to the sides of the treatment table. (b) A second strip was added to the first, under the chin, and secured to the table above the volunteer's head. After initial positioning, anterior and lateral photographs were acquired. Volunteers were positioned five times with each technique to allow calculation of inter‐fraction reproducibility measurements. To estimate intra‐fraction reproducibility, 5‐minute anterior and lateral videos were taken for each technique per volunteer. An in‐house software was used to analyze the photos and videos to assess setup reproducibility. The maximum intra‐fraction displacement for all volunteers was 2.8 mm. Intra‐fraction motion increased with time on table. The maximum inter‐fraction range of positions for all volunteers was 5.4 mm. The magnitude of inter‐fraction and intra‐fraction motion found using the “1‐strip” and “2‐strip” tape immobilization techniques was comparable to motion restrictions provided by a thermoplastic mask for whole‐brain radiotherapy. The results suggest that tape‐based immobilization techniques represent an economical and useful alternative to the thermoplastic mask.
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