Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study.

PURPOSE: To compare the ability of radiological semantic and quantitative texture features in lung cancer diagnosis of pulmonary nodules. MATERIALS AND METHODS: A total of N = 121 subjects with confirmed non-small-cell lung cancer were matched with 117 controls based on age and gender. Radiological semantic and quantitative texture features were extracted from CT images with or without contrast enhancement. Three different models were compared using LASSO logistic regression: "CS" using clinical and semantic variables, "T" using texture features, and "CST" using clinical, semantic, and texture variables. For each model, we performed 100 trials of fivefold cross-validation and the average receiver operating curve was accessed. The AUC of the cross-validation study (AUCCV) was calculated together with its 95% confidence interval. RESULTS: The AUCCV (and 95% confidence interval) for models T, CS, and CST was 0.85 (0.71-0.96), 0.88 (0.77-0.96), and 0.88 (0.77-0.97), respectively. After separating the data into two groups with or without contrast enhancement, the AUC (without cross-validation) of the model T was 0.86 both for images with and without contrast enhancement, suggesting that contrast enhancement did not impact the utility of texture analysis. CONCLUSIONS: The models with semantic and texture features provided cross-validated AUCs of 0.85-0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients. KEY POINTS: • Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way. • Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images. • Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.

An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms.

Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU-2 Image Quality (IQ) phantom is a time-consuming process that allows for interuser variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open-source plug-in to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows run-times between 3 and 4 min and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and maximum values on the order of 0.5% and 2%, respectively, over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU-2 IQ phantom for PET/CT scanner acceptance testing and QA/QC.

A Digital Reference Object to Analyze Calculation Accuracy of PET Standardized Uptake Value.

PURPOSE: To determine the extent of variations in computing standardized uptake value (SUV) by body weight (SUV(BW)) among different software packages and to propose a Digital Imaging and Communications in Medicine (DICOM) reference test object to ensure the standardization of SUV computation between medical image viewing workstations. MATERIALS AND METHODS: Research ethics board approval was not necessary because this study only evaluated images of a phantom. A synthetic set of positron emission tomographic (PET)/computed tomographic (CT) image data, called a digital reference object (DRO), with known SUV was created. The DRO was sent to 16 sites and evaluated on 21 different PET/CT display software packages. Users were asked to draw various regions of interest (ROIs) on specific features and report the maximum, minimum, mean, and standard deviation of the SUVs for each ROI. Numerical tolerances were defined for each metric, and the fraction of reported values within the tolerance was recorded, as was the mean, standard deviation, and range of the metrics. RESULTS: The errors in reported maximum SUV ranged from -37.8% to 0% for an isolated voxel with 4.11:1 target-to-background activity level, and errors in the reported mean SUV ranged from -1.6% to 100% for a region with controlled noise. There was also a range of errors in the less commonly used metrics of minimum SUV and standard deviation SUV. CONCLUSION: The variability of computed SUV(BW) between different software packages is substantial enough to warrant the introduction of a reference standard for medical image viewing workstations.

A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA.

Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT)-derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression-free survival (PFS) and overall survival (OS) predictions. Baseline FDG-PET scans were available for 1263 patients, 832 patients of these were cell-of-origin (COO)-evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low-, intermediate- and high-risk groups. The random forest model with COO subgroups identified a clearer high-risk population (45% 2-year PFS [95% confidence interval (CI) 40%-52%]; 65% 2-year OS [95% CI 59%-71%]) than the IPI (58% 2-year PFS [95% CI 50%-67%]; 69% 2-year OS [95% CI 62%-77%]). This study confirms that standard clinical risk factors can be combined with PET-derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.

Resolution Properties of a Prototype Continuous Miniature Crystal Element (cMiCE) Scanner.

Continuous miniature crystal element (cMiCE) detectors are a potentially lower cost alternative for high resolution discrete crystal PET detector designs. We report on performance characteristics of a prototype PET scanner consisting of two cMiCE detector modules. Each cMiCE detector is comprised of a 50 × 50 × 8 mm3 LYSO crystal coupled to a 64 channel multi-anode PMT. The cMiCE detectors use a statistics-based positioning method based upon maximum likelihood estimation for event positioning. By this method, cMiCE detectors can also provide some depth of interaction event positioning information. For the prototype scanner, the cMiCE detectors were positioned across from one another on a horizontal gantry with a detector spacing of 10.7 cm. Full tomographic data were collected and reconstructed using single slice rebinning and filtered back projection with no smoothing. The average image resolutions in X (radial), Y (transverse) and Z (axial) were 1.05 ± 0.08 mm, 0.99 ± 0.07 mm, 1.24 ± 0.31 mm FWHM. These initial imaging results from a prototype imaging system demonstrate the outstanding image resolution performance that can be achieved using the potentially lower cost cMiCE detectors.

Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer.

We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and mid-chemoradiation fluorodeoxyglucose-positron emission tomography (FDG PET) images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV/MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p < 0.001). Between PETpre and PETmid, MTV cluster pairs overlapped (Dice 0.70-0.87) and migrated 0.6-1.1 cm. PETmid ΔSUVmean response prediction was superior in MTV and MTVlo (RMSE = 0.17-0.21) compared to MTVhi (RMSE = 0.42-0.52, Friedman p < 0.001). PETmid ΔSUVmean response class prediction performance trended higher in MTVlo (AUC = 0.83-0.88) compared to MTVhi (AUC = 0.44-0.58, Friedman p = 0.052). Models were more sensitive to MTV/MTV cluster regions (Friedman p = 0.026) than feature sets/algorithms (Wilcoxon signed-rank p = 0.36). Top-ranked radiomic features included GLZSM-LZHGE (large-zone-high-SUV), GTSDM-CP (cluster-prominence), GTSDM-CS (cluster-shade) and NGTDM-CNT (contrast). Top-ranked features were consistent between MTVhi and MTVlo cluster pairs but varied between MTVhi-MTVlo clusters, reflecting distinct regional radiomic phenotypes. Variability in tumor voxel cluster response prediction can inform robust radiomic target definition for risk-adaptive chemoradiation in patients with LA-NSCLC. FLARE-RT trial: NCT02773238.

Cellular localization of bursicon using antisera against partial peptide sequences of this insect cuticle-sclerotizing neurohormone.

Bursicon is the final neurohormone released at the end of the molting cycle. It triggers the sclerotization (tanning) of the insect cuticle. Until now, its existence has been verified only by bioassays. In an attempt to identify this important neurohormone, bursicon was purified from homogenates of 2,850 nerve cords of the cockroach Periplaneta americana by using high performance liquid chromatography technology and two-dimensional gel electrophoresis. Bursicon bioactivity was found in four distinct protein spots at approximately 30 kDa between pH 5.3 and 5.9. The protein of one of these spots at pH 5.7 was subsequently microsequenced, and five partial amino acid sequences were retrieved. Evidence is presented that two of these sequences are derived from bursicon. Antibodies raised against the two sequences labeled bursicon-containing neurons in the central nervous systems of P. americana. One of these antisera labeled bursicon-containing neurons in the crickets Teleogryllus commodus and Gryllus bimaculatus, and the moth Manduca sexta. A cluster of four bilaterally paired neurons in the brain of Drososphila melanogaster was also labeled. In addition, this antiserum detected three spots corresponding to bursicon in Western blots of two-dimensional gels. The 12-amino acid sequence detected by this antiserum, thus, seems to be conserved even among species that are distantly related.

Biases in Multicenter Longitudinal PET Standardized Uptake Value Measurements.

This study investigates measurement biases in longitudinal positron-emission tomography/computed tomography (PET/CT) studies that are due to instrumentation variability including human error. Improved estimation of variability between patient scans is of particular importance for assessing response to therapy and multicenter trials. We used National Institute of Standards and Technology-traceable calibration methodology for solid germanium-68/gallium-68 ((68)Ge/(68)Ga) sources used as surrogates for fluorine-18 ((18)F) in radionuclide activity calibrators. One cross-calibration kit was constructed for both dose calibrators and PET scanners using the same 9-month half-life batch of (68)Ge/(68)Ga in epoxy. Repeat measurements occurred in a local network of PET imaging sites to assess standardized uptake value (SUV) errors over time for six dose calibrators from two major manufacturers and for six PET/CT scanners from three major manufacturers. Bias in activity measures by dose calibrators ranged from -50% to 9% and was relatively stable over time except at one site that modified settings between measurements. Bias in activity concentration measures by PET scanners ranged from -27% to 13% with a median of 174 days between the six repeat scans (range, 29 to 226 days). Corresponding errors in SUV measurements ranged from -20% to 47%. SUV biases were not stable over time with longitudinal differences for individual scanners ranging from -11% to 59%. Bias in SUV measurements varied over time and between scanner sites. These results suggest that attention should be paid to PET scanner calibration for longitudinal studies and use of dose calibrator and scanner cross-calibration kits could be helpful for quality assurance and control.

Assessment of patient selection criteria for quantitative imaging with respiratory-gated positron emission tomography.

The objective of this investigation was to propose techniques for determining which patients are likely to benefit from quantitative respiratory-gated imaging by correlating respiratory patterns to changes in positron emission tomography (PET) metrics. Twenty-six lung and liver cancer patients underwent PET/computed tomography exams with recorded chest/abdominal displacements. Static and adaptive amplitude-gated [[Formula: see text]]fluoro-D-glucose (FDG) PET images were generated from list-mode acquisitions. Patients were grouped by respiratory pattern, lesion location, or degree of lesion attachment to anatomical structures. Respiratory pattern metrics were calculated during time intervals corresponding to PET field of views over lesions of interest. FDG PET images were quantified by lesion maximum standardized uptake value ([Formula: see text]). Relative changes in [Formula: see text] between static and gated PET images were tested for association to respiratory pattern metrics. Lower lung lesions and liver lesions had significantly higher changes in [Formula: see text] than upper lung lesions (14 versus 3%, [Formula: see text]). Correlation was highest ([Formula: see text], [Formula: see text], [Formula: see text]) between changes in [Formula: see text] and nonstandard respiratory pattern metrics. Lesion location had a significant impact on changes in PET quantification due to respiratory gating. Respiratory pattern metrics were correlated to changes in [Formula: see text], though sample size limited statistical power. Validation in larger cohorts may enable selection of patients prior to acquisition who would benefit from respiratory-gated PET imaging.

Inhibition of BET bromodomain targets genetically diverse glioblastoma.

PURPOSE: Glioblastoma is refractory to conventional therapies. The bromodomain and extraterminal domain (BET) proteins are epigenetic readers that selectively bind to acetylated lysine residues on histone tails. These proteins recently emerged as important therapeutic targets in NUT midline carcinoma and several types of hematopoietic cancers. In this study, the therapeutic potential of a novel BET bromodomain inhibitor, JQ1, was assessed in a panel of genetically heterogeneous glioblastoma samples. EXPERIMENTAL DESIGN: The antineoplastic effects of JQ1 were shown using ex vivo cultures derived from primary glioblastoma xenograft lines and surgical specimens of different genetic background. The in vivo efficacy was assessed in orthotopic glioblastoma tumors. RESULTS: We showed that JQ1 induced marked G1 cell-cycle arrest and apoptosis, which was phenocopied by knockdown of individual BET family members. JQ1 treatment resulted in significant changes in expression of genes that play important roles in glioblastoma such as c-Myc, p21(CIP1/WAF1), hTERT, Bcl-2, and Bcl-xL. Unlike the observations in some hematopoietic cancer cell lines, exogenous c-Myc did not significantly protect glioblastoma cells against JQ1. In contrast, ectopically expressed Bcl-xL partially rescued cells from JQ1-induced apoptosis, and knockdown of p21(CIP1/WAF1) attenuated JQ1-induced cell-cycle arrest. Cells genetically engineered for Akt hyperactivation or p53/Rb inactivation did not compromise JQ1 efficacy, suggesting that these frequently mutated signaling pathways may not confer resistance to JQ1. Furthermore, JQ1 significantly repressed growth of orthotopic glioblastoma tumors. CONCLUSION: Our results suggest potentially broad therapeutic use of BET bromodomain inhibitors for treating genetically diverse glioblastoma tumors.

Quantitative assessment of dynamic PET imaging data in cancer imaging.

Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration. However, dynamic tracer imaging can provide considerably more information about in vivo biology by delineating both the temporal and spatial pattern of tracer uptake. In addition, several potential sources of error that occur in static imaging can be mitigated. This review focuses on the application of dynamic PET imaging to measuring regional cancer biologic features and especially in using dynamic PET imaging for quantitative therapeutic response monitoring for cancer clinical trials. Dynamic PET imaging output parameters, particularly transport (flow) and overall metabolic rate, have provided imaging end points for clinical trials at single-center institutions for years. However, dynamic imaging poses many challenges for multicenter clinical trial implementations from cross-center calibration to the inadequacy of a common informatics infrastructure. Underlying principles and methodology of PET dynamic imaging are first reviewed, followed by an examination of current approaches to dynamic PET image analysis with a specific case example of dynamic fluorothymidine imaging to illustrate the approach.

Identification of blood protein biomarkers that aid in the clinical assessment of patients with malignant glioma.

Analyzing molecular biomarkers using blood is an important approach for clinical assessment of malignant glioma. We investigated a molecular proteomic biomarker-based approach for glioblastoma using patients' blood samples. The expression levels of a list of candidate proteins were quantified in plasma and serum samples from two different cohorts of patients with malignant glioma and normal controls. The biological function was studied for one of the identified markers. Additionally, the prognostic significance of protein marker expression was measured by survival analysis. As a result, protein biomarkers associated with malignant glioma were identified from the blood specimens and five of the protein biomarkers were common to both cohorts. Immunohistochemical analysis demonstrated that many of the protein biomarkers identified in peripheral blood specimens were expressed in malignant gliomas. Staining levels for one of the biomarkers, MIP-1α, was found to correlate with WHO grade among invasive gliomas, and we demonstrate that MIP-1α promotes human glioblastoma cell proliferation and migration. Additionally, four prognostic protein biomarkers were identified. In conclusion, we demonstrate that both peripheral blood plasma and serum specimens are highly valuable and complementary to each other in the quest for protein biomarkers of malignant glioma. Sets of novel protein biomarkers were identified that may aid in the diagnosis and prognosis of patients with malignant glioma.

The impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging.

Our aim is to investigate the impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging using a population of patient respiratory traces. A total of 1295 respiratory traces acquired during whole body PET/CT imaging were classified into three types according to the qualitative shape of their signal histograms. Each trace was scaled to three diaphragm motion amplitudes (6 mm, 11 mm and 16 mm) to drive a whole body PET/CT computer simulation that was validated with a physical phantom experiment. Three lung lesions and one liver lesion were simulated with diameters of 1 cm and 2 cm. PET data were reconstructed using the OS-EM algorithm with attenuation correction using CT images at the end-expiration phase and respiratory-averaged CT. The errors of the lesion maximum standardized uptake values (SUV(max)) and lesion volumes between motion-free and motion-blurred PET/CT images were measured and analyzed. For respiration with 11 mm diaphragm motion and larger quiescent period fraction, respiratory motion can cause a mean lesion SUV(max) underestimation of 28% and a mean lesion volume overestimation of 130% in PET/CT images with 1 cm lesions. The errors of lesion SUV(max) and volume are larger for patient traces with larger motion amplitudes. Smaller lesions are more sensitive to respiratory motion than larger lesions for the same motion amplitude. Patient respiratory traces with relatively larger quiescent period fraction yield results less subject to respiratory motion than traces with long-term amplitude variability. Mismatched attenuation correction due to respiratory motion can cause SUV(max) overestimation for lesions in the lower lung region close to the liver dome. Using respiratory-averaged CT for attenuation correction yields smaller mismatch errors than those using end-expiration CT. Respiratory motion can have a significant impact on static oncological PET/CT imaging where SUV and/or volume measurements are important. The impact is highly dependent upon motion amplitude, lesion location and size, attenuation map and respiratory pattern. To overcome the motion effect, motion compensation techniques may be necessary in clinical practice to improve the tumor quantification for determining the response to therapy or for radiation treatment planning.