Structural Lung Disease and Clinical Phenotype in Bronchiectasis Patients: The EMBARC CT Study.

Rationale: Chest computed tomography (CT) scans are essential to diagnose and monitor bronchiectasis (BE). To date, few quantitative data are available about the nature and extent of structural lung abnormalities (SLAs) on CT scans of patients with BE. Objectives: To investigate SLAs on CT scans of patients with BE and the relationship of SLAs to clinical features using the EMBARC (European Multicenter Bronchiectasis Audit and Research Collaboration) registry. Methods: CT scans from patients with BE included in the EMBARC registry were analyzed using the validated Bronchiectasis Scoring Technique for CT (BEST-CT). The subscores of this instrument are expressed as percentages of total lung volume. The items scored are atelectasis/consolidation, BE with and without mucus plugging (MP), airway wall thickening, MP, ground-glass opacities, bullae, airways, and parenchyma. Four composite scores were calculated: total BE (i.e., BE with and without MP), total MP (i.e., BE with MP plus MP alone), total inflammatory changes (i.e., atelectasis/consolidation plus total MP plus ground-glass opacities), and total disease (i.e., all items but airways and parenchyma). Measurements and Main Results: CT scans of 524 patients with BE were analyzed. Mean subscores were 4.6 (range, 2.3-7.7) for total BE, 4.2 (1.2-8.1) for total MP, 8.3 (3.5-16.7) for total inflammatory changes, and 14.9 (9.1-25.9) for total disease. BE associated with primary ciliary dyskinesia was associated with more SLAs, whereas chronic obstructive pulmonary disease was associated with fewer SLAs. Lower FEV1, longer disease duration, Pseudomonas aeruginosa and nontuberculous mycobacterial infections, and severe exacerbations were all independently associated with worse SLAs. Conclusions: The type and extent of SLAs in patients with BE are highly heterogeneous. Strong relationships between radiological disease and clinical features suggest that CT analysis may be a useful tool for clinical phenotyping.

Descending thoracic aortic mural ulceration is associated with postoperative spinal cord ischemia after branched endovascular aortic aneurysm repair.

OBJECTIVE: Paraplegia is one of the most feared complications after thoracoabdominal aortic aneurysm repair. The purpose of this study is to determine whether aortic thrombus characteristics are associated with spinal cord ischemia (SCI) after branched endovascular aneurysm repair (BEVAR). METHODS: From April 2011 to April 2020, 62 patients underwent elective BEVAR for thoracoabdominal aortic aneurysm and pararenal aortic aneurysms using a low-profile device and had a complete preoperative computed tomography angiography of the aorta from the sinotubular junction to the aortic bifurcation. Aortic thrombus was evaluated for thrombus thickness ≥5 mm, thrombus >2/3 of aortic circumference, and the presence of an ulcer-like thrombus. One point was assigned at each 5 mm axial image if all 3 criteria were met, resulting in a total "shaggy score" for the entire aorta. Data on demographics, procedural details, and outcomes were collected prospectively. All patients underwent a standard spinal cord protection protocol, including routine cerebrospinal fluid drainage. In July 2016, an insulin infusion protocol (IIP) was initiated to maintain postoperative blood glucose levels <120 mg/dL for 48 hours. The primary clinical end point was postoperative SCI. RESULTS: 10 (16%) patients developed postoperative SCI: 6 with transient paraparesis, 2 with persistent paraparesis, and 2 with persistent paraplegia. Patients with SCI were older, had higher shaggy scores, and were less likely to have been on an IIP. There were no significant differences in demographics, aneurysm type, or operative parameters. In a logistic multivariate regression model for SCI, age (odds ratio [OR]: 1.2 [1.1-1.4], P = .02) and shaggy score (OR: 1.2 [1.1-1.4], P = .02) were independently associated with increased risk of SCI, whereas treatment with the IIP was associated with lower risk of SCI (OR: 0.04 [0.006-0.50], P = .05). Of the individual components of the shaggy score, higher descending thoracic aortic ulcer scores were the most strongly associated with postoperative SCI (P = .009). CONCLUSIONS: Preoperative characterization of aortic wall thrombus is an important adjunctive tool for individualized clinical decision-making and patient counseling about the risk of SCI after BEVAR.

Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.

PURPOSE: Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines. METHODS: A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality. RESULTS: Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings. CONCLUSION: A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.

Quantitative Imaging Reveals Distinct Contributions of SnRK2 and ABI3 in Plasmodesmatal Permeability in Physcomitrella patens.

Cell-to-cell communication is tightly regulated in response to environmental stimuli in plants. We previously used a photoconvertible fluorescent protein Dendra2 as a model reporter to study this process. This experiment revealed that macromolecular trafficking between protonemal cells in Physcomitrella patens is suppressed in response to abscisic acid (ABA). However, it remains unknown which ABA signaling components contribute to this suppression and how. Here, we show that ABA signaling components SUCROSE NON-FERMENTING 1-RELATED PROTEIN KINASE 2 (PpSnRK2) and ABA INSENSITIVE 3 (PpABI3) play roles as an essential and promotive factor, respectively, in regulating ABA-induced suppression of Dendra2 diffusion between cells (ASD). Our quantitative imaging analysis revealed that disruption of PpSnRK2 resulted in defective ASD onset itself, whereas disruption of PpABI3 caused an 81-min delay in the initiation of ASD. Live-cell imaging of callose deposition using aniline blue staining showed that, despite this onset delay, callose deposition on cross walls remained constant in the PpABI3 disruptant, suggesting that PpABI3 facilitates ASD in a callose-independent manner. Given that ABA is an important phytohormone to cope with abiotic stresses, we further explored cellular physiological responses. We found that the acquisition of salt stress tolerance is promoted by PpABI3 in a quantitative manner similar to ASD. Our results suggest that PpABI3-mediated ABA signaling may effectively coordinate cell-to-cell communication during the acquisition of salt stress tolerance. This study will accelerate the quantitative study for ABA signaling mechanism and function in response to various abiotic stresses.

Oligomeganephronia with PAX2 gene deletion diagnosed at the third renal biopsy: a case report.

Oligomeganephronia is a congenital anomaly of the kidney and urinary tract. It is often categorized as one of the hypoplastic kidney conditions. The pathological diagnosis of oligomeganephronia is challenged by the absence of clear diagnostic criteria, which often leads to subjective interpretations by pathologists. This report presents the case of a 7-year-old girl who was diagnosed with oligomeganephronia through a third renal biopsy, which was confirmed by gene analysis revealing PAX2 deletion. Two previous renal biopsies, with the naked eye through a microscope, failed to identify glomerular hypertrophy and sparse glomerular distribution density. However, using digital images, the glomeruli were larger than those of age-matched controls, and the number of glomeruli within the renal cortex area revealed sparse glomerular distribution density. Image processing allows for objective evaluation of the glomerular size and glomerular distribution density, providing a quantitative assessment. For earlier diagnosis of oligomeganephronia, an appropriate objective standardized method for measuring glomerular size and glomerular distribution density should be established.

3D-printed iodine-ink CT phantom for radiomics feature extraction - advantages and challenges.

BACKGROUND: To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D-printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms. PURPOSE: To present the design process of an anthropomorphic 3D-printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's X-ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset. METHODS: Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26 × 19 × 30 cm3 . This DICOM dataset was printed on paper using iodinated ink. The phantom was CT-scanned and compared to the original image dataset used for printing the phantom. The water-equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual-energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus. RESULTS: Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has X-ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2 ). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid-frequencies (e.g., for ω = 0.3942 mm-1 , |f(ω)|original  = 0.09 * |fmax |original and |f(ω)|phantom  = 0.12 * |fmax |phantom ). CONCLUSIONS: 3D-iodine-ink-printing technology can be used to print anthropomorphic phantoms with a water-equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual-energy CT analyses.

Multiplex immunohistochemistry and high-throughput image analysis for evaluation of spatial tumor immune cell markers in human breast cancer.

BACKGROUND: The clinicopathological significance of spatial tumor-infiltrating lymphocytes (TILs) subpopulations is not well studied due to lack of high-throughput scalable methodology for studies with large human sample sizes. OBJECTIVE: Establishing a cyclic fluorescent multiplex immunohistochemistry (mIHC/IF) method coupled with computer-assisted high-throughput quantitative analysis to evaluate associations of six TIL markers (CD3, CD8, CD20, CD56, FOXP3, and PD-L1) with clinicopathological factors of breast cancer. METHODS: Our 5-plex mIHC/IF staining was shown to be reliable and highly sensitive for labeling three biomarkers per tissue section. Through repetitive cycles of 5-plex mIHC/IF staining, more than 12 biomarkers could be detected per single tissue section. Using open-source software CellProfiler, the measurement pipelines were successfully developed for high-throughput multiplex evaluation of intratumoral and stromal TILs. RESULTS: In analyses of 188 breast cancer samples from the Nashville Breast Health Study, high-grade tumors showed significantly increased intratumoral CD3+CD8+ cytotoxic T lymphocyte density (P= 0.0008, false discovery rate (FDR) adjusted P= 0.0168) and intratumoral PD-L1 expression (P= 0.0061, FDR adjusted P= 0.0602) compared with low-grade tumors. CONCLUSIONS: The high- and low-grade breast cancers exhibit differential immune responses which may have clinical significance. The multiplexed imaging quantification strategies established in this study are reliable, cost-efficient and applicable in regular laboratory settings for high-throughput tissue biomarker studies, especially retrospective and population-based studies using archived paraffin tissues.

Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer.

The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63-0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis.

Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review.

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on 18F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.

Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.

PURPOSE: To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset. METHODS: A dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75-mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77). Remaining 1917 CTs were equally allocated between R1 and R2 for volumetric pancreas segmentation [ground truth (GT)]. This internal dataset was randomly divided into training (n = 1380), validation (n = 248), and test (n = 289) sets for the development of a two-stage 3D CNN model based on a modified U-net architecture for automated volumetric pancreas segmentation. Model's performance for pancreas segmentation and the differences in model-predicted pancreatic volumes vs GT volumes were compared on the test set. Subsequently, an external dataset from The Cancer Imaging Archive (TCIA) that had CT scans acquired at standard radiation dose and same scans reconstructed at a simulated 25% radiation dose was curated (n = 41). Volumetric pancreas segmentation was done on this TCIA dataset by R1 and R2 independently on the full dose and then at the reduced radiation dose CT images. Intra-reader and inter-reader reliability, model's segmentation performance, and reliability between model-predicted pancreatic volumes at full vs reduced dose were measured. Finally, model's performance was tested on the benchmarking National Institute of Health (NIH)-Pancreas CT (PCT) dataset. RESULTS: Three-dimensional CNN had mean (SD) Dice similarity coefficient (DSC): 0.91 (0.03) and average Hausdorff distance of 0.15 (0.09) mm on the test set. Model's performance was equivalent between males and females (P = 0.08) and across different CT slice thicknesses (P > 0.05) based on noninferiority statistical testing. There was no difference in model-predicted and GT pancreatic volumes [mean predicted volume 99 cc (31cc); GT volume 101 cc (33 cc), P = 0.33]. Mean pancreatic volume difference was -2.7 cc (percent difference: -2.4% of GT volume) with excellent correlation between model-predicted and GT volumes [concordance correlation coefficient (CCC)=0.97]. In the external TCIA dataset, the model had higher reliability than R1 and R2 on full vs reduced dose CT scans [model mean (SD) DSC: 0.96 (0.02), CCC = 0.995 vs R1 DSC: 0.83 (0.07), CCC = 0.89, and R2 DSC:0.87 (0.04), CCC = 0.97]. The DSC and volume concordance correlations for R1 vs R2 (inter-reader reliability) were 0.85 (0.07), CCC = 0.90 at full dose and 0.83 (0.07), CCC = 0.96 at reduced dose datasets. There was good reliability between model and R1 at both full and reduced dose CT [full dose: DSC: 0.81 (0.07), CCC = 0.83 and reduced dose DSC:0.81 (0.08), CCC = 0.87]. Likewise, there was good reliability between model and R2 at both full and reduced dose CT [full dose: DSC: 0.84 (0.05), CCC = 0.89 and reduced dose DSC:0.83(0.06), CCC = 0.89]. There was no difference in model-predicted and GT pancreatic volume in TCIA dataset (mean predicted volume 96 cc (33); GT pancreatic volume 89 cc (30), p = 0.31). Model had mean (SD) DSC: 0.89 (0.04) (minimum-maximum DSC: 0.79 -0.96) on the NIH-PCT dataset. CONCLUSION: A 3D CNN developed on the largest dataset of CTs is accurate for fully automated volumetric pancreas segmentation and is generalizable across a wide range of CT slice thicknesses, radiation dose, and patient gender. This 3D CNN offers a scalable tool to leverage biomarkers from pancreas morphometrics and radiomics for pancreatic diseases including for early pancreatic cancer detection.

Design of an anthropomorphic PET phantom with elastic lungs and respiration modeling.

PURPOSE: Respiratory motion during positron emission tomography (PET) scans can be a major detriment to image quality in oncological imaging. The impact of motion on lesion quantification and detectability can be assessed using phantoms with realistic anatomy representation and motion modeling. In this work, we develop an anthropomorphic phantom for PET imaging that combines anatomic fidelity and a realistic breathing mechanism with deformable lungs. METHODS: We start from a previously developed anatomically accurate but static phantom of a human torso, and add elastic lungs with a highly controllable actuation mechanism which replicates the physics of breathing. The space outside the lungs is filled with a radioactive water solution. To maintain anatomical accuracy and realistic gamma ray attenuation in the torso, all motion mechanisms and actuators are positioned outside of the phantom compartment. The actuation mechanism can produce custom respiratory waveforms with breathing rates up to 25 breaths per minute and tidal volumes up to 1200 mL. RESULTS: Several tests were performed to validate the performance of the phantom assembly, in which the phantom was filled with water and given respiratory waveforms to execute. All parts demonstrated expected performance. Force requirements were not exceeded and no leaks were detected, although continued use of the phantom is required to evaluate wear. The motion of the lungs was determined to be within a reasonable realistic range. CONCLUSIONS: The full mechanical design is described in this paper, as well as a software application with graphical user interface which was developed to plan and visualize respiratory patterns. Both are available online as open source files. The developed phantom will facilitate future work in evaluating the impact of respiratory motion on lesion quantification and detectability in clinical practice.

Determination of the optimal range for virtual monoenergetic images in dual-energy CT based on physical quality parameters.

PURPOSE: Virtual monoenergetic images (VMI) obtained from Dual-Energy Computed Tomography (DECT) with iodinated contrast are used in radiotherapy of the Head and Neck to improve the delineation of target volumes and organs at-risk (OAR). The energies used to vary from 40 to 70 keV, but noise at low keV and the use of Single Energy CT (SECT) at low kVp settings may shrink this interval. There is no guide about how to find out the optimal range where VMI has a significant improvement related to SECT images. Our study proposes a procedure to determine this optimal range, based on common image quality parameters, and establishes this range in a Siemens Somatom Confidence and a Head and Neck protocol. METHODS: We compared the quality of the VMI series at 40-60 keV versus single X-ray tube voltage computed tomography (SECT) at 80 and 120 kVp . Our reference was 120 kVp . DECT images were sequentially acquired using the Siemens Somatom Confidence RT Pro CT according to the head and neck protocol in our department. VMI series were constructed using the Syngo Via software Monoenergetic+ algorithm. Quality parameters were: image uniformity, high- and low-contrast resolution, noise, and sensitivity to the iodinated contrast. We used the Catphan 604 phantom for quality control, except when assessing iodine sensitivity. To evaluate high contrast resolution, we calculated the modulation transfer function (MTF) using the point spread function estimation of a point bead and the slanted edge methods. For the low-contrast resolution, we used a statistical method for assessing differences between contrast structures and local noise. To measure the absolute value of noise and compare its texture, we used the standard deviation and the noise power spectrum. We measured iodine sensitivity by dissolving the Optiray Ultraject iodinated contrast in water in concentrations of 0 to 4500 mg/l and then compared the contrast to noise ratio (CNR) and analyzed the linear correlation between concentration and HU. RESULTS: The entire series met the minimum quality requirements. However, the one at 40 keV presented uniformity at the limits of acceptability. The high- and low-contrast resolutions were similar between series. The noise of the VMI series decreased with increasing energy, while sensitivity to the contrast displayed the opposite behavior. All series showed linearity of HUs from very low iodine concentrations. Images at 60 keV presented lower iodine sensitivity than SECT at 80 kVp , while those at 55 keV were similar to them. CONCLUSIONS: Our method of image comparison based on standard quality parameters in phantom gave clear results about the optimal range and can be used as a guide to characterize any other DECT imaging protocols. The optimal range for using VMI images in iodinated contrasts in the Siemens system was 45-55 keV. Lower energies lacked noise and uniformity, while higher ones could be substituted by SECT images at low kilovoltage (80 kVp ).

Technical Note: Human tissue-equivalent MRI phantom preparation for 3 and 7 Tesla.

PURPOSE: While test objects (phantoms) in magnetic resonance imaging (MRI) are crucial for sequence development, protocol validation, and quality control, studies on the preparation of phantoms have been scarce, particularly at fields exceeding 3 Tesla. Here, we present a framework for the preparation of phantoms with well-defined T1 and T2 times at 3 and 7 Tesla. METHODS: Phantoms with varying concentrations of agarose and Gd-DTPA were prepared and measured at 3 and 7 Tesla using T1 and T2 mapping techniques. An empirical, polynomial model was constructed that best represents the data at both field strengths, enabling the preparation of new phantoms with specified combinations of both T1 and T2 . Instructions for three different tissue types (brain gray matter, brain white matter, and renal cortex) are presented and validated. RESULTS: T1 times in the samples ranged from 698 to 2820 ms and from 695 to 2906 ms, whereas T2 times ranged from 39 to 227 ms and from 34 to 235 ms for 3 and 7 Tesla scans, respectively. Models for both relaxation times used six parameters to represent the data with an adjusted R² of 0.998 and 0.997 for T1 and T2 , respectively. CONCLUSION: Based on the equations derived from the current study, it is now possible to obtain accurate weight specifications for a test object with desired T1 and T2 relaxation times. This will spare researchers the laborious task of trail-and-error approaches in test object preparation attempts.

Reproducibility of lung nodule radiomic features: Multivariable and univariable investigations that account for interactions between CT acquisition and reconstruction parameters.

PURPOSE: Recent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel. METHOD: A cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer-aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6 mm, 1 mm, and 2 mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well-known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC ≥ 0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA. RESULTS: All included radiomic features lacked intercondition reproducibility (OCCC < 0.9) among all the 36 conditions. Out of 226 radiomic features analyzed, only 17 and 18 features were considered reproducible (OCCC ≥ 0.9) to dose and kernel variation, respectively, within the corresponding condition subsets. Slice thickness demonstrated the largest impact on radiomic feature values where only one to five features were reproducible at a few condition subsets. ANOVA revealed significant interactions (P < 0.05) between CT parameters affecting the variability of >50% of radiomic features. CONCLUSION: We systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies.

7T MR Thermometry technique for validation of system-predicted SAR with a home-built radiofrequency wrist coil.

PURPOSE: A 7T magnetic resonance thermometry (MRT) technique was developed to validate the conversion factor between the system-measured transmitted radiofrequency (RF) power into a home-built RF wrist coil with the system-predicted SAR value. The conversion factor for a new RF coil developed for ultra high magnetic field MRI systems is used to ensure that regulatory limits on RF energy deposition in tissue, specifically the local 10g-averaged specific absorption rate (SAR10g ), are not exceeded. MRT can be used to validate this factor by ensuring that MRT-measured SAR values do not exceed those predicted by the system. METHODS: A 14-cm diameter high-pass birdcage RF coil was built to image the wrist at 7T. A high spatial and temporal resolution dual-echo gradient echo MRT technique, incorporating quasi-simultaneous RF-induced heating and temperature change measurements using the proton resonance frequency method, was developed. The technique allowed for high-temperature resolution measurements (~±0.1°C) to be performed every 20 s over a 4-min heating period, with high spatial resolution (2.56 mm3 voxel size) and avoiding phase discontinuities arising from severe magnetic susceptibility-induced B0 inhomogeneities. Magnetic resonance thermometry was performed on a phantom made from polyvinylpyrrolidone to mimic the dielectric properties of muscle tissue at 297.2 MHz. Temperature changes measured with MRT and four fiber optic temperature sensors embedded in the phantom were compared. Electromagnetic simulations of the coil and phantom were developed and validated via comparison of simulated and measured B1 + maps in the phantom. The position of maximum SAR within the coil was determined from simulations, and MRT was performed within a wrist-sized piece of meat positioned at that SAR hotspot location. MRT-measured and system-predicted SAR values for the phantom and meat were compared. RESULTS: Temperature change measurements from MRT matched closely to those from the fiber optic temperature sensors. The simulations were validated via close correlation between the simulated and MRT-measured B1 + and SAR maps. Using a coil conversion factor of 2 kg-1 , MRT-measured point-SAR values did not exceed the system-predicted SAR10g in either the uniform phantom or in the piece of meat mimicking the wrist located at the SAR hotspot location. CONCLUSIONS: A highly accurate MRT technique with high spatial and temporal resolution was developed. This technique can be used to ensure that system-predicted SAR values are not exceeded in practice, thereby providing independent validation of SAR levels delivered by a newly built RF wrist coil. The MRT technique is readily generalizable to perform safety evaluations for other RF coils at 7T.

Quantitative positron emission tomography imaging in the presence of iodinated contrast media using electron density quantifications from dual-energy computed tomography.

PURPOSE: As preparation for future positron emission tomography (PET)/dual-energy computed tomography (DECT)T imaging modality and new possible clinical applications, the study aimed to evaluate the utility of clinically available spectral results from a DECT system for improving attenuation corrections of PET acquisitions in the presence of iodinated contrast media. The dependence of the accuracy of PET quantification values, reconstructed with conventional and spectral-based attenuation corrections, was examined as a function of the amount of iodine content and x-ray radiation exposure. METHODS: Measurements were performed on commercial PET/CT and DECT systems, using a semi-anthropomorphic phantom with seven centrifuge tubes in its bore. Five different configurations of tube contents were scanned by both PET/CT and DECT. With the aim of mimicking clinically observed concentrations, in all phantom configurations the center tube contained a high concentration of radionuclide while the peripheral tubes contained a lower concentration of radionuclide. Iodine content was incrementally increased between phantom configurations by replacing iodine-free tubes with tubes that contained the original radionuclide concentration within a 10 mg/ml iodine dilution. DECT-based attenuation correction maps were generated by scaling electron density spectral results into corresponding 511 keV photon linear attenuation coefficients. RESULTS: Mean SUV values obtained from the nominal PET reconstruction, using conventional CT images as input for the attenuation correction, demonstrate a monotonic increase of 8.6% when the water and radionuclide mixtures were replaced by iodine, water, and radionuclide (same level of activity) mixture. Mean SUV values obtained from the DECT-based reconstruction, in which the attenuation correction utilizes electron density values as input, demonstrate different, more stable behavior across all iodine insert configurations, with a standard deviation to mean ratio of less than 1%. This observed behavior was independent of the area size used for measurement. A minor radiation dose dependency of the electron density values (below 0.5%) was observed. This resulted in consistent (iodine independent) PET quantification behavior, which persisted even at the lowest radiation dose levels tested in our experiment, that is, 25% of the radiation dose utilized for CT acquisition in the clinical PET/CT protocol. CONCLUSIONS: Utilization of DECT-generated electron density estimations for attenuation correction benefit PET quantification consistency in the presence of iodine and at nominal and low DECT radiation exposure levels. The ability to correctly account for iodinated contrast media in PET acquisitions will allow the development of new clinical applications that rely on the quantitative capabilities of spectral CT technologies and modern PET systems.

Quantitative 18F-FDG PET-CT scan characteristics correlate with tuberculosis treatment response.

BACKGROUND: There is a growing interest in the use of F-18 FDG PET-CT to monitor tuberculosis (TB) treatment response. Tuberculosis lung lesions are often complex and diffuse, with dynamic changes during treatment and persisting metabolic activity after apparent clinical cure. This poses a challenge in quantifying scan-based markers of burden of disease and disease activity. We used semi-automated, whole lung quantification of lung lesions to analyse serial FDG PET-CT scans from the Catalysis TB Treatment Response Cohort to identify characteristics that best correlated with clinical and microbiological outcomes. RESULTS: Quantified scan metrics were already associated with clinical outcomes at diagnosis and 1 month after treatment, with further improved accuracy to differentiate clinical outcomes after standard treatment duration (month 6). A high cavity volume showed the strongest association with a risk of treatment failure (AUC 0.81 to predict failure at diagnosis), while a suboptimal reduction of the total glycolytic activity in lung lesions during treatment had the strongest association with recurrent disease (AUC 0.8 to predict pooled unfavourable outcomes). During the first year after TB treatment lesion burden reduced; but for many patients, there were continued dynamic changes of individual lesions. CONCLUSIONS: Quantification of FDG PET-CT images better characterised TB treatment outcomes than qualitative scan patterns and robustly measured the burden of disease. In future, validated metrics may be used to stratify patients and help evaluate the effectiveness of TB treatment modalities.

A siRNA Mediated Screen During C2C12 Myogenesis.

Myogenesis is a multistep process taking place during pre- and postnatal stages for muscle formation, growth, and regeneration. It is a highly regulated process involving many molecular factors which act during myoblast proliferation and differentiation. To provide new insights into the molecular mechanisms and interactions behind the regulation of these different steps, RNA interference is an efficient methodology to implement. We developed a high-throughput siRNA screen in C2C12 murine myoblast cells for identification of genes relevant to signaling pathways controlling muscle growth. The proposed protocol is based on (1) the analyses of a maximum number of cells/myotubes to detect and quantify both clear and subtle phenotypes during proliferation/fusion cells and (2) the use of two cellular fluorescent markers, DAPI and myosin, decorating nuclei and myotubes respectively. Four phenotypic criteria were quantitatively assessed: cellular density, myotubes quantity, fusion index, and size and morphology of myotubes.

Development of a Composite Outcome Measure for Systemic Sclerosis Related Interstitial Lung Disease.

OBJECTIVE: While systemic sclerosis-related interstitial lung disease (SSc-ILD) trials predominantly use forced vital capacity (FVC) as the primary outcome, combining individual outcomes may lead to a more comprehensive measure of treatment response and minimize the risk of type 1 error. The present analysis aimed to develop a composite outcome measure to assess treatment response in SSc-ILD patients. METHODS: We used data from the Scleroderma Lung Study I (SLS-I) to create the composite outcome measure. SLS I was a multi-institutional, double-blind clinical trial, in which 158 patients with SSc-ILD were randomized to receive either oral cyclophosphamide (CYC) (titrated to 2.0 mg/kg once daily) or matching placebo for one year. To select the variables for inclusion in the composite outcome, we first performed a univariate analysis using all of the outcome variables measured in SLS I. We subsequently combined the variables with significant treatment effects (p<0.05) in a principal component analysis (PCA) to assess the difference between treatment groups. These variables included the FVC% predicted, computer-based score for quantitative lung fibrosis in the zone of maximum fibrosis (QLF-ZM) from thoracic high-resolution computer tomography (HRCT) scans, transitional dyspnea index (TDI), and the Health Assessment Questionnaire-Disability Index (HAQ-DI) at 12 months. RESULTS: Of the 158 patients, 82 had complete outcome data and were included in this analysis. There were no significant differences in baseline characteristics between the 82 patients included in this analysis and the remaining 76 patients. The regression model with the first principal component for FVC% predicted, QLF-ZM, TDI and HAQ-DI as the composite outcome demonstrated a significant treatment effect favoring cyclophosphamide (Estimate 0.7 [SE 0.2]; p=0.005). Eliminating FVC% predicted from the composite outcome model did not change the overall treatment effect (Estimate 0.8 [SE 0.2]; p=0.004). CONCLUSION: The CYC treatment effect observed from using the composite outcome of FVC% predicted, QLF-ZM, TDI and HAQ-DI was stronger than the effect observed using FVC% predicted alone. These findings suggest that combining patient-reported outcomes with structural and physiologic outcomes into a single outcome may serve as a more robust measure of treatment response compared with FVC alone in SSc-ILD trials.