CT-based Method Research Articles

Motion and anatomy dual aware lung ventilation imaging by integrating Jacobian map and average CT image using dual path fusion network.

Deep learning-based computed tomography (CT) ventilation imaging (CTVI) is a promising technique for guiding functional lung avoidance radiotherapy (FLART). However, conventional approaches, which rely on anatomical CT data, may overlook important ventilation features due to the lack of motion data integration. This study aims to develop a novel dual-aware CTVI method that integrates both anatomical information from CT images and motional information from Jacobian maps to generate more accurate ventilation images for FLART. A dataset of 66 patients with four-dimensional CT (4DCT) images and reference ventilation images (RefVI) was utilized to develop the dual-path fusion network (DPFN) for synthesizing ventilation images (CTVIDual). The DPFN model was specifically designed to integrate motion data from 4DCT-generated Jacobian maps with anatomical data from average 4DCT images. The DPFN utilized two specialized feature extraction pathways, along with encoders and decoders, designed to handle both 3D average CT images and Jacobian map data. This dual-processing approach enabled the comprehensive extraction of lung ventilation-related features. The performance of DPFN was assessed by comparing CTVIDual to RefVI using various metrics, including Spearman's correlation coefficients (R), Dice similarity coefficients of high-functional region (DSCh), and low-functional region (DSCl). Additionally, CTVIDual was benchmarked against other CTVI methods, including a dual-phase CT-based deep learning method (CTVIDLCT), a radiomics-based method (CTVIFM), a super voxel-based method (CTVISVD), a Unet-based method (CTVIUnet), and two deformable registration-based methods (CTVIJac and CTVIHU). In the test group, the mean R between CTVIDual and RefVI was 0.70, significantly outperforming CTVIDLCT (0.68), CTVIFM (0.58), CTVISVD (0.62), and CTVIUnet (0.66), with p<0.05. Furthermore, the DSCh and DSCl values of CTVIDual were 0.64 and 0.80, respectively, outperforming CTVISVD (0.63; 0.73) and CTVIUnet (0.62; 0.77). The performance of CTVIDual was also significantly better than that of CTVIJac and CTVIHU. A novel dual-aware CTVI model that integrates anatomical and motion information was developed to synthesize lung ventilation images. It was shown that the accuracy of lung ventilation estimation could be significantly enhanced by incorporating motional information, particularly in patients with tumor-induced blockages. This approach has the potential to improve the accuracy of CTVI, enabling more effective FLART.

CT-Based method for determining the elastic properties of an inhomogeneous medium element

The determination of mechanical parameters is one of the relevant directions in the calculation of inhomogeneous media elements. The need to build numerical models is primarily due to the development of modern approaches to the reconstruction of materials of various composite structures. The use of computational models in clinical practice also makes it possible to study the strength characteristics of objects of biological origin, the features of which include the uniqueness of the material structural distribution. The most widespread approaches in this case are based on the use of image data. Within the framework of this work, a method for constructing an elasticity matrix based on digital twin data is presented. During the work, images with a given material structure are constructed, as well as data extraction from CT scans of full-scale samples. The averaging of mechanical parameters was performed on the basis of the finite element method for the area of square or cubic geometry defining the unit cell. The calculations performed by the proposed method showed the convergence of the method with the analytical calculation data on the example of solving test problems, and also determined the qualitative correspondence of the results to the features of the structural distribution of the material in full-scale samples. The presented homogenization method makes it possible to determine the elastic characteristics of a medium element, taking into account the inhomogeneous properties of the forming material.

Advances in CT-based lung function imaging for thoracic radiotherapy.

The objective of this review is to examine the potential benefits and challenges of CT-based lung function imaging in radiotherapy over recent decades. This includes reviewing background information, defining related concepts, classifying and reviewing existing studies, and proposing directions for further investigation. The lung function imaging techniques reviewed herein encompass CT-based methods, specifically utilizing phase-resolved four-dimensional CT (4D-CT) or end-inspiratory and end-expiratory CT scans, to delineate distinct functional regions within the lungs. These methods extract crucial functional parameters, including lung volume and ventilation distribution, pivotal for assessing and characterizing the functional capacity of the lungs. CT-based lung ventilation imaging offers numerous advantages, notably in the realm of thoracic radiotherapy. By utilizing routine CT scans, additional radiation exposure and financial burdens on patients can be avoided. This imaging technique also enables the identification of different functional areas of the lung, which is crucial for minimizing radiation exposure to healthy lung tissue and predicting and detecting lung injury during treatment. In conclusion, CT-based lung function imaging holds significant promise for improving the effectiveness and safety of thoracic radiotherapy. Nevertheless, challenges persist, necessitating further research to address limitations and optimize clinical utilization. Overall, this review highlights the importance of CT-based lung function imaging as a valuable tool in radiotherapy planning and lung injury monitoring.

O83 ESTIMATING CARDIAC OUTPUT FROM ARTERIAL BLOOD PRESSURE WITHOUT PRESSURE WAVEFORMS

Background and Objective: Noninvasive estimations of cardiac Output (CO) using pulse wave analysis have been studied for over a century, yielding contrasting results. Our research group has developed a novel method for the noninvasive estimation of CO based on brachial blood pressure (BP) measurements and total arterial compliance (Ct), eliminating the need for arterial pulse wave analysis. This study compares all reported methods for estimating CO without pulse wave contours using the same dataset to assess their accuracy. Methods: A cross-sectional study was conducted involving 447 adult patients who underwent ambulatory transthoracic Doppler echocardiograms. Stroke volume (SV) was calculated as the product of the cross-sectional area of the aortic annulus and the average velocity of the left ventricular outflow tract. Simultaneously, oscillometric BP measurements were obtained, and CO was derived from Ct utilizing anthropometric variables, BP, and heart rates (HR). Calibration was conducted with 75% of the data using the formula: CO=K0+K1×estimator. The remaining 25% of the data was used for validation, with Bland-Altman analysis and the intraclass correlation coefficient (ICC). Calibration involved the following CO estimators: i) mean arterial pressure; ii) HR; iii) pulse pressure (PP); iv) PPxHR; v) Warner time-correction; vi) Liljestrand-Zander; vii) Wesseling-Langewouters; and viii) the new Ct-based method. Results: Of the participants, 51.2% were men, with an average age of 51.0±15.0 years, weight of 75.0±14.0 kg, and height of 166.0±9.5 cm. The CO by echocardiography had an average of 4.9±1.0 L/min, with a range of 2.0 to 8.5 L/min. Among the compared methods, the new Ct-based method showed the smallest mean difference and error, as well as the largest ICC. The Bland-Altman analysis shows a normal distribution for the new Ct-based method, with agreement limits between -1.56 to 1.48 L/min (Figure). Conclusions: The new Ct-based method developed surpasses other published methods and exhibits practical accuracy suitable for ambulatory use, such as hemodynamic monitoring of pathophysiological or therapeutic outcomes.

A dual-gene panel of two fragments of methylated IRF4 and one of ZEB2 in plasma cell-free DNA for gastric cancer detection

ABSTRACT Early detection is crucial for increasing the survival rate of gastric cancer (GC). We aimed to identify a methylated cell-free DNA (cfDNA) marker panel for detecting GC. The differentially methylated CpGs (DMCs) were selected from datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The selected DMCs were validated and further selected in tissue samples (40 gastric cancer and 36 healthy white blood cell samples) and in a quarter sample volume of plasma samples (37 gastric cancer, 12 benign gastric disease, and 43 healthy individuals). The marker combination selected was then evaluated in a normal sample volume of plasma samples (35 gastric cancer, 39 control diseases, and 40 healthy individuals) using real-time methylation-specific PCR (MSP). The analysis of the results compared methods based on 2−ΔΔCt values and Ct values. In the results, 30 DMCs were selected through bioinformatics methods, and then 5 were selected for biological validation. The marker combination of two fragments of IRF4 (IRF4–1 and IRF4–2) and one of ZEB2 was selected due to its good performance. The Ct-based method was selected for its good results and practical advantages. The assay, IRF4–1 and IRF4–2 in one fluorescence channel and ZEB2 in another, obtained 74.3% sensitivity for the GC group at any stage, at 92.4% specificity. In conclusion, the panel of IRF4 and ZEB2 in plasma cfDNA demonstrates good diagnostic performance and application potential in clinical settings.

CT-Based Evaluation of the Shape of the Diaphragm Using 3D Slicer.

The diaphragm is the main inspiratory muscle and separates the thorax and the abdomen. In COPD, the evaluation of the diaphragm shape is clinically important, especially in the case of hyperinflation. However, delineating the diaphragm remains a challenge as it cannot be seen entirely on CT scans. Therefore, the lungs, ribs, sternum, and lumbar vertebrae are used as surrogate landmarks to delineate the diaphragm. We herein describe a CT-based method for evaluating the shape of the diaphragm using 3D Slicer-a free software that allows delineation of the diaphragm landmarks-in ten COPD patients. Using the segmentation performed with 3D Slicer, the diaphragm shape was reconstructed with open-source Free Pascal Compiler. From this graduated model, the length of the muscle fibers, the radius of curvature, and the area of the diaphragm-the main determinants of its function-can be measured. Inter- and intra-user variabilities were evaluated with Bland and Altman plots and linear mixed models. Except for the coronal length (p = 0.049), there were not statistically significant inter- or intra-user differences (p values ranging from 0.326 to 0.910) suggesting that this method is reproducible and repeatable. In conclusion, 3D Slicer can be applied to CT scans for determining the shape of the diaphragm in COPD patients.

Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics.

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

Clinical Feasibility of Deep Learning-Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT.

We aimed to develop deep learning (DL)-based attenuation correction models for Tl-201 myocardial perfusion SPECT (MPS) images and evaluate their clinical feasibility. We conducted a retrospective study of patients with suspected or known coronary artery disease. We proposed a DL-based image-to-image translation technique to transform non-attenuation-corrected images into CT-based attenuation-corrected (CT AC ) images. The model was trained using a modified U-Net with structural similarity index (SSIM) loss and mean squared error (MSE) loss and compared with other models. Segment-wise analysis using a polar map and visual assessment for the generated attenuation-corrected (GEN AC ) images were also performed to evaluate clinical feasibility. This study comprised 657 men and 328 women (age, 65 ± 11 years). Among the various models, the modified U-Net achieved the highest performance with an average mean absolute error of 0.003, an SSIM of 0.990, and a peak signal-to-noise ratio of 33.658. The performance of the model was not different between the stress and rest datasets. In the segment-wise analysis, the myocardial perfusion of the inferior wall was significantly higher in GEN AC images than in the non-attenuation-corrected images in both the rest and stress test sets ( P < 0.05). In the visual assessment of patients with diaphragmatic attenuation, scores of 4 (similar to CT AC images) or 5 (indistinguishable from CT AC images) were assigned to most GEN AC images (65/68). Our clinically feasible DL-based attenuation correction models can replace the CT-based method in Tl-201 MPS, and it would be useful in case SPECT/CT is unavailable for MPS.

Segmentation of pelvic structures in T2 MRI via MR-to-CT synthesis

Segmentation of multiple pelvic structures in MRI volumes is a prerequisite for many clinical applications, such as sarcopenia assessment, bone density measurement, and muscle-to-fat volume ratio estimation. While many CT-specific datasets and automated CT-based multi-structure pelvis segmentation methods exist, there are few MRI-specific multi-structure segmentation methods in literature. In this pilot work, we propose a lightweight and annotation-free pipeline to synthetically translate T2 MRI volumes of the pelvis to CT, and subsequently leverage an existing CT-only tool called TotalSegmentator to segment 8 pelvic structures in the generated CT volumes. The predicted masks were then mapped back to the original MR volumes as segmentation masks. We compared the predicted masks against the expert annotations of the public TCGA-UCEC dataset and an internal dataset. Experiments demonstrated that the proposed pipeline achieved Dice measures ≥65% for 8 pelvic structures in T2 MRI. The proposed pipeline is an alternative method to obtain multi-organ and structure segmentations without being encumbered by time-consuming manual annotations. By exploiting the significant research progress in CTs, it is possible to extend the proposed pipeline to other MRI sequences in principle. Our research bridges the chasm between the current CT-based multi-structure segmentation and MRI-based segmentation. The manually segmented structures in the TCGA-UCEC dataset are publicly available.

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [18F]FDG PET-CT images

Utilisation of whole organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming. In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([18F]FDG) PET images of 50 patients with advanced Hodgkin lymphoma (HL). Manual segmentation was used as the reference method. The two automatic methods were also compared with a manually defined volume of interest (VOI) within the organ, a technique commonly performed in clinical settings. Both automatic methods provided accurate CT segmentations, with the deep learning method outperforming the multi-atlas with a DICE coefficient of 0.93 ± 0.03 (mean ± standard deviation) in liver and 0.87 ± 0.17 in spleen compared to 0.87 ± 0.05 (liver) and 0.78 ± 0.11 (spleen) for the multi-atlas. Similarly, a mean relative error of −3.2% for the liver and −3.4% for the spleen across patients was found for the mean standardized uptake value (SUVmean) using the deep learning regions while the corresponding errors for the multi-atlas method were −4.7% and −9.2%, respectively. For the maximum SUV (SUVmax), both methods resulted in higher than 20% overestimation due to the extension of organ boundaries to include neighbouring, high-uptake regions. The conservative VOI method which did not extend into neighbouring tissues, provided a more accurate SUVmax estimate. In conclusion, the automatic, and particularly the deep learning method could be used to rapidly extract information of the SUVmean within the liver and spleen. However, activity from neighbouring organs and lesions can lead to high biases in SUVmax and current practices of manually defining a volume of interest in the organ should be considered instead.

A Novel Multi-modal Population-graph based Framework for Patients of Esophageal Squamous Cell Cancer Prognostic Risk Prediction.

Prognostic risk prediction is pivotal for clinicians to appraise the patient's esophageal squamous cell cancer (ESCC) progression status precisely and tailor individualized therapy treatment plans. Currently, CT-based multi-modal prognostic risk prediction methods have gradually attracted the attention of researchers for their universality, which is also able to be applied in scenarios of preoperative prognostic risk assessment in the early stages of cancer. However, much of the current work focuses only on CT images of the primary tumor, ignoring the important role that CT images of lymph nodes play in prognostic risk prediction. Additionally, it is important to consider and explore the inter-patient feature similarity in prognosis when developing models. To solve these problems, we proposed a novel multi-modal population-graph based framework leveraging CT images including primary tumor and lymph nodes combined with clinical, hematology, and radiomics data for ESCC prognostic risk prediction. A patient population graph was constructed to excavate the homogeneity and heterogeneity of inter-patient feature embedding. Moreover, a novel node-level multi-task joint loss was proposed for graph model optimization through a supervised-based task and an unsupervised-based task. Sufficient experimental results show that our model achieved state-of-the-art performance compared with other baseline models as well as the gold standard on discriminative ability, risk stratification, and clinical utility. The core code is available at https://github.com/wuchengyu123/MPGSurv.

A comparative study of CT-based volumetric assessment methods for total lung capacity with the development of an adjustment factor: incorporating VR imaging for improved accuracy

A comparative study of CT-based volumetric assessment methods for total lung capacity with the development of an adjustment factor: incorporating VR imaging for improved accuracy

Pilot study on high-resolution radiological methods for the analysis of cerebrospinal fluid (CSF) shunt valves

ObjectivesDespite their life-saving capabilities, cerebrospinal fluid (CSF) shunts exhibit high failure rates, with a large fraction of failures attributed to the regulating valve. Due to a lack of methods for the detailed analysis of valve malfunctions, failure mechanisms are not well understood, and valves often have to be surgically explanted on the mere suspicion of malfunction.The presented pilot study aims to demonstrate radiological methods for comprehensive analysis of CSF shunt valves, considering both the potential for failure analysis in design optimization, and for future clinical in-vivo application to reduce the number of required shunt revision surgeries. The proposed method could also be utilized to develop and support in situ repair methods (e.g. by lysis or ultrasound) of malfunctioning CSF shunt valves. Materials and methodsThe primary methods described are contrast-enhanced radiographic time series of CSF shunt valves, taken in a favorable projection geometry at low radiation dose, and the machine-learning-based diagnosis of CSF shunt valve obstructions. Complimentarily, we investigate CT-based methods capable of providing accurate ground truth for the training of such diagnostic tools. Using simulated test and training data, the performance of the machine-learning diagnostics in identifying and localizing obstructions within a shunt valve is evaluated regarding per-pixel sensitivity and specificity, the Dice similarity coefficient, and the false positive rate in the case of obstruction free test samples. ResultsContrast enhanced subtraction radiography allows high-resolution, time-resolved, low-dose analysis of fluid transport in CSF shunt valves. Complementarily, photon-counting micro-CT allows to investigate valve obstruction mechanisms in detail, and to generate valid ground truth for machine learning-based diagnostics.Machine-learning-based detection of valve obstructions in simulated radiographies shows promising results, with a per-pixel sensitivity >70%, per-pixel specificity >90%, a median Dice coefficient >0.8 and <10% false positives at a detection threshold of 0.5. ConclusionsThis ex-vivo study demonstrates obstruction detection in cerebro-spinal fluid shunt valves, combining radiological methods with machine learning under conditions compatible to future in-vivo application.Results indicate that high-resolution contrast-enhanced subtraction radiography, possibly including time-series data, combined with machine-learning image analysis, has the potential to strongly improve the diagnostics of CSF shunt valve failures. The presented method is in principle suitable for in-vivo application, considering both measurement geometry and radiological dose. Further research is needed to validate these results on real-world data and to refine the employed methods.In combination, the presented methods enable comprehensive analysis of valve failure mechanisms, paving the way for improved product development and clinical diagnostics of CSF shunt valves.

Clinical and Pathological Validation of CT-Based Regional Harmonization Methods of Amyloid PET.

The CT-based regional direct comparison Centiloid (dcCL) method was developed to harmonize and quantify regional β-amyloid (Aβ) burden. In the present study, we aimed to investigate correlations between the CT-based regional dcCL scales and Aβ pathological burdens and to validate the clinical utility using thresholds derived from pathological assessment. We included a pathological cohort of 63 cases and a clinical cohort of 4062 participants, and obtained modified Consortium to Establish a Registry for Alzheimer's Disease criteria (mCERAD) scores by assessment of neuritic plaque burdens in multiple areas of each cortical region. PET and CT images were processed using the CT-based regional dcCL method to calculate scales in 6 distinct regions. The CT-based regional dcCL scales were correlated with neuritic plaque burdens represented by mCERAD scores, globally and regionally ( r = 0.56~0.76). In addition, striatum dcCL scales reflected Aβ involvement in the striatum ( P < 0.001). The regional dcCL scales could predict significant Aβ deposition in specific brain regions with high accuracy: area under the receiver operating characteristic curve of 0.81-0.97 with an mCERAD cutoff of 1.5 and area under the receiver operating characteristic curve of 0.88-0.93 with an mCERAD cutoff of 0.5. When applying the dcCL thresholds of 1.5 mCERAD scores, the G(-)R(+) group showed lower performances in memory and global cognitive functions and had less hippocampal volume compared with the G(-)R(-) group ( P < 0.001). However, when applying the dcCL thresholds of 0.5 mCERAD scores, there were no differences in the global cognitive functions between the 2 groups. The thresholds of regional dcCL scales derived from pathological assessments might provide clinicians with a better understanding of biomarker-guided diagnosis and distinguishable clinical phenotypes, which are particularly useful when harmonizing different PET ligands with only PET/CT.

Establishment of an NPM1 Mutation Copy Number Estimator for Xpert ® NPM1 Mutation Test

Objectives: The nucleophosmin ( NPM1)is the most mutated gene (~30%) in Acute Myeloid Leukemia (AML) 1. Three NPM1 mutations (type A, B, and D) represent ~84% in NPM1-mutated AML cases while other uncommon subtypes occupy ~16% 2. Xpert® NPM1 mutation, an automated cartridge-based test for measuring NPM1 mutation transcript levels (type A, B and D), is standardized to quantify the amount of mutated NPM1 relative to ABL1 control gene based on delta Ct in peripheral blood 3. Since mutated NPM1 level is crucial for risk assessment, medication selection, and ongoing therapeutic monitoring in AML 4,5, it is important to obtain the NPM1 mutation copy number (CN). The aim of this work is to develop NPM1 mutation CN estimator and to compare %NPM1 mutation/ABL1 reporting between delta Ct-based and CN-based methods. Methods: Five levels of NPM1 mutations (A, B, D) and ABL1 IVT-RNA panels as well as two lots of Xpert® NPM1 mutation tests were used to generate standard curves for CN and %CN reporting. The cell lysates from cell lines carrying either NPM1 mutation A, B, or D and AML clinical samples containing NPM1 mutations were examined to evaluate the CN and %CN between two lots of the Xpert® NPM1 mutation tests and to compare the delta Ct-based and CN-based methods for reporting %NPM1 mutation/ABL. Results: Linearity was demonstrated in Ct vs CN input for NPM1 mutation and ABL1 with R 2 above 0.96 for Lot1 and Lot2. Less than 3-fold difference was exhibited for CN and %CN across two lots of Xpert® NPM1 mutation test. Less than 3-fold difference was observed in %NPM1 mutation/ABL1 reporting between delta Ct-based and CN-based approaches. Conclusion: An NPM1 mutation copy number estimator for Xpert® NPM1 mutation test was established, which will provide diagnostic and prognostic values for NPM1-mutated AML patients.

Establishment of a PML-RARa Copy Number Estimator for Prototype Xpert ® PML-RARa Assay

Statement of the Problem: Acute Promyelocytic Leukemia (APL) represents 10-15% of Acute Myeloid Leukemia (AML). The PML-RARa fusion transcript is expressed in more than 95% of APL patients. Three PML-RARa isoforms (bcr1, bcr2, and bcr3) are identified in 90-95% of PML-RARa positive cases. Prototype Xpert ® PML-RARa, an automated cartridge-based assay for measuring PML-RARa fusion transcript levels (bcr1, bcr2, and bcr3), is standardized to quantify the amount of PML-RARA relative to ABL1 control gene based on delta Ct in peripheral blood. Since PML-RARa level is crucial for diagnosis and ongoing therapeutic monitoring in APL, it can be useful to obtain the PML-RARa copy number (CN). The aim of this work is to develop PML-RARa CN estimator and to compare %PML-RARa/ABL1 reporting between delta Ct-based and CN-based methods. Methodology &amp; Theoretical Orientation: Four levels of PML-RARa (bcr1, bcr2, and bcr3) and ABL1 IVT-RNA panels as well as two lots of prototype Xpert ® PML-RARa assay were used to generate standard curves for CN and %CN reporting. The samples with spiked-in bcr1 IVT-RNA and APL clinical samples containing PML-RARa fusion transcript were examined to evaluate the CN and %CN between two lots of the prototype Xpert ® PML-RARa assay and to compare the delta Ct-based and CN-based methods for reporting %PML-RARa/ABL1. Findings: Linearity was demonstrated in Ct vs CN input for PML-RARa (R 2&amp;gt;0.98) and ABL1 (R 2&amp;gt;0.97). Less than 2-fold difference was exhibited for CN and %CN across two different lots. Less than 2-fold difference was observed in %PML-RARa/ABL1 reporting between delta Ct-based and CN-based approaches. Conclusion &amp; Significance: A PML-RARa copy number estimator for prototype Xpert® PML-RARa assay was established, which will provide helpful information for diagnosis and prognosis of APL. (Product in development. Not for use in diagnostic procedures. Not reviewed by any regulatory body.)

Establishment of a BCR-ABL p190 Copy Number Estimator for Xpert ® BCR-ABL Ultra p190 Test

Statement of the Problem: BCR-ABL p190 (e1a2) is expressed in 20-30% of Acute Lymphoblastic Leukemia (ALL) and 1-2% of Chronic Myeloid Leukemia (CML). The co-expression of p190 and p210 is rare in both CML and ALL. Xpert ® BCR-ABL Ultra p190, an automated cartridge-based test for measuring BCR-ABL p190 transcript level, is standardized to quantify the amount of BCR-ABL p190 relative to ABL1 control gene based on delta Ct in peripheral blood of patients. Since BCR-ABL p190 level is crucial for risk stratification and treatment decisions in ALL as well as diagnosis and continuous therapeutic monitoring in CML, it can be useful to obtain the copy number (CN) of BCR-ABL p190. The purpose of this study is to establish a BCR-ABL p190 CN estimator with known CN of e1a2-ABL IVT-RNA as well as to compare %BCR-ABL p190/ABL1 reporting between delta Ct-based and CN-based methods. Methodology &amp; Theoretical Orientation: Nine levels of e1a2-ABL IVT-RNA as well as two lots of Xpert ® BCR-ABL Ultra p190 tests were used to generate standard curves for CN and %CN reporting. BCR-ABL p190 contrived samples and ALL clinical samples containing the BCR-ABL p190 transcript were examined to evaluate the CN and %CN between two lots of the Xpert ® BCR-ABL Ultra p190 tests and to compare the delta Ct-based and CN-based methods for reporting %BCR-ABL p190/ABL1. Findings: Linearity was demonstrated in Ct vs CN input for BCR-ABL p190 (R 2&amp;gt;0.99) and ABL1 (R 2&amp;gt;0.98). Less than 1.4-fold difference was exhibited for CN and %CN across two different lots. Less than 2-fold difference was observed in %BCR-ABL p190/ABL1 reporting between delta Ct-based and CN-based approaches. Conclusion &amp; Significance: A BCR-ABL p190 copy number estimator for Xpert ® BCR-ABL Ultra p190 test was established, which will provide helpful information for diagnosis and prognosis of diseases relating to BCR-ABL p190.

An ultrasound-based femoral artery calcification score

ObjectiveDuplex ultrasound (US) of the lower extremities is commonly used to assess patients with lower extremity atherosclerosis. Arterial calcification can often be visualized in these images; however, efforts to quantify its extent have been limited. We, thus, sought to develop a new scoring system to measure calcification on duplex US studies of the femoral artery and correlate it with standard computed tomography (CT)-based methods. We then made preliminary attempts to correlate US-based femoral artery calcification scores with limb-specific outcomes in patients with peripheral arterial disease. MethodsPatients who underwent CT evaluation of the lower extremities and arterial duplex US of either lower extremity within 6 months of each examination were included in the study. CT-based calcium scores of the femoral artery were generated using calcium scoring software. To determine the US score, five standard arterial segments (ie, common femoral artery, proximal superficial femoral artery [SFA], mid-SFA, distal SFA, and above the knee popliteal artery) were scored using a scale of 0 to 2 (0, a completely normal vessel segment; 1, a vessel with hyperechoic irregularities of the vessel wall; and 2, clear anechoic shadowing). The available scores were then averaged to yield a single femoral calcium score for each leg. Predictors of femoral calcification scores were then assessed and compared with the CT-based methods. The correlation between the US- and CT-based femoral calcification was assessed, and then the association between the US-based femoral calcification score and limb outcomes was evaluated. ResultsA total of 113 patients met the inclusion criteria and were included in the final analysis. US-based calcification scores were increased in patients with diabetes, renal failure, and the presence of chronic limb threatening ischemia similar to CT-based femoral calcification. The US- and CT-based calcification scores showed a moderate to strong correlation (r = 0.64). An elevated US-based femoral artery calcification score was associated with decreased amputation-free survival. ConclusionsA novel US-based method shows promise as a simple method for quantifying the extent of femoral artery calcification in patients with peripheral arterial disease. The US-based method correlates with standard CT-based methods. Preliminary studies show that it could be useful for predicating outcomes for patients with peripheral arterial disease.

CT-based quantification of trachea shape to detect invasion by thyroid cancer.

This study aims to develop a CT-based method for quantifying tracheal shape and evaluating its ability to distinguish between cases with or without tracheal invasion in patients with thyroid carcinoma. A total of 116 quantitative shape features, including 56 geometric moments and 60 bounding shape features, were defined. The tracheal lumen was semi-automatically defined with a CT threshold of less than - 500 HU. Three contiguous slices with the 1st, 2nd, and 3rd smallest trachea lumen areas were contiguously selected, and the appropriate number of slices to be included was determined. Fifty-six patients with differentiated thyroid carcinoma (DTC) invading the trachea and 22 patients with DTC but without invasion were retrospectively included. A receiver operating characteristic (ROC) curve was applied to select the representative shape features and determine the optimal threshold. 23.3%, 25.9%, and 24.1% of the features displayed an area under the ROC curve (AUC) ≥ 0.800 when derived from 1, 2, and 3 slices, respectively. Calculating feature values from two slices with the 1st and 2nd smallest tracheal lumen area were considered appropriate. Six final features, including 3 geometric moments and 3 bounding shape features, were selected to determine the tracheal invasion status of DTC and displayed AUCs of 0.875-0.918, accuracies of 0.821-0.891, sensitivities of 0.813-0.893, and specificities of 0.818-0.932, outperforming the visual evaluation results. Geometric moments and bounding shape features can quantify the tracheal shape and are reliable for identifying DTC tracheal invasion. The selected features quantified the extent of tracheal deformity in DTC patients with and without tracheal invasion. Six geometric features provide a non-invasive, semi-automated evaluation of the tracheal invasion status of thyroid cancer. • A novel method for quantifying tracheal shape using 56 geometric moments and 60 bounding shape features was developed. • Six features identify tracheal invasion by thyroid carcinoma. • The selected features quantified the extent of tracheal deformity in differentiated thyroid carcinoma patients with and without tracheal invasion.

Deep-learning-based methods of attenuation correction for SPECT and PET

Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.