Radiomics-based prediction of FIGO grade for placenta accreta spectrum.

BACKGROUND: Placenta accreta spectrum (PAS) is a rare, life-threatening complication of pregnancy. Predicting PAS severity is critical to individualise care planning for the birth. We aim to explore whether radiomic analysis of T2-weighted magnetic resonance imaging (MRI) can predict severe cases by distinguishing between histopathological subtypes antenatally. METHODS: This was a bi-centre retrospective analysis of a prospective cohort study conducted between 2018 and 2022. Women who underwent MRI during pregnancy and had histological confirmation of PAS were included. Radiomic features were extracted from T2-weighted images. Univariate regression and multivariate analyses were performed to build predictive models to differentiate between non-invasive (International Federation of Gynecology and Obstetrics [FIGO] grade 1 or 2) and invasive (FIGO grade 3) PAS using R software. Prediction performance was assessed based on several metrics including sensitivity, specificity, accuracy and area under the curve (AUC) at receiver operating characteristic analysis. RESULTS: Forty-one women met the inclusion criteria. At univariate analysis, 0.64 sensitivity (95% confidence interval [CI] 0.0-1.00), specificity 0.93 (0.38-1.0), 0.58 accuracy (0.37-0.78) and 0.77 AUC (0.56-.097) was achieved for predicting severe FIGO grade 3 PAS. Using a multivariate approach, a support vector machine model yielded 0.30 sensitivity (95% CI 0.18-1.0]), 0.74 specificity (0.38-1.00), 0.58 accuracy (0.40-0.82), and 0.53 AUC (0.40-0.85). CONCLUSION: Our results demonstrate a predictive potential of this machine learning pipeline for classifying severe PAS cases. RELEVANCE STATEMENT: This study demonstrates the potential use of radiomics from MR images to identify severe cases of placenta accreta spectrum antenatally. KEY POINTS: • Identifying severe cases of placenta accreta spectrum from imaging is challenging. • We present a methodological approach for radiomics-based prediction of placenta accreta. • We report certain radiomic features are able to predict severe PAS subtypes. • Identifying severe PAS subtypes ensures safe and individualised care planning for birth.

Bone segmentation in contrast enhanced whole-body computed tomography.

Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced whole-body scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities. This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. The proposed method achieved mean Dice coefficients of 0.979 ± 0.02, 0.965 ± 0.03, and 0.934 ± 0.06 on two internal datasets and one external test dataset respectively. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.

Enterprise imaging and big data: A review from a medical physics perspective.

In recent years enterprise imaging (EI) solutions have become a core component of healthcare initiatives, while a simultaneous rise in big data has opened up a number of possibilities in how we can analyze and derive insights from large amounts of medical data. Together they afford us a range of opportunities that can transform healthcare in many fields. This paper provides a review of recent developments in EI and big data in the context of medical physics. It summarizes the key aspects of EI and big data in practice, with discussion and consideration of the steps necessary to implement an EI strategy. It examines the benefits that a healthcare service can achieve through the implementation of an EI solution by looking at it through the lenses of: compliance, improving patient care, maximizing revenue, optimizing workflows, and applications of artificial intelligence that support enterprise imaging. It also addresses some of the key challenges in enterprise imaging, with discussion and examples presented for those in systems integration, governance, and data security and privacy.

Semiautomatic region-of-interest validation at the femur in (18)F-fluoride PET/CT.

UNLABELLED: The assessment of regional skeletal metabolism using (18)F-fluoride PET ((18)F-PET) requires segmentation of the tissue region of interest (ROI). The aim of this study was to validate a novel approach to define multiple ROIs at the proximal femur similar to those used in dual x-ray absorptiometry. Regions were first drawn on low-dose CT images acquired as a routine part of the PET/CT study and transferred to the (18)F-PET images for the quantitative analysis of bone turnover. METHODS: Four healthy postmenopausal women with a mean age of 65.1 y (range, 61.8-70.0 y), and with no history of metabolic bone disorder and not currently being administered treatment affecting skeletal metabolism, underwent dynamic (18)F-PET/CT at the hip with an injected activity of 180 MBq. The ROIs at the proximal femur included femoral shaft, femoral neck, and total hip and were segmented using both a semiautomatic method and manually by 8 experts at manual ROI delineation. The mean of the 8 manually drawn ROIs was considered the gold standard against which the performances of the semiautomatic and manual methods were compared in terms of percentage overlap and percentage difference. The time to draw the ROIs was also compared. RESULTS: The percentage overlaps between the gold standard and the semiautomatic ROIs for total hip, femoral neck, and femoral shaft were 86.1%, 37.8%, and 96.1%, respectively, and the percentage differences were 14.5%, 89.7%, and 4.7%, respectively. In the same order, the percentage overlap between the gold standard and the manual ROIs were 85.2%, 39.1%, and 95.2%, respectively, and the percentage differences were 19.9%, 91.6%, and 12.2%, respectively. The semiautomatic method was approximately 9.5, 2.5, and 67 times faster than the manual method for segmenting total-hip, femoral-neck, and femoral-shaft ROIs, respectively. CONCLUSION: We have developed and validated a semiautomatic procedure whereby ROIs at the hip are defined using the CT component of an (18)F-PET/CT scan. The percentage overlap and percentage difference results between the semiautomatic method and the manual method for ROI delineation were similar. Two advantages of the semiautomatic method are that it is significantly quicker and eliminates some of the variability associated with operator or reader input. The tube current used for the CT scan was associated with an effective dose 8 times lower than that associated with a typical diagnostic CT scan. These results suggest that it is possible to segment bone ROIs from low-dose CT for later transfer to PET in a single PET/CT procedure without the need for an additional high-resolution CT scan.

A method for improved clustering and classification of microscopy images using quantitative co-localization coefficients.

BACKGROUND: The localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization. FINDINGS: We have developed a novel method for combining two existing image analysis approaches, which results in highly efficient and accurate discrimination of proteins with seemingly similar distributions. We have combined image texture-based analysis with quantitative co-localization coefficients, a method that has traditionally only been used to study the spatial overlap between two populations of molecules. Here we describe and present a novel application for quantitative co-localization, as applied to the study of Rab family small GTP binding proteins localizing to the endomembrane system of cultured cells. CONCLUSIONS: We show how quantitative co-localization can be used alongside texture feature analysis, resulting in improved clustering of microscopy images. The use of co-localization as an additional clustering parameter is non-biased and highly applicable to high-throughput image data sets.

Comparison of six quantitative methods for the measurement of bone turnover at the hip and lumbar spine using 18F-fluoride PET-CT.

AIM: The aim of this study was to evaluate the relationship between different quantification methods used for the measurement of bone plasma clearance (K(i)) using F-PET at the hip and lumbar spine. METHODS: Twelve healthy postmenopausal women aged 52-71 years were recruited. Each participant underwent 60-min dynamic F-PET scans at the lumbar spine and hip on two separate occasions with an injected activity of 90 and 180 MBq, respectively. Image-derived input functions were obtained at the aorta from the lumbar spine scans. K(i) was evaluated using a three-compartment four-parameter model (K(i-4k)), three-compartment three-parameter model (K(i-3k)), Patlak analysis (K(i-Pat)), spectral analysis (K(i-Spec)) and deconvolution (K(i-Decon)). Standardized uptake values (SUVs) were also measured. RESULTS: The Pearson correlation between K(i-4k) and K(i-3k), K(i-Pat), K(i-Spec), K(i-Decon) and SUV were 0.91, 0.97, 0.94, 0.95 and 0.93, respectively, with a significance of P less than 0.0001. The differences between the correlations measured using Fisher's Z-test were not significant (P>0.05). Bland-Altman analysis showed that the limits of agreement for K(i) measured as the SD of the differences were 0.0082 (25.9%), 0.0062 (11.7%), 0.0098 (20.1%) and 0.0056 (25.5%) ml/min/ml, respectively, and the biases were -0.0081 (-23.8%), -0.0075 (-23.7%), -0.0107 (-29.5%) and -0.0015 (0.8%) ml/min/ml, respectively. CONCLUSION: All five methods of quantification (K(i-3k), K(i-Pat), K(i-Spec), K(i-Decon) and SUV) strongly correlated with K(i-4k). Although systematic differences of up to 29% were found between K(i-4k) and the other methods (K(i-3k), K(i-Pat), K(i-Spec) and K(i-Decon)), these should not affect the conclusions of clinical studies, provided the methods are applied consistently. However, care should be taken when comparing reports that use different methods of quantification.

Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images.

BACKGROUND: Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial. RESULTS: We have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells. CONCLUSIONS: This algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets, including high resolution confocal images, live cell time-lapse recordings, and high-throughput screening data sets.

Validation of image-derived arterial input functions at the femoral artery using 18F-fluoride positron emission tomography.

INTRODUCTION: The use of image-derived arterial input functions (IDAIF) for the dynamic quantification of bone metabolism using 18F-fluoride positron emission tomography 18F-PET is an attractive alternative to direct arterial blood sampling. PURPOSES: (a) To validate a method for obtaining the IDAIF by imaging the femoral artery against a method for deriving the IDAIF at the aorta that was previously validated against direct arterial sampling. (b) To compare the accuracy of bone plasma clearance measurements (Ki) at the total hip site obtained using the femoral artery IDAIF against Ki values at the same site obtained using the aorta IDAIF. METHODS: Twelve healthy postmenopausal women with a mean age of 62.6 years (range, 52.3-70.6 years) had 60-min dynamic 18F-PET scans of the lumbar spine and proximal femur 2 weeks apart. The femoral artery IDAIF was obtained from the proximal femur scan using four different algorithms: (a) fixed partial volume correction (PVC) method; (b) variable PVC method; (c) Chen method; and (d) Cook-Lodge method. The aorta IDAIF was obtained from the lumbar spine scan using a previously validated method and the respective Ki values in the hip were used to assess the performance of each of the femoral artery algorithms. RESULTS: When the femoral artery IDAIF methods were compared with the aorta IDAIF in terms of the area under the curve AUC values calculated in 4-min time intervals over 0-60 min, the absolute root mean square errors were: (a) fixed PVC, 0.52; (b) variable PVC, 0.54; (c) Chen, 0.72; and (d) Cook-Lodge, 0.49 in MBq s/ml. There were small, but statistically significant differences, in the Ki values found by all four femoral artery IDAIF methods when compared with the figures obtained using the aorta IDAIF. Bland-Altman plots of Ki values showed the best agreement for the fixed PVC method with a standard deviation of 0.0020 ml/min/ml, followed by variable PVC, Cook-Lodge and Chen method with standard deviations of 0.0022, 0.0024 and 0.0042 ml/min/ml, respectively. CONCLUSION: We have demonstrated that it is possible to measure regional bone turnover at the hip without the need to perform direct arterial sampling to acquire the arterial input function (AIF). The differences in the Ki values obtained at the hip by using aorta IDAIF and any of the four image-based AIF methods at the femoral artery were small and clinically insignificant. The performance of fixed PVC, variable PVC and Cook-Lodge method was similar although the latter was less robust than the other two methods.

Validation of new image-derived arterial input functions at the aorta using 18F-fluoride positron emission tomography.

OBJECTIVES: (i) To validate two new image-based methods for finding the plasma arterial input function (AIF) and evaluate the performance of these and two similar techniques against arterial sampling. (ii) To evaluate the performance of all four image-derived AIF (IDAIF) methods against arterial sampling for measuring the F plasma clearance (Ki) to the lumbar spine. METHODS: Eight healthy postmenopausal women had a F-fluoride positron emission tomography scan of the lumbar spine. Venous blood samples were used to estimate the IDAIFs from: (i) a fixed population-based partial volume correction (PVC) factor method, (ii) a variable PVC factor method, (iii) the Chen method, and (iv) the Cook-Lodge method. Continuous arterial sampling and the respective Ki values were used as the gold standard against which the performance of the IDAIF methods was compared. RESULTS: The IDAIFs were compared with direct arterial sampling in terms of the area under the curve values. The percentage root mean square error in area under the curves compared with arterial sampling were: (i) fixed PVC: 12.7%, (ii) variable PVC: 12.0%, (iii) Chen: 39.0%, and (iv) Cook-Lodge: 17.3%. There were small but significant differences in the Ki values found by all four methods compared with arterial sampling. Bland-Altman plots of Ki values showed the best agreement for the variable and fixed PVC methods with a standard deviation of 0.0026 and 0.0030 ml/min/ml, respectively. CONCLUSION: The differences in the Ki values obtained at the lumbar spine using direct arterial sampling and any of the IDAIF methods at the aorta were clinically nonsignificant. The variable PVC and fixed PVC methods performed better than the Cook-Lodge and Chen methods.

Applying DTI white matter orientations to finite element head models to examine diffuse TBI under high rotational accelerations.

The in-vivo mechanical response of neural tissue during impact loading of the head is simulated using geometrically accurate finite element (FE) head models. However, current FE models do not account for the anisotropic elastic material behaviour of brain tissue. In soft biological tissue, there is a correlation between internal microscopic structure and macroscopic mechanical properties. Therefore, constitutive equations are important for the numerical analysis of the soft biological tissues. By exploiting diffusion tensor techniques the anisotropic orientation of neural tissue is incorporated into a non-linear viscoelastic material model for brain tissue and implemented in an explicit FE analysis. The viscoelastic material parameters are derived from published data and the viscoelastic model is used to describe the mechanical response of brain tissue. The model is formulated in terms of a large strain viscoelastic framework and considers non-linear viscous deformations in combination with non-linear elastic behaviour. The constitutive model was applied in the University College Dublin brain trauma model (UCDBTM) (i.e. three-dimensional finite element head model) to predict the mechanical response of the intra-cranial contents due to rotational injury.

Bioluminescent imaging: a critical tool in pre-clinical oncology research.

Bioluminescent imaging (BLI) is a non-invasive imaging modality widely used in the field of pre-clinical oncology research. Imaging of small animal tumour models using BLI involves the generation of light by luciferase-expressing cells in the animal following administration of substrate. This light may be imaged using an external detector. The technique allows a variety of tumour-associated properties to be visualized dynamically in living models. The increasing use of BLI as a small-animal imaging modality has led to advances in the development of xenogeneic, orthotopic, and genetically engineered animal models expressing luciferase genes. This review aims to provide insight into the principles of BLI and its applications in cancer research. Many studies to assess tumour growth and development, as well as efficacy of candidate therapeutics, have been performed using BLI. More recently, advances have also been made using bioluminescent imaging in studies of protein-protein interactions, genetic screening, cell-cycle regulators, and spontaneous cancer development. Such novel studies highlight the versatility and potential of bioluminescent imaging in future oncological research.

Long-term precision of 18F-fluoride PET skeletal kinetic studies in the assessment of bone metabolism.

UNLABELLED: (18)F-Fluoride PET allows noninvasive evaluation of regional bone metabolism and has the potential to become a useful tool for assessing patients with metabolic bone disease and evaluating novel drugs being developed for these diseases. The main PET parameter of interest, termed K(i), reflects regional bone metabolism. The aim of this study was to compare the long-term precision of (18)F-fluoride PET with that of biochemical markers of bone turnover assessed over 6 mo. METHODS: Sixteen postmenopausal women with osteoporosis or significant osteopenia and a mean age of 64 y underwent (18)F-fluoride PET of the lumbar spine and measurements of biochemical markers of bone formation (bone-specific alkaline phosphatase and osteocalcin) and bone resorption (urinary deoxypyridinoline) at baseline and 6 mo later. Four different methods for analyzing the (18)F-fluoride PET data were compared: a 4k 3-compartmental model using nonlinear regression analysis (K(i-4k)), a 3k 3-compartmental model using nonlinear regression analysis (K(i-3k)), Patlak analysis (K(i-PAT)), and standardized uptake values. RESULTS: With the exception of a small but significant decrease in K(i-3k) at 6 mo, there were no significant differences between the baseline and 6-mo values for the PET parameters or biochemical markers. The long-term precision, expressed as the coefficient of variation (with 95% confidence interval in parentheses), was 12.2% (9%-19%), 13.8% (10%-22%), 14.4% (11%-22%), and 26.6% (19%-40%) for K(i-3k), K(i-PAT), mean standardized uptake value, and K(i-4k), respectively. For comparison, the precision of the biochemical markers was 10% (7%-15%), 18% (13%-27%), and 14% (10%-21%) for bone-specific alkaline phosphatase, osteocalcin, and urinary deoxypyridinoline, respectively. Intraclass correlation between the baseline and 6-mo values ranged from 0.44 for K(i-4k) to 0.85 for K(i-3k). No significant correlation was found between the repeated mean standardized uptake value measurements. CONCLUSION: The precision and intraclass correlation observed for K(i-3k) and K(i-PAT) was equivalent to that observed for biochemical markers. This study provided initial data on the long-term precision of (18)F-fluoride PET measured at the lumbar spine, which will aid in the accurate interpretation of changes in regional bone metabolism in response to treatment.