Quantitative Image Analysis Research Articles

Neural network model development for detecting atypical mitoses in histological slides

Background: Modern computer systems allow digitizing and examining images of histological preparations, which led the authors to the idea of using machine learning tools in digital pathohistology. The ability of neural networks to find sub-visual image features in digitized histological preparations provides the basis for better qualitative and quantitative image analysis. Existing machine learning methods provide good accuracy and speed in recognizing various images, which gives hope for their wide application, including in oncologic diagnostics. AIM: Use methods of mathematical modeling to identify pathological mitoses in histological preparations as the main sign of the difference between malignant and benign tumor growth. MATERIALS AND METHODS: Histological images of the N.N. Priorov National Medical Research Center of Traumatology and Orthopedics were used as a data set for the neural network model. The model was tested using 188 histologic slides from 67 patients treated at the institute. Histological preparations were scanned on a Leica Aperio CS2 microscope with a ×400 resolution and converted into JPEG format with further processing. Next, the test images were analyzed in streaming mode using the created neural network model in order to obtain the coordinates of the desired diagnostic object — pathological mitosis and the probability with which the model found the object of this category. The obtained images were analyzed by a pathologist to determine whether the detected object corresponded to pathological mitosis. RESULTS: The authors have chosen an architecture, developed a methodology for training a neural network, and created a model that can be used to detect pathologic mitoses in histologic preparations. The authors do not attempt to replace the physician, but show the possibility of an integrated approach to data analysis by a computer system and a pathologist. Conclusions: The developed mathematical model of neural network used as a part of technological solution for recognizing pathological mitoses in scanned histological preparations can be used as a tool to reduce the time of research and increase the accuracy of diagnosis by a pathologist.

Utilizing a Pathomics Biomarker to Predict the Effectiveness of Bevacizumab in Ovarian Cancer Treatment.

The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information regarding underlying tumor progression that is associated with tumor prognosis. However, this information cannot be readily identified by conventional visual examination. This study utilizes novel pathomics technology to quantify this meaningful information for treatment effectiveness prediction. Accordingly, a total of 9828 features were extracted from segmented tumor tissue, cell nuclei, and cell cytoplasm, which were categorized into geometric, intensity, texture, and subcellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver operating characteristic (ROC) curve of 0.8312. When examining the best model's confusion matrix, 37 and 25 cases were correctly predicted as responders and non-responders, respectively, achieving an overall accuracy of 0.7848. This investigation initially validated the feasibility of utilizing pathomics techniques to predict tumor responses to chemotherapy at an early stage.

Influence of Deposition Temperature and WC Concentration on the Microstructure of Electroless Ni-P-WC Nanocomposite Coatings with Improved Hardness and Corrosion Resistance

This study aims to develop Ni-P coatings with high P content (≥11 wt.%) reinforced with WC nanoparticles on F22 steel substrates. The introduction of conductive WC in the plating solution dramatically increases reactivity of the plating solution, and consequently a tuning of deposition parameters, in terms of temperature and WC concentration, is required to obtain nanocomposite coatings with improved mechanical properties. The coatings’ porosity and incorporation and dispersion of the reinforcing phase as a function of temperature and WC concentration were analyzed by quantitative image analysis from Scanning Electron Microscopy (SEM) micrographs. Increasing the temperature and concentration of nanoparticles leads to a faster plating rate and a dramatic increase in both porosity and agglomeration of the reinforcing phase, with detrimental effects on the coatings’ microhardness. The best compromise between coating parameters was obtained by deposition at 70 °C and 6.5 g/L of WC, with a plating rate ≈ 12 μm/h, porosity lower than 1.5%, and a good combination between particle incorporation and agglomeration. In these conditions, a hardness increase by 34% is achieved in comparison to standard Ni-P. Coatings were then heat treated in air at 200 °C for 2 h, to induce growing stress relaxation, or 400 °C for 1 h, to study effects of crystallization and precipitation. X-Ray Diffraction (XRD) analysis demonstrated that WC introduction does not alter the microstructure of Ni-P coatings, but delays grain growth coarsening of precipitates. Hardness improvement by 6.5% and 45% is registered after treatment at 200 °C and 400 °C, respectively. An increase in elastic modulus, measured by instrumented indentation, was found in WC-reinforced coatings compared with Ni-P. Potentiodynamic polarization tests revealed that both introduction of WC nanoparticles and heat treatment also enhance corrosion resistance.

Poster 332: Short Socket Anterior Cruciate Ligament Reconstruction Can Decrease Bone Tunnel Widening- A Quantitative MRI Imaging Analysis on Graft Maturation and Tunnel Dimensions

Poster 332: Short Socket Anterior Cruciate Ligament Reconstruction Can Decrease Bone Tunnel Widening- A Quantitative MRI Imaging Analysis on Graft Maturation and Tunnel Dimensions

Quantitative insights into osteocyte density developmental patterns in fossilized bone — A new method

This study presents a new methodology that combines quantitative image analysis, clustering, and statistical techniques to examine the 2D distribution of osteohistological features in an extinct stem-group bird. The driving force of our research was the need to map ontogenetic changes in the spatial density of osteocyte lacunae in the type specimen of Musivavis amabilis from the Lower Cretaceous of China. We particularly focused on developing tools to reveal quantitative aspects of the dynamics in the formation of avascular bone in this active flier. We achieve this goal by proposing an algorithm with the following methodological steps: 1) We obtain relevant coordinate details for pixel locations selected by thresholding the original image with shading as a criterion. 2) We estimate density using the Gaussian kernel estimator and refined it through observations and regression analysis. 3) After slicing the image, we apply k-means clustering to obtain one-dimensional representations of lacunar density. 4) We proceed by implementing weighted averaging employing the k-nearest neighbor approach. Having applied these steps, we are able to quantitatively disclose growth processes previously unnoticed and reveal dynamics in the formation of lacunar bone tissue in the enantiornithine birds capable of power flight.

A quantitative image analysis platform for assessing trophoblast differentiation

Immunofluorescence microscopy is extensively used in characterization of trophoblast differentiation in vitro. However, such data is primarily used to confirm the presence of protein markers or qualitatively compare levels of protein markers across experimental conditions. Imaging data, when processed and analyzed appropriately can provide quantitative and spatial information, and provide biological insight. Towards this end, here we present MATroph, an open-source MATLAB-based computational tool to process images generated by immunofluorescent microscopy. MATroph automatically executes a series of image processing operations, including the classification of red, blue, and green channels from images, background extraction, morphological operations, and image filtering. From the isolated blue channels corresponding to nuclear staining, this tool generates numerical values for cell number. Additionally, relative levels and spatial location of proteins are obtained by mapping red and green channel pixels to blue pixels by assigning minimum pixel distance between the blue and other color objects. Thus, this tool provides information about intracellular protein accumulation areas. Additionally, this tool can also classify cells as single cells or part of colonies, and extract information on protein levels for each; this is particularly useful for quantitative studies on extravillous trophoblast maturation. We provide a user-guide to analyze the relative levels of markers relevant to human trophoblast stem cell self-renewal and differentiation. Importantly, MATroph is composed of a simple MATLAB algorithm, and its implementation requires minimal expertise in programming.

Protective Effects of Changbudodam-tang on Cell Death Signals on the Bone Marrow-Derived Human Mesenchymal Stem Cells via Regulation of MKK7/JNK/c-Jun Signaling Pathway.

Polycystic ovary syndrome (PCOS) is one of the most common disorders and it shows up to 20% prevalence in reproductive-aged women populations, but no cures are available to date. We aimed to investigate the protective effects of Changbudodam-tang (CBD) on cell death signaling pathways, inflammation, and oxidative stress observed in Bone-Marrow derived human mesenchymal stem cell (BM-hMSC) by means of PCOS therapeutics in the future. BM-hMSCs were applied with cell deaths and injuries. Apoptosis and pyroptosis signals were quenched with their related signaling pathways using quantitative PCR, Western blot, and fluorescence image analysis. Our data clearly displayed hydrogen peroxide- and nigericin-treated cell death signaling pathways via regulations of mitochondrial integrity and interleukin (IL)-1β at the cellular levels (p < 0.01 or 0.001). We further observed that pre-treatment with CBD showed protective effects against oxidative stress by enhancement of antioxidant components at the cellular level, with respect to both protein and mRNA expression levels (p < 0.05, 0.01 or 0.001). The mechanisms of CBD were examined by Western blot analysis, and it showed anti-cell death, anti-inflammatory, and antioxidant effects via normalizations of the Jun N-terminal kinase/mitogen-activated protein kinase kinase 7/c-Jun signaling pathways. This study confirmed the pharmacological properties of CBD by regulation of cellular oxidation and the inflammation-provoked cell death condition of BM-hMSCs, which is mediated by the MKK7/JNK/c-Jun signaling pathway.

AI-based opportunistic quantitative image analysis of lung cancer screening CTs to reduce disparities in osteoporosis screening

Osteoporosis is underdiagnosed, especially in ethnic and racial minorities who are thought to be protected against bone loss, but often have worse outcomes after an osteoporotic fracture. We aimed to determine the prevalence of osteoporosis by opportunistic CT in patients who underwent lung cancer screening (LCS) using non-contrast CT in the Northeastern United States. Demographics including race and ethnicity were retrieved. We assessed trabecular bone and body composition using a fully-automated artificial intelligence algorithm. ROIs were placed at T12 vertebral body for attenuation measurements in Hounsfield Units (HU). Two validated thresholds were used to diagnose osteoporosis: high-sensitivity threshold (115–165 HU) and high specificity threshold (<115 HU). We performed descriptive statistics and ANOVA to compare differences across sex, race, ethnicity, and income class according to neighborhoods' mean household incomes. Forward stepwise regression modeling was used to determine body composition predictors of trabecular attenuation. We included 3708 patients (mean age 64 ± 7 years, 54 % males) who underwent LCS, had available demographic information and an evaluable CT for trabecular attenuation analysis. Using the high sensitivity threshold, osteoporosis was more prevalent in females (74 % vs. 65 % in males, p < 0.0001) and Whites (72 % vs 49 % non-Whites, p < 0.0001). However, osteoporosis was present across all races (38 % Black, 55 % Asian, 56 % Hispanic) and affected all income classes (69 %, 69 %, and 91 % in low, medium, and high-income class, respectively). High visceral/subcutaneous fat-ratio, aortic calcification, and hepatic steatosis were associated with low trabecular attenuation (p < 0.01), whereas muscle mass was positively associated with trabecular attenuation (p < 0.01). In conclusion, osteoporosis is prevalent across all races, income classes and both sexes in patients undergoing LCS. Opportunistic CT using a fully-automated algorithm and uniform imaging protocol is able to detect osteoporosis and body composition without additional testing or radiation. Early identification of patients traditionally thought to be at low risk for bone loss will allow for initiating appropriate treatment to prevent future fragility fractures. ClinicalTrials.gov identifierN/A

In-situ observation of solidification behaviors of Fe-Mn-Cr-Ni-Si alloy during TIG melt-run welding using synchrotron radiation X-ray

The solidification behaviors, including the solidification cracking and solidification mode of the Fe-Mn-Cr-Ni-Si alloys at the weld bead during tungsten inert gas (TIG) melt-run welding, were examined by time-resolved in-situ observations using synchrotron radiation X-ray imaging and X-ray diffraction. The dynamics of initiation and propagation of solidification cracking at the weld bead could be observed at the dendrite scale. For the specimen solidified in the austenite mode at a welding speed of 10 mm/s, solidification cracking with a zigzag interface developed via the coalescence of many small pores at the centerline, where the columnar dendrite tips impinged. Quantitative image analysis indicated that the local tensile strain was highly localized at the centerline, and the critical local tensile strain for the initiation of solidification cracking was approximately 0.04. For the specimen solidifying in the ferrite-austenite mode at the welding speed of 10 mm/s, the centerline solidification cracking was suppressed by the formation of many equiaxed γ-Fe dendrites ahead of the growing columnar δ-Fe dendrites. Grain refinement at the centerline can be achieved at a welding speed of more than 7 mm/s without requiring inoculants.

Effects of Sapindus mukorossi Seed Oil on Bone Healing Efficiency: An Animal Study.

Natural products have attracted great interest in the development of tissue engineering. Recent studies have demonstrated that unsaturated fatty acids found in natural plant seed oil may exhibit positive osteogenic effects; however, few in vivo studies have focused on the use of plant seed oil for bone regeneration. The aim of this study is to investigate the effects of seed oil found in Sapindus mukorossi (S. mukorossi) on the osteogenic differentiation of mesenchymal stem cells and bone growth in artificial bone defects in vivo. In this study, Wharton-jelly-derived mesenchymal stem cells (WJMSCs) were co-cultured with S. mukorossi seed oil. Cellular osteogenic capacity was assessed using Alizarin Red S staining. Real-time PCR was carried out to evaluate ALP and OCN gene expression. The potential of S. mukorossi seed oil to enhance bone growth was assessed using an animal model. Four 6 mm circular defects were prepared at the parietal bone of New Zealand white rabbits. The defects were filled with hydrogel and hydrogel-S. mukorossi seed oil, respectively. Quantitative analysis of micro-computed tomography (Micro-CT) and histological images was conducted to compare differences in osteogenesis between oil-treated and untreated samples. Although our results showed no significant differences in viability between WJMSCs treated with and without S. mukorossi seed oil, under osteogenic conditions, S. mukorossi seed oil facilitated an increase in mineralized nodule secretion and upregulated the expression of ALP and OCN genes in the cells (p < 0.05). In the animal study, both micro-CT and histological evaluations revealed that new bone formation in artificial bone defects treated with S. mukorossi seed oil were nearly doubled compared to control defects (p < 0.05) after 4 weeks of healing. Based on these findings, it is reasonable to suggest that S. mukorossi seed oil holds promise as a potential candidate for enhancing bone healing efficiency in bone tissue engineering.

Relationship between the sonographic foetal lung-to-liver ratio of mean grey level and progesterone concentration in canine late gestation

A study was conducted to perform the quantitative analysis of canine foetal lung and liver sonographic images in the late term of gestation (58-67 days) and to evaluate its association with serum progesterone concentration. Thirty-two pregnant bitches were selected for the study. Lung development of foetus occurs in three different stages viz the pseudo glandular (40-48), canalicular (49-56 d) and saccular phase (57-63 d) of pregnancy. Lungs of the foetus at the time of parturition are in saccular phase of development and the alveolar phase occurs in the neonatal period. In the present study, sonographic changes occurring in the lung during the saccular phase of foetal lung development was evaluated. Mean grey level of the foetal lung and liver sonographic images were estimated using dedicated image analysis software Image J. The lung-to-liver ratio (LLR) of mean grey level (MGL) was recorded. Corresponding progesterone concentrations were also estimated for each animal. Correlation statistical analysis revealed a significant correlation between LLR of MGL and progesterone concentration in the animals selected for the study. LLR of MGL is a prognostic indicator of foetal survival and since LLR of MGL and progesterone are positively correlated, it can be used as an indicator of foetal maturity in place of progesterone concentration. Keywords: Lung-to-liver ratio of mean grey level, ultrasonography, progesterone

Relating Macroscopic PET Radiomics Features to Microscopic Tumor Phenotypes Using a Stochastic Mathematical Model of Cellular Metabolism and Proliferation.

Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that "cluster shade" and "difference entropy" are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5-4 resolution units, corresponding to 8.7-10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response.

Geometric deep learning for enhanced quantitative analysis of microstructures in X-ray computed tomography data

Quantitative microstructural analysis of XCT 3D images is key for quality assurance of materials and components. In this paper we implement a Graph Convolutional Neural Network (GCNN) architecture to segment a complex Al-Si Metal Matrix composite XCT volume (3D image). We train the model on a synthetic dataset and we assess its performance on both synthetic and experimental, manually-labeled, datasets. Our simple GCNN shows a comparable performance, measured via the Dice score, to more standard machine learning methods, but uses a greatly reduced number of parameters (less than 1/10 of parameters), features low training time, and needs little hardware resources. Our GCNN thus achieves a cost-effective reliable segmentation.

Quantitative cellular pathology of the amygdala in temporal lobe epilepsy and correlation with magnetic resonance imaging volumetry, tissue microstructure, and sudden unexpected death in epilepsy risk factors.

Amygdala enlargement can occur in temporal lobe epilepsy, and increased amygdala volume is also reported in sudden unexpected death in epilepsy (SUDEP). Apnea can be induced by amygdala stimulation, and postconvulsive central apnea (PCCA) and generalized seizures are both known SUDEP risk factors. Neurite orientation dispersion and density imaging (NODDI) has recently provided additional information on altered amygdala microstructure in SUDEP. In a series of 24 surgical temporal lobe epilepsy cases, our aim was to quantify amygdala cellular pathology parameters that could predict enlargement, NODDI changes, and ictal respiratory dysfunction. Using whole slide scanning automated quantitative image analysis methods, parallel evaluation of myelin, axons, dendrites, oligodendroglia, microglia, astroglia, neurons, serotonergic networks, mTOR-pathway activation (pS6) and phosphorylated tau (pTau; AT8, AT100, PHF) in amygdala, periamygdala cortex, and white matter regions of interest were compared with preoperative magnetic resonance imaging data on amygdala size, and in 13 cases with NODDI and evidence of ictal-associated apnea. We observed significantly higher glial labeling (Iba1, glial fibrillary acidic protein, Olig2) in amygdala regions compared to cortex and a strong positive correlation between Olig2 and Iba1 in the amygdala. Larger amygdala volumes correlated with lower microtubule-associated protein (MAP2), whereas higher NODDI orientation dispersion index correlated with lower Olig2 cell densities. In the three cases with recorded PCCA, higher MAP2 and pS6-235 expression was noted than in those without. pTau did not correlate with SUDEP risk factors, including seizure frequency. Histological quantitation of amygdala microstructure can shed light on enlargement and diffusion imaging alterations in epilepsy to explore possible mechanisms of amygdala dysfunction, including mTOR pathway activation, that in turn may increase the risk for SUDEP.

Atherosclerotic carotid artery disease Radiomics: A systematic review with meta-analysis and radiomic quality score assessment

BackgroundStroke, a leading global cause of mortality and neurological disability, is often associated with atherosclerotic carotid artery disease. Distinguishing between symptomatic and asymptomatic carotid artery disease is crucial for appropriate treatment decisions. Radiomics, a quantitative image analysis technique, and ML have emerged as promising tools in medical imaging, including neuroradiology. This systematic review and meta-analysis aimed to evaluate the methodological quality of studies employing radiomics for atherosclerotic carotid artery disease analysis and ML algorithms for culprit plaque identification using CT or MRI. Materials and methodsPubmed, WoS and Scopus databases were searched for relevant studies published from January 2005 to May 2023. RQS assessed methodological quality of studies included in the review. QUADAS-2 assessed the risk of bias. A meta-analysis and three meta regressions were conducted on study performance based on model type, imaging modality and segmentation method. ResultsRQS assessed methodological quality, revealing an overall low score and consistent findings with other radiology domains. QUADAS-2 indicated an overall low risk, except for a single study with high bias. The meta-analysis demonstrated that radiomics-based ML models for predicting culprit plaques had a satisfactory performance, with an AUC of 0.85, surpassing clinical models. However, combining radiomics with clinical features yielded the highest AUC of 0.89. Meta-regression analyses confirmed these findings. MRI-based models slightly outperformed CT-based ones, but the difference was not significant. ConclusionIn conclusion, radiomics and ML hold promise for assessing carotid plaque vulnerability, aiding in early cerebrovascular event prediction. Combining radiomics with clinical data enhances predictive performance.

Positional contrastive learning for improved thigh muscle segmentation in MR images.

The accurate segmentation of individual muscles is essential for quantitative MRI analysis of thigh images. Deep learning methods have achieved state-of-the-art results in segmentation, but they require large numbers of labeled data to perform well. However, labeling individual thigh muscles slice by slice for numerous volumes is a laborious and time-consuming task, which limits the availability of annotated datasets. To address this challenge, self-supervised learning (SSL) emerges as a promising technique to enhance model performance by pretraining the model on unlabeled data. A recent approach, called positional contrastive learning, exploits the information given by the axial position of the slices to learn features transferable on the segmentation task. The aim of this work was to propose positional contrastive SSL for the segmentation of individual thigh muscles from MRI acquisitions in a population of elderly healthy subjects and to evaluate it on different levels of limited annotated data. An unlabeled dataset of 72 T1w MRI thigh acquisitions was available for SSL pretraining, while a labeled dataset of 52 volumes was employed for the final segmentation task, split into training and test sets. The effectiveness of SSL pretraining to fine-tune a U-Net architecture for thigh muscle segmentation was compared with that of a randomly initialized model (RND), considering an increasing number of annotated volumes (S= 1, 2, 5, 10, 20, 30, 40). Our results demonstrated that SSL yields substantial improvements in Dice similarity coefficient (DSC) when using a very limited number of labeled volumes (e.g., for = 1, DSC 0.631 versus 0.530 for SSL and RND, respectively). Moreover, enhancements are achievable even when utilizing the full number of labeled subjects, with DSC = 0.927 for SSL and 0.924 for RND. In conclusion, positional contrastive SSL was effective in obtaining more accurate thigh muscle segmentation, even with a very low number of labeled data, with a potential impact of speeding up the annotation process in clinics.

Topical Diclofenac Reduces Joint Synovitis in Hand Osteoarthritis: A Pilot Investigation Using Fluorescent Optical Imaging.

Synovitis, the inflammation of joint synovia, is a prominent feature of osteoarthritis (OA) manifested by enhanced synovial vascularity, endothelial leakage, and perivascular oedema. In this pilot study, we assessed the effect of topical diclofenac in hand OA (HOA) using the established semi-quantitative methods Magnetic Resonance Imaging (MRI) and Ultrasonography (US), and compared them with Fluorescent Optical Imaging (FOI), an emerging imaging modality. Ten patients with symptomatic and diagnosed HOA used topical diclofenac for 14 days, with FOI, MRI, US, and subjective pain assessed at Baseline and after 7 (Day 8), and 14 (Day 15) days of treatment. Changes in synovitis were assessed for all 10 joints of the hand (via sum scores), and separately for the two joints most affected by synovitis. A new, fully quantitative approach for objective synovitis assessment based on the FOI images was also developed and applied. The semi-quantitative analysis of the sum scores showed a small decrease in synovitis throughout the treatment duration across the different imaging modalities. The effect of the treatment was more prominent on the two most affected joints, with a synovitis reduction vs Baseline of 21.1% and 34.2% on Day 8 and Day 15, respectively, in the FOI. The quantitative FOI pixel analysis further strengthened the evidence for this effect, with observed reduction of 17.8% and 42.4% for Days 8 and 15, respectively. A similar trend was observed for subjective pain perception, with a reduction of 7.2 and 13.3 mm on Days 8 and 15. This pilot study evidenced the effect of topical diclofenac on reducing synovitis in hand OA in semi- and fully quantitative analyses, with the effect being stronger in the most affected joints. Further, supporting studies are needed to probe the accuracy of the quantitative pixel analysis of FOI images.

Experimental study on the dynamic characteristics of biodiesel/ethanol droplets impacting upon the inclined heated wall

The investigation of droplet impingement plays a crucial role in understanding the dynamics of fuel impact on the walls inside engine combustion chambers. To study the dynamics and heat transfer characteristics of two-component mixed droplets impacting upon an inclined stainless steel heated wall, the effect of wall tilt angle (0°–40°), Weber numbers (We, ranging from 50 to 210), and wall temperatures (Tw, between 25 and 350 °C) on the heat transfer models and spreading diameters is conducted. Through quantitative analysis of high-speed images captured during the experiments, it is observed that the spreading diameter of droplet impact on the inclined wall is closely correlated with both the wall temperature and We of the droplet. The wall tilt angle is found to modify the heat transfer state transition of the droplet to some extent. Additionally, the influence of gravity results in significant differences between the front and back spreading coefficients of the droplet. Based on the available data, a new empirical model is proposed that incorporates temperature terms and inclination to predict the maximum spreading coefficient of droplets impact on an inclined heated wall. Compared with experimental data of this study and in the literature, the maximum prediction error is less than 12.3%.

Correlation of ctDNA and radiographic imaging features derived from 18F-FDG PET/CT for patients with metastatic melanoma treated with immunotherapy.

3057 Background: 18F-FDG PET/CT scans are used to assess tumor response for patients undergoing immunotherapy for metastatic melanoma (MM). Evaluating the relationship between features derived from quantitative medical image analysis and markers in blood samples such as circulating tumor DNA (ctDNA) may present an opportunity to discover imaging and blood biomarkers that can influence future patient care. This study evaluates the correlation of ctDNA and radiographic imaging features derived from FDG PET/CT. Methods: Whole-body FDG PET/CT scans from MM patients between 2014-2020 were retrospectively collected under IRB-approved protocol. Patients received pembrolizumab (n=20), ipilimumab (n=4), nivolumab (n=7), or a combination of ipilimumab and nivolumab (n=19). TRAQinform IQ software (AIQ Solutions) identified and quantified regions of interest suspicious of cancer (lesion-ROI), enabling extraction of imaging features including SUVmax, SUVmean, and SUVtotal in baseline (BL) and follow-up (FU) images and change. Plasma ctDNA concentration (copies/ml) and frequency abundance (FA) were evaluated for the first sample (1) after immunotherapy had started (between baseline and first on-treatment scan), and the next available sample (2). Patients were further grouped by ctDNA mutation for analysis. Correlation between imaging features and ctDNA features was assessed using Spearman coefficient (ρ). Results: The patient cohort included 32 males and 19 females with average age of 62 years (range 23-83). For all 51 patients, moderate correlation was observed in SUVmax,BL with ctDNA copies/ml1 (ρ = 0.48, p&lt;&lt; 0.001) and ctDNA FA1 (ρ = 0.49, p &lt;&lt; 0.001) and in SUVtotal,BL with ctDNA FA1 (ρ= 0.41, p &lt; 0.001). In BRAF mutation patients (N=32), moderate correlations existed between SUVmax,BL with ctDNA copies/ml1 (ρ = 0.49, p = 0.0041 and ctDNA FA1 (ρ = 0.05, p = 0.0035), and SUVtotal,BL with ctDNA copies/ml2 (ρ = 0.42, p = 0.017), ctDNA FA1 (ρ = 0.40, p = 0.023), and ctDNA FA2 (ρ = 0.43, p = 0.012). All the other combinations displayed weak correlations (ρ &lt; 0.39, p &gt; 0.05). Conclusions: This study shows that in patients with MM receiving immunotherapy, quantitative features from blood biomarkers such as ctDNA correlate with FDG PET/CT imaging features. Combining blood biomarkers with imaging features that are spatially localizable may strengthen prognostication in this patient group. Further analysis is being performed with more types of ctDNA mutations.

Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning.

Quantitative imaging in life sciences has evolved into a powerful approach combining advanced microscopy acquisition and automated analysis of image data. The focus of the present study is on the imaging-based evaluation of the posterior cricoarytenoid muscle (PCA) influenced by long-term functional electrical stimulation (FES), which may assist the inspiration of patients with bilateral vocal fold paresis. To this end, muscle cross-sections of the PCA of sheep were examined by quantitative image analysis. Previous investigations of the muscle fibers and the collagen amount have not revealed signs of atrophy and fibrosis due to FES by a laryngeal pacemaker. It was therefore hypothesized that regardless of the stimulation parameters the fat in the muscle cross-sections would not be significantly altered. We here extending our previous investigations using quantitative imaging of intramuscular fat in cross-sections. In order to perform this analysis both reliably and faster than a qualitative evaluation and time-consuming manual annotation, the selection of the automated method was of crucial importance. To this end, our recently established deep neural network IMFSegNet, which provides more accurate results compared to standard machine learning approaches, was applied to more than 300 H&E stained muscle cross-sections from 22 sheep. It was found that there were no significant differences in the amount of intramuscular fat between the PCA with and without long-term FES, nor were any significant differences found between the low and high duty cycle stimulated groups. This study on a human-like animal model not only confirms the hypothesis that FES with the selected parameters has no negative impact on the PCA, but also demonstrates that objective and automated deep learning-based quantitative imaging is a powerful tool for such a challenging analysis.