ABSTRACT
As of 2022, lung cancer is the most commonly diagnosed cancer worldwide, with the highest mortality rate. There are three main histological types of lung cancer, and it is more important than ever to accurately identify the subtypes since the development of personalized, type-specific targeted therapies that have improved mortality rates. Traditionally, the gold standard for the confirmation of histological subtyping is tissue biopsy and histopathology. This, however, comes with its own challenges, which call for newer sampling techniques and adjunctive tools to assist in and improve upon the existing diagnostic workflow. This review aims to list and describe studies from the last decade (n = 47) that investigate three such potential omics techniques-namely (1) transcriptomics, (2) proteomics, and (3) metabolomics, as well as immunohistochemistry, a tool that has already been adopted as a diagnostic adjunct. The novelty of this review compared to similar comprehensive studies lies with its detailed description of each adjunctive technique exclusively in the context of lung cancer subtyping. Similarities between studies evaluating individual techniques and markers are drawn, and any discrepancies are addressed. The findings of this study indicate that there is promising evidence that supports the successful use of omics methods as adjuncts to the subtyping of lung cancer, thereby directing clinician practice in an economical and less invasive manner.
ABSTRACT
The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep learning, and computer vision, fundamentally transforming the analysis of retinal images. By utilizing a wide array of visual cues extracted from retinal fundus images, sophisticated artificial intelligence models have been developed to diagnose various retinal disorders. This paper concentrates on the detection of Age-Related Macular Degeneration (AMD), a significant retinal condition, by offering an exhaustive examination of recent machine learning and deep learning methodologies. Additionally, it discusses potential obstacles and constraints associated with implementing this technology in the field of ophthalmology. Through a systematic review, this research aims to assess the efficacy of machine learning and deep learning techniques in discerning AMD from different modalities as they have shown promise in the field of AMD and retinal disorders diagnosis. Organized around prevalent datasets and imaging techniques, the paper initially outlines assessment criteria, image preprocessing methodologies, and learning frameworks before conducting a thorough investigation of diverse approaches for AMD detection. Drawing insights from the analysis of more than 30 selected studies, the conclusion underscores current research trajectories, major challenges, and future prospects in AMD diagnosis, providing a valuable resource for both scholars and practitioners in the domain.
ABSTRACT
Wilms tumor (WT), or nephroblastoma, is the predominant renal malignancy in the pediatric population. This narrative review explores the evolution of personalized care strategies for WT, synthesizing critical developments in molecular diagnostics and treatment approaches to enhance patient-specific outcomes. We surveyed recent literature from the last five years, focusing on high-impact research across major databases such as PubMed, Scopus, and Web of Science. Diagnostic advancements, including liquid biopsies and diffusion-weighted MRI, have improved early detection precision. The prognostic significance of genetic markers, particularly WT1 mutations and miRNA profiles, is discussed. Novel predictive tools integrating genetic and clinical data to anticipate disease trajectory and therapy response are explored. Progressive treatment strategies, particularly immunotherapy and targeted agents such as HIF-2α inhibitors and GD2-targeted immunotherapy, are highlighted for their role in personalized treatment protocols, especially for refractory or recurrent WT. This review underscores the necessity for personalized management supported by genetic insights, with improved survival rates for localized disease exceeding 90%. However, knowledge gaps persist in therapies for high-risk patients and strategies to reduce long-term treatment-related morbidity. In conclusion, this narrative review highlights the need for ongoing research, particularly on the long-term outcomes of emerging therapies and integrating multi-omic data to inform clinical decision-making, paving the way for more individualized treatment pathways.
ABSTRACT
COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e., 1 s t and 2 n d order markers), morphology/shape markers (i.e., spherical harmonics), and diffusivity markers (i.e., Fractional Anisotropy (FA) & Mean Diffusivity (MD)), (ii) apply markers fusion based on the integrated markers, and (iii) determine the decision and corresponding performance metrics based on the most-promising classifier. The current study is unusual in that it ensemble bags the learned and fine-tuned ML classifiers and diagnoses olfactory bulb (OB) anosmia using majority voting. In the 5-fold approach, it achieved an accuracy of 94.1%, a balanced accuracy (BAC) of 92.18%, precision of 91.6%, recall of 90.61%, specificity of 93.75%, F1 score of 89.82%, and Intersection over Union (IoU) of 82.62%. In the 10-fold approach, stacking continued to demonstrate impressive results with an accuracy of 94.43%, BAC of 93.0%, precision of 92.03%, recall of 91.39%, specificity of 94.61%, F1 score of 91.23%, and IoU of 84.56%. In the leave-one-subject-out (LOSO) approach, the model continues to exhibit notable outcomes, achieving an accuracy of 91.6%, BAC of 90.27%, precision of 88.55%, recall of 87.96%, specificity of 92.59%, F1 score of 87.94%, and IoU of 78.69%. These results indicate that stacking and majority voting are crucial components of the CAD system, contributing significantly to the overall performance improvements. The proposed technology can help doctors assess which patients need more intensive clinical care.
ABSTRACT
As the population of Somaliland continues to grow rapidly, the demand for electricity is anticipated to rise exponentially over the next few decades. The provision of reliable and cost-effective electricity service is at the core of the economic and social development of Somaliland. Wind energy might offer a sustainable solution to the exceptionally high electricity prices. In this study, a techno-economic assessment of the wind energy potential in some parts of the western region of Somaliland is performed. Measured data of wind speed and wind direction for three sites around the capital city of Hargeisa are utilized to characterize the resource using Weibull distribution functions. Technical and economic performances of several commercial wind turbines are examined. Out of the three sites, Xumba Weyne stands out as the most favorable site for wind energy harnessing with average annual power and energy densities at 80 m hub height of 317 kW/m2 and 2782 kWh/m2, respectively. Wind turbines installed in Xumba Weyne yielded the lowest levelized cost of electricity (LCOE) of not more than 0.07 $/kWh, shortest payback times (i.e., less than 7.2 years) with minimum return on investment (ROI) of approximately 150%.
ABSTRACT
Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.
ABSTRACT
Rapid advancements in the critical care management of acute brain injuries have facilitated the survival of numerous patients who may have otherwise succumbed to their injuries. The probability of conscious recovery hinges on the extent of structural brain damage and the level of metabolic and functional cerebral impairment, which remain challenging to assess via laboratory, clinical, or functional tests. Current research settings and guidelines highlight the potential value of fluorodeoxyglucose-PET (FDG-PET) for diagnostic and prognostic purposes, emphasizing its capacity to consistently illustrate a metabolic reduction in cerebral glucose uptake across various disorders of consciousness. Crucially, FDG-PET might be a pivotal tool for differentiating between patients in the minimally conscious state and those in the unresponsive wakefulness syndrome, a persistent clinical challenge. In patients with disorders of consciousness, PET offers utility in evaluating the degree and spread of functional disruption, as well as identifying irreversible neural damage. Further, studies that capture responses to external stimuli can shed light on residual or revived brain functioning. Nevertheless, the validity of these findings in predicting clinical outcomes calls for additional long-term studies with larger patient cohorts suffering from consciousness impairment. Misdiagnosis of conscious illnesses during bedside clinical assessments remains a significant concern. Based on the clinical research settings, current clinical guidelines recommend PET for diagnostic and/or prognostic purposes. This review article discusses the clinical categories of conscious disorders and the diagnostic and prognostic value of PET imaging in clinically unresponsive patients, considering the known limitations of PET imaging in such contexts.
Subject(s)
Brain Injuries , Consciousness Disorders , Humans , Consciousness Disorders/diagnosis , Consciousness Disorders/metabolism , Fluorodeoxyglucose F18/metabolism , Brain/metabolism , Persistent Vegetative State/diagnostic imaging , Persistent Vegetative State/metabolism , Positron-Emission Tomography/methodsABSTRACT
The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing the correlation between COVID-19 lesions and the level of respiratory support provided to the patients. Computed tomography (CT) imaging will be used to analyze the three levels of respiratory support received by the patient: Level 0 (minimum support), Level 1 (non-invasive support such as soft oxygen), and Level 2 (invasive support such as mechanical ventilation). The system will begin by segmenting the COVID-19 lesions from the CT images and creating an appearance model for each lesion using a 2D, rotation-invariant, Markov-Gibbs random field (MGRF) model. Three MGRF-based models will be created, one for each level of respiratory support. This suggests that the system will be able to differentiate between different levels of severity in COVID-19 patients. The system will decide for each patient using a neural network-based fusion system, which combines the estimates of the Gibbs energy from the three MGRF-based models. The proposed system were assessed using 307 COVID-19-infected patients, achieving an accuracy of [Formula: see text], a sensitivity of [Formula: see text], and a specificity of [Formula: see text], indicating a high level of prediction accuracy.
Subject(s)
COVID-19 , Humans , COVID-19/diagnostic imaging , Tomography, X-Ray Computed , Neural Networks, Computer , Oxygen , PatientsABSTRACT
The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular eye examinations. Age-related macular degeneration (AMD), a prevalent condition in individuals over 45, is a leading cause of vision impairment in the elderly. This paper presents a comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. This is crucial for early detection and precise diagnosis of age-related macular degeneration (AMD), enabling timely intervention and personalized treatment strategies. We have developed a novel system that extracts both local and global appearance markers from fundus images. These markers are obtained from the entire retina and iso-regions aligned with the optical disc. Applying weighted majority voting on the best classifiers improves performance, resulting in an accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 of 93.72%, ROC of 95.85%, balanced accuracy of 95.81%, and weighted sum of 95.38%. This system not only achieves high accuracy but also provides a detailed assessment of the severity of each retinal region. This approach ensures that the final diagnosis aligns with the physician's understanding of AMD, aiding them in ongoing treatment and follow-up for AMD patients.
Subject(s)
Geographic Atrophy , Wet Macular Degeneration , Humans , Aged , Fundus Oculi , Retina , Wet Macular Degeneration/diagnosis , Geographic Atrophy/diagnostic imaging , Machine LearningABSTRACT
This paper reviews and analyzes the innovations and advances in using algae and their derivatives in different parts of Li-ion batteries. Applications in Li-ion battery anodes, electrolytes, binders, and separators were discussed. Algae provides a sustainable feedstock for different materials that can be used in Li-ion batteries, such as carbonaceous material, biosilica, biopolymers, and other materials that have unique micro- and nano-structures that act as biotemplates for composites structure design. Natural materials and biotemplates provided by algae have various advantages, such as electrochemical and thermal stability, porosity that allows higher storage capacity, nontoxicity, and other properties discussed in the paper. Results reveal that despite algae and its derivatives being a promising renewable feedstock for different applications in Li-ion batteries, more research is yet to be performed to evaluate its feasibility of being used in the industry.
Subject(s)
Industry , Ions , Electrodes , Physical Phenomena , PorosityABSTRACT
Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.
ABSTRACT
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
ABSTRACT
Kidney transplantation is the preferred treatment for end-stage renal failure, but the limited availability of donors and the risk of immune rejection pose significant challenges. Early detection of acute renal rejection is a critical step to increasing the lifespan of the transplanted kidney. Investigating the clinical, genetic, and histopathological markers correlated to acute renal rejection, as well as finding noninvasive markers for early detection, is urgently needed. It is also crucial to identify which markers are associated with different types of acute renal rejection to manage treatment effectively. This short review summarizes recent studies that investigated various markers, including genomics, histopathology, and clinical markers, to differentiate between different types of acute kidney rejection. Our review identifies the markers that can aid in the early detection of acute renal rejection, potentially leading to better treatment and prognosis for renal-transplant patients.
ABSTRACT
In this study a lipid bilayer membrane model was used in which the bilayer is tethered to a solid substrate with molecular tethers. Voltage-current (V-I) measurements of the tethered bilayer membranes (tBLM) and tBLM with benzyl alcohol (BZA) incorporated in their structures, were measured using triangular voltage ramps of 0-500 mV. The temperature dependence of the conductance deduced from the V-I measurements are described. An evaluation of the activation energies for electrical conductance showed that BZA decreased the activation/ Born energies for ionic conduction of tethered lipid membranes. It is concluded that BZA increased the average pore radius of the tBLM.
Subject(s)
Benzyl Alcohol , Lipid Bilayers , Lipid Bilayers/chemistry , Benzyl Alcohol/pharmacologyABSTRACT
BACKGROUND AND OBJECTIVE: Lung cancer is an important cause of death and morbidity around the world. Two of the primary computed tomography (CT) imaging markers that can be used to differentiate malignant and benign lung nodules are the inhomogeneity of the nodules' texture and nodular morphology. The objective of this paper is to present a new model that can capture the inhomogeneity of the detected lung nodules as well as their morphology. METHODS: We modified the local ternary pattern to use three different levels (instead of two) and a new pattern identification algorithm to capture the nodule's inhomogeneity and morphology in a more accurate and flexible way. This modification aims to address the wide Hounsfield unit value range of the detected nodules which decreases the ability of the traditional local binary/ternary pattern to accurately classify nodules' inhomogeneity. The cut-off values defining these three levels of the novel technique are estimated empirically from the training data. Subsequently, the extracted imaging markers are fed to a hyper-tuned stacked generalization-based classification architecture to classify the nodules as malignant or benign. The proposed system was evaluated on in vivo datasets of 679 CT scans (364 malignant nodules and 315 benign nodules) from the benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and an external dataset of 100 CT scans (50 malignant and 50 benign). The performance of the classifier was quantitatively assessed using a Leave-one-out cross-validation approach and externally validated using the unseen external dataset based on sensitivity, specificity, and accuracy. RESULTS: The overall accuracy of the system is 96.17% with 97.14% sensitivity and 95.33% specificity. The area under the receiver-operating characteristic curve was 0.98, which highlights the robustness of the system. Using the unseen external dataset for validating the system led to consistent results showing the generalization abilities of the proposed approach. Moreover, applying the original local binary/ternary pattern or using other classification structures achieved inferior performance when compared against the proposed approach. CONCLUSIONS: These experimental results demonstrate the feasibility of the proposed model as a novel tool to assist physicians and radiologists for lung nodules' early assessment based on the new comprehensive imaging markers.
Subject(s)
Lung Neoplasms , Solitary Pulmonary Nodule , Humans , Lung Neoplasms/diagnosis , Lung/pathology , Tomography, X-Ray Computed/methods , Algorithms , ROC Curve , Solitary Pulmonary Nodule/diagnostic imaging , Radiographic Image Interpretation, Computer-AssistedABSTRACT
Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.
ABSTRACT
Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.
ABSTRACT
Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient's condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively.
Subject(s)
Macula Lutea , Wet Macular Degeneration , Humans , Fluorescein Angiography , Fundus Oculi , Retina/diagnostic imagingABSTRACT
Objectives: We aimed to describe Familial Hemophagocytic Lymphohistiocytosis (F-HLH) patients' clinical features, intensive care courses, and outcomes. Methods: Multi-center retrospective cohort study of pediatric patients diagnosed with F-HLH from 2015 to 2020 in five tertiary centers in Saudi Arabia. Patients were classified as F-HLH based on their genetic confirmation of known mutation or on their clinical criteria, which include a constellation of abnormalities, early disease onset, recurrent HLH in the absence of other causes, or a family history of HLH. Results: Fifty-eight patients (28 male, 30 female), with a mean age of 21.0 ± 33.9 months, were included. The most common principal diagnosis was hematological or immune dysfunction (39.7%), followed by cardiovascular dysfunction in 13 (22.4%) patients. Fever was the most common clinical presentation in 27.6%, followed by convulsions (13.8%) and bleeding (13.8%). There were 20 patients (34.5%) who had splenomegaly, and more than 70% of patients had hyperferritinemia >500â mg/dl, hypertriglyceridemia >150â mg/dl and hemophagocytosis in bone marrow biopsy. Compared to deceased patients 18 (31%), survivors had significantly lower PT (p = 041), bilirubin level of <34.2â mmol/L (p = 0.042), higher serum triglyceride level (p = 0.036), and lesser bleeding within the initial 6â h of admission (p = 0.004). Risk factors for mortality included requirements of higher levels of hemodynamic (61.1% vs. 17.5%, p = 0.001) and respiratory (88.9% vs. 37.5%, p < 0.001) support, and positive fungal cultures (p = 0.046). Conclusions: Familial HLH still represents a challenge in the pediatric critical care setting. Earlier diagnosis and prompt initiation of appropriate treatment could improve F-HLH survival.
ABSTRACT
Wilms' tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms' tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms' tumors. A total of 63 patients (age range: 6 months-14 years) were included in this study, after receiving their guardians' informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based features from Wilms' tumors before chemotherapy. The proposed system consists of six steps: (i) delineate the tumors' images across the three contrast phases; (ii) characterize the texture of the tumors using first- and second-order textural features; (iii) extract the shape features by applying a parametric spherical harmonics model, sphericity, and elongation; (iv) capture the intensity changes across the contrast phases to describe the tumors' functionality; (v) apply features fusion based on the extracted features; and (vi) determine the final prediction as responsive or non-responsive via a tuned support vector machine classifier. The system achieved an overall accuracy of 95.24%, with 95.65% sensitivity and 94.12% specificity. Using the support vector machine along with the integrated features led to superior results compared with other classification models. This study integrates novel imaging markers with a machine learning classification model to make early predictions about how a Wilms' tumor will respond to preoperative chemotherapy. This can lead to personalized management plans for Wilms' tumors.