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BACKGROUND: In polyglutamine (polyQ) diseases, the identification of modifiers and the construction of prediction model for progression facilitate genetic counseling, clinical management and therapeutic interventions. METHODS: Data were derived from the longest longitudinal study, with 642 examinations by International Cooperative Ataxia Rating Scale (ICARS) from 82 SCA3 participants. Using different time scales of disease duration, we performed multiple different linear, quadratic and piece-wise linear growth models to fit the relationship between ICARS scores and duration. Models comparison was employed to determine the best-fitting model according to goodness-of-fit tests, and the analysis of variance among nested models. RESULTS: An acceleration was detected after 13 years of duration: ICARS scores progressed 2.445 (SE: 0.185) points/year before and 3.547 (SE: 0.312) points/year after this deadline. Piece-wise growth model fitted better to studied data than other two types of models. The length of expanded CAG repeat (CAGexp) in ATXN3 gene significantly influenced progression. Age at onset of gait ataxia (AOga), a proxy for aging process, was not an independent modifier but affected the correlation between CAGexp and progression. Additionally, gender had no significant effect on progression rate of ICARS. The piece-wise growth models were determined as the predictive models, and ICARS predictions from related models were available. CONCLUSIONS: We first confirmed that ICARS progressed as a nonlinear pattern and varied according to different stages in SCA3. In addition to ATXN3 CAGexp, AOga or aging process regulated the progression by interacting with CAGexp.
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Enfermedad de Machado-Joseph , Edad de Inicio , Progresión de la Enfermedad , Humanos , Estudios Longitudinales , Enfermedad de Machado-Joseph/genéticaRESUMEN
OBJECTIVE: The present study aimed to investigate if CT-based radiomics features could correlate to the risk of metastatic progression in high-risk prostate cancer patients treated with radical RT and long-term androgen deprivation therapy (ADT). MATERIALS AND METHODS: A total of 157 patients were investigated and radiomics features extracted from the contrast-free treatment planning CT series. Three volumes were segmented: the prostate gland only (CTV_p), the prostate gland with seminal vesicles (CTV_psv), and the seminal vesicles only (CTV_sv). The patients were split into two subgroups of 100 and 57 patients for training and validation. Five clinical and 62 radiomics features were included in the analysis. Considering metastases-free survival (MFS) as an endpoint, the predictive model was used to identify the subgroups with favorable or unfavorable prognoses (separated by a threshold selected according to the Youden method). Pure clinical, pure radiomic, and combined predictive models were investigated. RESULTS: With a median follow-up of 30.7 months, the MFS at 1 and 3 years was 97.2%⯱ 1.5 and 92.1%⯱ 2.0, respectively. Univariate analysis identified seven potential predictors for MFS in the CTV_p group, 11 in the CTV_psv group, and 9 in the CTV_sv group. After elastic net reduction, these were 4 predictors for MFS in the CTV_p group (positive lymph nodes, Gleason score, H_Skewness, and NGLDM_Contrast), 5 in the CTV_psv group (positive lymph nodes, Gleason score, H_Skewnesss, Shape_Surface, and NGLDM_Contrast), and 6 in the CTV_sv group (positive lymph nodes, Gleason score, H_Kurtosis, GLCM_Correlation, GLRLM_LRHGE, and GLZLM_SZLGE). The patients' group of the training and validation cohorts were stratified into favorable and unfavorable prognosis subgroups. For the combined model, for CTV_p, the mean MFS was 134⯱ 14.5 vs. 96.9⯱ 22.2 months for the favorable and unfavorable subgroups, respectively, and 136.5⯱ 14.6 vs. 70.5⯱ 4.3 months for CTV_psv and 150.0⯱ 4.2 vs. 91.1⯱ 8.6 months for CTV_sv, respectively. CONCLUSION: Radiomic features were able to predict the risk of metastatic progression in high-risk prostate cancer. Combining the radiomic features and clinical characteristics can classify high-risk patients into favorable and unfavorable prognostic groups.
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Neoplasias de la Próstata , Antagonistas de Andrógenos , Humanos , Masculino , Pronóstico , Próstata/patología , Neoplasias de la Próstata/diagnóstico por imagen , Neoplasias de la Próstata/patología , Neoplasias de la Próstata/radioterapia , Vesículas Seminales/patologíaRESUMEN
Alzheimer's disease is a common neurodegenerative brain disease that affects the elderly population worldwide. Its early automatic detection is vital for early intervention and treatment. A common solution is to perform future cognitive score prediction based on the baseline brain structural magnetic resonance image (MRI), which can directly infer the potential severity of disease. Recently, several studies have modelled disease progression by predicting the future brain MRI that can provide visual information of brain changes over time. Nevertheless, no studies explore the intra correlation of these two solutions, and it is unknown whether the predicted MRI can assist the prediction of cognitive score. Here, instead of independent prediction, we aim to predict disease progression in multi-view, i.e., predicting subject-specific changes of cognitive score and MRI volume concurrently. To achieve this, we propose an end-to-end integrated framework, where a regression model and a generative adversarial network are integrated together and then jointly optimized. Three integration strategies are exploited to unify these two models. Moreover, considering that some brain regions, such as hippocampus and middle temporal gyrus, could change significantly during the disease progression, a region-of-interest (ROI) mask and a ROI loss are introduced into the integrated framework to leverage this anatomical prior knowledge. Experimental results on the longitudinal Alzheimer's Disease Neuroimaging Initiative dataset demonstrated that the integrated framework outperformed the independent regression model for cognitive score prediction. And its performance can be further improved with the ROI loss for both cognitive score and MRI prediction.
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Enfermedad de Alzheimer , Anciano , Enfermedad de Alzheimer/diagnóstico por imagen , Encéfalo/diagnóstico por imagen , Progresión de la Enfermedad , Humanos , Imagen por Resonancia Magnética , NeuroimagenRESUMEN
BACKGROUND: Lumbar disc degeneration (LDD) may be related to aging, biomechanical and genetic factors. Despite the extensive work on understanding its etiology, there is currently no automated tool for accurate prediction of its progression. PURPOSE: We aim to establish a novel deep learning-based pipeline to predict the progression of LDD-related findings using lumbar MRIs. MATERIALS AND METHODS: We utilized our dataset with MRIs acquired from 1,343 individual participants (taken at the baseline and the 5-year follow-up timepoint), and progression assessments (the Schneiderman score, disc bulging, and Pfirrmann grading) that were labelled by spine specialists with over ten years clinical experience. Our new pipeline was realized by integrating the MRI-SegFlow and the Visual Geometry Group-Medium (VGG-M) for automated disc region detection and LDD progression prediction correspondingly. The LDD progression was quantified by comparing the Schneiderman score, disc bulging and Pfirrmann grading at the baseline and at follow-up. A fivefold cross-validation was conducted to assess the predictive performance of the new pipeline. RESULTS: Our pipeline achieved very good performances on the LDD progression prediction, with high progression prediction accuracy of the Schneiderman score (Accuracy: 90.2 ± 0.9%), disc bulging (Accuracy: 90.4% ± 1.1%), and Pfirrmann grading (Accuracy: 89.9% ± 2.1%). CONCLUSION: This is the first attempt of using deep learning to predict LDD progression on a large dataset with 5-year follow-up. Requiring no human interference, our pipeline can potentially achieve similar predictive performances in new settings with minimal efforts.
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Degeneración del Disco Intervertebral , Humanos , Degeneración del Disco Intervertebral/diagnóstico por imagen , Degeneración del Disco Intervertebral/genética , Vértebras Lumbares/diagnóstico por imagen , Imagen por Resonancia MagnéticaRESUMEN
INTRODUCTION: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. METHODS: We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated. RESULTS: A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R2 = 24%) and memory (R2 = 25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%-75%. DISCUSSION: Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.
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Enfermedad de Alzheimer , Disfunción Cognitiva , Progresión de la Enfermedad , Aprendizaje Automático , Adulto , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/patología , Biomarcadores/líquido cefalorraquídeo , Disfunción Cognitiva/genética , Disfunción Cognitiva/patología , Femenino , Humanos , Imagen por Resonancia Magnética , Masculino , Tomografía de Emisión de PositronesRESUMEN
BACKGROUND: Chronic kidney disease progression to kidney failure is diverse, and progression may be different according to genetic aspects and settings of care. We aimed to describe kidney failure risk equation prognostic accuracy in an Australian population. METHODS: A retrospective cohort study was undertaken in a public hospital community-based chronic kidney disease service in Brisbane, Australia, which included a cohort of 406 adult patients with chronic kidney disease Stages 3-4 followed up over 5 years (1/1/13-1/1/18). Risk of progression to kidney failure at baseline using Kidney Failure Risk Equation models with three (eGFR/age/sex), four (add urinary-ACR) and eight variables (add serum-albumin/phosphate/bicarbonate/calcium) at 5 and 2 years were compared to actual patient outcomes. RESULTS: Of 406 patients followed up over 5 years, 71 (17.5%) developed kidney failure, while 112 died before reaching kidney failure. The overall mean difference between observed and predicted risk was 0.51% (p = 0.659), 0.93% (p = 0.602), and - 0.03% (p = 0.967) for the three-, four- and eight-variable models, respectively. There was small improvement in the receiver operating characteristic-area under the curve from three-variable to four-variable models: 0.888 (95%CI = 0.819-0.957) versus 0.916 (95%CI = 0.847-0.985). The eight-variable model showed marginal receiver operating characteristic-area under the curve improvement: 0.916 (95%CI = 0.847-0.985) versus 0.922 (95%CI = 0.853-0.991). The results were similar in predicting 2 year risk of kidney failure. CONCLUSIONS: The kidney failure risk equation accurately predicted progression to kidney failure in an Australian chronic kidney disease population. Younger age, male sex, lower estimated glomerular filtration rate, higher albuminuria, diabetes mellitus, tobacco smoking and non-Caucasian ethnicity were associated with increased risk of kidney failure. Cause-specific cumulative incidence function for progression to kidney failure or death, stratified by chronic kidney disease stage, demonstrated differences within different chronic kidney disease stages, highlighting the interaction between comorbidity and outcome.
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Fallo Renal Crónico , Insuficiencia Renal Crónica , Insuficiencia Renal , Adulto , Humanos , Masculino , Fallo Renal Crónico/epidemiología , Pruebas de Función Renal , Estudios Retrospectivos , Estudios de Cohortes , Australia/epidemiología , Insuficiencia Renal Crónica/diagnóstico , Insuficiencia Renal Crónica/epidemiología , Insuficiencia Renal Crónica/complicaciones , Tasa de Filtración Glomerular , Progresión de la Enfermedad , Factores de RiesgoRESUMEN
In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.
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Simulación por Computador , Modelos Neurológicos , Esclerosis Múltiple , Humanos , Conectoma/métodos , Progresión de la Enfermedad , Esclerosis Múltiple/fisiopatología , Esclerosis Múltiple/diagnóstico por imagenRESUMEN
The utilization of evolutive models and algorithms for predicting the evolution of hepatic steatosis holds immense potential benefits. These computational approaches enable the analysis of complex datasets, capturing temporal dynamics and providing personalized prognostic insights. By optimizing intervention planning and identifying critical transition points, they promise to revolutionize our approach to understanding and managing hepatic steatosis progression, ultimately leading to enhanced patient care and outcomes in clinical settings. This paradigm shift towards a more dynamic, personalized, and comprehensive approach to hepatic steatosis progression signifies a significant advancement in healthcare. The application of evolutive models and algorithms allows for a nuanced characterization of disease trajectories, facilitating tailored interventions and optimizing clinical decision-making. Furthermore, these computational tools offer a framework for integrating diverse data sources, creating a more holistic understanding of hepatic steatosis progression. In summary, the potential benefits encompass the ability to analyze complex datasets, capture temporal dynamics, provide personalized prognostic insights, optimize intervention planning, identify critical transition points, and integrate diverse data sources. The application of evolutive models and algorithms has the potential to revolutionize our understanding and management of hepatic steatosis, ultimately leading to improved patient outcomes in clinical settings.
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Introduction: Because Alzheimer's disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD. Methods: This study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging. Results: We leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging. Discussion: The representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis.
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OBJECTIVES: Accurate outcome prediction is important for making informed clinical decisions in cancer treatment. In this study, we assessed the feasibility of using changes in radiomic features over time (Delta radiomics: absolute and relative) following chemotherapy, to predict relapse/progression and time to progression (TTP) of primary mediastinal large B-cell lymphoma (PMBCL) patients. MATERIAL AND METHODS: Given the lack of standard staging PET scans until 2011, only 31 out of 103 PMBCL patients in our retrospective study had both pre-treatment and end-of-treatment (EoT) scans. Consequently, our radiomics analysis focused on these 31 patients who underwent [18F]FDG PET-CT scans before and after R-CHOP chemotherapy. Expert manual lesion segmentation was conducted on their scans for delta radiomics analysis, along with an additional 19 EoT scans, totaling 50 segmented scans for single time point analysis. Radiomics features (on PET and CT), along with maximum and mean standardized uptake values (SUVmax and SUVmean), total metabolic tumor volume (TMTV), tumor dissemination (Dmax), total lesion glycolysis (TLG), and the area under the curve of cumulative standardized uptake value-volume histogram (AUC-CSH) were calculated. We additionally applied longitudinal analysis using radial mean intensity (RIM) changes. For prediction of relapse/progression, we utilized the individual coefficient approximation for risk estimation (ICARE) and machine learning (ML) techniques (K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)) including sequential feature selection (SFS) following correlation analysis for feature selection. For TTP, ICARE and CoxNet approaches were utilized. In all models, we used nested cross-validation (CV) (with 10 outer folds and 5 repetitions, along with 5 inner folds and 20 repetitions) after balancing the dataset using Synthetic Minority Oversampling TEchnique (SMOTE). RESULTS: To predict relapse/progression using Delta radiomics between the baseline (staging) and EoT scans, the best performances in terms of accuracy and F1 score (F1 score is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives) were achieved with ICARE (accuracy = 0.81 ± 0.15, F1 = 0.77 ± 0.18), RF (accuracy = 0.89 ± 0.04, F1 = 0.87 ± 0.04), and LDA (accuracy = 0.89 ± 0.03, F1 = 0.89 ± 0.03), that are higher compared to the predictive power achieved by using only EoT radiomics features. For the second category of our analysis, TTP prediction, the best performer was CoxNet (LASSO feature selection) with c-index = 0.67 ± 0.06 when using baseline + Delta features (inclusion of both baseline and Delta features). The TTP results via Delta radiomics were comparable to the use of radiomics features extracted from EoT scans for TTP analysis (c-index = 0.68 ± 0.09) using CoxNet (with SFS). The performance of Deauville Score (DS) for TTP was c-index = 0.66 ± 0.09 for n = 50 and 0.67 ± 03 for n = 31 cases when using EoT scans with no significant differences compared to the radiomics signature from either EoT scans or baseline + Delta features (p-value> 0.05). CONCLUSION: This work demonstrates the potential of Delta radiomics and the importance of using EoT scans to predict progression and TTP from PMBCL [18F]FDG PET-CT scans.
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In this paper, we propose a novel efficient multi-task learning formulation for the class of progression problems in which its state will continuously change over time. To use the shared knowledge information between multiple tasks to improve performance, existing multi-task learning methods mainly focus on feature selection or optimizing the task relation structure. The feature selection methods usually fail to explore the complex relationship between tasks and thus have limited performance. The methods centring on optimizing the relation structure of tasks are not capable of selecting meaningful features and have a bi-convex objective function which results in high computation complexity of the associated optimization algorithm. Unlike these multi-task learning methods, motivated by a simple and direct idea that the state of a system at the current time point should be related to all previous time points, we first propose a novel relation structure, termed adaptive global temporal relation structure (AGTS). Then we integrate the widely used sparse group Lasso, fused Lasso with AGTS to propose a novel convex multi-task learning formulation that not only performs feature selection but also adaptively captures the global temporal task relatedness. Since the existence of three non-smooth penalties, the objective function is challenging to solve. We first design an optimization algorithm based on the alternating direction method of multipliers (ADMM). Considering that the worst-case convergence rate of ADMM is only sub-linear, we then devise an efficient algorithm based on the accelerated gradient method which has the optimal convergence rate among first-order methods. We show the proximal operator of several non-smooth penalties can be solved efficiently due to the special structure of our formulation. Experimental results on four real-world datasets demonstrate that our approach not only outperforms multiple baseline MTL methods in terms of effectiveness but also has high efficiency.
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Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P â+ âS, 5%), and a proportion of non-progressors (N, 52%) â¼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81-0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52-0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P â+ âS (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.
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Background: Lumbar disc degeneration (LDD) is considered as one of the main causes of low back pain. For clinical diagnosis of LDD, magnetic resonance imaging (MRI) is commonly used. Schmorl's node, high intensity zone (HIZ), Modic changes, and other MRI biomarkers of intervertebral disc (IVD) degeneration are also associated with low back pain. However, the progression and natural history of these features are unclear and there is limited predictive capacity with MRI. Purpose: We aim to establish and validate a deep learning pipeline, EDPP-Flow, for the 5-year progression prediction of Schmorl's node, HIZ, and Modic changes, based on clinical MRIs. Materials and methods: An MRI dataset developed on 1152 volunteers was used in this study. For each volunteer, two MRI scans, at baseline and 5-year follow-up, were collected and pathology labels were annotated as present or absent (with/without pathology) by two specialists with over 10 years of clinical experience. Our pipeline contained the published MRI-SegFlow and state-of-the-art convolutional neural network for progression prediction of endplate defects. The label distribution of the dataset is unbalanced, where the number of present samples was much smaller than absent samples. The resampling and data augmentation strategies were adopted to increase the number of present samples in the training process and balance the influence of different samples on the model, which can improve the prediction accuracy. Results: Our pipeline achieved high weighted accuracy, sensitivity, and specificity for progression prediction of Schmorl's node (89.46 ± 3.71%, 89.19 ± 2.70%, 89.72 ± 2.42%), HIZ (91.75 ± 2.48%, 93.07 ± 3.96%, 90.43 ± 2.51%), and Modic changes (87.51 ± 2.23%, 87.93 ± 1.72%, 87.10 ± 1.99%), on the unbalanced dataset (present sample's percentages of the 3 pathologies above were 4.3%, 11.7%, and 6.7%). Conclusion: We developed and validated a deep learning pipeline, for the progression prediction of endplate defects, which showed high prediction accuracy on unbalanced data. The method has significant potential for clinical implementation.
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BACKGROUND: The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of Ellipsoid Zone (EZ) At-Risk to study progression in nonexudative age-related macular degeneration (AMD). METHODS: Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based EZ At-Risk percentage area measurement was calculated. Random forest-based feature ranking of EZ At-Risk was compared to previously validated quantitative OCT-based biomarkers. RESULTS: The model achieved a detection accuracy of 99% (sensitivity = 99%; specificity = 100%) for EZ At-Risk. Automatic EZ At-Risk measurement achieved an accuracy of 90% (sensitivity = 90%; specificity = 84%) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean EZ At-Risk correlated with higher progression to GA at year 5 (p < 0.001). EZ At-Risk was a top ranked feature in the random forest assessment for GA prediction. CONCLUSIONS: This report describes a novel high performance DL-based model for the detection and measurement of EZ At-Risk. This biomarker showed promising results in predicting progression in nonexudative AMD patients.
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Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. Identifying patients at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Recently, deep-learning-based models have been developed and achieved superior performance for late AMD prediction. However, most existing methods are limited to the color fundus photography (CFP) from the last ophthalmic visit and do not include the longitudinal CFP history and AMD progression during the previous years' visits. Patients in different AMD subphenotypes might have various speeds of progression in different stages of AMD disease. Capturing the progression information during the previous years' visits might be useful for the prediction of AMD progression. In this work, we propose a Contrastive-Attention-based Time-aware Long Short-Term Memory network (CAT-LSTM) to predict AMD progression. First, we adopt a convolutional neural network (CNN) model with a contrastive attention module (CA) to extract abnormal features from CFPs. Then we utilize a time-aware LSTM (T-LSTM) to model the patients' history and consider the AMD progression information. The combination of disease progression, genotype information, demographics, and CFP features are sent to T-LSTM. Moreover, we leverage an auto-encoder to represent temporal CFP sequences as fixed-size vectors and adopt k-means to cluster them into subphenotypes. We evaluate the proposed model based on real-world datasets, and the results show that the proposed model could achieve 0.925 on area under the receiver operating characteristic (AUROC) for 5-year late-AMD prediction and outperforms the state-of-the-art methods by more than 3%, which demonstrates the effectiveness of the proposed CAT-LSTM. After analyzing patient representation learned by an auto-encoder, we identify 3 novel subphenotypes of AMD patients with different characteristics and progression rates to late AMD, paving the way for improved personalization of AMD management. The code of CAT-LSTM can be found at GitHub.
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Patient-derived organoids are being considered as models that can help guide personalized therapy through in vitro anticancer drug response evaluation. However, attempts to quantify in vitro drug responses in organoids and compare them with responses in matched patients remain inadequate. In this study, we investigated whether drug responses of organoids correlate with clinical responses of matched patients and disease progression of patients. Organoids were established from 54 patients with colorectal cancer who (except for one patient) did not receive any form of therapy before, and tumor organoids were assessed through whole-exome sequencing. For comparisons of in vitro drug responses in matched patients, we developed an 'organoid score' based on the variable anticancer treatment responses observed in organoids. Very interestingly, a higher organoid score was significantly correlated with a lower tumor regression rate after the standard-of-care treatment in matched patients. Additionally, we confirmed that patients with a higher organoid score (≥ 2.5) had poorer progression-free survival compared with those with a lower organoid score (< 2.5). Furthermore, to assess potential drug repurposing using an FDA-approved drug library, ten tumor organoids derived from patients with disease progression were applied to a simulation platform. Taken together, organoids and organoid scores can facilitate the prediction of anticancer therapy efficacy, and they can be used as a simulation model to determine the next therapeutic options through drug screening. Organoids will be an attractive platform to enable the implementation of personalized therapy for colorectal cancer patients.
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Antineoplásicos , Neoplasias Colorrectales , Antineoplásicos/farmacología , Antineoplásicos/uso terapéutico , Neoplasias Colorrectales/tratamiento farmacológico , Neoplasias Colorrectales/genética , Neoplasias Colorrectales/patología , Progresión de la Enfermedad , Humanos , Organoides , Medicina de PrecisiónRESUMEN
Atherosclerotic plaque progression and rupture play an important role in cardiovascular disease development and the final drastic events such as heart attack and stroke. Medical imaging and image-based computational modeling methods advanced considerably in recent years to quantify plaque morphology and biomechanical conditions and gain a better understanding of plaque evolution and rupture process. This article first briefly reviewed clinical imaging techniques for coronary thin-cap fibroatheroma (TCFA) plaques used in image-based computational modeling. This was followed by a summary of different types of biomechanical models for coronary plaques. Plaque progression and vulnerability prediction studies based on image-based computational modeling were reviewed and compared. Much progress has been made and a reasonable high prediction accuracy has been achieved. However, there are still some inconsistencies in existing literature on the impact of biomechanical and morphological factors on future plaque behavior, and it is very difficult to perform direct comparison analysis as differences like image modality, biomechanical factors selection, predictive models, and progression/vulnerability measures exist among these studies. Encouraging data and model sharing across the research community would partially resolve these differences, and possibly lead to clearer assertive conclusions. In vivo image-based computational modeling could be used as a powerful tool for quantitative assessment of coronary plaque vulnerability for potential clinical applications.
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Aterosclerosis , Enfermedad de la Arteria Coronaria , Placa Aterosclerótica , Fenómenos Biomecánicos , Simulación por Computador , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Vasos Coronarios/diagnóstico por imagen , Humanos , Placa Aterosclerótica/diagnóstico por imagenRESUMEN
Alzheimer's disease (AD) is a neurodegenerative disorder with a long prodromal phase. Predicting AD progression will clinically help improve diagnosis and empower sufferers in taking proactive care. However, most existing methods only target individuals with a fixed number of historical visits, and only predict the cognitive scores once at a fixed time horizon in the future, which cannot meet practical requirements. In this study, we consider a flexible yet more challenging scenario in which individuals may suffer from the (arbitrary) modality-missing issue, as well as the number of individuals' historical visits and the length of target score trajectories being not prespecified. To address this problem, a multi-modal sequence learning framework, highlighted by deep latent representation collaborated sequence learning strategy, is proposed to flexibly handle the incomplete variable-length longitudinal multi-modal data. Specifically, the proposed framework first employs a deep multi-modality fusion module that automatically captures complementary information for each individual with incomplete multi-modality data. A comprehensive representation is thus learned and fed into a sequence learning module to model AD progression. In addition, both the multi-modality fusion module and sequence learning module are collaboratively trained to further promote the performance of AD progression prediction. Experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset validate the superiority of our method.
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Enfermedad de Alzheimer , Disfunción Cognitiva , Humanos , Enfermedad de Alzheimer/diagnóstico por imagen , Neuroimagen/métodos , Aprendizaje , Progresión de la Enfermedad , Imagen por Resonancia Magnética/métodos , Disfunción Cognitiva/diagnóstico por imagenRESUMEN
The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic research on leveraging consecutive rounds of multimodal clinical examination and laboratory test data to facilitate clinical decision-making for the treatment of COVID-19. To address this issue, we propose a multistage multimodal deep learning (MMDL) model to (1) first assess the patient's current condition (i.e., the mild and severe symptoms), then (2) give early warnings to patients with mild symptoms who are at high risk to develop severe illness. In MMDL, we build a sequential stage-wise learning architecture whose design philosophy embodies the model's predicted outcome and does not only depend on the current situation but also the history. Concretely, we meticulously combine the latest round of multimodal clinical data and the decayed past information to make assessments and predictions. In each round (stage), we design a two-layer multimodal feature extractor to extract the latent feature representation across different modalities of clinical data, including patient demographics, clinical manifestation, and 11 modalities of laboratory test results. We conduct experiments on a clinical dataset consisting of 216 COVID-19 patients that have passed the ethical review of the medical ethics committee. Experimental results validate our assumption that sequential stage-wise learning outperforms single-stage learning, but history long ago has little influence on the learning outcome. Also, comparison tests show the advantage of multimodal learning. MMDL with multimodal inputs can beat any reduced model with single-modal inputs only. In addition, we have deployed the prototype of MMDL in a hospital for clinical comparison tests and to assist doctors in clinical diagnosis.
Asunto(s)
COVID-19 , Aprendizaje Profundo , Humanos , Gravedad del Paciente , Pacientes , Brotes de EnfermedadesRESUMEN
Background: Automated tools for characterising dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer's disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction. Methods: Age- and sex-matched 44 probable AD (Age: 69.13 ± 7.13; MMSE: 27-30) and 22 non-demented control (Age: 69.38 ± 7.21; MMSE: 27-30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong's test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed. Results: AD-RAI had excellent same-day repeatability and excellent 2-week reproducibility. AD-RAI's AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients. Conclusions: The AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.