Value of Radiomics Features From Adrenal Gland and Periadrenal Fat in CT Images for Predicting COVID-19 Prognosis

AbstractBackgroundValue of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied.MethodsA total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed 3D V-Net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted to predict disease progression in patients with COVID-19.ResultsThe auto-segmentation framework yielded a dice value of 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.712, 0.692, 0.763, 0.791, and 0.806, respectively in the training set. FM and RN had better predictive efficacy than CM (P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was more than 0.3 in the validation set or between 0.4 and 0.8 in the test set, it could gain more net benefits using RN than FM and CM.ConclusionRadiomics features extracted from the adrenal gland and periadrenal fat CT images may predict progression in patients with COVID-19.FundingThis study was funded by Science and Technology Foundation of Guizhou Province (QKHZC [2020]4Y002, QKHPTRC [2019]5803), the Guiyang Science and Technology Project (ZKXM [2020]4), Guizhou Science and Technology Department Key Lab. Project (QKF [2017]25), Beijing Medical and Health Foundation (YWJKJJHKYJJ-B20261CS) and the special fund for basic Research Operating Expenses of public welfare research institutes at the central level from Chinese Academy of Medical Sciences (2019PT320003).

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Deep learning to predict subtypes of poorly differentiated lung cancer from biopsy whole slide images.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.8536 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 8536-8536

Author(s):

Gouji Toyokawa ◽

Fahdi Kanavati ◽

Seiya Momosaki ◽

Kengo Tateishi ◽

Hiroaki Takeoka ◽

...

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Learning Model ◽

Test Set ◽

Cancer Subtypes ◽

Independent Test ◽

Poorly Differentiated ◽

Test Sets ◽

Deep Learning Model ◽

Whole Slide Images

8536 Background: Lung cancer is the leading cause of cancer-related death in many countries, and its prognosis remains unsatisfactory. Since treatment approaches differ substantially based on the subtype, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC) and small cell lung cancer (SCLC), an accurate histopathological diagnosis is of great importance. However, if the specimen is solely composed of poorly differentiated cancer cells, distinguishing between histological subtypes can be difficult. The present study developed a deep learning model to classify lung cancer subtypes from whole slide images (WSIs) of transbronchial lung biopsy (TBLB) specimens, in particular with the aim of using this model to evaluate a challenging test set of indeterminate cases. Methods: Our deep learning model consisted of two separately trained components: a convolutional neural network tile classifier and a recurrent neural network tile aggregator for the WSI diagnosis. We used a training set consisting of 638 WSIs of TBLB specimens to train a deep learning model to classify lung cancer subtypes (ADC, SCC and SCLC) and non-neoplastic lesions. The training set consisted of 593 WSIs for which the diagnosis had been determined by pathologists based on the visual inspection of Hematoxylin-Eosin (HE) slides and of 45 WSIs of indeterminate cases (64 ADCs and 19 SCCs). We then evaluated the models using five independent test sets. For each test set, we computed the receiver operator curve (ROC) area under the curve (AUC). Results: We applied the model to an indeterminate test set of WSIs obtained from TBLB specimens that pathologists had not been able to conclusively diagnose by examining the HE-stained specimens alone. Overall, the model achieved ROC AUCs of 0.993 (confidence interval [CI] 0.971-1.0) and 0.996 (0.981-1.0) for ADC and SCC, respectively. We further evaluated the model using five independent test sets consisting of both TBLB and surgically resected lung specimens (combined total of 2490 WSIs) and obtained highly promising results with ROC AUCs ranging from 0.94 to 0.99. Conclusions: In this study, we demonstrated that a deep learning model could be trained to predict lung cancer subtypes in indeterminate TBLB specimens. The extremely promising results obtained show that if deployed in clinical practice, a deep learning model that is capable of aiding pathologists in diagnosing indeterminate cases would be extremely beneficial as it would allow a diagnosis to be obtained sooner and reduce costs that would result from further investigations.

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The Endocrine System: Adrenal Glands

10.2310/im.2272 ◽

2017 ◽

Author(s):

Abbas Al-Kurd ◽

Haggi Mazeh

Keyword(s):

Adrenal Gland ◽

Key Words ◽

Patient Group ◽

Clinical Relevance ◽

Perioperative Management ◽

Adrenal Glands ◽

Preoperative Evaluation ◽

Endocrine System ◽

Anatomy And Physiology ◽

Complicated Patient

The adrenal glands represent an essential component of the endocrine system, and their failure can have catastrophic consequences to several aspects of bodily homeostasis. Each adrenal gland can be divided into two different endocrine components, the cortex and the medulla, each with distinct functions. This in-depth review of normal adrenal embryology, anatomy, and physiology also emphasizes the clinical relevance of various irregularities in adrenal functioning. Every surgeon attempting to manage adrenal diseases is expected to be familiar with the detailed pathophysiology of these conditions because such an understanding is essential for sound preoperative evaluation and perioperative management of this potentially complicated patient group. This review contains 4 figures, 1 table, and 70 references. Key words: adrenal, adrenal glands, adrenal pathophysiology, adrenal physiology, anatomy of adrenal glands, cortex, embryology, endocrine system, medulla

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The Endocrine System: Adrenal Glands

10.2310/fm.2272 ◽

2017 ◽

Author(s):

Abbas Al-Kurd ◽

Haggi Mazeh

Keyword(s):

Adrenal Gland ◽

Key Words ◽

Patient Group ◽

Clinical Relevance ◽

Perioperative Management ◽

Adrenal Glands ◽

Preoperative Evaluation ◽

Endocrine System ◽

Anatomy And Physiology ◽

Complicated Patient

The adrenal glands represent an essential component of the endocrine system, and their failure can have catastrophic consequences to several aspects of bodily homeostasis. Each adrenal gland can be divided into two different endocrine components, the cortex and the medulla, each with distinct functions. This in-depth review of normal adrenal embryology, anatomy, and physiology also emphasizes the clinical relevance of various irregularities in adrenal functioning. Every surgeon attempting to manage adrenal diseases is expected to be familiar with the detailed pathophysiology of these conditions because such an understanding is essential for sound preoperative evaluation and perioperative management of this potentially complicated patient group. This review contains 4 figures, 1 table, and 70 references. Key words: adrenal, adrenal glands, adrenal pathophysiology, adrenal physiology, anatomy of adrenal glands, cortex, embryology, endocrine system, medulla

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The Endocrine System: Adrenal Glands

DeckerMed Surgery ◽

10.2310/surg.2272 ◽

2017 ◽

Author(s):

Abbas Al-Kurd ◽

Haggi Mazeh

Keyword(s):

Adrenal Gland ◽

Key Words ◽

Patient Group ◽

Clinical Relevance ◽

Perioperative Management ◽

Adrenal Glands ◽

Preoperative Evaluation ◽

Endocrine System ◽

Anatomy And Physiology ◽

Complicated Patient

The adrenal glands represent an essential component of the endocrine system, and their failure can have catastrophic consequences to several aspects of bodily homeostasis. Each adrenal gland can be divided into two different endocrine components, the cortex and the medulla, each with distinct functions. This in-depth review of normal adrenal embryology, anatomy, and physiology also emphasizes the clinical relevance of various irregularities in adrenal functioning. Every surgeon attempting to manage adrenal diseases is expected to be familiar with the detailed pathophysiology of these conditions because such an understanding is essential for sound preoperative evaluation and perioperative management of this potentially complicated patient group. This review contains 4 figures, 1 table, and 70 references. Key words: adrenal, adrenal glands, adrenal pathophysiology, adrenal physiology, anatomy of adrenal glands, cortex, embryology, endocrine system, medulla

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The Endocrine System: Adrenal Glands

10.2310/7ccsp.2272 ◽

2017 ◽

Author(s):

Abbas Al-Kurd ◽

Haggi Mazeh

Keyword(s):

Adrenal Gland ◽

Key Words ◽

Patient Group ◽

Clinical Relevance ◽

Perioperative Management ◽

Adrenal Glands ◽

Preoperative Evaluation ◽

Endocrine System ◽

Anatomy And Physiology ◽

Complicated Patient

The adrenal glands represent an essential component of the endocrine system, and their failure can have catastrophic consequences to several aspects of bodily homeostasis. Each adrenal gland can be divided into two different endocrine components, the cortex and the medulla, each with distinct functions. This in-depth review of normal adrenal embryology, anatomy, and physiology also emphasizes the clinical relevance of various irregularities in adrenal functioning. Every surgeon attempting to manage adrenal diseases is expected to be familiar with the detailed pathophysiology of these conditions because such an understanding is essential for sound preoperative evaluation and perioperative management of this potentially complicated patient group. This review contains 4 figures, 1 table, and 70 references. Key words: adrenal, adrenal glands, adrenal pathophysiology, adrenal physiology, anatomy of adrenal glands, cortex, embryology, endocrine system, medulla

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Using Connectionist Modules for Decision Support

Methods of Information in Medicine ◽

10.1055/s-0038-1634790 ◽

1990 ◽

Vol 29 (03) ◽

pp. 167-181 ◽

Cited By ~ 6

Author(s):

G. Hripcsak

Keyword(s):

Decision Support ◽

Standard Deviation ◽

Confidence Interval ◽

Posterior Probability ◽

Back Propagation ◽

Connectionist Model ◽

Test Set ◽

The Third ◽

Independent Test ◽

Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

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Protective Effect of Hypericum perforatum L. Extract on Hexavalent Chromium Induced Toxicity in Rat Adrenal Gland

Revista de Chimie ◽

10.37358/rc.17.9.5813 ◽

2017 ◽

Vol 68 (9) ◽

pp. 2014-2017

Author(s):

Jelena Savici ◽

Oana Maria Boldura ◽

Cornel Balta ◽

Diana Brezovan ◽

Florin Muselin ◽

...

Keyword(s):

Drinking Water ◽

Adrenal Gland ◽

Hexavalent Chromium ◽

Structural Damage ◽

Hypericum Perforatum ◽

Adrenal Glands ◽

Expression Level ◽

Apoptotic Pathway ◽

Hypericum Perforatum L ◽

Induction Of Apoptosis

This study was carried out to test the possibility of hexavalent chromium administration through drinking water to induce the structural damage in rat�s adrenal glands and the possibility of Hypericum perforatum extract to faith against chromium aggression. Chromium induced cellular stress was determined by the expression level assessment of the Bcl2 genes family, known to modulate the apoptotic pathway. Obtained results showed that exposure to chromium altered adrenal glands morphology, by induction of apoptosis. When Hypericum perforatum extract was administered expression level of Bcl2 genes and histological lesions in adrenal glands were significantly reduced.

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Evaluation of Adrenal Cortical Function in Neonatal and Weanling Laboratory Beagle Dogs

Toxicologic Pathology ◽

10.1177/01926233211009492 ◽

2021 ◽

pp. 019262332110094

Author(s):

Janet M. Petruska ◽

Maria Adamo ◽

Jeffrey McCartney ◽

Ahamat Aboulmali ◽

Thomas J. Rosol

Keyword(s):

Adrenal Gland ◽

Pituitary Gland ◽

Endocrine System ◽

Beagle Dogs ◽

Cortical Function ◽

Test Species ◽

Toxicity Studies ◽

Gland Function ◽

Common Target ◽

Adrenal Cortical Function

The most common target organ for toxicity in the endocrine system is the adrenal gland, and its function is dependent upon the hypothalamus and pituitary gland. Histopathologic examination of the adrenal glands and pituitary gland is routinely performed in toxicity studies. However, the function of the adrenal gland is not routinely assessed in toxicity studies. Assessment of adrenal cortical function may be necessary to determine whether a histopathologic finding in the adrenal cortex results in a functional effect in the test species. As juvenile toxicity studies are more commonly performed in support of pediatric indications for pharmaceuticals, it is important to establish historical control data for adrenal gland function. In this study, adrenal cortical function was assessed in control neonatal and weanling beagle dogs as part of an ongoing juvenile toxicology program. Measurements of serum adrenocorticotropic hormone (ACTH), cortisol prior to and following administration of exogenous ACTH, and aldosterone were conducted beginning at 2 weeks of age continuing through 26 weeks of age. Serum electrolyte concentrations were determined at 4, 13, and 26 weeks of age. Dogs as young as 2 weeks of age synthesize and secrete adrenal cortical hormones and exhibit a functional hypothalamic pituitary adrenal axis.

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Feature-Weighted Sampling for Proper Evaluation of Classification Models

Applied Sciences ◽

10.3390/app11052039 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2039

Author(s):

Hyunseok Shin ◽

Sejong Oh

Keyword(s):

Random Sampling ◽

Sampling Method ◽

Classification Model ◽

Training Set ◽

Test Set ◽

Feature Importance ◽

Proper Training ◽

Machine Learning Applications ◽

Test Sets ◽

The Given

In machine learning applications, classification schemes have been widely used for prediction tasks. Typically, to develop a prediction model, the given dataset is divided into training and test sets; the training set is used to build the model and the test set is used to evaluate the model. Furthermore, random sampling is traditionally used to divide datasets. The problem, however, is that the performance of the model is evaluated differently depending on how we divide the training and test sets. Therefore, in this study, we proposed an improved sampling method for the accurate evaluation of a classification model. We first generated numerous candidate cases of train/test sets using the R-value-based sampling method. We evaluated the similarity of distributions of the candidate cases with the whole dataset, and the case with the smallest distribution–difference was selected as the final train/test set. Histograms and feature importance were used to evaluate the similarity of distributions. The proposed method produces more proper training and test sets than previous sampling methods, including random and non-random sampling.

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