scholarly journals Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma

2022 ◽  
Vol 15 ◽  
Author(s):  
Jia-Qi Chen ◽  
Nuo Zhang ◽  
Zhi-Lin Su ◽  
Hui-Guo Qiu ◽  
Xin-Guo Zhuang ◽  
...  
Keyword(s):  
Molecular Therapy ◽  
Integrated Analysis ◽  
Differential Analysis ◽  
Potential Mechanism ◽  
Prognostic Signature ◽  
Targeted Molecular Therapy ◽  
Test Set ◽  
Independent Test ◽  
The Central Nervous System ◽  
Prognostic Prediction

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
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.

A Comparison of Two Aerial Photo Volume Tables for Pine Stands in Central Mississippi

1981 ◽  
Vol 5 (2) ◽  
pp. 92-96 ◽  
Author(s):  
James L. Smith ◽  
Roy A. Mead
Keyword(s):  
Prediction Models ◽  
Quadratic Model ◽  
Aerial Photo ◽  
Test Set ◽  
Volume Table ◽  
Independent Test ◽  
Pine Stands ◽  
Negative Volume

Abstract Two aerial photo volume prediction models, Avery's Composite Aerial Volume Table, and Mead's Quadratic Model, were compared using graphs and a small independent test set. The graphs indicated that Mead's model predicted higher merchantable volumes for pine stands in central Mississippi than did Avery's model. Both models tended to underpredict ground merchantable volume. However, only Avery's model underpredicted in a statistically significant manner. Even though the possibility of negative volume predictions exists when using Mead's Quadratic Model, it was deemed the superior model of the two investigated.

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

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.

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

2021 ◽  
Author(s):  
Mudan zhang ◽  
Xuntao Yin ◽  
Wuchao Li ◽  
Yan Zha ◽  
Xianchun Zeng ◽  
...  
Keyword(s):  
Adrenal Gland ◽  
Adrenal Glands ◽  
Endocrine System ◽  
Ct Images ◽  
Threshold Probability ◽  
Test Set ◽  
Clinical Model ◽  
Disease Prognosis ◽  
Independent Test ◽  
Test Sets

Abstract Background: Endocrine system plays an important role in infectious disease prognosis. Our goal is to assess the value of radiomics features extracted from adrenal gland and periadrenal fat CT images in predicting disease prognosis in patients with COVID-19. Methods: A total of 1,325 patients (765 moderate and 560 severe patients) from three centers were enrolled in the retrospective study. We proposed a 3D cascade V-Net to automatically segment adrenal glands in onset CT images. Periadrenal fat areas were obtained using inflation operations. Then, the radiomics features were automatically extracted. Five models were established to predict the disease prognosis in patients with COVID-19: a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], fusion of adrenal gland and periadrenal fat model [FM]), and a radiomics nomogram model (RN).Data from one center (1,183 patients) were utilized as training and validation sets. The remaining two (36 and 106 patients) were used as 2 independent test sets to evaluate the models’ performance. Results: The auto-segmentation framework achieved an average dice of 0.79 in the test set. CM, AM, PM, FM, and RN obtained AUCs of 0.716, 0.755, 0.796, 0.828, and 0.825, respectively in the training set, and the mean AUCs of 0.754, 0.709, 0.672, 0.706 and 0.778 for 2 independent test sets. Decision curve analysis showed that if the threshold probability was more than 0.3, 0.5, and 0.1 in the validation set, the independent-test set 1 and the independent-test set 2 could gain more net benefits using RN than FM and CM, respectively. Conclusion: Radiomics features extracted from CT images of adrenal glands and periadrenal fat are related to disease prognosis in patients with COVID-19 and have great potential for predicting its severity.

Targeted molecular therapy of malignant gliomas

2005 ◽  
Vol 5 (3) ◽  
pp. 186-197 ◽  
Author(s):  
Santosh Kesari ◽  
Naren Ramakrishna ◽  
Claire Sauvageot ◽  
Charles D. Stiles ◽  
Patrick Y. Wen
Keyword(s):  
Malignant Gliomas ◽  
Molecular Therapy ◽  
Targeted Molecular Therapy
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