scholarly journals Prediction Models for Malignant Pulmonary Nodules Based-on Texture Features of CT Image

10.5772/14766 ◽  
2011 ◽  
Author(s):  
Guo Xiuhua ◽  
Sun Tao ◽  
Wang huan ◽  
Liang Zhigang
Keyword(s):  
Prediction Models ◽  
Pulmonary Nodules ◽  
Texture Features ◽  
Ct Image

scholarly journals Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 696
Author(s):  
Barbara Palumbo ◽  
Francesco Bianconi ◽  
Isabella Palumbo ◽  
Mario Luca Fravolini ◽  
Matteo Minestrini ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Prediction Models ◽  
Classification Tree ◽  
Pulmonary Nodules ◽  
Texture Features ◽  
Imaging Features ◽  
Solitary Pulmonary Nodules ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

In this paper, we investigate the role of shape and texture features from 18F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules. To this end, we retrospectively evaluated cross-sectional data from 111 patients (64 males, 47 females, age = 67.5 ± 11.0) all with histologically confirmed benign (n=39) or malignant (n=72) solitary pulmonary nodules. Eighteen three-dimensional imaging features, including conventional, texture, and shape features from PET and CT were tested for significant differences (Wilcoxon-Mann-Withney) between the benign and malignant groups. Prediction models based on different feature sets and three classification strategies (Classification Tree, k-Nearest Neighbours, and Naïve Bayes) were also evaluated to assess the potential benefit of shape and texture features compared with conventional imaging features alone. Eight features from CT and 15 from PET were significantly different between the benign and malignant groups. Adding shape and texture features increased the performance of both the CT-based and PET-based prediction models with overall accuracy gain being 3.4–11.2 pp and 2.2–10.2 pp, respectively. In conclusion, we found that shape and texture features from 18F-FDG PET/CT can lead to a better discrimination between benign and malignant lung nodules by increasing the accuracy of the prediction models by an appreciable margin.

Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image

2010 ◽  
Vol 74 (1) ◽  
pp. 124-129 ◽  
Author(s):  
Huan Wang ◽  
Xiu-Hua Guo ◽  
Zhong-Wei Jia ◽  
Hong-Kai Li ◽  
Zhi-Gang Liang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pulmonary Nodules ◽  
Texture Features ◽  
Ct Image

scholarly journals Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Yongfeng Gao ◽  
Jiaxing Tan ◽  
Zhengrong Liang ◽  
Lihong Li ◽  
Yumei Huo
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
Ct Image ◽  
Computer Aided Detection ◽  
Ct Scanner ◽  
Human Visual Perception ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Cade System

AbstractComputer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

Evaluation Challenges for Computer-Aided Diagnostic Characterization

Data Mining ◽  
2013 ◽  
pp. 1794-1818
Author(s):  
William H. Horsthemke ◽  
Daniela S. Raicu ◽  
Jacob D. Furst ◽  
Samuel G. Armato
Keyword(s):  
Gold Standard ◽  
Prediction Models ◽  
Pulmonary Nodules ◽  
Ground Truth ◽  
Predictive Performance ◽  
Reference Standard ◽  
Domain Experts ◽  
Computer Aided ◽  
Visual Characteristics ◽  
Aided Diagnosis

Evaluating the success of computer-aided decision support systems depends upon a reliable reference standard, a ground truth. The ideal gold standard is expected to result from the marking, labeling, and rating by domain experts of the image of interest. However experts often disagree, and this lack of agreement challenges the development and evaluation of image-based feature prediction of expert-defined “truth.” The following discussion addresses the success and limitation of developing computer-aided models to characterize suspicious pulmonary nodules based upon ratings provided by multiple expert radiologists. These prediction models attempt to bridge the semantic gap between images and medically-meaningful, descriptive opinions about visual characteristics of nodules. The resultant computer-aided diagnostic characterizations (CADc) are directly usable for indexing and retrieving in content-based medical image retrieval and supporting computer-aided diagnosis. The predictive performance of CADc models are directly related to the extent of agreement between radiologists; the models better predict radiologists’ opinions when radiologists agree more with each other about the characteristics of nodules.

scholarly journals Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer?

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0194755 ◽  
Author(s):  
Hae Young Kim ◽  
Young Hoon Kim ◽  
Gabin Yun ◽  
Won Chang ◽  
Yoon Jin Lee ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Peritoneal Carcinomatosis ◽  
Advanced Gastric Cancer ◽  
Texture Features ◽  
Ct Image ◽  
Preoperative Ct

scholarly journals The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hui-Bin Tan ◽  
Fei Xiong ◽  
Yuan-Liang Jiang ◽  
Wen-Cai Huang ◽  
Ye Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Value ◽  
Laboratory Data ◽  
Texture Features ◽  
Chest Ct ◽  
Automatic Machine ◽  
Classification Model ◽  
Control Group ◽  
Ct Image ◽  
Severe Group

Abstract To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.

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