Radiomics with artificial intelligence: a practical guide for beginners

Koçak, Burak; Durmaz, Emine Şebnem; Ateş, Ece; Kılıçkesmez, Özgür

doi:10.5152/dir.2019.19321

Cited by 261 publications

(197 citation statements)

References 62 publications

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“…In order to predict the PM status of patients with GC, we used the least absolute shrinkage and selection operator (LASSO) logistic regression model to select the optimal radiomics features from the primary texture features, and then, the development of the radiomics score (Rad-score) was constructed in the training cohort (21). For further detecting and addressing the collinearity among features, scatterplot correlation matrix with Person correlation coefficient was applied to investigate the interrelationship among the primary selected features and PM status, and if features had a correlation coefficient that was higher than 0.80 between each other, then the one with the highest collinearity was excluded from the analysis (22)(23)(24). In this study, we used the R software (version 3.5.3) with the "glmnet" package to perform the LASSO regression (25,26).…”

Section: Radiomics Feature Selection and Signature Developmentmentioning

confidence: 99%

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

et al. 2020

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The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703-0.799), 0.802 (95%CI, 0.691-0.912), and 0.745 (95%CI, 0.683-0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748-0.836), 0.870 (95%CI, 0.795-0.946), and 0.815 (95%CI, 0.763-0.867), respectively. Conclusions: CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.

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Section: Radiomics Feature Selection and Signature Developmentmentioning

confidence: 99%

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

et al. 2020

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“…Together with 14 shape features of the original image, 1688 features were extracted for this study. For more information on the methods and parameters of feature extraction in radiomics 16 , see Table 2 .…”

Section: Methodsmentioning

confidence: 99%

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

Tan

Xiong

Jiang

et al. 2020

Sci Rep

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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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“…Radiomics, sometimes referred to as quantitative imaging, implies the extraction of a vast number of features from medical images, and its conversion to high-dimensional data [ 21 ]. Imaging features such as size, shape, physical transport properties, and texture are extracted from conventional CT, MRI, or positron emission tomography (PET) images into a database, and are then used to create statistical models that can improve diagnostic, prognostic, and predictive accuracy [ 22 ]. The principle of radiomics is that the gray-scale values of an image (i.e., pixels), and their spatial and temporal relationships, contain information on phenotype, pathophysiology, and biology (e.g., genomics, proteomics, and metabolomics) [ 23 , 24 , 25 ].…”

Section: Definition Of Radiomicsmentioning

confidence: 99%

Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions

2020

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Radiomics, also known as quantitative imaging or texture analysis, involves extracting a large number of features traditionally unmeasured in conventional radiological cross-sectional images and converting them into mathematical models. This review describes this approach and its use in the evaluation of pancreatic cystic lesions (PCLs). This discipline has the potential of more accurately assessing, classifying, risk stratifying, and guiding the management of PCLs. Existing studies have provided important insight into the role of radiomics in managing PCLs. Although these studies are limited by the use of retrospective design, single center data, and small sample sizes, radiomic features in combination with clinical data appear to be superior to the current standard of care in differentiating cyst type and in identifying mucinous PCLs with high-grade dysplasia. Combining radiomic features with other novel endoscopic diagnostics, including cyst fluid molecular analysis and confocal endomicroscopy, can potentially optimize the predictive accuracy of these models. There is a need for multicenter prospective studies to elucidate the role of radiomics in the management of PCLs.

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Radiomics with artificial intelligence: a practical guide for beginners

Cited by 261 publications

References 62 publications

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer

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

Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions

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