Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study

Wang, Gongzheng; Kang, Bing; Cui, Jingjing; Deng, Yan; Zhao, Yun; Ji, Chaonan; Wang, Ximing

doi:10.1007/s00330-022-09182-8

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Decision curve analysis shows that both models can improve the clinical benefits of patients, and the radiomics-clinical combination model achieves higher clinical benefits than the radiomics model. The features of radiomics, including shape, grayscale, and texture, help to build radiomics models (26). This study establishes the correlation between radiomics features and CT imaging signs, and the study reveals that "mean lesion density" is negatively correlated with "original glrlm ShortRunLowGrayLevelEmphasis, " "wavelet-LHL firstorder Median," "normalize glrlm GrayLevelNonUniformityNormalized," and "specklenoise glrlm ShortRunLowGrayLevelEmphasis"; and is positively correlated with "wavelet-HLL firstorder Skewness"; "consolidation pattern" is negatively correlated with "original glrlm ShortRunLowGrayLevelEmphasis" and "normalize glrlm GrayLevelNonUniformityNormalized"; "air bronchogram sign" is negatively correlated with "original glrlm ShortRunLowGrayLevelEmphasis," "normalize glrlm GrayLevelNonUniformityNormalized," and "specklenoise glrlm ShortRunLowGrayLevelEmphasis"; "Interlobular_septal_ thickening" is negatively correlated with "discretegaussian glszm SizeZoneNonUniformity"; and the correlation coefficients were all greater than 0.35.…”

Section: Discussionmentioning

confidence: 99%

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Meng,

Wang,

Zhuo

et al. 2024

Front. Med.

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PurposeThis study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs.Materials and methodsIn a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively.ResultsFor the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35.ConclusionThe combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

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Section: Discussionmentioning

confidence: 99%

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Meng,

Wang,

Zhuo

et al. 2024

Front. Med.

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“…An increasing number of studies have recently investigated the application of radiomics-based approaches to diagnose afflictions of the adrenal glands. Radiomics has the ability to predict adrenocortical carcinomas, metastatic carcinomas, pheochromocytomas, and APAs by quantitatively extracting the features of adrenal lesions before surgery [12][13][14][15]. One study reported that contrastenhanced CT (CECT)-based radiomics can be used to identify non-functioning adrenal adenomas (NAAs) in patients with EH and APAs in patients with PA [16].…”

Section: Introductionmentioning

confidence: 99%

Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma

Yang,

Hao,

et al. 2023

Bioengineering

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To evaluate the secretory function of adrenal incidentaloma, this study explored the usefulness of a contrast-enhanced computed tomography (CECT)-based radiomics model for distinguishing aldosterone-producing adenoma (APA) from non-functioning adrenal adenoma (NAA). Overall, 68 APA and 60 NAA patients were randomly assigned (8:2 ratio) to either a training or a test cohort. In the training cohort, univariate and least absolute shrinkage and selection operator regression analyses were conducted to select the significant features. A logistic regression machine learning (ML) model was then constructed based on the radiomics score and clinical features. Model effectiveness was evaluated according to the receiver operating characteristic, accuracy, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis. In the test cohort, the area under the curve (AUC) of the Radscore model was 0.869 [95% confidence interval (CI), 0.734–1.000], and the accuracy, sensitivity, specificity, and F1 score were 0.731, 1.000, 0.583, and 0.900, respectively. The Clinic–Radscore model had an AUC of 0.994 [95% CI, 0.978–1.000], and the accuracy, sensitivity, specificity, and F1 score values were 0.962, 0.929, 1.000, and 0.931, respectively. In conclusion, the CECT-based radiomics and clinical radiomics ML model exhibited good diagnostic efficacy in differentiating APAs from NAAs; this non-invasive, cost-effective, and efficient method is important for the management of adrenal incidentaloma.

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Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics

Liu,

Zhao,

Bao

et al. 2024

Vis. Comput. Ind. Biomed. Art

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Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

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Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study

Cited by 5 publications

References 35 publications

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma

Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics

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