Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study

Bianconi, Francesco; Fravolini, Mario Luca; Palumbo, Isabella; Pascoletti, Giulia; Nuvoli, Susanna; Rondini, Maria; Spanu, Angela; Palumbo, Barbara

doi:10.3390/diagnostics11071224

Cited by 9 publications

(7 citation statements)

References 55 publications

(74 reference statements)

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“…[22][23][24] Recently, new technologies, such as methylated DNA marker detection and single cell sequencing, are also used to diagnose malignant pleural effusion. 25,26 In addition, Bianconi et al 27 and Palumbo et al 15 used machine learning methods based on medical imaging, such as computed tomography (CT), which is a completely non-invasive testing method, to identify benign and malignant pulmonary nodules resulting in a good performance. In a future study, we could combine the data from both the CT image and the tumor markers to improve the diagnostic accuracy of MPE.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models

et al. 2023

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Background To evaluate the diagnostic value of combinations of tumor markers carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 125, CA153, and CA19-9 in identifying malignant pleural effusion (MPE) from non-malignant pleural effusion (non-MPE) using machine learning, and compare the performance of popular machine learning methods. Methods A total of 319 samples were collected from patients with pleural effusion in Beijing and Wuhan, China, from January 2018 to June 2020. Five machine learning methods including Logistic regression, extreme gradient boosting (XGBoost), Bayesian additive regression tree, random forest, and support vector machine were applied to evaluate the diagnostic performance. Sensitivity, specificity, Youden's index, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of different diagnostic models. Results For diagnostic models with a single tumor marker, the model using CEA, constructed by XGBoost, performed best (AUC = 0.895, sensitivity = 0.80), and the model with CA153, also by XGBoost, showed the largest specificity 0.98. Among all combinations of tumor markers, the combination of CEA and CA153 achieved the best performance (AUC = 0.921, sensitivity = 0.85) in identifying MPE under the diagnostic model constructed by XGBoost. Conclusions Diagnostic models for MPE with a combination of multiple tumor markers outperformed the models with a single tumor marker, particularly in sensitivity. Using machine learning methods, especially XGBoost, could comprehensively improve the diagnostic accuracy of MPE.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models

et al. 2023

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“…The stability of texture features was investigated in previous studies [ 33 , 34 , 35 ]. In addition to different scanner techniques and image reconstruction methods, the lesion size should be considered as a possible factor influencing texture features.…”

Section: Discussionmentioning

confidence: 99%

CT Texture Analysis of Pulmonary Neuroendocrine Tumors—Associations with Tumor Grading and Proliferation

Meyer

Leonhardi

Höhn

et al. 2021

JCM

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Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, “S(1,1)SumEntrp” was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed “S(4,4)InvDfMom” (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. “Sumentrp” might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.

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“…The lack of a practical guidelines suggesting how to set extraction options is currently a major issue in radiomics [ 27 ]. Therefore, we referred to a scientific paper that reported a study carried out on the same dataset (LIDC-IDRI) to set the value of the bin size [ 28 ]. As a result, we set it equal to 64, while we left the Pyradiomics default settings for all other extraction options.…”

Section: Case Studymentioning

confidence: 99%

matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model

et al. 2022

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Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) feature selection, and (iv) model fitting. Many radiomics tools were developed to fulfill the steps mentioned above. However, to date, users must switch different software to complete the radiomics workflow. To address this issue, we developed a new free and user-friendly radiomics framework, namely matRadiomics, which allows the user: (i) to import and inspect biomedical images, (ii) to identify and segment the target, (iii) to extract the features, (iv) to reduce and select them, and (v) to build a predictive model using machine learning algorithms. As a result, biomedical images can be visualized and segmented and, through the integration of Pyradiomics into matRadiomics, radiomic features can be extracted. These features can be selected using a hybrid descriptive–inferential method, and, consequently, used to train three different classifiers: linear discriminant analysis, k-nearest neighbors, and support vector machines. Model validation is performed using k-fold cross-Validation and k-fold stratified cross-validation. Finally, the performance metrics of each model are shown in the graphical interface of matRadiomics. In this study, we discuss the workflow, architecture, application, future development of matRadiomics, and demonstrate its working principles in a real case study with the aim of establishing a reference standard for the whole radiomics analysis, starting from the image visualization up to the predictive model implementation.

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Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study

Cited by 9 publications

References 55 publications

Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models

Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models

CT Texture Analysis of Pulmonary Neuroendocrine Tumors—Associations with Tumor Grading and Proliferation

matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model

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