Dataset Information

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

ABSTRACT: Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.

SUBMITTER: Kalpathy-Cramer J

PROVIDER: S-EPMC5279995 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

Kalpathy-Cramer Jayashree J Mamomov Artem A Zhao Binsheng B Lu Lin L Cherezov Dmitry D Napel Sandy S Echegaray Sebastian S Rubin Daniel D McNitt-Gray Michael M Lo Pechin P Sieren Jessica C JC Uthoff Johanna J Dilger Samantha K N SK Driscoll Brandan B Yeung Ivan I Hadjiiski Lubomir L Cha Kenny K Balagurunathan Yoganand Y Gillies Robert R Goldgof Dmitry D

Tomography (Ann Arbor, Mich.) 20161201 4

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting ...[more]

PMID: 28149958

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Project description:ObjectivesTo explore the predictive value of radiomics nomogram using pretreatment ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer (TNBC).Methods and materialsA total of 486 TNBC patients from 3 different institutions were consecutively recruited for this study. They were categorized into the primary cohort (n = 216), as well as the internal validation cohort (n = 108) and external validation cohort (n = 162). In primary cohort, least absolute shrinkage and selection operator logistic regression algorithm was used to select recurrence-related radiomics features extracted from the breast tumor and peritumor regions, and a radiomics signature was constructed derived from the grayscale ultrasound images. A radiomic nomogram integrating independent clinicopathological variables and radiomic signature was established with uni- and multivariate cox regressions. The predictive nomogram was validated using an internal cohort and an independent external cohort regarding abilities of discrimination, calibration and clinical usefulness.ResultsThe patients with higher Rad-score had a worse prognostic outcome than those with lower Rad-score in primary cohort and two validation cohorts (All p < 0.05).The radiomics nomogram indicated more effective prognostic performance compared with the clinicopathological model and tumor node metastasis staging system (p < 0.01), with a training C-index of 0.75 (95% confidence interval (CI), 0.71-0.80), an internal validation C-index of 0.73 (95% CI, 0.69-0.78) and an external validation 0.71 (95% CI,0.66-0.76). Moreover, the calibration curves revealed a good consistency for survival prediction of the radiomics model.ConclusionsThe ultrasound-based radiomics signature was a promising biomarker for risk stratification for TNBC patients. Furthermore, the proposed radiomics modal integrating the optimal radiomics features and clinical data provided individual relapse risk accurately.Advances in knowledgeThe radiomics model integrating radiomic signature and independent clinicopathological variables could improve individual prognostic evaluation and facilitate therapeutic decision-making, which demonstrated the incremental value of the radiomics signature for prognostic prediction in TNBC.

Project description:BackgroundAccurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.MethodsThis study enrolled 5-20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model.ResultsLung cancer risk models were developed among four nodule groups: all nodules (410 benign vs. 761 malignant; 1:1.86), nodules ≤10 mm (185 benign vs. 224 malignant; 1:1.21), solid nodules (306 benign vs. 242 malignant; 1.26:1), and SSNs (104 benign vs. 104 malignant; 1:1 matched). Significant clinical and radiomics predictors were selected for each group. The accuracy, area under the ROC curve, sensitivity, and specificity of the best model on validation dataset was 0.86, 0.91, 0.93, 0.73 for all nodules (XGBOOST), 0.82, 0.90, 0.86, 0.76 for nodules ≤10 mm (XGBOOST), 0.80, 0.89, 0.78, 0.82 for solid nodules (XGBOOST) and 0.70, 0.73, 0.73, 0.67 for SSNs (Random Forest). Except for the SSN models, the established clinical-radiomics models were superior to the Mayo model.ConclusionsPredictive models based on both clinical and radiomics features can be used to assess the malignancy of small solid and subsolid pulmonary nodules, even for nodules that are 10 mm or smaller.

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Project description:BackgroundThe accurate classification of lung nodules is critical to achieving personalized lung cancer treatment and prognosis prediction. The treatment options for lung cancer and the prognosis of patients are closely related to the type of lung nodules, but there are many types of lung nodules, and the distinctions between certain types are subtle, making accurate classification based on traditional medical imaging technology and doctor experience challenging. This study adopts a novel approach, using computed tomography (CT) radiomics to analyze the quantitative features in CT images to reveal the characteristics of lung nodules, and then employs diversity-weighted ensemble learning to enhance the accuracy of classification by integrating the predictive results of multiple models.MethodsWe extracted lung nodules from the Lung Image Database Consortium image collection (LIDC-IDRI) dataset and derived radiomics features from the nodules. For the classification tasks of seven types of lung nodules, each was split into binary classifications. Two model-building methods were employed: M1 (equal-weighted voting ensemble classifier) and M2 (diversity-weighted voting ensemble classifier). Models were evaluated using 10-fold cross-validation with metrics including the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity.ResultsBoth methods effectively completed classification tasks. The M2 method outperformed M1, particularly in classifying texture, calcification, and the benign and malignant nature of lung nodules. The AUC values of the M2 method in the four subclassifications of texture types of lung nodules were 0.9913, 0.8838, 0.9525, and 0.8845, with the corresponding accuracies of 0.9651, 0.8116, 0.9000, and 0.8284, respectively. In the classification of the degree of calcification of lung nodules, the AUC value of the M2 method was 0.9775 with an accuracy of 0.9642. In the classification of the benign and malignant nature of lung nodules, the AUC value of the M2 method was 0.8953 with an accuracy of 0.8168. The combination of CT radiomics and diversity-weighted ensemble learning effectively identifies lung nodule types, providing a novel method for the precise classification of lung nodules and aiding personalized lung cancer treatment and prognosis prediction.ConclusionsThe combination of CT radiomics and ensemble learning for diversity weighting can be well realized to identify the type of lung nodules.

Dataset Information

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

Publications

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

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