Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules.
Ontology highlight
ABSTRACT:
SUBMITTER: Balagurunathan Y
PROVIDER: S-EPMC6561979 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundNon-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.MethodsProtein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index.ResultsAge, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy.ConclusionsA combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.
| S-EPMC10057862 | biostudies-literature
Project description:This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care.
| S-EPMC11352131 | biostudies-literature
Project description:PurposeTo quantitatively assess the value of dual-energy CT (DECT) in differentiating malignancy and benignity of solitary pulmonary nodules.Materials and methodsSixty-three patients with solitary pulmonary nodules detected by CT plain scan underwent contrast enhanced CT scans in arterial phase (AP) and venous phase (VP) with spectral imaging mode for tumor type differentiation. The Gemstone Spectral Imaging (GSI) viewer was used for image display and data analysis. Region of interest was placed on the relatively homogeneous area of the nodule to measure iodine concentration (IC) on iodine-based material decomposition images and CT numbers on monochromatic image sets to generate spectral HU curve. Normalized IC (NIC), slope of the spectral HU curve (?HU) and net CT number enhancement on 70keV images were calculated. The two-sample t-test was used to compare quantitative parameters. Receiver operating characteristic curves were generated to calculate sensitivity and specificity.ResultsThere were 63 nodules, with 37 malignant nodules (59%) and 26 benign nodules (41%). NIC, ?HU and net CT number enhancement on 70keV images for malignant nodules were all greater than those of benign nodules. NIC and ?HU had intermediate to high performances to differentiate malignant nodules from benign ones with the areas under curve of 0.89 and 0.86 respectively in AP, 0.96 and 0.89 respectively in VP. Using 0.30 as a threshold value for NIC in VP, one could obtain sensitivity of 93.8% and specificity of 85.7% for differentiating malignant from benign solitary pulmonary nodules. These values were statistically higher than the corresponding values of 74.2% and 53.8% obtained with the conventional CT number enhancement.ConclusionsDECT imaging with GSI mode provides more promising value in quantitative way for distinguishing malignant nodules from benign ones than CT enhancement numbers.
| S-EPMC4739615 | biostudies-literature
Project description: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.
| S-EPMC5279995 | biostudies-literature
Project description:ObjectivesDistinction of malignant pulmonary nodules from the benign ones based on computed tomography (CT) images can be time-consuming but significant in routine clinical management. The advent of artificial intelligence (AI) has provided an opportunity to improve the accuracy of cancer risk prediction.MethodsA total of 8950 detected pulmonary nodules with complete pathological results were retrospectively enrolled. The different radiological manifestations were identified mainly as various nodules densities and morphological features. Then, these nodules were classified into benign and malignant groups, both of which were subdivided into finer specific pathological types. Here, we proposed a deep convolutional neural network for the assessment of lung nodules named DeepLN to identify the radiological features and predict the pathologic subtypes of pulmonary nodules.ResultsIn terms of density, the area under the receiver operating characteristic curves (AUCs) of DeepLN were 0.9707 (95% confidence interval, CI: 0.9645-0.9765), 0.7789 (95%CI: 0.7569-0.7995), and 0.8950 (95%CI: 0.8822-0.9088) for the pure-ground glass opacity (pGGO), mixed-ground glass opacity (mGGO) and solid nodules. As for the morphological features, the AUCs were 0.8347 (95%CI: 0.8193-0.8499) and 0.9074 (95%CI: 0.8834-0.9314) for spiculation and lung cavity respectively. For the identification of malignant nodules, our DeepLN algorithm achieved an AUC of 0.8503 (95%CI: 0.8319-0.8681) in the test set. Pertaining to predicting the pathological subtypes in the test set, the multi-task AUCs were 0.8841 (95%CI: 0.8567-0.9083) for benign tumors, 0.8265 (95%CI: 0.8004-0.8499) for inflammation, and 0.8022 (95%CI: 0.7616-0.8445) for other benign ones, while AUCs were 0.8675 (95%CI: 0.8525-0.8813) for lung adenocarcinoma (LUAD), 0.8792 (95%CI: 0.8640-0.8950) for squamous cell carcinoma (LUSC), 0.7404 (95%CI: 0.7031-0.7782) for other malignant ones respectively in the malignant group.ConclusionsThe DeepLN based on deep learning algorithm represented a competitive performance to predict the imaging characteristics, malignancy and pathologic subtypes on the basis of non-invasive CT images, and thus had great possibility to be utilized in the routine clinical workflow.
| S-EPMC9130467 | biostudies-literature
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.
| S-EPMC8339772 | biostudies-literature
Project description:Over 80,000 people undergo pulmonary resection for a lung nodule in the United States each year. Small nodules are frequently missed or difficult to find despite preoperative imaging. We hypothesized that near-infrared (NIR) imaging technology could be used to identify and locate lung nodules during surgery.We enrolled 18 patients who were diagnosed with a pulmonary nodule that required resection. All patients had a fine-cut 1-mm computed tomography scan preoperatively. The patients were given systemic 5 mg/kg indocyanine green and then underwent an open thoracotomy 24 hours later. The NIR imaging was used to identify the primary nodule and search for additional nodules that were not found by visual inspection or manual palpation of the ipsilateral lung.Manual palpation and visual inspection identified all 18 primary pulmonary nodules and no additional lesions. Intraoperative NIR imaging detected 16 out of the 18 primary nodules. The NIR imaging also identified 5 additional subcentimeter nodules; 3 metastatic adenocarcinomas and 2 metastatic sarcomas. This technology could identify nodules as small as 0.2 cm and as deep as 1.3 cm from the pleural surface. This approach discovered 3 nodules that were in different lobes than the primary tumor. Nodule fluorescence was independent of size, metabolic activity, histology, tumor grade and vascularity.This is the first-in-human demonstration of identifying pulmonary nodules during thoracic surgery with NIR imaging without a priori knowledge of their location or existence. The NIR imaging can detect pulmonary nodules during lung resections that are poorly visualized on computed tomography and difficult to discriminate on finger palpation.
| S-EPMC4185369 | biostudies-literature
Project description:OBJECTIVES:The post-imaging, mathematical predictive model was established by combining demographic and imaging characteristics with a pulmonary nodule risk score. The prediction model provides directions for the treatment of pulmonary nodules. Many studies have established predictive models for pulmonary nodules in different populations. However, the predictive factors contained in each model were significantly different. We hypothesized that applying different models to local research groups will make a difference in predicting the benign and malignant lung nodules, distinguishing between early and late lung cancers, and between adenocarcinoma and squamous cell carcinoma. In the present study, we compared four widely used and well-known mathematical prediction models. MATERIALS AND METHODS:We performed a retrospective study of 496 patients from January 2017 to October 2019, they were diagnosed with nodules by pathological. We evaluate models' performance by viewing 425 malignant and 71 benign patients' computed tomography results. At the same time, we use the calibration curve and the area under the receiver operating characteristic curve whose abbreviation is AUC to assess one model's predictive performance. RESULTS:We find that in distinguishing the Benign and the Malignancy, Peking University People's Hospital model possessed excellent performance (AUC = 0.63), as well as differentiating between early and late lung cancers (AUC = 0.67) and identifying lung adenocarcinoma (AUC = 0.61). While in the identification of lung squamous cell carcinoma, the Veterans Affairs model performed the best (AUC = 0.69). CONCLUSIONS:Geographic disparities are an extremely important influence factors, and which clinical features contained in the mathematical prediction model are the key to affect the precision and accuracy.
| S-EPMC7048676 | biostudies-literature
Project description:BackgroundTo develop and validate a deep learning-based model on CT images for the malignancy and invasiveness prediction of pulmonary subsolid nodules (SSNs).Materials and methodsThis study retrospectively collected patients with pulmonary SSNs treated by surgery in our hospital from 2012 to 2018. Postoperative pathology was used as the diagnostic reference standard. Three-dimensional convolutional neural network (3D CNN) models were constructed using preoperative CT images to predict the malignancy and invasiveness of SSNs. Then, an observer reader study conducted by two thoracic radiologists was used to compare with the CNN model. The diagnostic power of the models was evaluated with receiver operating characteristic curve (ROC) analysis.ResultsA total of 2,614 patients were finally included and randomly divided for training (60.9%), validation (19.1%), and testing (20%). For the benign and malignant classification, the best 3D CNN model achieved a satisfactory AUC of 0.913 (95% CI: 0.885-0.940), sensitivity of 86.1%, and specificity of 83.8% at the optimal decision point, which outperformed all observer readers' performance (AUC: 0.846±0.031). For pre-invasive and invasive classification of malignant SSNs, the 3D CNN also achieved satisfactory AUC of 0.908 (95% CI: 0.877-0.939), sensitivity of 87.4%, and specificity of 80.8%.ConclusionThe deep-learning model showed its potential to accurately identify the malignancy and invasiveness of SSNs and thus can help surgeons make treatment decisions.
| S-EPMC8351466 | biostudies-literature
Project description:The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.
| S-EPMC10341085 | biostudies-literature