Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images

Contourlet textual features: Improving the diagnosis of solitary pulmonary nodules in two dimensional ct images

Materials and Methods: A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.Results: Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.Objective: To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Copyright:Conclusion: Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer

Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer

Identification of EGFR mutations is critical to the treatment of primary lung cancer and brain metastases (BMs). Here, we explored whether radiomic features of contrast-enhanced T1-weighted images (T1WIs) of BMs predict EGFR mutation status in primary lung cancer cases. In total, 1209 features were extracted from the contrast-enhanced T1WIs of 61 patients with 210 measurable BMs. Feature selection and classification were optimized using several machine learning algorithms. Ten-fold cross-validation was applied to the T1WI BM dataset (189 BMs for training and 21 BMs for the test set). Area under receiver operating characteristic curves (AUC), accuracy, sensitivity, and specificity were calculated. Subgroup analyses were also performed according to metastasis size. For all measurable BMs, random forest (RF) classification with RF selection demonstrated the highest diagnostic performance for identifying EGFR mutation (AUC: 86.81). Support vector machine and AdaBoost were comparable to RF classification. Subgroup analyses revealed that small BMs had the highest AUC (89.09). The diagnostic performance for large BMs was lower than that for small BMs (the highest AUC: 78.22). Contrast-enhanced T1-weighted image radiomics of brain metastases predicted the EGFR mutation status of lung cancer BMs with good diagnostic performance. However, further study is necessary to apply this algorithm more widely and to larger BMs.ope

Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes

Radiomics has shown potential in disease diagnosis, but its feasibility for non-small cell lung carcinoma (NSCLC) subtype classification is unclear. This study aims to explore the diagnosis value of texture and colour features from positron emission tomography computed tomography (PET-CT) images in differentiation of NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Two patient cohorts were retrospectively collected into a dataset of 341 18F-labeled 2-deoxy-2fluoro-d-glucose ([18F] FDG) PET-CT images of NSCLC tumours (125 ADC, 174 SqCC, and 42 cases with unknown subtype). Quantification of texture and colour features was performed using freehand regions of interest. The relation between extracted features and commonly used parameters such as age, gender, tumour size, and standard uptake value (SUVmax) was explored. To classify NSCLC subtypes, support vector machine algorithm was applied on these features and the classification performance was evaluated by receiver operating characteristic curve analysis. There was a significant difference between ADC and SqCC subtypes in texture and colour features (P  <  0.05); this showed that imaging features were significantly correlated to both SUVmax and tumour diameter (P  <  0.05). When evaluating classification performance, features combining texture and colour showed an AUC of 0.89 (95% CI, 0.78–1.00), colour features showed an AUC of 0.85 (95% CI, 0.71–0.99), and texture features showed an AUC of 0.68 (95% CI, 0.48–0.88). DeLong's test showed that AUC was higher for features combining texture and colour than that for texture features only (P  =  0.010), but not significantly different from that for colour features only (P  =  0.328). HSV colour features showed a similar performance to RGB colour features (P  =  0.473). The colour features are promising in the refinement of NSCLC subtype differentiation, and features combining texture and colour of PET-CT images could result in better classification performance

Intelligent support system for CVA diagnosis by cerebral computerized tomography

The Cerebral Vascular Accident (CVA) is one of the major causes of death in USA and developed countries, immediately following cardiac diseases and tumors. The increasing number of CVA’s and the requirement of short time diagnosis to minimize morbidity and mortality encourages the development of computer aided diagnosis systems. Early stages of CVA are often undetected by human eye observation of Computer Tomographic (CT) images, thus incorporation of intelligent based techniques on such systems is expected to highly improve their performance. This thesis presents a Radial Basis Functions Neural Network (RBFNN) based diagnosis system for automatic identification of CVA through analysis of CT images. The research hereby reported included construction of a database composed of annotated CT images, supported by a web-based tool for Neuroradiologist registration of his/her normal or abnormal interpretation of each CT image; in case of an abnormal identification the medical doctor was indicted by the software application to designate the lesion type and to identify the abnormal region on each CT’s slice image. Once provided the annotated database each CT image processing considered a pre-processing stage for artefact removal and tilted images’ realignment followed by a feature extraction stage. A large number of features was considered, comprising first and second order pixel intensity statistics as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line of each image. The policy conducted during the intelligent-driven image processing system development included the design of a neural network classifier. The architecture was determined by a Multi Objective Genetic Algorithm (MOGA) where the classifier structure, parameters and image features (input features) were chosen based on the use of different (often conflicting) objectives, ensuring maximization of the classification precision and a good generalization of its performance for unseen data Several scenarios of choosing proper MOGA’s data sets were conducted. The best result was obtained from the scenario where all boundary data points of an enlarged dataset were included in the MOGA training set. Confronted with the NeuroRadiologist annotations, specificity values of 98.01% and sensitivity values of 98.22% were obtained by the computer aided system, at pixel level. These values were achieved when an ensemble of non-dominated models generated by MOGA in the best scenario, was applied to a set of 150 CT slices (1,867,602 pixels). Present results show that the MOGA designed RBFNN classifier achieved better classification results than Support Vector Machines (SVM), despite the huge difference in complexity of the two classifiers. The proposed approach compares also favorably with other similar published solutions, both at lesion level specificity and at the degree of coincidence of marked lesions