Lung cancer imaging

The value of chest computed tomography in evaluating lung cancer in a lobe affected by stable pulmonary tuberculosis in middle-aged and elderly patients: A preliminary study

Introduction

Lung cancer can be masked by coexisting stable tuberculosis lesions, which may result in delayed lung cancer diagnosis and treatment. Information about pulmonary tuberculosis patients who are at high-risk of developing lung cancer is scarce. We aimed to examine the value of chest computed tomography (CT) in evaluating lung cancer in a lobe affected by stable pulmonary tuberculosis in middle-aged and elderly patients.

Methods

In this single-centered, retrospective, observational study, we enrolled 41 middle-aged and elderly patients with pulmonary tuberculosis who developed lung cancer in the same lobe from January 30, 2011 to December 30, 2020. Comparisons of the clinical and chest CT data were made with age-matched and sex-matched control groups of patients with stable pulmonary tuberculosis but no lung cancer diagnosis (n = 38).

Results

Seventeen patients in the lung cancer group (41%) were initially misdiagnosed. Compared to lesions in the control group, lesions in the lung cancer group were significantly more likely to demonstrate the following CT features: large size, vessel convergence, lobulation, spiculation, spinous protuberance, bronchial obstruction or stenosis, vacuolation, ground-glass opacification, heterogeneous or homogeneous enhancement, and gradual increase in size. Nodular enlargement showed the best diagnostic performance in the diagnosis of lung cancer in a lobe affected by tuberculosis (area under the receiver operating characteristic curve = 0.974; P <0.001; accuracy = 98.2%; sensitivity =94.7%; specificity = 100%).

Conclusion

Chest CT might play an important role in early diagnosis of lung cancer in a lobe affected by tuberculosis. Regular CT re-examination is necessary in continuous controls monitoring of patients with stable pulmonary tuberculosis. The study indicates necessity of prospective study in this field.

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM

Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes. After that, we applied the genetic algorithm for selection of the best model. In the tests' stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules. The proposed work presents promising results at the classification into malignant and benign, achieving accuracy of 92.52%, sensitivity of 93.1% and specificity of 92.26%. The results demonstrated a good rate of correct detections using texture features. Since a precocious detection allows a faster therapeutic intervention, thus a more favorable prognostic to the patient, we propose herein a methodology that contributes to the area in this aspect.

3D shape analysis to reduce false positives for lung nodule detection systems

Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC-IDRI database, and cross-validation with k-fold, where [Formula: see text], was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer.

Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM

Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.