Statistical tools for the temporal analysis and classification of lung lesions.

BACKGROUND AND OBJECTIVE: Lung cancer remains one of the most common cancers globally. Temporal evaluation is an important tool for analyzing the malignant behavior of lesions during treatment, or of indeterminate lesions that may be benign. This work proposes a methodology for the analysis, quantification, and visualization of small (local) and large (global) changes in lung lesions. In addition, we extract textural features for the classification of lesions as benign or malignant. METHODS: We employ the statistical concept of uncertainty to associate each voxel of a lesion to a probability that changes occur in the lesion over time. We employ the Jensen divergence and hypothesis test locally to verify voxel-to-voxel changes, and globally to capture changes in lesion volumes. RESULTS: For the local hypothesis test, we determine that the change in density varies by between 3.84 and 40.01% of the lesion volume in a public database of malignant lesions under treatment, and by between 5.76 and 35.43% in a private database of benign lung nodules. From the texture analysis of regions in which the density changes occur, we are able to discriminate lung lesions with an accuracy of 98.41%, which shows that these changes can indicate the true nature of the lesion. CONCLUSION: In addition to the visual aspects of the density changes occurring in the lesions over time, we quantify these changes and analyze the entire set using volumetry. In the case of malignant lesions, large b-divergence values are associated with major changes in lesion volume. In addition, this occurs when the change in volume is small but is associated with significant changes in density, as indicated by the histogram divergence. For benign lesions, the methodology shows that even in cases where the change in volume is small, a change of density occurs. This proves that even in lesions that appear stable, a change in density occurs.

The surprising outcome of a giant primary mediastinal synovial sarcoma treated with neoadjuvant chemotherapy.

There are only a few cases of primary mediastinal synovial sarcoma in the literature. Normally, they do not respond well to chemotherapy. In our case, a 30-year-old patient was admitted due to thoracic pain, dyspnea, orthopnea, cough, hoarseness and weight loss over a 3-month period as well as a dramatic worsening a week before the admission. A chest radiography showed a completely white left hemithorax and contralateral mediastinal shift; in addition, a chest tomography revealed a giant heterogeneous mediastinal mass, lung atelectasia and a small pleural effusion. The patient was submitted to Chamberlain procedure (biopsy) under local anesthesia and the diagnosis of a synovial sarcoma was obtained after immunohistochemical analysis. Due to his poor general condition, he received chemotherapy first, with a dramatic response, after what, the mass that had been reduced was removed surgically. After a 5-year- follow-up period there are no signs of disease recurrence.

Automatic segmentation of lung nodules with growing neural gas and support vector machine.

Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancer. Unfortunately, this disease is often diagnosed late, affecting the treatment outcome. In order to help specialists in the search and identification of lung nodules in tomographic images, many research centers have developed computer-aided detection systems (CAD systems) to automate procedures. This work seeks to develop a methodology for automatic detection of lung nodules. The proposed method consists of the acquisition of computerized tomography images of the lung, the reduction of the volume of interest through techniques for the extraction of the thorax, extraction of the lung, and reconstruction of the original shape of the parenchyma. After that, growing neural gas (GNG) is applied to constrain even more the structures that are denser than the pulmonary parenchyma (nodules, blood vessels, bronchi, etc.). The next stage is the separation of the structures resembling lung nodules from other structures, such as vessels and bronchi. Finally, the structures are classified as either nodule or non-nodule, through shape and texture measurements together with support vector machine. The methodology ensures that nodules of reasonable size be found with 86% sensitivity and 91% specificity. This results in a mean accuracy of 91% for 10 experiments of training and testing in a sample of 48 nodules occurring in 29 exams. The rate of false positives per exam was of 0.138, for the 29 exams analyzed.