Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Utilidade do PET/CT na caracterização do nódulo solitário pulmonar

Introdução – O cancro de pulmão/traqueia e brônquios é a principal causa de morte por neoplasia na União Europeia. A técnica de duas aquisições de imagem em tempos diferentes no Positron Emission Tomography/Computed Tomography (PET/CT) tem sido referenciada em alguns artigos como uma mais-valia no diagnóstico do cancro do pulmão. O objectivo deste estudo consiste em avaliar a eficiência diagnóstica do PET/CT com a aquisição das duas imagens em tempos diferentes na caracterização do nódulo solitário pulmonar (NSP), tendo em conta a histologia e o tamanho do nódulo. Metodologia – Foram analisados 115 NSP, num total de 110 pacientes, dos quais 65 nódulos eram malignos. Adquiriram-se duas imagens, a primeira a um tempo médio de 52 minutos e a segunda a um tempo médio de 125 minutos após administração do 18F-2-fluoro-2-deoxi-D-glucose (18F-FDG). Para a análise das imagens obteve-se o standard uptake value máximo (SUVmax) e a percentagem de variação dos SUVmax (%variação). Resultados – A %variação apresenta valores de eficiência diagnóstica superiores à análise dos SUVmax em separado. Existem também diferenças significativas na histologia e no SUVmax, registando-se um aumento do SUVmax2 comparativamente ao SUVmax1 nas patologias malignas. Conclusão – A técnica da aquisição de duas imagens em tempos diferentes mostrou ser mais eficaz na caracterização do NSP do que a análise de apenas uma imagem

The Advantage of PET and CT Integration in Examination of Lung Tumors

Purpose. To evaluate the diagnosis value of integrated positron emission tomography and computed tomography (PET/CT) with lung masses, this study emphasized the correlation between tumor size and maximum standardized uptake value (SUVmax) in selected regions of interest (ROI) of lung masses. Material and Methods. A retrospective analysis was performed on 85 patients with solid pulmonary lesions, all verified by pathology. The morphology, edge (speculated margins and lobule), size, density of pulmonary masses, and on-chest CT images were reviewed. The SUVmax in ROI of pulmonary masses was calculated. Results. Among the 85 patients with lung masses, 59 patients presented with pulmonary malignant neoplasm and 26 patients with benign lesions. The sensitivity, specificity, and accuracy were 89.8%, 61.5%, 81.2%, respectively, for PET measurement only, 88.1%, 65.4%, 81.2% for CT only, and 96.6%, 80.8%, 91.8% for PET/CT. The size of pulmonary malignant neoplasm in the 59 patients was apparently correlated with the ROI's SUVmax (r=0.617, P<.001). However, the size of pulmonary benign mass in the 26 patients was not correlated with the SUVmax. Conclusion. PET/CT is of greater value in characterization of lung masses than PET and CT performed separately. The examination of lung tumor can be further specified by the correlation between the size of pulmonary malignant neoplasm and the ROI's SUVmax

Volumetric Measurements of Lung Nodules with Multi-Detector Row CT: Effect of Changes in Lung Volume

OBJECTIVE: To evaluate how changes in lung volume affect volumetric measurements of lung nodules using a multi-detector row CT. MATERIALS AND METHODS: Ten subjects with asthma or chronic bronchitis who had one or more lung nodules were included. For each subject, two sets of CT images were obtained at inspiration and at expiration. A total of 33 nodules (23 nodules > or =3 mm) were identified and their volume measured using a semiautomatic volume measurement program. Differences between nodule volume on inspiration and expiration were compared using the paired t-test. Percent differences, between on inspiration and expiration, in nodule attenuation, total lung volume, whole lung attenuation, and regional lung attenuation, were computed and compared with percent difference in nodule volume determined by linear correlation analysis. RESULTS: The difference in nodule volume observed between inspiration and expiration was significant (p or =3 mm. The volume of nodules was measured to be larger on expiration CT than on inspiration CT (28 out of 33 nodules; 19 out of 23 nodules > or =3 mm). A statistically significant correlation was found between the percent difference of lung nodule volume and lung volume or regional lung attenuation (p or =3 mm. CONCLUSION: Volumetric measurements of pulmonary nodules were significantly affected by changes in lung volume. The variability in this respiration-related measurement should be considered to determine whether growth has occurred in a lung nodule.Supported by in part NIH NHLBI, RO1 HL 69149 and by a grant from Electronics and Telecommunications Research Institute

The Pretreatment of Maximum Standardized Uptake Values (SUVmax) of the Primary Tumor Is Predictor for Poor Prognosis for Patients with Epithelial Ovarian Cancer

The purpose of this study was to evaluate prognostic factors for epithelial ovarian cancer. We found that the pretreatment values of maximum standardized uptake (SUVmax) of the primary tumor by positron emission tomography/computed tomography (PET/CT), tumor marker CA125 and C-reactive protein (CRP) were correlated with clinical characteristics and prognosis for such patients. The clinical parameters and prognoses and their correlations with SUVmax of primary tumor, CA125 and CRP were examined for 51 patients with primary ovarian cancer. The SUVmax of the primary tumor had a statistically significant association with stage (p＝0.010) and histology (p＝0.001). CA125 was significant associated with stage (p＝0.011), histology (p＝0.005) and lymph node metastasis (p＝0.025). CRP was also significantly associated with stage (p＝0.049). Disease-free survival rates of patients exhibiting a high SUVmax, CA125 and CRP were significantly lower than those exhibiting a low SUVmax, CA125 and CRP levels (p＝0.008, 0.034, and 0.037, respectively). Furthermore, overall survival rates of patients exhibiting a high SUVmax were significantly lower than those exhibiting a low SUVmax (p＝0.049).The high SUVmax of primary tumor is an important factor for identifying ovarian cancer patients with a predictor for poor prognosis