ABSTRACT
ABSTRACT The objective of this work is to identify the malignant lung nodules accurately and early with less false positives. 'Nodule' is the 3mm to 30mm diameter size tissue clusters present inside the lung parenchyma region. Segmenting such a small nodules from consecutive CT scan slices are a challenging task. In our work Auto-seed clustering based segmentation technique is used to segment all the possible nodule candidates. Efficient shape and texture features (2D and 3D) were computed to eliminate the false nodule candidates. The change in centroid position of nodule candidates from consecutive slices was used as a measure to remove the vessels. The two-stage classifier is used in this work to classify the malignant and benign nodules. First stage rule-based classifier producing 100 % sensitivity, but with high false positive of 12.5 per patient scan. The BPN based ANN classifier is used as the second-stage classifier which reduces a false positive to 2.26 per patient scan with a reasonable sensitivity of 88.8%. The Rate of Nodule Growth (RNG) was computed in our work to measure the nodules growth between the two scans of the same patient taken at different time interval. Finally, the nodule growth predictive measure was modeled through the features such as compactness (CO), mass deficit (MD), mass excess (ME) and isotropic factor(IF). The developed model results show that the nodules which have low CO, low IF, high MD and high ME values might have the potential to grow in future.
ABSTRACT
Previous investigations have shown that changes in total prostate volume (TPV) are highly variable among aging men, and a considerable proportion of aging men have a stable or decreasing prostate size. Although there is an abundance of literature describing prostatic enlargement in association with benign prostatic hyperplasia, less is known about the appropriate age cut-off points for TPV growth rate. In this community-based cohort study, TPV was examined once a year in men who had consecutive health checkup, during a follow-up of 4 years. A total of 5058 men (age 18-92 years old) were included. We applied multiple regression analyses to estimate the correlation between TPV growth rate and age. Overall, 3232 (63.9%) men had prostate growth, and 1826 (36.1%) had a stable or decreased TPV during the study period. The TPV growth rate was correlated negatively with baseline TPV (r=-0.32, P<0.001). Among 2620 men with baseline TPV <15 cm, the TPV growth rate increased with age (β=0.98, 95% CI: 0.77%-1.18%) only up to 53 years old. Among 2188 men with baseline TPV of 15-33.6 cm, the TPV growth rate increased with age (β=0.84, 95% CI, 0.66%-1.01%) only up to 61 years old after adjusting for factors of hypertension, obesity, baseline TPV, diabetes mellitus and dyslipidemia. In this longitudinal study, the TPV growth rate increased negatively with baseline TPV, only extending to a certain age and not beyond. Further research is needed to identify the mechanism underlying such differences in prostate growth.