Name
Title | ||
|---|---|---|
Nonlinear dimensionality reduction of CT histogram based feature space for predicting recurrence-free survival in non-small-cell lung cancer |
Abstract | ||
|---|---|---|
Advantages of CT scanners with high resolution have allowed the improved detection of lung cancers. The release of positive results from the National Lung Screening Trial (NLST) in the US showed that CT screening does in fact have a positive impact on the reduction of lung cancer related mortality. While this study does show the efficacy of CT based screening, physicians often face the problems of deciding appropriate management strategies for maximizing patient survival and for preserving lung function. Several key manifold-learning approaches efficiently reveal intrinsic low-dimensional structures latent in high-dimensional data spaces. This study was performed to investigate whether the dimensionality reduction can identify embedded structures from the CT histogram feature of non-small-cell lung cancer (NSCLC) space to improve the performance in predicting the likelihood of recurrence-free survival (RFS) for patients with NSCLC. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2081719 | Proceedings of SPIE |
Keywords | Field | DocType |
non-small-cell lung cancer,recurrent-free survival,nonlinear dimensionality reduction,CT histogram | Lung cancer,Histogram,Ct scanners,Dimensionality reduction,Lung,Artificial intelligence,National Lung Screening Trial,Nonlinear dimensionality reduction,Computer vision,Feature vector,Medical physics,Radiology,Physics | Conference |
Volume | ISSN | Citations |
9414 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 2 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshiki Kawata | 1 | 192 | 54.44 |
| Noboru Niki | 2 | 188 | 66.10 |
| Hironobu Ohmatsu | 3 | 138 | 45.23 |
| keiju aokage | 4 | 0 | 1.35 |
| masahiko kusumoto | 5 | 46 | 16.28 |
| takaaki tsuchida | 6 | 0 | 4.39 |
| Kenji Eguchi | 7 | 129 | 42.78 |
| Masahiro Kaneko | 8 | 55 | 19.24 |