Name
Title | ||
|---|---|---|
Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection |
Abstract | ||
|---|---|---|
In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of non-small-cell lung cancer (maximum lesion size of 3 cm), we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/12.2043390 | Proceedings of SPIE |
Keywords | Field | DocType |
lung cancer,recurrent-free survival,topic model-based categorization,quantitative CT biomarker | Lung cancer,Computer vision,Categorization,Latent Dirichlet allocation,Lung,Resection,Biomarker (medicine),Artificial intelligence,Topic model,Radiology,Physics | Conference |
Volume | ISSN | Citations |
9035 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshiki Kawata | 1 | 192 | 54.44 |
| Noboru Niki | 2 | 188 | 66.10 |
| Hironobu Ohmatsu | 3 | 138 | 45.23 |
| mitsuo satake | 4 | 0 | 0.34 |
| masahiko kusumoto | 5 | 46 | 16.28 |
| takaaki tsuchida | 6 | 0 | 4.39 |
| keiju aokage | 7 | 0 | 1.35 |
| Kenji Eguchi | 8 | 129 | 42.78 |
| Masahiro Kaneko | 9 | 55 | 19.24 |
| Noriyuki Moriyama | 10 | 148 | 50.47 |