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Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy
| Content Provider | Scilit |
|---|---|
| Author | Li, Hongming Galperin-Aizenberg, Maya Pryma, Daniel Simone, Charles B. Fan, Yong |
| Copyright Year | 2018 |
| Description | Journal: Radiotherapy and Oncology Background and purpose To predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data. Material and methods This study was performed based on an$ ^{18}$F-FDG-PET dataset of 100 consecutive patients who were treated with SBRT for early stage NSCLC. Each patient's tumor was characterized by 722 radiomic features. An unsupervised two-way clustering method was used to identify groups of patients and radiomic features simultaneously. The groups of patients were compared in terms of survival and freedom from nodal failure. Meta-features were computed for building survival models to predict survival and free of nodal failure. Results Differences were found between 2 groups of patients when the patients were clustered into 3 groups in terms of both survival (p = 0.003) and freedom from nodal failure (p = 0.038). Average concordance measures for predicting survival and nodal failure were |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261331/pdf http://www.thegreenjournal.com/article/S0167814018333401/pdf |
| Ending Page | 226 |
| Page Count | 9 |
| Starting Page | 218 |
| ISSN | 01678140 |
| e-ISSN | 18790887 |
| DOI | 10.1016/j.radonc.2018.06.025 |
| Journal | Radiotherapy and Oncology |
| Issue Number | 2 |
| Volume Number | 129 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2018-07-04 |
| Access Restriction | Open |
| Subject Keyword | Journal: Radiotherapy and Oncology Medical Informatics Radiology, Nuclear Medicine and Imaging Unsupervised Machine Learning Non-small Cell Lung Cancer Stereotactic Body Radiation Therapy |
| Content Type | Text |
| Resource Type | Article |
| Subject | Radiology, Nuclear Medicine and Imaging Hematology Oncology |
