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Radiother Oncol. 2016 Aug;120(2):258-66. doi: 10.1016/j.radonc.2016.05.024. Epub 2016 Jun 10.

CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer.

Author information

1
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA. Electronic address: ehuynh@lroc.harvard.edu.
2
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
3
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.

Abstract

BACKGROUND:

Radiomics uses a large number of quantitative imaging features that describe the tumor phenotype to develop imaging biomarkers for clinical outcomes. Radiomic analysis of pre-treatment computed-tomography (CT) scans was investigated to identify imaging predictors of clinical outcomes in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS:

CT images of 113 stage I-II NSCLC patients treated with SBRT were analyzed. Twelve radiomic features were selected based on stability and variance. The association of features with clinical outcomes and their prognostic value (using the concordance index (CI)) was evaluated. Radiomic features were compared with conventional imaging metrics (tumor volume and diameter) and clinical parameters.

RESULTS:

Overall survival was associated with two conventional features (volume and diameter) and two radiomic features (LoG 3D run low gray level short run emphasis and stats median). One radiomic feature (Wavelet LLH stats range) was significantly prognostic for distant metastasis (CI=0.67, q-value<0.1), while none of the conventional and clinical parameters were. Three conventional and four radiomic features were prognostic for overall survival.

CONCLUSION:

This exploratory analysis demonstrates that radiomic features have potential to be prognostic for some outcomes that conventional imaging metrics cannot predict in SBRT patients.

KEYWORDS:

Imaging; Lung cancer; Radiomics; Stereotactic body radiation therapy

PMID:
27296412
DOI:
10.1016/j.radonc.2016.05.024
[Indexed for MEDLINE]

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