Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy

Angel Moran, Megan E Daly, Stephen S.F. Yip, Tokihiro Yamamoto

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Background: Over 50% of patients who receive stereotactic body radiotherapy (SBRT) develop radiographic evidence of radiation-induced lung injury. Radiomics is an emerging approach that extracts quantitative features from image data, which may provide greater value and a better understanding of pulmonary toxicity than conventional approaches. We aimed to investigate the potential of computed tomography-based radiomics in characterizing post-SBRT lung injury. Methods: A total of 48 diagnostic thoracic computed tomography scans (acquired prior to SBRT and at 3, 6, and 9 months post-SBRT) from 14 patients were analyzed. Nine radiomic features (ie, 7 gray level co-occurrence matrix [GLCM] texture features and 2 first-order features) were investigated. The ability of radiomic features to distinguish radiation oncologist-defined moderate/severe lung injury from none/mild lung injury was assessed using logistic regression and area under the receiver operating characteristic curve (AUC). Moreover, dose-response curves (DRCs) for radiomic feature changes were determined as a function of time to investigate whether there was a significant dose-response relationship. Results: The GLCM features (logistic regression P-value range, 0.012-0.262; AUC range, 0.643-0.750) outperformed the first-order features (P-value range, 0.100-0.990; AUC range, 0.543-0.661) in distinguishing lung injury severity levels. Eight of 9 radiomic features demonstrated a significant dose-response relationship at 3, 6, and 9 months post-SBRT. Although not statistically significant, the GLCM features showed clear separations between the 3- or 6-month DRC and the 9-month DRC. Conclusion: Radiomic features significantly correlated with radiation oncologist-scored post-SBRT lung injury and showed a significant dose-response relationship, suggesting the potential for radiomics to provide a quantitative, objective measurement of post-SBRT lung injury.

Original languageEnglish (US)
JournalClinical Lung Cancer
DOIs
StateAccepted/In press - Jan 25 2017

Fingerprint

Radiosurgery
Lung Injury
Radiation
Area Under Curve
Logistic Models
Tomography
ROC Curve
Thorax
Lung

Keywords

  • Biomarkers
  • Computed tomography
  • Non-small-cell lung cancer
  • Quantitative imaging
  • Texture

ASJC Scopus subject areas

  • Oncology
  • Pulmonary and Respiratory Medicine
  • Cancer Research

Cite this

Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy. / Moran, Angel; Daly, Megan E; Yip, Stephen S.F.; Yamamoto, Tokihiro.

In: Clinical Lung Cancer, 25.01.2017.

Research output: Contribution to journalArticle

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abstract = "Background: Over 50{\%} of patients who receive stereotactic body radiotherapy (SBRT) develop radiographic evidence of radiation-induced lung injury. Radiomics is an emerging approach that extracts quantitative features from image data, which may provide greater value and a better understanding of pulmonary toxicity than conventional approaches. We aimed to investigate the potential of computed tomography-based radiomics in characterizing post-SBRT lung injury. Methods: A total of 48 diagnostic thoracic computed tomography scans (acquired prior to SBRT and at 3, 6, and 9 months post-SBRT) from 14 patients were analyzed. Nine radiomic features (ie, 7 gray level co-occurrence matrix [GLCM] texture features and 2 first-order features) were investigated. The ability of radiomic features to distinguish radiation oncologist-defined moderate/severe lung injury from none/mild lung injury was assessed using logistic regression and area under the receiver operating characteristic curve (AUC). Moreover, dose-response curves (DRCs) for radiomic feature changes were determined as a function of time to investigate whether there was a significant dose-response relationship. Results: The GLCM features (logistic regression P-value range, 0.012-0.262; AUC range, 0.643-0.750) outperformed the first-order features (P-value range, 0.100-0.990; AUC range, 0.543-0.661) in distinguishing lung injury severity levels. Eight of 9 radiomic features demonstrated a significant dose-response relationship at 3, 6, and 9 months post-SBRT. Although not statistically significant, the GLCM features showed clear separations between the 3- or 6-month DRC and the 9-month DRC. Conclusion: Radiomic features significantly correlated with radiation oncologist-scored post-SBRT lung injury and showed a significant dose-response relationship, suggesting the potential for radiomics to provide a quantitative, objective measurement of post-SBRT lung injury.",
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