CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy

Radiation Oncology, Sep 2017

Eduardo G. Moros, Jin Qi, Jongphil Kim, Kujtim Latifi, Matthew B. Schabath, Qian Li, Robert J. Gillies, Thomas J. Dilling, Ying Liu, Yoganand Balagurunathan, et al.

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CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy

Li et al. Radiation Oncology CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy Qian Li 0 4 Jongphil Kim 2 Yoganand Balagurunathan 3 Jin Qi 0 3 4 Ying Liu 0 4 Kujtim Latifi 1 Eduardo G. Moros 1 3 Matthew B. Schabath 5 Zhaoxiang Ye 0 4 Robert J. Gillies 3 Thomas J. Dilling 1 0 Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer , Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin 300060 , China 1 Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute , 12902 Magnolia Drive, Tampa, FL 33612 , USA 2 Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute , Tampa, FL , USA 3 Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute , Tampa, FL , USA 4 Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer , Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin 300060 , China 5 Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute , Tampa, FL , USA Background: Predicting recurrence after stereotactic body radiotherapy (SBRT) in non-small cell lung cancer (NSCLC) patients is problematic, but critical for the decision of following treatment. This study aims to investigate the association of imaging features derived from the first follow-up computed tomography (CT) on lung cancer patient outcomes following SBRT, and identify patients at high risk of recurrence. Methods: Fifty nine biopsy-proven non-small cell lung cancer patients were qualified for this study. The first follow-up CTs were performed about 3 months after SBRT (median time: 91 days). Imaging features included 34 manually scored radiological features (semantics) describing the lesion, lung and thorax and 219 quantitative imaging features (radiomics) extracted automatically after delineation of the lesion. Cox proportional hazard models and Harrel's C-index were used to explore predictors of overall survival (OS), recurrence-free survival (RFS), and loco-regional recurrence-free survival (LR-RFS). Five-fold cross validation was performed on the final prognostic model. Results: The median follow-up time was 42 months. The model for OS contained Eastern Cooperative Oncology Group (ECOG) performance status (HR = 3.13, 95% CI: 1.17-8.41), vascular involvement (HR = 3.21, 95% CI: 1.29-8.03), lymphadenopathy (HR = 3.59, 95% CI: 1.58-8.16) and the 1st principle component of radiomic features (HR = 1.24, 95% CI: 1.02-1.51). The model for RFS contained vascular involvement (HR = 3.06, 95% CI: 1.40-6.70), vessel attachment (HR = 3.46, 95% CI: 1.65-7.25), pleural retraction (HR = 3.24, 95% CI: 1.41-7.42), lymphadenopathy (HR = 6.41, 95% CI: 2.58-15.90) and relative enhancement (HR = 1.40, 95% CI: 1.00-1.96). The model for LR-RFS contained vascular involvement (HR = 4.96, 95% CI: 2.23-11.03), lymphadenopathy (HR = 2.64, 95% CI: 1.19-5.82), circularity (F13, HR = 1.60, 95% CI: 1.10-2.32) and 3D Laws feature (F92, HR = 1.96, 95% CI: 1.35-2.83). Five-fold cross-validated the areas under the receiver operating characteristic curves (AUC) of these three models were all above 0.8. Conclusions: Our analysis reveals disease progression could be prognosticated as early as 3 months after SBRT using CT imaging features, and these features would be helpful in clinical decision-making. Stereotactic body radiotherapy (SBRT); Computed tomography; Survival; Radiomics; Semantics; Image features Introduction Stereotactic body radiotherapy (SBRT) is a guidelinerecommended treatment of choice for patients with early stage non-small cell lung cancer (NSCLC) who are inoperable or do not accept the risk of surgery [ 1 ]. SBRT delivers high doses of radiation in five or fewer fractions to the targeted area with high local control and sparing normal tissues. There have been a few studies showing improved overall survival in patients treated with SBRT [ 2 ] and it has replaced conventionally fractionated radiotherapy as standard of care in treatment of stage I disease. One of the most serious complications of SBRT is radiation induced lung injury (RILI). Acute radiation pneumonitis is generally seen in approximately 10% of patients and fibrosis in most of the cases [ 3 ], which makes the follow-up response assessment especially difficult. Although most mass-like consolidations in RILI decrease in size with time, there are also RILI cases with transient size increases. Conversely, patients with recurrence may show temporary size decreases [ 4 ]. It has been shown in several studies that size alone is not a reliable criterion (...truncated)


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Eduardo G. Moros, Jin Qi, Jongphil Kim, Kujtim Latifi, Matthew B. Schabath, Qian Li, Robert J. Gillies, Thomas J. Dilling, Ying Liu, Yoganand Balagurunathan, Zhaoxiang Ye. CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy, Radiation Oncology,