Pretreatment F-18-FDG PET Textural Features in Locally Advanced Non Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235

• Authors: Nitin Ohri, Montefiore Medical Center; Fenghai Duan, Brown University; Bradley S. Snyder, Brown University; Bo Wei, Emory University; Mitchell Machtay, University Hospital Seidman Cancer Center; Abass Alavi, University of Pennsylvania; Barry A. Siegel, Washington University; Douglas W. Johnson, Baptist Cancer Institute; Jeffrey Bradley, Emory University; Albert DeNittis, Lankenau Hospital; Maria Werner-Wasik, Thomas Jefferson University; Issam El Naqa, University of Michigan
• Journal Title: Journal of Nuclear Medicine
• Volume: Volume 57, Number 6
• Publisher: Society of Nuclear Medicine and Molecular Imaging | 2016-06-01, Pages 842-848
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.2967/jnumed.115.166934
• Subjects: Health Sciences, Oncology; Health Sciences, Radiology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Radiology, Nuclear Medicine & Medical Imaging; non-small cell lung cancer; chemoradiotherapy; (18)FDG-PET; textural features; FDG-PET; PROGNOSTIC VALUE; HETEROGENEITY; PREDICTION; RADIOTHERAPY; SURVIVAL; THERAPY; IMAGES; VALIDATION; CONCURRENT

Abstract

In a secondary analysis of American College of Radiology Imaging Network (ACRIN) 6668/RTOG 0235, high pretreatment metabolic tumor volume (MTV) on 18F-FDG PET was found to be a poor prognostic factor for patients treated with chemoradiotherapy for locally advanced non-small cell lung cancer (NSCLC). Here we utilize the same dataset to explore whether heterogeneity metrics based on PET textural features can provide additional prognostic information. Methods: Patients with locally advanced NSCLC underwent 18F-FDG PET prior to treatment. A gradient-based segmentation tool was used to contour each patient's primary tumor. MTV, maximum SUV, and 43 textural features were extracted for each tumor. To address overfitting and high collinearity among PET features, the least absolute shrinkage and selection operator (LASSO) method was applied to identify features that were independent predictors of overall survival (OS) after adjusting for MTV. Recursive binary partitioning in a conditional inference framework was utilized to identify optimal thresholds. Kaplan-Meier curves and log-rank testing were used to compare outcomes among patient groups. Results: Two hundred one patients met inclusion criteria. The LASSO procedure identified 1 textural feature (SumMean) as an independent predictor of OS. The optimal cutpoint for MTV was 93.3 cm3, and the optimal Sum- Mean cutpoint for tumors above 93.3 cm3 was 0.018. This grouped patients into three categories: low tumor MTV (n = 155; median OS, 22.6 mo), high tumor MTV and high SumMean (n = 23; median OS, 20.0 mo), and high tumor MTV and low SumMean (n = 23; median OS, 6.2 mo; log-rank P < 0.001). Conclusion: We have described an appropriate methodology to evaluate the prognostic value of textural PET features in the context of established prognostic factors. We have also identified a promising feature that may have prognostic value in locally advanced NSCLC patients with large tumors who are treated with chemoradiotherapy. Validation studies are warranted.