Nitin Ohri1, Fenghai Duan2, Bradley S Snyder2, Bo Wei3, Mitchell Machtay4, Abass Alavi5, Barry A Siegel6, Douglas W Johnson7, Jeffrey D Bradley6, Albert DeNittis8, Maria Werner-Wasik9, Issam El Naqa10. 1. Department of Radiation Oncology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, New York ohri.nitin@gmail.com. 2. Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, Rhode Island. 3. Emory University, Atlanta, Georgia. 4. Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Comprehensive Cancer Center and Case Western Reserve University, Cleveland, Ohio. 5. Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania. 6. Mallinckrodt Institute of Radiology and the Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri. 7. Department of Radiation Oncology, Baptist Cancer Institute, Jacksonville, Florida. 8. Department of Radiation Oncology, Lankenau Hospital and Lankenau Institute for Medical Research, Lower Merion, Pennsylvania. 9. Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania; and. 10. University of Michigan Ann Arbor, Ann Arbor, Michigan.
Abstract
UNLABELLED: In a secondary analysis of American College of Radiology Imaging Network (ACRIN) 6668/RTOG 0235, high pretreatment metabolic tumor volume (MTV) on (18)F-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 (18)F-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 cm(3), and the optimal SumMean cutpoint for tumors above 93.3 cm(3) 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.
UNLABELLED: In a secondary analysis of American College of Radiology Imaging Network (ACRIN) 6668/RTOG 0235, high pretreatment metabolic tumor volume (MTV) on (18)F-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 (18)F-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 cm(3), and the optimal SumMean cutpoint for tumors above 93.3 cm(3) was 0.018. This grouped patients into three categories: low tumorMTV (n = 155; median OS, 22.6 mo), high tumorMTV and high SumMean (n = 23; median OS, 20.0 mo), and high tumorMTV 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 NSCLCpatients with large tumors who are treated with chemoradiotherapy. Validation studies are warranted.
Authors: Jianhua Yan; Jason Lim Chu-Shern; Hoi Yin Loi; Lih Kin Khor; Arvind K Sinha; Swee Tian Quek; Ivan W K Tham; David Townsend Journal: J Nucl Med Date: 2015-07-30 Impact factor: 10.057
Authors: Nitin Ohri; Maria Werner-Wasik; Inga S Grills; José Belderbos; Andrew Hope; Di Yan; Larry L Kestin; Matthias Guckenberger; Jan-Jakob Sonke; Jean-Pierre Bissonnette; Ying Xiao Journal: Int J Radiat Oncol Biol Phys Date: 2012-11-01 Impact factor: 7.038
Authors: Ralph T H Leijenaar; Sara Carvalho; Emmanuel Rios Velazquez; Wouter J C van Elmpt; Chintan Parmar; Otto S Hoekstra; Corneline J Hoekstra; Ronald Boellaard; André L A J Dekker; Robert J Gillies; Hugo J W L Aerts; Philippe Lambin Journal: Acta Oncol Date: 2013-09-09 Impact factor: 4.089
Authors: Gary J R Cook; Connie Yip; Muhammad Siddique; Vicky Goh; Sugama Chicklore; Arunabha Roy; Paul Marsden; Shahreen Ahmad; David Landau Journal: J Nucl Med Date: 2012-11-30 Impact factor: 10.057
Authors: Yi Luo; Daniel L McShan; Martha M Matuszak; Dipankar Ray; Theodore S Lawrence; Shruti Jolly; Feng-Ming Kong; Randall K Ten Haken; Issam El Naqa Journal: Med Phys Date: 2018-06-04 Impact factor: 4.071
Authors: Ke Nie; Hania Al-Hallaq; X Allen Li; Stanley H Benedict; Jason W Sohn; Jean M Moran; Yong Fan; Mi Huang; Michael V Knopp; Jeff M Michalski; James Monroe; Ceferino Obcemea; Christina I Tsien; Timothy Solberg; Jackie Wu; Ping Xia; Ying Xiao; Issam El Naqa Journal: Int J Radiat Oncol Biol Phys Date: 2019-01-31 Impact factor: 7.038
Authors: Yi Luo; Daniel McShan; Dipankar Ray; Martha Matuszak; Shruti Jolly; Theodore Lawrence; Feng Ming Kong; Randall Ten Haken; Issam El Naqa Journal: IEEE Trans Radiat Plasma Med Sci Date: 2018-05-02