title: The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT.
abstract: OBJECTIVE
  There is evidence that computed tomography (CT) and positron emission tomography (PET) imaging metrics are prognostic and predictive in nonsmall cell lung cancer (NSCLC) treatment outcomes. However, few studies have explored the use of standardized uptake value (SUV)-based image features of nodal regions as predictive features. The authors investigated and compared the use of tumor and node image features extracted from the radiotherapy target volumes to predict relapse in a cohort of NSCLC patients undergoing chemoradiation treatment.
  
  
  METHODS
  A prospective cohort of 25 patients with locally advanced NSCLC underwent 4DPET/4DCT imaging for radiation planning. Thirty-seven image features were derived from the CT-defined volumes and SUVs of the PET image from both the tumor and nodal target regions. The machine learning methods of logistic regression and repeated stratified five-fold cross-validation (CV) were used to predict local and overall relapses in 2 yr. The authors used well-known feature selection methods (Spearman's rank correlation, recursive feature elimination) within each fold of CV. Classifiers were ranked on their Matthew's correlation coefficient (MCC) after CV. Area under the curve, sensitivity, and specificity values are also presented.
  
  
  RESULTS
  For predicting local relapse, the best classifier found had a mean MCC of 0.07 and was composed of eight tumor features. For predicting overall relapse, the best classifier found had a mean MCC of 0.29 and was composed of a single feature: the volume greater than 0.5 times the maximum SUV (N).
  
  
  CONCLUSIONS
  The best classifier for predicting local relapse had only tumor features. In contrast, the best classifier for predicting overall relapse included a node feature. Overall, the methods showed that nodes add value in predicting overall relapse but not local relapse.
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada.
authors.forename: Heyse
authors.initials: H
authors.lastname: Li
authors.affiliation: Radiation Oncology, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada.
authors.forename: Nathan
authors.initials: N
authors.lastname: Becker
authors.affiliation: Radiation Oncology, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada.
authors.forename: Srinivas
authors.initials: S
authors.lastname: Raman
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5, Canada.
authors.forename: Timothy C Y
authors.initials: TC
authors.lastname: Chan
authors.affiliation: Radiation Oncology, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5, Canada.
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Bissonnette
mesh_terms: D000328:Adult; D000368:Aged; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000628:therapy; D059248:Chemoradiotherapy; D005260:Female; D006801:Humans; D016015:Logistic Models; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000628:therapy; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis* / Q000000981:diagnostic imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D011379:Prognosis; D011446:Prospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*