title: Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.
abstract: To develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer.
  
  
  
  In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP).
  
  
  
  Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05).
  
  
  
  We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
authors.forename: Jia
authors.initials: J
authors.lastname: Wu
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
authors.forename: Michael F
authors.initials: MF
authors.lastname: Gensheimer
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
authors.forename: Xinzhe
authors.initials: X
authors.lastname: Dong
authors.affiliation: Department of Radiology, Stanford University School of Medicine, Stanford, California; Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, California.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
authors.affiliation: Department of Radiology, Stanford University School of Medicine, Stanford, California.
authors.forename: Sandy
authors.initials: S
authors.lastname: Napel
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California.
authors.forename: Maximilian
authors.initials: M
authors.lastname: Diehn
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California.
authors.forename: Billy W
authors.initials: BW
authors.lastname: Loo
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California. Electronic address: rli2@stanford.edu.
authors.forename: Ruijiang
authors.initials: R
authors.lastname: Li
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002140:California / Q000453:epidemiology; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000401:mortality / Q000532:radiotherapy*; D008297:Male; D000072662:Margins of Excision; D008875:Middle Aged; D000072078:Positron Emission Tomography Computed Tomography / Q000379:methods*; D015995:Prevalence; D011379:Prognosis; D019275:Radiopharmaceuticals; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D047368:Tumor Burden
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18