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SU‐D‐207B‐05: Robust Intra‐Tumor Partitioning to Identify High‐Risk Subregions for Prognosis in Lung Cancer
Author(s) -
Wu J,
Gensheimer M,
Dong X,
Rubin D,
Napel S,
Diehn M,
Loo B,
Li R
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4955673
Subject(s) - lung cancer , medicine , concordance , positron emission tomography , nuclear medicine , cancer , population , radiation therapy , fluorodeoxyglucose , stage (stratigraphy) , log rank test , survival analysis , primary tumor , retrospective cohort study , oncology , metastasis , biology , paleontology , environmental health
Purpose: To develop an intra‐tumor partitioning framework for identifying high‐risk subregions from 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) and CT imaging, and to test whether tumor burden associated with the high‐risk subregions is prognostic of outcomes in lung cancer. Methods: In this institutional review board‐approved retrospective study, we analyzed the pre‐treatment FDG‐PET and CT scans of 44 lung cancer patients treated with radiotherapy. A novel, intra‐tumor partitioning method was developed based on a two‐stage clustering process: first at 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). Results: Three spatially distinct subregions were identified within each tumor, which 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 = 0.66–0.67. When restricting the analysis to patients with stage III disease (n = 32), the same subregion achieved an even higher CI = 0.75 (HR = 3.93, logrank p = 0.002) for predicting OS, and a CI = 0.76 (HR = 4.84, logrank p = 0.002) for predicting OFP. In comparison, conventional imaging markers including tumor volume, SUVmax and MTV50 were not predictive of OS or OFP, with CI mostly below 0.60 (p < 0.001). Conclusion: We propose a robust intra‐tumor 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.