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Int J Radiat Oncol Biol Phys. 2016 Aug 1;95(5):1504-1512. doi: 10.1016/j.ijrobp.2016.03.018. Epub 2016 Mar 24.

Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.

Author information

1
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
2
Department of Radiology, Stanford University School of Medicine, Stanford, California; Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, California.
3
Department of Radiology, Stanford University School of Medicine, Stanford, California.
4
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.
5
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California.
6
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.

Abstract

PURPOSE:

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.

METHODS AND MATERIALS:

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).

RESULTS:

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).

CONCLUSION:

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.

PMID:
27212196
PMCID:
PMC4969127
DOI:
10.1016/j.ijrobp.2016.03.018
[Indexed for MEDLINE]
Free PMC Article

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