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Title: SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer

Abstract

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 =more » 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.« less

Authors:
; ; ; ; ; ; ;  [1]
  1. Stanford University, Palo Alto, CA (United States)
Publication Date:
OSTI Identifier:
22624416
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; BIOLOGICAL MARKERS; BIOMEDICAL RADIOGRAPHY; FLUORINE 18; FLUORODEOXYGLUCOSE; IMAGE PROCESSING; LUNGS; NEOPLASMS; PATIENTS; POSITRON COMPUTED TOMOGRAPHY; RADIOTHERAPY

Citation Formats

Wu, J, Gensheimer, M, Dong, X, Rubin, D, Napel, S, Diehn, M, Loo, B, and Li, R. SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer. United States: N. p., 2016. Web. doi:10.1118/1.4955673.
Wu, J, Gensheimer, M, Dong, X, Rubin, D, Napel, S, Diehn, M, Loo, B, & Li, R. SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer. United States. doi:10.1118/1.4955673.
Wu, J, Gensheimer, M, Dong, X, Rubin, D, Napel, S, Diehn, M, Loo, B, and Li, R. Wed . "SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer". United States. doi:10.1118/1.4955673.
@article{osti_22624416,
title = {SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer},
author = {Wu, J and Gensheimer, M and Dong, X and Rubin, D and Napel, S and Diehn, M and Loo, B and Li, R},
abstractNote = {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.},
doi = {10.1118/1.4955673},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}