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. 2016 Jan;278(1):214-22.
doi: 10.1148/radiol.2015142920. Epub 2015 Jul 15.

Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors

David V Fried  1 , Osama Mawlawi  1 , Lifei Zhang  1 , Xenia Fave  1 , Shouhao Zhou  1 , Geoffrey Ibbott  1 , Zhongxing Liao  1 , Laurence E Court  1
Affiliations

Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors

David V Fried et al. Radiology. 2016 Jan.

Abstract

Purpose: To determine whether quantitative imaging features from pretreatment positron emission tomography (PET) can enhance patient overall survival risk stratification beyond what can be achieved with conventional prognostic factors in patients with stage III non-small cell lung cancer (NSCLC).

Materials and methods: The institutional review board approved this retrospective chart review study and waived the requirement to obtain informed consent. The authors retrospectively identified 195 patients with stage III NSCLC treated definitively with radiation therapy between January 2008 and January 2013. All patients underwent pretreatment PET/computed tomography before treatment. Conventional PET metrics, along with histogram, shape and volume, and co-occurrence matrix features, were extracted. Linear predictors of overall survival were developed from leave-one-out cross-validation. Predictive Kaplan-Meier curves were used to compare the linear predictors with both quantitative imaging features and conventional prognostic factors to those generated with conventional prognostic factors alone. The Harrell concordance index was used to quantify the discriminatory power of the linear predictors for survival differences of at least 0, 6, 12, 18, and 24 months. Models were generated with features present in more than 50% of the cross-validation folds.

Results: Linear predictors of overall survival generated with both quantitative imaging features and conventional prognostic factors demonstrated improved risk stratification compared with those generated with conventional prognostic factors alone in terms of log-rank statistic (P = .18 vs P = .0001, respectively) and concordance index (0.62 vs 0.58, respectively). The use of quantitative imaging features selected during cross-validation improved the model using conventional prognostic factors alone (P = .007). Disease solidity and primary tumor energy from the co-occurrence matrix were found to be selected in all folds of cross-validation.

Conclusion: Pretreatment PET features were associated with overall survival when adjusting for conventional prognostic factors in patients with stage III NSCLC.

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Figures

Figure 1:
Figure 1:
Illustration of voxel exclusion based on percentage of voxel within the PETedge contour. A, Original contour. B, Illustration of analyzed and excluded voxels.
Figure 2:
Figure 2:
Extracted quantitative PET features. * = Features were calculated for primary disease, nodal disease, and total disease (primary plus nodal). † = Feature was only calculated for nodal disease and total disease (primary plus nodal).
Figure 3:
Figure 3:
Illustration of solidity metric.
Figure 4:
Figure 4:
Box-and-whisker plot shows k-means clustering of linear predictor from cross-validation. Whiskers are maximum of 1.5 × interquartile range.
Figure 5:
Figure 5:
A, Graph of Kaplan-Meier risk groups based on linear predictors from conventional prognostic factors. B, Graph of Kaplan-Meier risk groups based on linear predictors from conventional prognostic factors and quantitative imaging features. C, Concordance index (C-Index) values from both models at various time points. CPFs = conventional prognostic factors, CV = cross-validation, QIFs = quantitative imaging features.
See this image and copyright information in PMC

References

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