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Randomized Controlled Trial
. 2014 Jul 2;9(7):e100244.
doi: 10.1371/journal.pone.0100244. eCollection 2014.

Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic

Glen J Weiss  1 , Balaji Ganeshan  2 , Kenneth A Miles  2 , David H Campbell  3 , Philip Y Cheung  4 , Samuel Frank  5 , Ronald L Korn  6
Affiliations
Randomized Controlled Trial

Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic

Glen J Weiss et al. PLoS One. .

Abstract

Background: Non-invasive characterization of a tumor's molecular features could enhance treatment management. Quantitative computed tomography (CT) based texture analysis (QTA) has been used to derive tumor heterogeneity information, and the appearance of the tumors has been shown to relate to patient outcome in non-small cell lung cancer (NSCLC) and other cancers. In this study, we examined the potential of tumoral QTA to differentiate K-ras mutant from pan-wildtype tumors and its prognostic potential using baseline pre-treatment non-contrast CT imaging in NSCLC.

Methods: Tumor DNA from patients with early-stage NSCLC was analyzed on the LungCarta Panel. Cases with a K-ras mutation or pan-wildtype for 26 oncogenes and tumor suppressor genes were selected for QTA. QTA was applied to regions of interest in the primary tumor. Non-parametric Mann Whitney test assessed the ability of the QTA, clinical and patient characteristics to differentiate between K-ras mutation from pan-wildtype. A recursive decision tree was developed to determine whether the differentiation of K-ras mutant from pan-wildtype tumors could be improved by sequential application of QTA parameters. Kaplan-Meier survival analysis assessed the ability of these markers to predict survival.

Results: QTA was applied to 48 cases identified, 27 had a K-ras mutation and 21 cases were pan-wildtype. Positive skewness and lower kurtosis were significantly associated with the presence of a K-ras mutation. A five node decision tree had sensitivity, specificity, and accuracy values (95% CI) of 96.3% (78.1-100), 81.0% (50.5-97.4), and 89.6% (72.9-97.0); respectively. Kurtosis was a significant predictor of OS and DFS, with a lower kurtosis value linked with poorer survival.

Conclusions: Lower kurtosis and positive skewness are significantly associated with K-ras mutations. A QTA feature such as kurtosis is prognostic for OS and DFS. Non-invasive QTA can differentiate the presence of K-ras mutation from pan-wildtype NSCLC and is associated with patient survival.

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Conflict of interest statement

Competing Interests: GJW is on the speaker's bureau for Genentech, Pfizer, Celgene, and Eli Lilly, and has received honoraria from Quintiles and Medscape. Sequenom, Inc. provided his lab the LungCarta Panel assay analysis, and had no role in the control of the data and information submitted for publication. RLK is an employee and stockholder in Imaging Endpoints, a core lab that supports clinical trials. BG and KM are shareholders in TexRAD Ltd, a company developing and marketing the tumor textural analysis software described in this manuscript, all other authors had control of the data and information submitted for publication. These disclosures do not alter the authors' adherence to PLOS ONE policies on sharing data and materials. There are no other author disclosures.

Figures

Figure 1
Figure 1. Box plot of pan-wildtype vs. K-ras mutation.
A) (Top Panel): Positive skewness with fine-texture significantly differentiates K-ras mutation from pan-wildtype (p = 0.031). B) (Bottom Panel): Lower kurtosis with coarse-texture significantly differentiates K-ras mutation from pan-wildtype (p = 0.009).
Figure 2
Figure 2. Decision-tree for identification of KRAS mutations using QTA parameters.
M-Numbers of mutants; WT-number of pan-wildtype; SD-standard deviation; ssf-spatial scale factor.
Figure 3
Figure 3. Overall survival curves based on QTA.
A) (Left Panel): Without filtration, higher skewness (green line, median OS 30.7 months) vs. lower skewness (blue line, median OS 89.4 months) was associated with inferior OS (p = 0.012). B) (Right Panel): Without filtration, lower kurtosis (green line, median OS 61.6 months) vs. higher kurtosis (blue line, median OS 93.2 months) was associated with inferior OS (p = 0.012).
Figure 4
Figure 4. Disease free survival curves based on QTA.
Lower kurtosis with coarse-texture (green line, median DFS 59.6 months) had inferior OS vs. higher kurtosis (blue line, median DFS 93.2 months)(p = 0.048).
See this image and copyright information in PMC

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