Non-small cell lung cancer: Whole-lesion histogram analysis of the apparent diffusion coefficient for assessment of tumor grade, lymphovascular invasion and pleural invasion

Abstract

Purpose: Investigating the diagnostic accuracy of histogram analyses of apparent diffusion coefficient (ADC) values for determining non-small cell lung cancer (NSCLC) tumor grades, lymphovascular invasion, and pleural invasion.

Materials and methods: We studied 60 surgically diagnosed NSCLC patients. Diffusion-weighted imaging (DWI) was performed in the axial plane using a navigator-triggered single-shot, echo-planar imaging sequence with prospective acquisition correction. The ADC maps were generated, and we placed a volume-of-interest on the tumor to construct the whole-lesion histogram. Using the histogram, we calculated the mean, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC, skewness, and kurtosis. Histogram parameters were correlated with tumor grade, lymphovascular invasion, and pleural invasion. We performed a receiver operating characteristics (ROC) analysis to assess the diagnostic performance of histogram parameters for distinguishing different pathologic features.

Results: The ADC mean, 10th, 25th, 50th, 75th, 90th, and 95th percentiles showed significant differences among the tumor grades. The ADC mean, 25th, 50th, 75th, 90th, and 95th percentiles were significant histogram parameters between high- and low-grade tumors. The ROC analysis between high- and low-grade tumors showed that the 95th percentile ADC achieved the highest area under curve (AUC) at 0.74. Lymphovascular invasion was associated with the ADC mean, 50th, 75th, 90th, and 95th percentiles, skewness, and kurtosis. Kurtosis achieved the highest AUC at 0.809. Pleural invasion was only associated with skewness, with the AUC of 0.648.

Conclusions: ADC histogram analyses on the basis of the entire tumor volume are able to stratify NSCLCs' tumor grade, lymphovascular invasion and pleural invasion.

Fig 1. A 67-year-old woman with adenocarcinoma of the right upper lobe.

The outline of the tumor is drawn on the axial low-b value DW image (b = 0 s/mm²) (A). The tumor's boundaries are meticulously identified with reference to the coronal and sagittal reformatted DW images (B, C). The data acquired from each slice are summed to generate volumes of interest (VOIs) (D). The contours of the VOI are automatically copied to the exact same location of the corresponding ADC maps.

Fig 2. ROC curves of percentiles of ADC in predicting high-grade.

The AUC was highest for the 95th percentile ADC (AUC = 0.74, cut-off value of 1634.1 × 10⁻⁶ mm²/sec, sensitivity 84.6%, specificity 66.7%).

Fig 3

ROC curves of percentiles of ADC (A), kurtosis and skewness (B) in predicting lymphovascular invasion. The AUC was highest for the kurtosis (AUC = 0.809, cut-off value of 1.0815×10^-6mm²/sec, sensitivity 61.2%, specificity 90.9%) in predicting lymphovascular invasion.

Fig 4. ROC curve of skewness in predicting pleural invasion.

The AUC of the skewness was 0.648 (cut-off value 0.824×10⁻⁶ mm²/sec, sensitivity 60.0%, specificity 73.3%) in predicting pleural invasion.