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

PurposeInvestigating 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 methodsWe 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.ResultsThe 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.ConclusionsADC histogram analyses on the basis of the entire tumor volume are able to stratify NSCLCs' tumor grade, lymphovascular invasion and pleural invasion.

Performance of Multiparametric Functional Imaging and Texture Analysis in Predicting Synchronous Metastatic Disease in Pancreatic Ductal Adenocarcinoma Patients by Hybrid PET/MR: Initial Experience

“…Another study showed that skewness and kurtosis of histogram analysis from ADC map were able to differentiate thyroid carcinoma with lymph node metastasis from that without metastasis (32). In the study of non-small cell lung cancer, higher ADC skewness and kurtosis were associated with lymphovascular invasion and pleural invasion (34). In a study of pediatric diffuse intrinsic potine glioma using 18 F-FDG PET and MRI ADC histogram, higher ADC skewness and kurtosis of the enhancing portion of the tumor were associated with shorter PFS (16).…”

Imaging Assessment of Visceral Pleural Surface Invasion by Lung Cancer: Comparison of CT and Contrast-Enhanced Radial T1-Weighted Gradient Echo 3-Tesla MRI

“…Dynamic free-breathing steady-state free precession MRI has been used to assess the movement of a tumor abutting the chest wall during breathing with 88.5% accuracy [ 18 ]. The whole-lesion histogram analysis of the apparent diffusion coefficient could assist in the assessment of pleural invasion [ 19 ]. However, to the best of our knowledge, no previous studies have distinguished PL1 from PL2 pleural invasion.…”

Histogram Analysis of ADC Maps for Differentiating Brain Metastases From Different Histological Types of Lung Cancers

“…27 In their study investigating NSCLC type lung cancers, Tsuchiya et al found an association between skewness, kurtosis and pleural, lymphovascular invasion, while no association was reported between these histogram parameters and tumor grade which might support our results. 28…”

“…Fourth, we lacked the information that might alter the ADC histogram results such as genetic mutations in lung cancer histological types and NSCLC histological grade information. 25,28 Fifth, DWI data obtained with only 1.5-T MRI system was included in the study. Future studies utilizing different magnetic fields are warranted to confirm our findings and compare the results of different scanners.…”