Research Article | Open Access
Dan Liu, Shuai-Xiang Gao, Hong-Fan Liao, Jing-Mei Xu, Ming Wen, "A Comparative Study of 2 Different Segmentation Methods of ADC Histogram for Differentiation Genetic Subtypes in Lower-Grade Diffuse Gliomas", BioMed Research International, vol. 2020, Article ID 9549361, 13 pages, 2020. https://doi.org/10.1155/2020/9549361
A Comparative Study of 2 Different Segmentation Methods of ADC Histogram for Differentiation Genetic Subtypes in Lower-Grade Diffuse Gliomas
Background. To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) histogram parameters for differentiating the genetic subtypes in lower-grade diffuse gliomas and explore which segmentation method (ROI-1, the entire tumor ROI; ROI2, the tumor ROI excluding cystic and necrotic portions) performs better. Materials and Methods. We retrospectively evaluated 56 lower-grade diffuse gliomas and divided them into three categories: IDH-wild group (IDHwt, 16cases); IDH mutant with the intact 1p or 19q group (IDHmut/1p19q+, 18cases); and IDH mutant with the 1p/19q codeleted group (IDHmut/1p19q−, 22cases). Histogram parameters of ADC maps calculated with the two different ROI methods: ADCmean, min, max, mode, P5, P10, P25, P75, P90, P95, kurtosis, skewness, entropy, StDev, and inhomogenity were compared between these categories using the independent test or Mann–Whitney test. For statistically significant results, a receiver operating characteristic (ROC) curves were constructed, and the optimal cutoff value was determined by maximizing Youden’s index. Area under the curve (AUC) results were compared using the method of Delong et al. Results. The inhomogenity from the two different ROI methods for distinguishing IDHwt gliomas from IDHmut gliomas both showed the biggest AUC (0.788, 0.930), the optimal cutoff value was 0.229 (sensitivity, 81.3%; specificity, 75.0%) for the ROI-1 and 0.186 (sensitivity, 93.8%; specificity, 82.5%) for the ROI-2, and the AUC of the inhomogenity from the ROI-2 was significantly larger than that from another segmentation, but no significant differences were identified between the AUCs of other same parameters from the two different ROI methods. For the differentiaiton of IDHmut/1p19q− tumors and IDHmut/1p19q+ tumors, with the ROI-1, the ADCmode showed the biggest AUC (AUC: 0.784; sensitivity, 61.1%; specificity, 90.9%), with the ROI-2, and the skewness performed best (AUC, 0.821; sensitivity, 81.8%; specificity, 77.8%), but no significant differences were identified between the AUCs of the same parameters from the two different ROI methods. Conclusion. ADC values analyzed by the histogram method could help to classify the genetic subtypes in lower-grade diffuse gliomas, no matter which ROI method was used. Extracting cystic and necrotic portions from the entire tumor lesions is preferable for evaluating the difference of the intratumoral heterogeneity and classifying IDH-wild tumors, but not significantly beneficial to predicting the 1p19q genotype in the lower-grade gliomas.
Glioma is the most common neuroepithelial tumor in the brain which accounts for 80% of the malignant brain tumors. The severity of gliomas is further distinguished by malignant grades (I to IV) on the basis of the histopathological and clinical criteria . The grade II and III gliomas are sometimes described as lower-grade gliomas, which present approximately one-third of all gliomas. Lower-grade gliomas form a biologically heterogeneous group of tumors. They are usually less aggressive tumors with a longer, indolent clinical course, but a subset of these gliomas will progress to glioblastoma (WHO grade IV gliomas) within months [2, 3]. Histology alone is often insufficient to make accurate prognostic estimates, and tumors belonging to the same WHO grade may display different malignant behavior, depending on their molecular profile. The Cancer Genome Atlas (TCGA) Analysis Working Group grouped LGGs into three robust molecular classes on the basis of mutations in isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2, hereafter collectively referred to as IDH) and codeletion of chromosomes 1p and 19q. LGGs without IDH mutation are associated with the most aggressive clinical behavior and worst outcome, similar to that of glioblastomas (WHO grade IV). LGGs with IDH mutation and 1p/19q codeletion are associated with the most favorable clinical outcome and possibly improved sensitivity to procarbazine, lomustine, and vincristine chemotherapy compared with noncodeleted neoplasms. LGGs with IDH mutation and no 1p/19q codeletion are associated with an intermediate outcome, worse than those with 1p/19q codeletion, but far more favorable than IDHwt neoplasms . This molecular classification has been integrated into the 2016 WHO classification of brain tumors .
Diffusion-weighted imaging (DWI) is a physiologic imaging modality that exploits the diffusion of water molecules to create contrast between tissues. Apparent diffusion coefficient (ADC) calculated from DWI is used as a quantitative parameter to assess the grade of restrictive diffusion and to provide information about tissue structure and cellularity [5, 6]. Previous studies have demonstrated the ability of ADC to differentiate the IDH-wild gliomas from the mutant ones, the 1p19q codeleted gliomas from those noncodeleted ones [7–9]. However, previous studies were limited to using the mean value of ADCs based on regions of interest (ROIs) from a single representative slice of a lesion or region of interest from tumor volume, which may dilute or even mask the small but important differences between different disease entities. Additionally, they may not precisely depict the tumor status due to the intrinsic heterogeneous environment of tumors. Histogram analysis of the whole lesion may offer multiple parameters containing not only the quantitative accumulated ADC parameters, such as percentiles, minimal and maximal values, and mode but also the distribution parameters, such as the kurtosis, skewness, range, StDev, inhomogenity, and entrophy, thus providing more information about the tumor heterogeneity than the mean values [10, 11]. In previous studies, two main ROI placement methods of the ADC histogram were used, including the tumor ROI excluding cystic and necrotic portions [12–14] and the entire tumor ROI containing cystic and necrotic areas [11, 15–17]. The theoretical basis of the former mentioned method is that necrotic and cystic-appearing areas may increase ADC values, which may be a confusing factor for differentiating the subtypes of gliomas based on ADC maps, but the latter method contained all compositions of the tumor, theoretically, it can better assess the heterogeneity of the tumor in its entirety. So, the purposes of this retrospective study were to evaluate whether the ADC values analyzed by the histogram method could help to classify IDH-wild tumors from IDH-mutated ones as well as IDHmut-NonCodel tumors from IDHmut-Codel ones in lower-grade diffuse gliomas and determine which segmentation method performs better.
2. Materials and Methods
We searched the electronic hospital information system and picture archiving and communication system to identify patients from January 2016 to August 2019 who met the following inclusion criteria: (1) final histopathologic results were WHO grade II–III diffuse gliomas on the basis of the WHO classification for tumors of the central nervous system; (2) diffusion-weighted MRI, T2-weighted, and postcontrast T1-weighted anatomical scan performed at initial diagnosis and prior to any surgery; (3) and known IDH1 mutation and 1p/19q codeletion status. On the other hand, patients were excluded for the poor DWI images quality, which influence the consequent image analysis. The institutional review board at the First Affiliated Hospital of Chongqing Medical University approved this study. Thus, 56 consecutive patients were included in the final study cohort. The patients were divided into the following categories: IDH wild-group (IDHwt), IDH mutant with the intact 1p or 19q group (IDHmut/1p19q+), and IDH mutant with the 1p/19q codeleted group (IDHmut/1p19q−).
There were 40 cases in the IDH-mutated group (18 men, 22 women; age range, 23-66 years; mean age, years; WHO grade II gliomas, ; grade III gliomas, ). Among the IDH-mutated group, there were 18 cases of IDHmut/1p19q+gliomas (8 men, 10 women; age range, 24-59 years; mean age, years; WHO grade II gliomas, ; grade III gliomas, ) and 22 cases of the IDHmut/1p19q− group (10 men, 12 women; age range, 23-66 years; mean age, years; WHO grade II glomas, ; grade III gliomas, ). There were 16 cases in the IDHwt group (9 men, 7women; age range, 21-73 years; mean age, years; WHO grade II gliomas, ; grade III gliomas, ).
2.2. ADC Histogram Measurement
Each case was investigated using DWI (multishot echo-planar-imaging sequence with values of 0 and 1000 s/mm2) obtained with a 3.0 T MRI scanner (Signa HDxt, GE Medical System, WI). The ADC maps were digitally transferred from the picture archiving and communication system workstation to a personal computer and processed with an in-house software (Firevoxel, available at https://wp.nyu.edu/firevoxel/). For each case, the ROI was manually drawn by two independent radiologists with no knowledge of the final pathologic results. 2 different types of segmentation were completed: ROI-1, the entire tumor ROI containing all compositions; ROI-2, the entire tumor ROI excluding cystic and necrotic areas (cystic or necrotic portions met conditions: first, no enhancement with the contrast agent in the T1-weighted images and second, high intensity, like cerebrospinal fluid (CSF), in the T2-weighted images). According to Kang and Lue’s methods, the tumor boundaries were defined with reference to the high-signal-intensity areas thought to represent the tumor tissue on the T2-weighted images [9, 11]. The ROIs were placed carefully inside the mass to avoid regions influenced by the partial volume effect, and the position of the ROIs was verified using postcontrast T1-weighted images, T2-weighted images, and T2-FLAIR imaging. Then, the following accumulated ADC parameters: mean ADC (ADCmean), maximum ADC (ADCmax), minimum ADC (ADCmin), mode ADC (ADCmode), 5th (P5 ADC), 10th (P10 ADC), 25th (P25 ADC), 75th (P75 ADC), 90th (P90 ADC), and 95th (P95 ADC) were calculated, as well as distribution parameters—the kurtosis, skewness, entropy, StDev, and inhomogenity—were also estimated. Skewness reflects the asymmetry of the distribution, being positive if more values lie to the left of the mean, and negative if the opposite. Kurtosis is a measure of the peakedness of the distribution, and a higher kurtosis indicates a sharper peak of the histogram. In case of a normal distribution, skewness equals 0 and kurtosis equals 3. Entropy, StDev, and inhomogenity represent the statistical measure of variation that can be used to characterize the image texture . For further analyses of ADC histogram parameters, the results of two readers were averaged. The two different ROI segmentations and the corresponding ADC histograms are shown in Figures 1–3.
2.3. Histopathology and Molecular Analysis
All tissue samples underwent analysis at The First Affiliated Hospital of Chongqing Medical University’s neuropathology department and center for molecular medicine testing according to the World Health Organization (WHO) 2016 guidance on immunohistochemistry testing for glioma. IDH R132H immunonegative tumors underwent multiple gene Sanger sequencing. The 1p/19q codeletion status was determined by fluorescence in situ hybridization-specific probes for the 1p36 and 19q13 loci.
2.4. Statistical Analysis
All statistical testing was performed with SPSS 22 (IBM) and MedClac Version15.6.1. The intraclass correlation coefficient (ICC) was calculated to evaluate interobserver agreement of all histogram parameters (, poor correlation; , fair correlation; , moderate correlation; , good correlation; , excellent correlation) [15, 16]. The Shapiro-Wilk test was used to check whether the measurement data followed a normal distribution. Normally distributed continuous variables were compared using the independent test, and nonnormally distributed continuous variables were compared using the Mann–Whitney test between different molecular groups. For statistically significant results, receiver operating characteristic (ROC) curves were constructed to determine the optimal threshold for each histogram parameter to differentiate the molecular subtypes, and the optimal cutoff value was determined by maximizing Youden’s index. Area under the curve (AUC) results were compared using the method of Delong et al. The results with values of less than 0.05 were considered to be statistically significant.
3.1. Comparison of Clinical Characteristics of the 56 Patients
The mean age was greater in the IDHwt group than in the IDHmut group (, ). The proportion of grade III gliomas in the IDHwt group was statistically significantly larger than in the IDHmut group (Fisher’s exact test ). Compared with the IDHmut/1p19q+ group, neither the greater mean age or larger proportion of grade III gliomas in the IDHmut/1p19q− group was statistically significant (, for age, Fisher’s exact test for the proportion of grade III gliomas). The histological and molecular characteristics of the patient population are listed in Table 1.
#Pearson’s chi-square test , ##Fisher’s exact test ; , ; , .
3.2. Comparison of ADC Histogram Parameters for Each Method
When applying the ROI-1 segmentation, the interobserver agreements were good to excellent for all parameters in the three groups, with ICCs of 0.980-0.999, 0.868-0.999, and 0.809-0.999. With the second segmentation，the interobserver agreements were moderate to excellent for all parameters in the three groups, with ICCs of 0.880-0.987, 0.792-0.957, and 0.808-0.965.
For the IDHwt tumors, the ADCmean (), P75ADC (), P90ADC (), P95ADC (), ADCmax (), StDev (), inhomogenity (), and range () obtained from the ROI-1 were proved to be significantly greater than those from the second segmentation. Other histogram parameters did not show significant differences between the two segmentation methods (Table 2).
a value; b value.
For the IDHmut/1p19q+ tumors, only the ADCmax () differed significantly between the two segmentation methods, and other histogram parameters did not show significant differences between the two segmentation methods (Table 3).
a, t value; b, Z value.
For the IDHmut/1p19q− tumors, the greater P90ADC (), P95ADC (), ADCmax (), kurtosis (), skewness (), StDev (), inhomogenity (), and range () calculated from the ROI-1 were all statistically significant than those from the second segmentation, and other histogram parameters did not show significant differences between the two segmentation methods (Table 4).
a value; b value.
3.3. Ability to Differentiate the Genetic Subtypes of Diffuse Lower-Grades Gliomas
The P75ADC (), P90ADC (), P95ADC (), ADCmax (), StDev (), inhomogenity (), and range () from the ROI-1 were all statistically significant greater in the IDHwt group than in the IDHmut group, and the ADCmin () and kurtosis () in the IDHwt group were smaller (Table 5).
1ROI-1; 2ROI-2; a value; b value.
The StDev (), inhomogenity (), and range () from the ROI-2 differed significantly between IDHwt gliomas and IDHmut gliomas and decreased with the IDH mutation, but the ADCmin (), P5 ADC (), and kurtosis () in the IDHwt gliomas were lower than the other group (Table 5).
The ADCmean (), P5ADC (), P10ADC (), P25 ADC (), P50ADC (), P75ADC (), P90ADC (), and ADCmode () from the ROI-1 were significantly lower in IDHmut/1p19q−gliomas, and the skewness () was greater in IDHmut/1p19q−gliomas; the similar results were obtained from the ROI-2, but the P95 ADC () from the ROI-2 was also significantly lower in IDHmut/1p19q−gliomas, and the kurtosis () from the ROI-2 was lager in IDHmut/1p19q−gliomas (Table 6).