Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma

Abstract

Background: Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor.

Objective: To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival.

Methods: Preoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation.

Results: This technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning.

Conclusion: Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.

Figure 1. Retrospective study results

Left panel presents an estimated map for tumor infiltration from preoperative MRI analysis; yellow arrow points to a regions estimated to be relatively more infiltrated. Right panel represents the corresponding MRIs after tumor resection and subsequent recurrence (red arrow) for the same patient. Recurrence occurred in the vicinity of peritumoral tissue originally estimated to be highly infiltrated. The first row represents a three-dimensional rendering of the images. The second, third, and fourth rows show T₁-weighted with contrast-fused infiltration map, T₁weighted with contrast, and T₂-FLAIR respectively.

Figure 2. Replication study results

Left panel presents an estimated map for tumor infiltration from preoperative MRI analysis; yellow arrow points to a region estimated to be relatively more infiltrated. Right panel represents the corresponding MR images after tumor resection and subsequent recurrence (red arrow) for the same patient. Recurrence occurred in the vicinity of peritumoral tissue originally estimated to be highly infiltrated. The first row represents a three-dimensional rendering of the images. The second, third, and fourth rows show T₁-weighted with contrast fused infiltration map, T₁-weighted with contrast, and T₂-FLAIR respectively.

Figure 3. Accuracy Analysis

ROC curve for the replication study.

Figure 4. Imaging characteristics of recurrent and nonrecurrent tissue

The figures demonstrate the imaging characteristics of the recurrence and nonrecurrence regions within the peritumoral edema on the preoperative MRI. Red represents the probability density function of the recurrence tissues, whereas blue represents the nonrecurrence tissues. T1, T₁-weighted (AUC = 0.58); T1gad, T₁-weighted contrast-enhanced (AUC,= 0.60); FL, T₂–fluid-attenuated inversion recovery (AUC = 0.62); T2, T₂-weighted (AUC = 0.76); TR, trace (AUC = 0.73); FA, fractional anisotropy (AUC = 0.73); RAD, radial diffusivity (AUC = 0.74); AX, axial diffusivity (AUC = 0.72); PC1, first principal component (AUC = 0.56); PC2, second principal component (AUC = 0.66); PC3, third principal component (AUC = 0.60); RCBV, relative cerebral blood volume (AUC = 0.72); Inf Index, infiltration index (AUC = 0.89). X-axis shows the intensity in arbitrary unit scaled between 0 and 255 and Y-axis is the number of voxels. The P value of a t-test showed a significant difference between the 2 groups in all modalities (P < .001).