Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma

Abstract

Glioblastoma is the most common primary brain tumor. Standard therapy consists of maximum safe resection combined with adjuvant radiochemotherapy followed by chemotherapy with temozolomide, however prognosis is extremely poor. Assessment of the residual tumor after surgery and patient stratification into prognostic groups (i.e., by tumor volume) is currently hindered by the subjective evaluation of residual enhancement in medical images (magnetic resonance imaging [MRI]). Furthermore, objective evidence defining the optimal time to acquire the images is lacking. We analyzed 144 patients with glioblastoma, objectively quantified the enhancing residual tumor through computational image analysis and assessed the correlation with survival. Pathological enhancement thickness on post-surgical MRI correlated with survival (hazard ratio: 1.98, p < 0.001). The prognostic value of several imaging and clinical variables was analyzed individually and combined (radiomics AUC 0.71, p = 0.07; combined AUC 0.72, p < 0.001). Residual enhancement thickness and radiomics complemented clinical data for prognosis stratification in patients with glioblastoma. Significant results were only obtained for scans performed between 24 and 72 h after surgery, raising the possibility of confounding non-tumor enhancement in very early post-surgery MRI. Regarding the extent of resection, and in agreement with recent studies, the association between the measured tumor remnant and survival supports maximal safe resection whenever possible.

Figure 1

Kaplan–Meier survival curves for the enhancement thickness analysis, according to high and low mean (left) and maximum (right) thickness. Censored data are indicated with tick marks.

Figure 2

ROC of different prognostic models: perfusion variables (99th percentile cerebral blood volume [rCBV], maximum percentage of signal recovery [PSR]), mean thickness of enhancement and all variables (perfusion, thickness, age, Karnofsky performance status [KPS] and radiomics) together for predicting survival (left panel). The model with clinical variables age and KPS is shown in the right panel for clarity.

Figure 3

Pipeline of the applied methods. Registration is performed with the contrast-T1w scan as the reference image and the radiomics features are extracted from the contrast-T1w. The thickness is calculated from the 3D distance transform of the volume of interest, depicted as a colormap.