Adam Hasse^{1}, Mark Dapash^{2}, Yong Jeong^{3}, Star Su^{4}, Abrianna Cummings^{4}, Sameer Ansari^{5}, Daniel Ginat^{6}, and Timothy J Carroll^{6}

A data analysis pipeline was developed to study relationships between geometric imaging features and the underlying tumor phenotype. This pipeline was run on the tumors of 203 patients clinically diagnosed with glioblastoma multiforme. For each tumor, the tumor bulk and percent non-enhancing volume was calculated, along with the surface regularity of any tumor with a 3D T1-weighted post-contrast MR scan. These features were compared to the expression of P53 and Ki67 sampled from these tumors. Although there were significant differences between multiple features for both genes, only the surface regularity was a significant predictor of Ki-67 proliferative index.

A cohort of 203 patients (142 males, 111 females, average age of 57.5 years) with confirmed diagnoses of GBM were selected from a larger dataset. These were specifically chosen due to the presence of some sort of genetic data and T1-weighted, post-contrast MR images, both of which were required for this analysis. Software was developed in MATLAB so a trained operator could outline the contrast-enhanced tumor on axial T1-weighted post-contrast images, along with the necrotic core if it was present. The contrast-enhanced outlines were then slightly modified by a dilation, followed by a gray level thresholding method to more accurately conform to the outline of the contrast-enhanced region.4 This “shrink-wrapping” method was validated using two separate users on twenty cases, resulting in a dice similarity coefficient of overlap outlines of 0.92. Further, the non-enhancing region of the tumor was also outlined, if present. These outlines were confirmed by a board-certified radiologist with 7 years of experience.

To obtain the tumor
bulk, defined as the contrast-enhanced region and the associated necrosis, the
outlines were smoothed in the axial direction to reduce the sharp steps from
slice-to-slice. Tumor bulk (*TB*) then
was calculated for each imaging set by summing the volumes of truncated
pyramids (see *Equation 1*), where *h* is the slice thickness and *A _{n}* is the area of the tumor
bulk on the

$$$TB = \sum_{n} \frac{h}{3} (A_{n} + A_{n+1} + \sqrt{A_{n}A_{n+1}})$$$ *Equation 1*

Similarly, the volume of the non-enhancing area was calculated.

Additionally,
the surface regularity was calculated for each outline.^{5} However,
due to the dependence of this value on the surface area (*TS*), only 3D T1-weighted post-contrast images were used for this
analysis. The surface regularity (*SR*)
was calculated using *Equation 2*.

$$$SR = 6\sqrt{\pi} \frac{TB}{\sqrt{TS^{3}}}$$$ *Equation 2*

After these features were calculated, two genetic markers were
highlighted for analysis due to the large number of cases present. P53, a tumor
suppression gene, and Ki67, a proliferative marker, were analyzed in this
study. Because these genes are measured by the fraction of cells stained via
immunohistochemistry, the median value was chosen for each (P53 labeling index
(P53-LI) = 0.1, Ki67 proliferative index (ki67-PI) = 0.2) to separate each data
set into two classes (see *Figure 1*).
T-tests and ROC analysis were performed to determine separation of the classes
and the predicting power of each feature.

1. Hanif F, Muzaffar K, Perveen K, Malhi SM, Simjee SU. Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through Clinical Presentation and Treatment. Asian Pac J of Cancer Prev. 18(1), 3-9 (2017).

2. Stupp R et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 10(5), 459-66 (2009).

3. Verhaak, RGW et al. An integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDFGRA, IDH1, EGFR, and NF1. Cancer Cell 17(1), 98 (2010).

4. Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62-6 (1979).

5. Perez-Beteta, J. et al. Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma. Radiology 00, 1-8 (2018).