Research Article: Analysis of peritumoral hyperintensity on pre-operative T2-weighted MR images in glioblastoma: Additive prognostic value of Minkowski functionals

Date Published: May 31, 2019

Publisher: Public Library of Science

Author(s): Yangsean Choi, Kook Jin Ahn, Yoonho Nam, Jinhee Jang, Na-Young Shin, Hyun Seok Choi, So-Lyung Jung, Bum-soo Kim, Pan Lin.


The extent of peritumoral tumor cell infiltrations in glioblastoma contributes to poor prognosis. We aimed to assess additive prognostic value of Minkowski functionals in analyzing heterogeneity of peritumoral hyperintensity on T2WI in glioblastoma patients.

Clinical data (age, sex, extent of surgical resection), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and pre-operative T2WI of 113 pathologically confirmed glioblastoma patients (from our institution, n = 61; from the Cancer Imaging Archive, n = 52) were retrospectively reviewed. The patients were randomly grouped into a training set (n = 80) and a test set (n = 33). Peritumoral T2 hyperintensity was manually segmented and Minkowski functionals—a texture analysis method capturing heterogeneity of MR images—were computed as a function of 11 grayscale thresholds. The Cox proportional hazards models were fitted with clinical variables, Minkowski functionals features as well as both combined. The risk prediction performances of the Minkowski functionals and combined models were validated on a separate test dataset. The sex-specific survival difference of the entire cohort was analyzed according to MGMT methylation status via Kaplan-Meier survival curves.

Thirty-three Minkowski features (11 area, 11 perimeter and 11 genus) for each patient were acquired giving a total of 3729 features. Cox regression models fitted with clinical data, Minkowski features, and both combined had incremental concordance indices of 0.577 (P = 0.02), 0.706 (P = 0.02) and 0.714 (P = 0.01) respectively. The prediction error rate of the combined model—having clinical and Minkowski features—was lower than that of Minkowski functionals model (0.135 and 0.161, respectively) when validated on a test dataset. No sex-specific survival difference was found according to MGMT methylation status (male, P = 0.2; female, P = 0.22).

Minkowski functionals features computed from peritumoral hyperintensity can capture heterogeneity of glioblastoma on T2WI and have additive prognostic value in predicting survival, demonstrating their potential in complementing currently available prognostic parameters.

Partial Text

Glioblastoma is the most common primary malignant brain tumor [1] notorious for its aggressive clinical course and poor prognosis. Even with the current standard therapy of surgical resection followed by concurrent chemoradiation with temozolomide, the median survival after initial diagnosis of glioblastoma is around 18–24 months [2]. Making appropriate prognostic evaluation on initial diagnosis is therefore important for risk stratification in glioblastoma patients.

The institutional review board of The Catholic University of Korea, Seoul St. Mary’s Hospital approved this retrospective study (approval number: KC18DESI0497) and the requirement for informed consent was waived.

Survival analysis of the entire cohort demonstrated no sex-specific survival differences according to MGMT methylation status (Fig 5A and 5B).

In this study, we analyzed whether MF can capture heterogeneity of extensive peritumoral T2 hyperintensity associated with glioblastoma and its potential value as prognostic marker. Compared to a prior study on analyzing peritumoral infiltration of glioblastoma via MF histogram analysis [25], our study extended the scope by applying MF as texture analysis with emphasis on patients’ prognosis. MF were chosen as modality of texture analysis because of their relatively automated and reliable image analysis with parameterization of image heterogeneity [19]. We only applied 11 thresholded images per slice because previous studies showed that increased numbers of thresholds of over 11 provided no additional benefits [17–19]. Moreover, our dataset consists of two separate cohorts randomly combined, thereby increasing not only the sample size but also the possible heterogeneity of population investigated.

Application of MF as texture analysis may be useful in analyzing heterogeneity of peritumoral hyperintensity of glioblastoma on pre-operative T2WI. Furthermore, when combined with clinical variables MF features showed additive prognostic potential in predicting overall survival of treatment-naïve glioblastoma patients. We propose that MF carry a complementary prognostic role and should be investigated further in future researches.




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