Evaluating automated longitudinal tumor measurements for glioblastoma response assessment

Yannick Suter; Michelle Notter; Raphael Meier; Tina Loosli; Philippe Schucht; Roland Wiest; Mauricio Reyes; Urspeter Knecht

doi:10.3389/fradi.2023.1211859

Evaluating automated longitudinal tumor measurements for glioblastoma response assessment

Front Radiol. 2023 Sep 7:3:1211859. doi: 10.3389/fradi.2023.1211859. eCollection 2023.

Authors

Yannick Suter^{1

2}, Michelle Notter^{1

3}, Raphael Meier⁴, Tina Loosli⁴, Philippe Schucht⁵, Roland Wiest⁴, Mauricio Reyes^{1

2}, Urspeter Knecht^{1

6}

Affiliations

¹ ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
² Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
³ Cantonal Hospital of Graubünden, Chur, Switzerland.
⁴ Support Center for Advanced Neuroimaging, Inselspital, Bern, Switzerland.
⁵ Department of Neurosurgery, Inselspital, Bern, Switzerland.
⁶ Radiology Department, Spital Emmental, Burgdorf, Switzerland.

Abstract

Automated tumor segmentation tools for glioblastoma show promising performance. To apply these tools for automated response assessment, longitudinal segmentation, and tumor measurement, consistency is critical. This study aimed to determine whether BraTumIA and HD-GLIO are suited for this task. We evaluated two segmentation tools with respect to automated response assessment on the single-center retrospective LUMIERE dataset with 80 patients and a total of 502 post-operative time points. Volumetry and automated bi-dimensional measurements were compared with expert measurements following the Response Assessment in Neuro-Oncology (RANO) guidelines. The longitudinal trend agreement between the expert and methods was evaluated, and the RANO progression thresholds were tested against the expert-derived time-to-progression (TTP). The TTP and overall survival (OS) correlation was used to check the progression thresholds. We evaluated the automated detection and influence of non-measurable lesions. The tumor volume trend agreement calculated between segmentation volumes and the expert bi-dimensional measurements was high (HD-GLIO: 81.1%, BraTumIA: 79.7%). BraTumIA achieved the closest match to the expert TTP using the recommended RANO progression threshold. HD-GLIO-derived tumor volumes reached the highest correlation between TTP and OS (0.55). Both tools failed at an accurate lesion count across time. Manual false-positive removal and restricting to a maximum number of measurable lesions had no beneficial effect. Expert supervision and manual corrections are still necessary when applying the tested automated segmentation tools for automated response assessment. The longitudinal consistency of current segmentation tools needs further improvement. Validation of volumetric and bi-dimensional progression thresholds with multi-center studies is required to move toward volumetry-based response assessment.

Keywords: RANO; automated response assessment; glioblastoma; longitudinal; segmentation; tumor burden measurements.

Grants and funding

This study was part of a project funded by Swiss Cancer Research (Krebsliga Schweiz) (grant KFS-3979-08-2016).