Evaluating automated longitudinal tumor measurements for glioblastoma response assessment

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

glioblastoma, segmentation, automated response assessment, RANO, tumor burden measurements, longitudinal

Subject (DDC)

330 - Wirtschaft

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ISSN

2673-8740

Language

English

Created during FHNW affiliation

No

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Publication status

Published

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Peer review of the complete publication

Open access category

Gold

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Citation

Suter, Y., Notter, M., Meier, R., Loosli, T., Schucht, P., Wiest, R., Reyes, M., & Knecht, U. (2023). Evaluating automated longitudinal tumor measurements for glioblastoma response assessment. Frontiers in Radiology, 3. https://doi.org/10.3389/fradi.2023.1211859

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