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Deep Learning Derived Tumor Infiltration Maps for Personalized Target Definition in Glioblastoma Radiotherapy - 2019

Deep Learning Derived Tumor Infiltration Maps For Personalized Target Definition In Glioblastoma Radiotherapy

Research Area:  Machine Learning

Abstract:

Glioblastoma is routinely treated by concomitant radiochemotherapy. Current target definition guidelines use anatomic MRI (magnetic resonance imaging) scans, taking into account contrast enhancement and the rather unspecific hyperintensity on the fluid-attenuated inversion recovery (FLAIR) sequence.We applied deep learning based free water correction of diffusion tensor imaging (DTI) scans to estimate the infiltrative gross tumor volume (iGTV) inside of the FLAIR hyperintense region. We analyzed the resulting iGTVs and their impact on target volume definition in a retrospective cohort of 33 GBM patients.iGTVs were significantly smaller compared to standard pre- and post-operative gross tumor volume (GTV) definitions. Two novel infiltrative tumor GTVs (nGTVPRE-OP and nGTVPOST-OP) defined as the conjunction volume of the standard GTV and the iGTV showed only a moderate increase in size compared to standard GTV definitions. On postoperative scans, the iGTV was predominantly covered by the two clinical target volume (CTV) concepts CTVEORTC and CTVROTG1. A novel infiltrative tumor CTV (nCTV) [nGTVPOST-OP + 2 cm margin] was significantly smaller compared to CTVROTG1 but larger than CTVEORTC. The overlap volume and conformity index demonstrated a distinct spatial configuration of the nCTV. Tumor recurrences overlapped with the iGTV in all but one patients and were completely covered by the nCTV in all patients. After reducing the margin to 1 cm recurrences coverage was at least in-field in all patients.To conclude, free water corrected DTI scans may help to define infiltrative tumor areas of GBM that could ultimately be used to individualize RT treatment planning in terms of dose sparing or dose escalation.

Keywords:  

Author(s) Name:  Jan C. Peeken, Miguel Molina-Romero, Christian Diehl, Bjoern H. Menze, Christoph Straube, Bernhard Meyer, Claus Zimmer, Benedikt Wiestler, Stephanie E. Combs

Journal name:  Radiotherapy and Oncology

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.radonc.2019.06.031

Volume Information:  Volume 138, September 2019, Pages 166-172