Abstract
PURPOSETo construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis.PATIENTS AND METHODSWe retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort.RESULTSThese predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP.CONCLUSIONImaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.
Highlights
Cancers display hallmarks of spatial and temporal heterogeneity at various scales, contributing to unfavorable prognosis and treatment failure.[1]
PATIENTS AND METHODS We retrospectively identified data for patients with newly diagnosed glioblastoma multiforme (GBM) from two institutions who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine progression-free survival (PFS), and available presurgical multiparametric MRI (MP-MRI)
We address the problem of constructing personalized prognostic signatures of GBM related to PFS and recurrence pattern (RP) by leveraging Cancer imaging phenomics (CIPh) signatures generated from the Cancer Imaging Phenomics Toolkit (CaPTk) platform
Summary
Cancers display hallmarks of spatial and temporal heterogeneity at various scales, contributing to unfavorable prognosis and treatment failure.[1] Clinical imaging offers the possibility of elucidating multifaceted phenotypic aspects of cancer structure and physiology through acquisition of diverse modalities.[1,2,3] Semantic features such as descriptors of size, morphology, and location that are commonly measured from radiologic images, are limited in revealing the underlying cancer heterogeneity.[4,5] Cancer imaging phenomics (CIPh) is an emerging field for quantitative analysis of oncologic multiparametric imaging. Through mathematical measurements of the aforementioned features, commonly known as radiomic features, CIPh provides a broad spectrum of phenotypic imaging signatures, which potentially brings increased precision to diagnosis, prognosis, and prediction of response to therapy.[6,7].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
