Abstract
PurposeEarly recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma.Patients and MethodsA total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness.ResultsThe nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram.ConclusionThis study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.
Highlights
Glioblastoma multiforme (GBM) is the most malignant primary brain tumor [1] and represents one third of primary brain tumors with 79,000 new cases worldwide per year [2]
We evaluated preoperative brain MRI scans for both radiomic features analyzed by computational methods and the conventional radiological characteristics included in the Visually Accessible Rembrandt Images (VASARI) feature set assessed by neuroradiologists through visual inspection [6]
A total of 122 patients with GBM were included in this study, including 65 with early recurrence
Summary
Glioblastoma multiforme (GBM) is the most malignant primary brain tumor [1] and represents one third of primary brain tumors with 79,000 new cases worldwide per year [2]. Early recurrence may occur due to the aggressiveness and diffuse infiltrative growth of GBM [7, 8]. Pretreatment identification of patients at risk for early GBM recurrence has several benefits [9]. A more aggressive treatment strategy, such as a more extensive resection [10], with concurrent extended individualized radiotherapy or new radiotherapy-based methods, may be warranted. Conventional MRI evaluation is not adequate for predicting early recurrence in GBM [19, 20]. There is a need to assess additional imaging biomarkers analyzed by computational methods for predicting GBM recurrence [21, 22]
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