Abstract 3040: Radiomics discriminates pseudo-progression from true progression in glioblastoma patients: A large-scale multi-institutional study

Abstract

BACKGROUND: Treatment-related imaging changes are often difficult to distinguish from true tumor progression. Treatment-related changes or pseudoprogression (PsP) subsequently subside or stabilize without any further treatment, whereas progressive tumor requires a more aggressive approach in patient management. Pseudoprogression can mimic true progression radiographically and may potentially alter the physician's judgment about the recurrent disease. Hence, it can predispose a patient to overtreatment or be categorized as a non-responder and exclude him from the clinical trials. This study aims at assessing the potential of radiomics to discriminate PsP from progressive disease (PD) in glioblastoma (GBM) patients. METHODS: We retrospectively evaluated 304 GBM patients with new or increased enhancement on conventional MRI after treatment, of which it was uncertain for PsP versus PD. 149 patients had the histopathological evidence of PD and 27 of PsP. Remaining 128 patients were categorized into PD and PsP based on RANO criteria performed by a board-certified radiologist. Volumetrics using 3D slicer 4.3.1 and radiomics texture analysis were performed of the enhancing lesion(s) in question. RESULTS: Using the MRMR feature selection method, we identified 100 significant features that were used to build a SVM model. Five texture features (E, CS, SA, MP, CP) were found to be most predictive of pseudoprogression. On Leave One Out Cross-Validation (LOOCV), sensitivity, specificity and accuracy were 97%, 72%, and 90%, respectively. Using 70% of the patient data for training and 30% for validation, an AUC of 94% was achieved, with the sensitivity of 97% and specificity of 75%. CONCLUSION: 3D radiomic texture features of conventional MRI successfully discriminated pseudoprogression from true progression in a large cohort of GBM patients. Citation Format: Srishti Abrol, Aikaterini Kotrotsou, Ahmed Hassan, Nabil Elshafeey, Tagwa Idris, Naveen Manohar, Anand Agarwal, Islam Hassan, Kamel Salek, Nikdokht Farid, Carrie McDonald, Shiao-Pei Weathers, Naeim Bahrami, Samuel Bergamaschi, Ahmed Elakkad, Kristin Alfaro-Munoz, Fanny Moron, Jason Huse, Jeffrey Weinberg, Sherise Ferguson, Evangelos Kogias, Amy Heimberger, Raymond Sawaya, Ashok Kumar, John de Groot, Meng Law, Pascal Zinn, Rivka R. Colen. Radiomics discriminates pseudo-progression from true progression in glioblastoma patients: A large-scale multi-institutional study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3040.

Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

“…The experiments show that a combination of the least absolute shrinkage and selection operator (LASSO) and SVM achieves the best prognostic prediction performance and the highest stability. Lu et al (Krizhevsky et al, 2012) proposed and evaluated an approach, which uses the AlexNet deep learning network (Abrol et al, 2018) as a feature extractor and applies transfer learning to train a model for brain disease detection in magnetic resonance imaging (MRI) data. The last three layers of AlexNet are replaced by a fully connected layer, a softmax layer, and a classification layer to implement the feature extractor function.…”