Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

Abstract

Purpose Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. Materials and methods One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis. Results Our multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values < 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively). Conclusion ML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

“…This is critical not only by a medico-legal point of view, but also by a "human" point of view, because, like Coppola et al [81] stressed, we must not superintend the meaning of the irreplaceable doctor-patient bond. Connecting doctors and patients directly will always be an important phase of healthcare services that artificial intelligence can never replace [2,20,68,[102][103][104][105][106][107][108][109].…”

“…Statistical features define the individual voxel values distribution, the associations between neighboring voxels allowing for extraction from medical image features linked to lesion heterogeneity and the quantification of successive voxels with equal intensities along certain directions. Higher order statistical metrics are acquired through the application of filters or mathematical transformations to the images [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

“…In the context of personalized medicine, this is evident as the possibility to predict several prognostic markers allows us to identify the best treatment for a specific patient [41][42][43][44][45][46]. Radiomics analysis could be a promising tool to evaluate a lesion "virtually", with the possibility to analyze the whole tumor during the disease history to obtain those markers which can affect the treatment choice [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. In addition, this approach is safe and inexpensive since radiomics data are obtained from radiological studies which a patient should be subjected during staging and follow-up [66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84].…”

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

“…Usually, ultrasound is the initial imaging tool used to detect small (and usually incidental) renal lesions [35]. US is a widely accessible, inexpensive, and noninvasive method [36] and is a very sensitive technique for detecting renal masses, being generally reliable in differentiating between solid and cystic lesions [37,38]. In fact, simple and uncomplicated renal cysts have a typical US appearance: well-confined anechoic lesions with thin walls and without septa and vascularity; posterior acoustic shadowing may be present [39].…”