Abstract
Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. Materials and Methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401–610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients’ dataset (Siemens and GE scans). Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.
Highlights
Primary tumours of the central nervous system (CNS) have an incidence of five cases per 100,000 inhabitants/year in Europe and cause 2% of all deaths from cancer.In recent decades, there has been a progressive increase in incidence, for the larger diffusion of improved imaging methods that allow a more accurate diagnosis, but a significant increase was noted in the age group >65 years, where the incidence has more than doubled [1]
The ability to discriminate between high-grade and low-grade CNS tumors may be evaluated with [18F]-Fluorodeoxiglucose (FDG) positron emission tomography (PET), in our research we investigate the diagnostic power of brain PET images acquired through [11C]-methionine (MET), which shows potential advantages, through a radiomics approach
Starting from the 44 features obtained for each region of interest (ROI) and volume of interest (VOI), the reduction and selection method described in Section 2.2.3 was capable to identify the most relevant features on the two segmentations, i.e., the VOI obtained using the thresholding method and the fixed
Summary
Primary tumours of the central nervous system (CNS) have an incidence of five cases per 100,000 inhabitants/year in Europe and cause 2% of all deaths from cancer.In recent decades, there has been a progressive increase in incidence, for the larger diffusion of improved imaging methods that allow a more accurate diagnosis, but a significant increase was noted in the age group >65 years, where the incidence has more than doubled [1]. For the latest version of the WHO classification of CNS tumours, grade of disease was correlated to an idealized clinical-biological behaviour; for instance, WHO grade I tumours were curable if they could be surgically removed; at the other end of the spectrum, WHO grade IV tumours were highly malignant, leading to death in relatively short periods of time in the absence of effective therapy [4]. In view of this classification, the importance of having a tool able to predict the grade of disease at diagnosis of CNS tumours has become a crucial point of clinical interest
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