Pattern recognition of abscesses and brain tumors through MR spectroscopy: Comparison of experimental conditions and radiological findings

Abstract

Introduction: The interpretation of brain tumors and abscesses MR spectra is complex and subjective. In clinical practice, different experimental conditions such as field strength or echo time (TE) reveal different metabolite information. Our study aims to show in which scenarios magnetic resonance spectroscopy can differentiate among brain tumors, normal tissue and abscesses using classification algorithms. Methods: Pairwise classification between abscesses, brain tumor classes, and healthy subjects tissue spectra was performed, also the multiclass classification between meningiomas, grade I-II-III gliomas, and glioblastomas and metastases, in 1.5T short TE (n = 195), 1.5T long TE (n = 231) and 3.0T long TE (n = 59) point resolved spectroscopy setups, using LCModel metabolite concentration as input to classifiers. Results: Areas under the curve of the Receiver Operating Characteristic above 0.9 were obtained for the classification between abscesses and all classes except glioblastomas, reaching 0.947 when classifying against metastases, grade I-II gliomas and glioblastomas (0.980), meningiomas and glioblastomas (0.956), grade I-II gliomas and metastases (0.989), meningiomas and metastases (0.990), and between healthy tissue and all other classes in both conditions except for anaplastic astrocytomas in short TE 1.5T setup. When the multiclass classification agrees with radiological diagnosis the accuracy reaches 96.8% for short TE and 98.9% for long TE. Conclusions: The results in the three conditions were similar, highlighting comparable quality, robust quantification and good regularization and flexibility in either algorithm. Multiclass classification provides useful information to the radiologist. These findings show the potential of the development of decision support systems as well as tools for the accompaniment of treatments.

Evaluation of the INTERPRET decision-support system: can it improve the diagnostic value of magnetic resonance spectroscopy of the brain?

“…The reason could be that this tumour can be difficult to differentiate from other tumours with regard to cellular architecture and molecular patterns, also for the pathologist [ 17 , 18 ]. A recent study using cases from the INTERPRET database showed that even for the binary classification of anaplastic astrocytoma compared to other tumour grades or healthy tissue, an area under curve > 0.9 in receiver operating characteristics operating analysis was infrequently achieved at 1.5 T with a short TE [ 19 ]. In an earlier study on the other hand, the use of INTERPRET was better than MRI for characterisation of anaplastic astrocytoma [ 2 ].…”

Cancer metabolism in a snapshot: MRS(I)

“…With respect to diagnosis, the need for multicentre studies seems well established since the INTERPRET experience on preoperative brain tumour typing/grading at 1.5 T . The fact that the curated INTERPRET dataset has been made accessible to research groups allows them to develop classifiers comparing single‐centre cohorts with that dataset, for example to differentiate abscesses from tumours at 3 T . Diagnosis of paediatric brain tumours has also been the subject of two recent works at 1.5 T on a large cohort of 90 patients and at 3 T in a multicentre study on 41 patients, to distinguish among the three main paediatric brain tumours (medulloblastoma versus pilocytic astrocytoma versus ependymoma).…”

“…The problem of imbalanced classes has also been tackled with the use of AdaBoost . Supervised pattern recognition techniques such as support vector machines, linear discriminant analysis or several different algorithms, and classifier fusion, are also used in the latest literature, mainly for diagnostic questions. These methodologies are, generally too sophisticated to be put in practice by purely clinical groups, and when methodologies are tested the norm is availability of very small datasets, although there are exceptions such as Reference 113 or 109.…”

Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning

“…The reason for this scarcity may be the difficulty in examining and interpreting MRS signals. Therefore, artificial intelligence and computer-aided diagnosis (CAD) are novel and effective methods that can contribute to overcoming the problems mentioned above 16,17,18 . To the best of our knowledge, very few studies have addressed the determination of MS types with the help of combined approaches, adopting both MRS and advanced machine learning algorithms.…”