Prospective diagnostic performance evaluation of single‐voxel ¹H MRS for typing and grading of brain tumours

Abstract

The purpose of this study was to evaluate whether single-voxel (1)H MRS could add useful information to conventional MRI in the preoperative characterisation of the type and grade of brain tumours. MRI and MRS examinations from a prospective cohort of 40 consecutive patients were analysed double blind by radiologists and spectroscopists before the histological diagnosis was known. The spectroscopists had only the MR spectra, whereas the radiologists had both the MR images and basic clinical details (age, sex and presenting symptoms). Then, the radiologists and spectroscopists exchanged their predictions and re-evaluated their initial opinions, taking into account the new evidence. Spectroscopists used four different systems of analysis for (1)H MRS data, and the efficacy of each of these methods was also evaluated. Information extracted from (1)H MRS significantly improved the radiologists' MRI-based characterisation of grade IV tumours (glioblastomas, metastases, medulloblastomas and lymphomas) in the cohort [area under the curve (AUC) in the MRI re-evaluation 0.93 versus AUC in the MRI evaluation 0.85], and also of the less malignant glial tumours (AUC in the MRI re-evaluation 0.93 versus AUC in the MRI evaluation 0.81). One of the MRS analysis systems used, the INTERPRET (International Network for Pattern Recognition of Tumours Using Magnetic Resonance) decision support system, outperformed the others, as well as being better than the MRI evaluation for the characterisation of grade III astrocytomas. Thus, preoperative MRS data improve the radiologists' performance in diagnosing grade IV tumours and, for those of grade II-III, MRS data help them to recognise the glial lineage. Even in cases in which their diagnoses were not improved, the provision of MRS data to the radiologists had no negative influence on their predictions.

Randomized pilot study and qualitative evaluation of a clinical decision support system for brain tumour diagnosis based on SV 1H MRS: Evaluation as an additional information procedure for novice radiologists

“…We compare next our work with previously published evaluations involving CDSS for brain tumour diagnosis ([4], [9] and [10]). As a main difference with respect to their approaches, we have focused our work to study the impact on novice radiologists.…”

“…In [4], [9] and [10], they evaluated 16, 10 and 40 patient cases respectively. However, they distributed the set of cases among the different radiologists, which increased the total number of evaluated cases, although involving repetition.…”

“…Horská and Baker [19] suggested that automated procedures to analyze MRS and display its results are needed to overcome this issue. For this reason, translational research has focused its attention in developing and evaluating clinical decision support systems (CDSS) based on 1 H MRS to help radiologists in the diagnosis of brain tumors [4] [8][9][10]. Additionally, a CDSS for brain tumour diagnosis may be of special interest to novice radiologists, where the lack of clinical experience on large number of real cases of specific tumor types, offers an optimal opportunity for the use of a CDSS [15].…”

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

“…INTERPRET version 3.0 proved to be superior for the characterization of grade Ⅲ astrocytomas when compared to the spectroscopic and the radiologists' evaluation [48] .…”

Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization

“…MRS is inherently non-invasive and can be used either on its own, or in conjunction with other MR mo-dalities [1] with the aim to improve diagnostic accuracy. Although MRS can be used to investigate a wide range of tissue types [2], it is mainly used for diseases of the central nervous system (CNS), and has proven a powerful tool in assessing a broad spectrum of diseases such as metabolic disorders, epilepsy, Alzheimer and Parkinson, amongst others.…”