Extraction of artefactual MRS patterns from a large database using non‐negative matrix factorization

Hernández-Villegas, Yanisleydis; Ortega‐Martorell, Sandra; Arús, Carles; Vellido, Alfredo; Julià‐Sapé, Margarida

doi:10.1002/nbm.4193

Cited by 9 publications

(6 citation statements)

References 38 publications

(68 reference statements)

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“…Hernández‐Villegas et al 43 propose a convex nonnegative matrix factorization for the same multicenter studies used in Reference 41 to distinguish between good and poor quality spectra. The method first iteratively factorizes observations into a source matrix (of data centroids) and a mixing matrix (containing combination weights).…”

Section: Discussionmentioning

confidence: 99%

A review of machine learning applications for the proton MR spectroscopy workflow

van de Sande,

Merkofer,

Amirrajab

et al. 2023

Magnetic Resonance in Med

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This literature review presents a comprehensive overview of machine learning (ML) applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state‐of‐the‐art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the MR field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.

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Section: Discussionmentioning

confidence: 99%

A review of machine learning applications for the proton MR spectroscopy workflow

van de Sande,

Merkofer,

Amirrajab

et al. 2023

Magnetic Resonance in Med

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“…Convergence was considered to be achieved when the reconstruction error of the signals was below the threshold value of 10 −5 . Given that in a previous study 18 we reported that the application of cNMF to a similar dataset often includes a variable number of sources that are either artifactual or very unstable, in this study we performed a previous robustness evaluation of the sources extracted. To this end, we extracted k = 2, 3, 4, 10, and 20 sources, and repeated each extraction 100 times.…”

Section: Methodsmentioning

confidence: 99%

“…The robustness of the set of solutions obtained decreased when increasing the number of sources, similarly to previous studies using the same approach. 18 For two and three sources, the same set was obtained in 100% of the repetitions. Beyond three sources, the solutions were exceedingly unstable.…”

Section: Source Extraction From Scer Spectramentioning

confidence: 99%

Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous

Ungan,

Pons‐Escoda,

Ulinic

et al. 2024

NMR in Biomedicine

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The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow‐up study to evaluate treatment response is performed 1 month after concomitant treatment, when contrast‐enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow‐up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2 months later. We extracted the underlying metabolic patterns of the contrast‐enhancing regions with a blind‐source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramér's V. We identified three distinct metabolic patterns—proliferative, necrotic, and responsive, which were associated with status 2 months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast‐enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast‐enhancing regions are also metabolically heterogeneous 1 month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers.

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“…NMF and Convex-NMF are not new to the neuro-oncology domain, where they have been used to differentiate abnormal masses 19 ; to distinguish non-tumoral, responding and non-responding tumoral tissue in glioblastoma through source extraction in a semi-supervised way 20 ; for tumor type classification 21 ; or for spectral data quality control 22 , to name just a few applications.…”

Section: Convex-nmfmentioning

confidence: 99%

Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction

Núñez

Romero

Julià‐Sapé

et al. 2020

Sci Rep

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Glioblastoma is the most frequent aggressive primary brain tumor amongst human adults. Its standard treatment involves chemotherapy, for which the drug temozolomide is a common choice. These are heterogeneous and variable tumors which might benefit from personalized, data-based therapy strategies, and for which there is room for improvement in therapy response follow-up, investigated with preclinical models. This study addresses a preclinical question that involves distinguishing between treated and control (untreated) mice bearing glioblastoma, using machine learning techniques, from magnetic resonance-based data in two modalities: MRI and MRSI. It aims to go beyond the comparison of methods for such discrimination to provide an analytical pipeline that could be used in subsequent human studies. This analytical pipeline is meant to be a usable and interpretable tool for the radiology expert in the hope that such interpretation helps revealing new insights about the problem itself. For that, we propose coupling source extraction-based and radiomics-based data transformations with feature selection. Special attention is paid to the generation of radiologist-friendly visual nosological representations of the analyzed tumors.

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Extraction of artefactual MRS patterns from a large database using non‐negative matrix factorization

Cited by 9 publications

References 38 publications

A review of machine learning applications for the proton MR spectroscopy workflow

A review of machine learning applications for the proton MR spectroscopy workflow

Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous

Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction

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