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A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

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A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

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dc.contributor.author Cerdà Alberich, Leonor es_ES
dc.contributor.author Sangüesa Nebot, Cinta es_ES
dc.contributor.author Alberich-Bayarri, Angel es_ES
dc.contributor.author Carot Sierra, José Miguel es_ES
dc.contributor.author Martinez de las Heras, Blanca es_ES
dc.contributor.author Veiga Canuto, Diana es_ES
dc.contributor.author Cañete, Adela es_ES
dc.contributor.author Martí-Bonmatí, Luis es_ES
dc.date.accessioned 2023-05-16T18:01:23Z
dc.date.available 2023-05-16T18:01:23Z
dc.date.issued 2020-12 es_ES
dc.identifier.uri http://hdl.handle.net/10251/193438
dc.description.abstract [EN] There is growing interest in applying quantitative diffusion techniques to magnetic resonance imaging for cancer diagnosis and treatment. These measurements are used as a surrogate marker of tumor cellularity and aggressiveness, although there may be factors that introduce some bias to these approaches. Thus, we explored a novel methodology based on confidence habitats and voxel uncertainty to improve the power of the apparent diffusion coefficient to discriminate between benign and malignant neuroblastic tumor profiles in children. We were able to show this offered an improved sensitivity and negative predictive value relative to standard voxel-based methodologies. Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor. es_ES
dc.description.sponsorship This study was funded by PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers), a Horizon 2020 vertical bar RIA project (Topic SC1-DTH-07-2018), grant agreement no: 826494. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Cancers es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Tumor clustered habitats es_ES
dc.subject Confidence maps es_ES
dc.subject Data smearing es_ES
dc.subject Uncertainty exclusion es_ES
dc.subject Reproducible imaging biomarkers es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/cancers12123858 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/826494/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Cerdà Alberich, L.; Sangüesa Nebot, C.; Alberich-Bayarri, A.; Carot Sierra, JM.; Martinez De Las Heras, B.; Veiga Canuto, D.; Cañete, A.... (2020). A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors. Cancers. 12(12):1-14. https://doi.org/10.3390/cancers12123858 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/cancers12123858 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 12 es_ES
dc.identifier.eissn 2072-6694 es_ES
dc.identifier.pmid 33371218 es_ES
dc.identifier.pmcid PMC7767170 es_ES
dc.relation.pasarela S\427586 es_ES
dc.contributor.funder European Commission es_ES


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