Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma.
Simple Summary: Mantle cell lymphoma (MCL) is an aggressive lymphoid tumour with a poor prognosis. There exist no routine biomarkers for the early prediction of relapse. Our study compared the potential of radiomics-based machine learning and 3D deep learning models as non-invasive biomarkers to ris...
| Publicado en: | Cancers Vol. 14; no. 8; pp. 2008 - 2030 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156504708&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156504708 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Apr2022 vid: 14 iid: 8 pid: 97109 pub: MDPI artinfo: ui: 156504708 156504708 156504708 10.3390/cancers14082008 156504708 ppf: 2008 ppct: 22 formats: tig: atl: Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma. aug: au: Lisson, Catharina Silvia Lisson, Christoph Gerhard Mezger, Marc Fabian Wolf, Daniel Schmidt, Stefan Andreas Thaiss, Wolfgang M. Tausch, Eugen Beer, Ambros J. Stilgenbauer, Stephan Beer, Meinrad Goetz, Michael affil: Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany sug: subj: Algorithms Lymphoma, Non-Hodgkin's Physiopathology Machine Learning Recurrence Lymphoma, Non-Hodgkin's Diagnosis Prediction Models Human Tomography, X-Ray Computed Algorithms Classification Factor Analysis Rare Diseases Lymphoma, Non-Hodgkin's Prognosis Lymphoma, Non-Hodgkin's Classification Lymphoma, Non-Hodgkin's Symptoms Chromosome Aberrations Intracellular Signaling Peptides and Proteins Metabolism ab: Simple Summary: Mantle cell lymphoma (MCL) is an aggressive lymphoid tumour with a poor prognosis. There exist no routine biomarkers for the early prediction of relapse. Our study compared the potential of radiomics-based machine learning and 3D deep learning models as non-invasive biomarkers to risk-stratify MCL patients, thus promoting precision imaging in clinical oncology. Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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