Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma.
Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 3; pp. 605 - 613 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2022
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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=157184693&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157184693 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2022 vid: 35 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157184693 155520590 157184693 157184693 10.1007/s10278-022-00607-w 157184693 ppf: 605 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma. aug: au: Liu, Gengbo Poon, Mini Zapala, Matthew A. Temple, William C. Vo, Kieuhoa T. Matthay, Kathrine K. Mitra, Debasis Seo, Youngho affil: Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA sug: subj: Machine Learning Decision Support Systems, Clinical Neuroblastoma Prognosis Predictive Value of Tests Tomography, X-Ray Computed Human Retrospective Design Neural Networks (Computer) Radiologists ROC Curve Regression Algorithms ab: Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of 65 neuroblastoma patients are analyzed. An artificial neural network (ANN) is used on tumor radiomic features extracted from 3D CT images. A pre-trained 2D convolutional neural network (CNN) is used on slices of the same images. ML models are trained for various pathologically investigated outcomes of these patients. A subspecialty-trained pediatric radiologist independently reviewed the manually segmented primary tumors. Pyradiomics library is used to extract 105 radiomic features. Six ML algorithms are compared to predict the following outcomes: mortality, presence or absence of metastases, neuroblastoma differentiation, mitosis-karyorrhexis index (MKI), presence or absence of MYCN gene amplification, and presence of image-defined risk factors (IDRF). The prediction ranges over multiple experiments are measured using the area under the receiver operating characteristic (ROC-AUC) for comparison. Our results show that the radiomics-based ANN method slightly outperforms the other algorithms in predicting all outcomes except classification of the grade of neuroblastic differentiation, for which the elastic regression model performed the best. Contributions of the article are twofold: (1) noninvasive models for the prognosis from CT images of neuroblastoma, and (2) comparison of relevant ML models on this medical imaging problem. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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