Non-invasive prediction of lymph node risk in oral cavity cancer patients using a combination of supervised and unsupervised machine learning algorithms

A. Traversoa*, A. Hosni-Abdalaty, M. Hasan, T. Tadic, T. Patel, M. Giuliani, J. Kim, J. Ringash, J. Cho, S. Bratman, A. Bayley, J. Waldron, B. O'Sullivan, J. Irish, D. Chepeha, J. De Almeida, D. Goldstein, D. Jaffray, L. Wee, A. DekkerA. Hope, A. Krol, B.S. Gimi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

In oral cavity (OC) squamous cell cancer, the incidence of occult nodal metastases varies from 20% to 50% depending and tumor size and thickness. Besides clinical and histopathological factors, image-derived biomarkers may help estimate the probability of LN (lymph nodes) metastasis using a non-invasive approach to further stratify patients' need for neck dissection. We investigated the role of MR-based radiomics in predicting positive lymph nodes in OC patients, prior to surgery. We also investigated different supervised and unsupervised dimensionality reduction techniques, as well as different classifiers. Results showed that the combination of radiomics+clinical factors outperform radiomics and clinical predictors alone. Overall, a combination of supervised and supervised machine learning algorithms seems more suitable for better performances in radiomic studies.
Original languageEnglish
Title of host publicationMEDICAL IMAGING 2020: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING
EditorsA Krol, BS Gimi
PublisherSPIE-INT SOC OPTICAL ENGINEERING
Number of pages7
Volume11317
ISBN (Print)9781510634022
DOIs
Publication statusPublished - 2021
EventSPIE Medical Imaging Conference: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: 15 Feb 202020 Feb 2020

Publication series

SeriesProgress in Biomedical Optics and Imaging
Number113172C
Volume11317
ISSN1605-7422

Conference

ConferenceSPIE Medical Imaging Conference
Abbreviated titleMI106
Country/TerritoryUnited States
CityHouston
Period15/02/2020/02/20

Keywords

  • magnetic resonance imaging
  • machine learning
  • oral cavity cancers
  • radiomics
  • RADIOMICS

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